Hey, HTC here. I'm in the mudfighter. Whenever I review a GI Joe airplane, I like to fly him. I like to test him out. Wait, what's that? MUD! DIE, MUDS! DIE! I fight all the MUDS! There's more MUD over there! I MUD DIE! Hello everybody, HoodedCobraCommander788 here. This is the show where we review every vintage GI Joe toy from 1982 to 1994. When you think of GI Joe airplanes, you think of the Skystriker, the Conquest X-30, the Phantom X-19, the MUD Fighter? Why is it called the MUD Fighter? Why does it fight MUD? Well, MUD can be rather infuriating. That's why you need dogfight with MUD fighting action. I honestly can't think of anything else to say and I have nothing else written on this page. So let's just do it. HTC 788 presents the MUD Fighter and Dogfight. This is the MUD Fighter and Pilot Dogfight from 1989. This vehicle and figure set were first introduced in 1989. They were also available in 1990. They were discontinued for 1991. MUD Fighter and Dogfight were only sold together. They were not sold separately. During their retail run, in addition to being broadly available at retail, they were also released as an exclusive to Benny's, packaged with the His2, the Track Viper, and two bonus figures. Benny's was a retailer based in New England. We never had a Benny's in my state, so I never would have seen that exclusive. There were no later versions of the MUD Fighter or Dogfight in the vintage or any other era. I don't know if that's indicative of them not being popular or if the molds just were not available later. We will take a thorough look at Dogfight later, but I'm going to set him aside for now so we can take a closer look at the MUD Fighter. The name MUD Fighter has always been a puzzle. It's an aircraft. You would expect a MUD Fighter to be a land vehicle. There is a real aircraft called a MUD Fighter, the Aries MUD Fighter. The Aries was a close support aircraft designed for the U.S. Army. That aircraft, though, didn't fly until 1990. The G.I. Joe MUD Fighter couldn't have been directly inspired by it. That seems to be the role of G.I. Joe's MUD Fighter. It provides air support for ground forces. It flies in the air, but it still plays in the mud. Don't confuse it with the MUD Buster, which was a four-wheel drive land rover from 1993. You see, the MUD Fighter fights mud, and the MUD Buster busts mud. Two totally different things. Either way, MUD's name is MUD. The MUD Fighter is a propeller-driven aircraft, not a jet. That makes it unique in the vintage era of the G.I. Joe toy line. Most fixed-wing aircraft were jets, as you would expect. There was another G.I. Joe propeller aircraft after the vintage era, the Sargent Savage P-40 Warhawk. When lined up with G.I. Joe's other aircraft, the MUD Fighter is rather small and underpowered, being a prop plane in the era of jets. There are propeller-driven aircraft used by the U.S. Armed Forces even today. Cargo planes and gunships such as the AC-130 variants are turboprops. The C-12 is a small turboprop aircraft like the MUD Fighter. Even though the MUD Fighter isn't quite the high-performance jet fighter that the Sky Striker is, it isn't necessarily unrealistic. Let's look at the parts and the features of the MUD Fighter. I have the blueprints for the MUD Fighter, and I will use this to describe some of the features on this vehicle. Let's start with this overall green body. This fits its role as a close support fighter. It's camouflaged for the ground against attacks from above. On the port side of the fuselage next to the canopy, we have four Cobra kill marks. On the front underside of the MUD Fighter, we have two machine guns. These are not named on the blueprints, surprisingly. They are most likely 20 or 25mm Gatling guns, like the M61 Vulcan Cannon or the GAU-12 Equalizer. I would guess the smaller, lighter weight Vulcan. These are molded into the hull, so they are the same green color as the body. They would look pretty fierce if they didn't blend in with the body of the plane. There are also black wheels on the front for landing gear. They are not retractable. Back on the top, we have this black canopy. The blueprints call this an anti-laser, puncture-proof titanium shield. There are cut-out windows. There is no clear plastic over those windows, so you just have to imagine windows on this canopy. It is hinged at the back, and it clips at the front, so you can pull it up to open it. It doesn't open very far. In fact, there's a stopper here to prevent it opening any farther. If you push up too far on it, it will just pop off. Opening the canopy reveals a single seat cockpit. There is a texture pattern on that seat. There is some technical detail on each side of the seat and a minimal control panel. No control stick. You can place Dogfight in the cockpit and close the canopy, and he fits completely in there, and the canopy clips closed so he should not fall out. Since there is no control stick, Dogfight has to control the mudfighter with his mind. On that green body of the mudfighter on the top and on the bottom, there are some cut-out vents. There are even some cut-out vents at the back. There is some additional panel and technical detail on the top and on the bottom. There are some stickers. There's a GI Joe logo. There is a number eight, and there is the Mudfighter Library, this crest, with a banner that says, Ground Assault Wing. There are also these clips for the bomb racks that are visible from the top of the plane. These are a little unsightly, but we'll get to those in a moment when we talk about the bomb racks. On each side, there are these black winglets. The blueprints call these flexi-steel vehicle winglet with computer-enhanced aerodynamics. On the underside of those winglets, there are fins with attached wheels that serve as the landing gear. They are always deployed. That means the landing gear for the mudfighter do not retract. There should be some additional stickers on these winglets that are missing on my example. Most of the action is on the underside, where the Mudfighter has an unbelievable number of bombs. There are 16 bombs total. Each wing has a black plastic bomb rack, and each of those holds four bombs, and there's an additional eight bombs on the belly of the plane. These 16 bombs are all the same. They peg onto the plane or the bomb racks with these dumbbell-shaped pegs. They are in silver plastic. They are football-shaped with small fins. What are these bombs exactly? The blueprints are not very helpful. All 16 bombs are exactly the same, but the blueprints have two listings for them. They are either AS-99 2000 pound delayed detonation air-to-surface bombs, or they are M7A 1500 pound cluster bombs with pre-contact detonation mechanism. The best I can tell is the bombs on the belly are supposed to be the 2000 pound bombs, and the bombs on the wings are supposed to be the 1500 pound bombs. That would be a total of 28,000 pounds of bombs on this plane. It's amazing it can get off the ground. Here's what the Mudfighter looks like without the bombs. These black bomb racks are, of course, removable. Let's flip the plane back over to the top and take a look at that for a moment. Of these unsightly clips for the bomb racks, the back of the box for the Mudfighter says, bomb release mechanism drops bombs from racks. It doesn't seem to really work that way because if you squeeze these clips, it doesn't drop the bombs off the rack. It drops the entire rack. Does that really count as a feature? In the TV commercial for the Mudfighter, this clip that drops the entire bomb rack is advertised like a feature, but I don't think I buy it. Here is the bottom of the Mudfighter with everything removed, so you can see some of the details that are covered up with the bombs and the racks. There is this ridge portion here at the tail. The blueprints describe this as low-angle assault fan with infrared tracking beam capability. I have no idea what that means. Finally, we get to the very back, which is where the propeller is attached. It is in black plastic. It does spin. The blueprints call this 1000 FW 1670 pounds unducted propeller with forward slash reverse power modes. It's a black propeller. It looks like a screw for a watercraft. These may be scimitar propeller blades, but the curve is extreme even for that type of curved propeller blade. Is the propeller 1670 pounds or the engine? I assume this means the engine is that weight, not just the propeller itself. The blueprints give a lot of weight measurements for components of the Mudfighter. The bombs total 28,000 pounds altogether depending on how you count it. The engine seems to weigh 1670 pounds. If the Gatling guns are M61 Vulcans, the lightest variation of that weapon is 202 pounds. That's 404 pounds total since there are two of them. So there's at least 30,074 pounds hanging off of this aircraft, not counting the ammunition, the airframe itself, the pilot, and the fuel. For reference, the Beechcraft C12 Huron has a max takeoff weight of 12,500 pounds. Some of the military variants have a max weight of 16,500 pounds. The Mudfighter nearly doubles that. Now let's look at Dogfight, the Mudfighter pilot. There isn't much written about Dogfight in the various G.I. Joe reference sources. He looks a little bit like Jamie Heineman from Mythbusters. A Dogfight is an aerial battle between fighter aircraft. While that seems like a good code name for a pilot, the Mudfighter is not designed for aerial combat. It is a close air support aircraft for ground forces and a bomber. When writing this episode, how many times did I accidentally type Dongfight instead of Dogfight? A lot. Let's take a look at Dogfight's only accessory, his pistol. The pistol is in black plastic. It has a long barrel and a scope. I do not know if this is based on any real world weapon. It is very small and it is frequently missing. It often gets lost so you'll see a lot of Dogfight action figures without the pistol. Dogfight probably doesn't need this pistol. It's something to lose inside the Mudfighter and rattle around in there forever. But of course we wouldn't want to leave Dogfight unarmed. After he runs out of ammunition in the Mudfighter's two Gatling guns and after he drops the Mudfighter's 16 bombs, he would be totally unarmed without his hand cannon. Let's take a look at Dogfight's articulation. He had the articulation that was standard for GI Joe figures well before 1989 so he could turn his head from left to right and look up and down. He could swing his arm up at the shoulder and swivel at the shoulder all the way around. He had a hinge at the elbow that allowed him to bend his arm at the elbow about 90 degrees. He had a swivel at the bicep so he could swivel his arm all the way around. This was an O-ring figure meaning the figure was held together with a rubber O-ring that looped around the inside. That allowed him to move at the torso a bit. He could move his legs apart about so far. He could bend his legs at the hip about 90 degrees and bend at the knee about 90 degrees. Let's take a look at the sculpt design and color of Dogfight starting with his head.  He has silver headset and silver sunglasses and orange hair and an orange handlebar mustache. This cap looks like a World War II US Army Air Corps war training service visor cap and badge. This would have been anachronistic by the time Dogfight wore it in the late 1980s but perhaps he wanted to honor a relative who served in World War II. On his chest he has a brown flight jacket that is open in the front to reveal a white button-up shirt. There's an additional detail on that shirt. I'm not sure what it's supposed to be. It may be a microphone, but it's not picked out in a different color plastic so it kind of blends in with the white shirt. On the left side of the jacket he has a silver wing badge and on the right side he has a blank silver disc. On the artwork on the card there is some detail on that disc but not on the figure. The blank disc on the chest makes the figure feel a little blank. This chest reminds me a bit of the Lannard Core Figure Gunner O'Grady. That's not a good thing. A G.I. Joe figure shouldn't compare to the cheaper and generally less detailed core figures. This relatively plain chest is acceptable for the core but not for G.I. Joe. We expect it and usually got more for G.I. Joe. The arms have long brown sleeves for that jacket and bare hands. The arms are rather plain. Dogfight is made up of all unique parts but the arms are so generic they could have been used on numerous other figures. The waist piece is in blue plastic. He has a black belt with a silver belt buckle. There is a strip of that black belt that runs down the right side to the holster that is on the right leg. There is a texture pattern all over that belt. His legs feature blue trousers. The left leg is rather plain but on the right leg there is a black pistol holster with a texture pattern on it and there is a silver pistol in the holster. His lower legs he has unpainted knee pads and it's the type of knee pad that extends above the knee a bit. I do like those. I don't know why he needs that detail but I do like them.  The blank silver circle on the chest, the minimally detailed torso, the generic arms, the unpainted knee pads. These all make the figure feel a little cheap. Is this a G.I. Joe figure? We tend to expect higher quality from G.I. Joe figures. At least he has some character with that hat and that crazy mustache. Let's take a look at Dogfight's file card. It has his faction as G.I. Joe. It has a portrait of Dogfight here and this portrait is not taken from the Mudfighter box art. The box art doesn't actually show Dogfight. His codename is Dogfight. He is the Mudfighter pilot. His filename is James R. King. There's a missed opportunity not naming him Harry. Harry, bud. Incorrect. Primary military specialty Mudfighter pilot, secondary military specialty electronics technician, birthplace Providence, Rhode Island and grade is 02. Most G.I. Joe file cards were written by Larry Hamma but not all. On a few occasions, Hasbro tried to produce the file cards in-house. When that happened, they often placed the character's birthplace in New England because that's where Hasbro headquarters is located. In this case, despite Dogfight being from Rhode Island, I think this is still a Larry Hamma file card. This paragraph says, Dogfight was permanently 86 from every carnival and county fair in Alabama for winning too many stuffed bears. Alabama? How did he get there? 86 has an asterisk. It means forcibly ejected and forbidden to return. 86 does sound like something Larry would write. The combination of his uncanny depth perception, precise hand-eye coordination and powerful throwing arm enabled him to knock down all the wooden bottles with just one pitch every time. Now he uses the same skills to destroy Cobra, his two tanks and maggots. So does he throw the bombs with his powerful throwing arms? Also, is this a reference to the Benny's his two-two pack? This bottom paragraph has a quote. It says, The Cobra is here the mudfighter first.  Chances are good that Dogfight will be diving out of the sun with his thumb dancing a Fandango on his bomb release trigger and all the mudfighters guns blazing. By that time, it's far too late for evasive action or return fire. This is a common trope for GI Joe file cards. He has an innate ability. He's just born with it and he applies that ability to his job. Looking at how the mudfighter and dogfight were used in GI Joe media, the mudfighter was animated for a TV commercial, but dogfight was not. Dogfight was never animated, not for the animated series and not for a TV commercial. If he were animated, he would talk like Looking at how the mudfighter and dogfight are used in the GI Joe comic book series, in the main GI Joe series, they first appeared in issue number 111 in the Benzine War story arc. Well, the mudfighter appears and dogfight is flying it, but dogfight is barely seen. The mudfighter assisted in delivering Ninja Force on an infiltration mission. In issue number 115, dogfight appears as a co-pilot for ace in a sky striker during a crash landing on the USS flag. Dogfight had his most significant appearance in the special mission series in issue number 28, which was the final issue of that series. The mudfighter takes off from the deck of the USS flag without a catapult. In that issue, dogfight actually engages in a dogfight with cobra python patrol conquests. He fares better than you would expect, taking out a couple of them. The mudfighter is given a couple air-to-air missiles, which the toy doesn't have. In the end, dogfight and the mudfighter are saved by the defiant shuttle. That issue showcased a lot of GI Joe's air and space vehicles and was a great swan song for the series. On the final page, there is a team shot in which Hawke announces that the series is canceled, but the regular series will continue. Dogfight is front and center in that team shot. Looking at the mudfighter and dogfight overall, the mudfighter is cheap and short on features. It has no retractable landing gear, no removable engine covers, no clear plastic canopy. I'm not buying the bomb release mechanism clips as a feature. Even so, it's surprisingly fun. The mudfighter has an ungodly number of bombs. It also has twin gatling guns that are almost entirely ignored by the toy packaging and blueprints. The guns kind of blend into the plane since they are just molded in. The mudfighter is perfectly equipped for its role as a ground assault aircraft. This is a propeller driven plane in a jet fighter world. Despite the depiction in the comic book, the mudfighter is not going to match up against Cobra Night Ravens or Python Patrol Conquest. The mudfighter's Cobra counterpart is actually the Cobra Rattler, which fulfilled exactly the same role for Cobra. The Cobra Rattler though had a lot more features. It was a jet based on a real world aircraft and it just had a lot more going on for it. The best thing about the mudfighter is the size. It's much smaller and lighter than the Sky Striker or the Phantom. It's easy to play with and fly around. I imagine a kid would have a lot of fun with this back in 1989. Dogfight is an okay figure. He looks kind of like a cartoon character with the hat and the bushy orange mustache. I think it needs more detail. That blank silver disc on the jacket doesn't look like a G.I. Joe detail. Something was supposed to go there and it was cut out. Probably for cost. They had to pay for those 16 bombs somehow. I understand pilots need weapons for close quarters combat. But as a practical matter, I would prefer this pistol be given to somebody else. This thing is going to get lost in the mudfighter cockpit and give the plane a permanent rattle. That was my review of the mudfighter and dogfight. I hope you enjoyed it. If you did, please give this video a thumbs up on YouTube and subscribe to the YouTube channel and share this video with your friends. That's what helps this channel grow. You can find me on social media, on Facebook and Twitter, and I have a website, hcc788.com. I can only continue doing these videos with the support of my friends on Patreon. If you'd like to support the channel, that's a great way to do it. You can even get your name in videos. You see all the names scrolling on the screen right now? Your name could be there. Next week, I have no idea what I'm doing. This is a rare occasion where I haven't planned anything, so I will be just as surprised as you are. I'll see you then, and until then remember, only G.I. Joe is G.I. Joe.


Good afternoon everyone. We're here today to announce the first round of approved projects for the community recovery and revitalization program As you might recall this was an important initiative that we strongly advocated for during the last session and Worked with the legislature to secure 40 million dollars in federal ARPA funding to support projects across the state Which will help our communities grow and recover Of the 40 million dollars about 10 million will go out in this first round to 31 approved projects That funding will help leverage and support project costs totaling more than a hundred and fifty-three million Mr. Goldstein will offer more details on the program and the first round of award winners And we're grateful to have at least one of them here We're going to have to but the weather might have delayed them a bit to talk about how this funding will support their revitalization projects and their communities These first 31 represent diverse areas like housing child care and agriculture They're also supporting the arts entertainment and hospitality sectors which suffered significant losses during the pandemic as I've long said We must be strategic and make lasting investments with this one-time federal money My administration has been focused on that goal and this program will help businesses and organizations over the long term ARPA funding has received a lot of attention over the last year Mostly on how and where to spend it and we're now beginning to see how transformative it can be With projects like these But we can't stop here We have to continue to look down the road and make smart investments that will build on progress we're making The funding decisions we make today need to work in tandem with the many state programs. We already have It's why in my budget we increase funding for our downtown organizations and regional development organizations So those on the ground can help prioritize projects that need financial support to help create a vibrant downtown structure It's also why we must support economic development initiative initiatives like making sure we have industrial space Available and ready So it's a so it's there when a company looks to move or expand here or continue to fund brownfield redevelopment projects as I've said, there's no quick fix for the state It will take all of these programs working together to make lasting changes in all 14 counties So we hope the legislature will continue to work with us to prioritize investments that help the parts of the our state They need it most and with that I'll turn it over to Commissioner Goldstein to talk more specifically about the program and those 31 projects Thank You governor and hello everybody our team in the Department of Economic Development has been hard at work reviewing these community recovery and revitalization program applications to ensure that the awarded projects are Making capital improvements or capital expansions that spur the economic recovery in communities that need the revitalization All across the state as the governor mentioned 31 projects have been approved for funding thus far And we've spent just a little over 10 million dollars of the 40 million dollars available has been allocated these These projects will be This 10 million will be supporting a hundred and fifty three million dollars worth of project costs So just this bit of government money helps for additional capital investment These projects are expected to support 354 existing jobs and enable the creation of 205 new jobs 79 new affordable housing units 196 new child care slots for a load of moderate income families the awards are spread across 12 counties with 20 projects coming in from the priority groups We had historically underrepresented groups and towns with stagnant or decline in grand list values were looked at first this program also was made available to municipalities and We're happy to say three were awarded in the first round. That's Highgate Manchester and Killington And we're very pleased with the Variety of projects that we're seeing all types of projects that are being supported through this program the demand We knew was acute when we first developed it and we want to impact areas as diverse as child care affordable housing Tourism the arts and agriculture to really make this truly make this a unique program in total We've received 83 applications Including the 31 that have been approved and that is to support more than 408 million dollars in project costs So what's what's so inspiring about having this pot of funding is to see the great projects that are being done all over the State by all kinds of people in all kinds of organizations Applications not approved in this round remain under review And they'll be announced in the coming weeks as they are approved applications will continue to be accepted Until the funds are allocated as the governor mentioned the ARPA funded projects are a great start And it's great to see ARPA dollars at work But we know we need to do more and we need to maximize these investments And that's why we need the legislative support to support our full slate and portfolio of economic development initiatives this year including brownfields the rural industrial development fund the Vermont training program and new worker incentives So we can continue the work of ensuring our communities are able to recover Grow and continue to be revitalized in the future with that I'm going to turn it over to Mowee Moulton and Letty Moulton from neck of the woods daycare Which is based in Waitsfield one of our recipients. Thank you Good afternoon. I am Mowee Moulton. I'm the executive director of the neck of the woods program This is Letty We'll see if she can make it through this one neck of the woods is a child care center located in Waitsfield, Vermont with me here today also is one of our amazing toddler teachers destiny Miller and We're hoping that let he can manage to hold out her squeals of appreciation for this grant quicker than than I can We're absolutely honored to be here today and we would first like to express our utmost gratitude to governor Scott To the state legislature the agency of commerce and community development and those administering the community recovery and revitalization program that is hard to say when you haven't slept in three weeks Similar to the rest of the state the matter of valley has a dramatic shortage of child care available to families with children infant through preschool age Many families have to patch together child care options often with one parent staying home and out of the workforce Providing high-quality child care is a critical component to young working families considering moving to Vermont Neck of the woods programming began in the early months of 2020 with our team's vision of providing high-quality child care and early education to the Madover Valley and the surrounding communities in 2021 we bought our campus from small dog electronics, which includes 11 acres of land a 10,000 square foot building and a 5600 square foot warehouse At the time of purchase we began a two and a half million dollar capital campaign To renovate the main building and turn it into a much needed child care facility Since 2021 we've renovated the main floor of the building to include five classrooms and bathrooms and improve the building envelope In HVAC systems We currently have 45 children enrolled per day ages six weeks to five years of age and We'll be expanding to allow available slots to 68 children per day by the end of this year this grant that we were awarded of $468,000 will give us the opportunity to continue to expand our facility By renovating the second floor to open up more classrooms Adding handicap access with a new elevator and entryway It will allow us to continue to update the building envelope and systems to meet all state and federal requirements and in a county where there are over 1100 slots needed for infant through preschool age children to meet the demand for child care We will be able to provide 125 slots for children in these age ranges once the second floor is complete We would again like to express our appreciation for this grant and the opportunity to help fill the critical need for child care for Young families and our beautiful state of Vermont. Thank you Thank you so much and we'll now open up for questions Maybe we can stay on topic first and then we'll take the off topic questions afterwards Is that specific We're we have a whole slate of economic Growth initiatives that we'd like them to to adopt So I'll let Yes, so for the community revitalization We're not asking for additional funding but we're asking for 10 million dollars for a brown fields revitalization And 10 million dollars for industrial development funds to help spur development Of industrial facilities all around the state so that we're ready when businesses are growing Or when we're recruiting businesses from canada as an example The other asks our five million dollars for a Vermont training program and four million dollars for the relocated worker You mentioned housing being one of the sectors to which these grants are going Do you have a sense of how many new units will come on board as a result of with this With this 40 million dollar grant with it with the 10 million that is being appropriated in this round If you can talk about 31 net increase in housing units So of this in the 31 projects, we're going to have 79 new affordable units. That's in white river junction So there's that's one project in white river junction. That is that's correct. Okay For the first round we have many housing applications in q There'll be more I Guess it's probably different for different projects, but when you're just you're vetting these applications What are some of the criteria that you guys are waiting? Sure. Um, thanks for the question project readiness weighs very heavily as does the Sourcing of other funds most of these projects have many sources of funds and if it's early days We really are kind of deferring those and asking them to come back when they've located some other sources of funds I'd say those are the two most heavily weighted We also look at geographic distribution. We look at child care and affordable housing You know, so there's a variety of things that go into it But I'd say the two largest components are readiness and the leveraging of other sources Is it moe? You talked about This grant giving your organization the opportunity to be able to create more slots for kids Presumably, you're going to need more people Employed in order to do that. Can you talk about? Have you think you're going to be able to address some of the workforce issues that other Organizations like yours are running into That's a tough one. We We definitely all in child care see difficulty in hiring staff. Is that what you're asking? Yeah So we do we do find that it's a very difficult balance Because in child care, you don't necessarily get paid an extremely large amount of money So I think what we've been doing is we've been working with our board to piece together How that balance is going to look and making sure that we continue to Build programs so that we can use a multitude of programs that will funnel The money in together so that we can distribute out more money to our staff than we otherwise would Be able to with just one or two programs. So we kind of use A multi-tiered approach to those things Yeah Any other questions on topic anybody on the line? Yeah, tim sarah edd or guy have on topic questions Want to go now we can circle back for off topics All right, thank you very much she was very good As you know before Fortown meeting day break the senate voted out s5 Looks a little different has a has that check back has a study Associated with it does does the current proposal that was voted out of the senate meet your concerns? No, no it doesn't But we still hope that they will be able to make the changes in the legislature as it moves through Through the other body and we'll go from there What specifically I want to build to specifically say We're going to take this back in And then we're going to I've listed it in the in my veto letter from last year Um exactly what it's going to cost and what it's going to mean and what we're going to look for And I think it really has to be specific and go through the normal bill process And it really doesn't say that it says a check back of some sort and and that could be changed In many different ways and it really is in the wording Because who knows who's going to be here when that comes back from the puc Um some of us may not be here. I may not be governor. Who who knows? So, uh, that leaves it to interpretation And when you go to interpretation, you have to go by the the words Not what the the intent was If I'm staying in a hotel right now, government is paying for me to stay in a hotel in a hotel And I hit that exit date right whether it's may 31st or June 31st What what's next for me Well, we've been doing, um, I think something that's uh, that hasn't been done before Um, and because a lot of this is new to us in some especially the expansive nature of all the folks who are in the enrolled in the hotel motel program But we've gone in with a team to actually Talk with with them to find out what their needs are And what they're going to do or what brought them here and what they're, uh, what we're going to do in the future. So With this, uh, type of approach We'll have a better handle On where they go next and have provide some of the services they need Whether it's in recovery, uh, whether it's in transitional housing Whether it's in any type of other type of temporary housing, uh, as well as for long-term housing So finding job opportunities and so forth. So we're working, uh, to try and make sure this is soft landing for them as possible So they can get back on their feet And we can get them into permanent housing in the long run What would the temporary housing options look like for some of our But we still have our program, right? I mean it's still it's still there still viable So the program a general assistance program isn't going away. It's just changing Um, but um, but with some of the investments we've made In some of the the organizations There will be more housing available in temporary in nature There as well Man ask, uh, Secretary Samuelson if she could take it from here All right, thank you I'm going to address that question quite well. Um, I just want to reinforce that The agency and the administration they've had a very active role investing in Housing having to re-house more than 3,000 People over the period of the pandemic We continue to work on making unprecedented investment bringing the units online As well as um adding shelter bed And shelter units As we work move forward As the governor reiterated the agency conservative is in part that across all of the departments as well as with other Other agencies And working to assess the needs Both the health care needs housing needs the economic needs and the workforce needs In the hotels, which is that's more information than we've ever had before but also As a stronger resource casement approach To working with individuals who are spending often We have confident that these approaches will give us the information need to continue To move forward and to help the clients that we're working Forward into housing or other situations in the future Secretary Samuelson, I Understand there are about 1,800 households and motels and hotels right now when we Go into the new iteration of this program on june 1st of july 1st or whatever it is How many households where there'll be capacity for In in that program We are currently Lately And across our shelters across the work we're doing for the Vermont housing for the end of What I can say is there are Many individuals who were formerly housed off the family and friends units We came This point in time in in those analysis, but We can we can and have supported on going family and Houses as their loved ones We're being in creative ways each day Providing financial support to individuals experiencing the font all turn What I will be Our current in program not favoritizing the most vulnerable and the number of units that are available for decline the for all of us to Begin to look at our programs going forward To make sure that we're housing children and youth That we're housing individuals with disabilities and older adults as we go because right now we're not able to accommodate them in our current programs I think you probably got part of that right? Yeah, I guess I'm just wondering is there a number that you can give us for the number of households that will that the New that the next version of the program will be able to accommodate at any given time Thank you That's why we want to continue investing in the v-hip program for instance providing for accessory dwellings We think that that's provided a lot of units For a lot less money than the traditional housing program So we want to continue to invest in that area and hope the legislature goes along with us You're gonna in the not too distant future Have a bill arrive at your desk that seeks to address The issue of suicide in vermont by instituting a 72 hour waiting period for firearms purchases A requirement that gun owners keep their firearms securely locked away when they're not using them Um What's your inclination right now? What's the bill member again? 230 So that that's past the house. It's it's uh in judiciary right now In judiciary so hasn't got through the house yet, right? It's Okay, and then so it gets through the house and then it goes to the senate. Yeah. Yeah, so it's it's got a ways to go First of all, yeah a lot of changes there. I think I've made it very fairly clear I don't believe I personally don't believe we need to make any direct dramatic changes in our gun laws at this point And from my perspective That would be problematic So we'll take a look at it when the final version finally does arrive But I'd say it still has a long ways to go Problematic in what way for me In what why is it a way? Well, like I said, we we took as you Probably remember about three or four years ago. We took a lot of steps towards implementing gun safety regulations and oversight And and I think we can still improve in that red flag laws and so forth There are some provisions that we could we could adopt and go along with but If it's too expansive then it's probably a non-starter from my perspective But what's the problem that's created for gun owners in the state by the provisions that I was just referencing Which one's uh, we had 72 our waiting period and again, I've I've been resistant To the 72 hour waiting period. I think I've even vetoed one. It was for Handgun legislation in particular Um, I just don't think that that's uh, workable. I just don't I don't think that that helps the situation And say storage and you're uh, well again, who's going to who's going to have oversight of that? um, like who is going to um Someone going to go into your home and inspect your home and determine whether you've Put your firearms away I mean, how far are we going to go with this? I mean that those are the types of things that people are going to ask And they're going to wonder does this give um Uh, give law enforcement the ability to to come into your home to inspect your Whether you have your firearms stored correctly Governor as you also Probably seen the budget adjustment is uh coming your way. I think it was voted out of conference committee or they're working on it They did voted voted out. They did. I know you would raise concerns about the size and scope of some of the spending I mean, what would you make of the? My concerns are still there. Um, it's 50 million dollars over What we had asked for in budget adjustment About half of that was for the housing piece that uh, that peter was referencing um, and the other um There was 25 million more for Vcb as well So it's not where it's directed in terms of housing vcb and so forth But where's the money going to come from? Uh, you know, we presented a budget that we think was Balanced and made the appropriate investments. So it's got to come out of something And with all the other All the other initiatives they're talking about now. We're talking about I mean, there's a lot of money at stake here Hundreds of millions of dollars Is are those concerns enough to warrant a veto? We'll take a look when it gets here first of all. I mean, we have a bill review process When we get the bill we have five days to act upon it And uh, we'll take a look to make sure that there are no mistakes within the bill I think that's something that a lot of folks miss That it's not just uh, we don't just take the word for it because we've seen I think it was last session We had a number of bills that had mistakes there. I mean they were rushed through and There's some oversight, but but you miss things when there's so much at the end So we want to make sure that it's workable. We'll have all our agencies and departments Look at the the portions of the bill that they are responsible for And then give us any feedback they might have at that point. I'll determine whether to sign or not sign Um or whether to veto As we do with every bill Yeah, I mean we are in constant contact with emergency management And I had a cabinet meeting this morning We went around the table and discussed our areas of responsibility and how we were doing Obviously the the southern part of the state got hit the hardest and this isn't over We have we have some concerns about What the high winds are going to to bring and more snow The other regions of the state a lot of power outages in the southern part Before this press conference I I learned that we are setting up. I believe An emergency shelter in the Brattleboro area Route 9 is closed. Route 7a is closed a portion of that route 12. I believe in Bethel is closed. So This is you know, it's it's a significant event northeaster And it's not over. So we have our share of concerns anything uh, commissioner morrison you'd like to add to that Uh, sorry, I'm going to add that as a half an hour ago we had over 36,000 averages reported So it will be a multi-day restoration period. Um We have uh I've authorized the opening of a partial virtual emergency operations center to coordinate our response and to support the utilities in their work in whatever way possible Um, so yeah this one the the snow accumulation has gone much further north Then we anticipated uh, and We ran a higher volume and so I think once those winds pick up we're going to be really concerned with more trees and lines coming down the governor alluded to several roads that are closed. We had multiple reports of trees online or or uh Just trees across roads too. So This will be a messy one to pick up from everybody should be very very cautious stay home if you don't need to be outdone But we have you know, we're prepared as well as we could Green Mountain power did a great job in bringing over some of our partners from the north and canada So we were all set up ready to go In anticipating that there was going to be power outages as a result of the storm Questions online Yeah, go ahead New cc has the only landfilling provide they have the virtual To read These are why I'm bringing this office because they're most recent business strategy has been to virtually vertically integrate Which means they're buying out independent hollers. So they have control of the waste chain from the garbage can to the landfill So Recently last they made purchase of a company up here and at the same time they got post a couple of hollers from another company That company couldn't replace the drivers. So they had to keep up those routes um Just wondering if there's any concern on the administration's part that they're going to develop a virtual not leading state and we'll set their prices And they did increase their rates out last year and they increased them again from back in february I just wanted what you're taking is on how we can manage the facilities that are protected by A company that really is operating and also about production Yeah, um, well a couple of things. Uh, first of all, I don't know is there are many companies many entities who didn't Increase their rates over the last year or two. So I'm not surprised by that As well, I would say we do have another major competitor Myers out of the chitin county area And there's some others throughout the state as well So I don't think we're at a point where there's concern about a monopoly But I understand your point and we'll continue to monitor that In any way that we can This is uh, you know a problematic In terms of having just one facility that takes all of the Trash throughout the state. Uh, and so again, um, I don't have all the answers to that but um But I get your point when we'll continue to monitor Okay In January, I'll have a one-to-one pick up in January. It cost me eight dollars plus a dollar 61 type of fuel tax And then I'll talk in February On the office in three dollars sixty-one seven full tax I guess I'm wondering if Seeing how they have a lot of tariffs were locked up Um, and in the same position as if I was going to try to shop around for another electric utility Oh Yeah, well again, I don't think we're at that point right now, but we'll continue to monitor the situation Thank you S18 I'm not familiar with the bill All flavored tobacco Um, yeah, you know, um, I think in the past we've we've commented on that I'm I'm I'm a proponent of as much prevention as possible I think one in six for monitors currently smoke and and I believe that Many of our youth that's when they get hooked And so we need to continue to focus as best we can on prevention I haven't looked at that bill in particular But but anything we can do to prevent someone From from getting addicted to smoking. I think it would be beneficial for Themselves their families and society in general. So I'll take a look at it. Um, but it's again It's going through the senate has to go to the house Has a long ways to go before it comes back To to here to me I think I just think it's unnecessary I think wages have been inflated. I said this before I wasn't in favor of Uh, a $15 minimum wage before the pandemic And I still am not I believe that supply and demand Works and in this case, I I believe most people are making well over $15 an hour Not to get smarter, but why Not raise the minimum wage by legislation in that case that demand has to put the patients up Uh, I won't get smart and ask you why if we need it, but Um, I again, I just think it's unnecessary. I I just don't understand why we would have to go through this whole contortion Um, when it's not needed at all and and I wasn't supportive of of artificially raising The minimum wage when we had provisions within the bill, um, that we have passed over the years I voted for it when I was in the senate And uh put a a multiplier on there due to the cost of living And from my standpoint, that's still working. Um, so I just think it's unnecessary Okay Just a clarification, um, am I hearing you say you don't mean principle You still need to look at the bill to see what's in there, but in principle You don't have concerns with a statewide ban on flavored tobacco from Vegas Yeah, I mean I again, I would like to look at the bill, um, but um, But personally I I don't have a concern for that Thank you all very much


My name is Josh, I'm a Malaysian and I completed the MBA Global program from the University of New Castle, graduated in the year 2020. I currently work as an internal sales engineer at KNR Hydraulics. Hi, I'm Karthika. I studied Masters of Business Administration Global at the University of New Castle and I'm working as a consultant at EY right now. My name is Harjit Soni. I have recently graduated from Master of Business Administration Global from University of New Castle. MBA Global offers courses in accounting, marketing, digital marketing, finance which are some of the core subjects that you learn in a normal MBA. Master of Business Administration Global focuses on each aspect of the management so it's like a bird-eye view of every aspect of the company. MBA Global has internships and international study tour. This will give you experience in the industry while you study your theory as well that will really prepare you for work. The MBA Global helped me to get this job and also thrive in this job simply because it actually improved my communication skills. Sitting in the right heart of the CBD, you get a lot of connection with the industry leaders. They are actually well connected with lots of industries around this region. If you are an international student, there are a lot of networking opportunities. The unique things I found about this university is the resources and the support you start. The university really supports the students. The lecturers they want to hear answers from us and it got us to come up with new ideas. The more you have exposure and experience in the industry, the more you work ready.


When you look at that kind of moment or service, what's missing from your perspective there? There is great potential to not just think about quality and efficiency all the time. And this, I also think, is really important within public services as well. There's a crafting of an experience in the service. And so, how do we create these experiential rich experiences? And so, I think ritual is one of those things that lifts that.


So, this is the link where you can open it up and basically this is the repository. It has the slides as well as the code. I would encourage you to check out the notebooks as we go along if you get time or you can refer to it later because it's a 45 minute presentation. So, we may not get time to cover each and everything line by line, but the intent is to understand what's going on, how do you visualize data, what are the main techniques or strategies we use, and then we have some nice bonus content also. So, how many of you have seen the agenda from the conference side? Which part do you find the most interesting or which part are you here for? Bonus, okay. We'll try to cover the initial parts quickly. So, I can even walk through some code about some interesting deep learning use cases which I have been working on for one of my books. So, yeah, I expected the bonus will be, that's why I added it in. So, yeah, okay. Good to see a full house. I mean, honestly, there are three other great talks going on. So, thank you for coming to this one. I myself wanted to attend another one. So, I really thank you all for coming here. So, honestly, I was expecting maybe 10, 15 people, but great. I underestimated myself. So, yeah, we have four minutes. So, what are you expecting from this talk? Anything in particular you want to see or anything you are kind of, why did you come here in general? Like, what are you looking at? Yeah, go ahead. Okay, sure. Yes, yeah, that was one of my, that was going to be one of my questions. So, how many of you here use Python or R in general for data visualization? Okay, wow. And the commercial tools like Tableau or anything else, okay. Good mix. Yeah, so today's code or examples are mostly in Python because that is what we use internally also for our use cases or even I use in general. But you can do a lot of stuff in R also and definitely you have the other open source as well as commercial tools. We won't be showcasing examples in different things, obviously, due to the lack of time, but we'll try to cover some interesting problems in Python and basically start with structured data as you have seen in the agenda, which I'll be showing you shortly and definitely cover the bonus content, show some nice examples also. And in case of any issues with accessing the link, just let me know. I'll reach out to you. So yeah, we'll get started since there's a lot of content, but want to cover the interesting parts also towards the end. So today's talk will be on data visualization as you have all seen the session, I mean the agenda. So basically, this is the agenda which I've also put in the website. So a basic introduction about what is visualization, why do we need it? Why do we need effective data visualization? Then moving on to the interesting part of how do you structure a visualization using Grammar of Graphics, which is a very interesting thing, instead of playing around all over the place. Talk about some tools and frameworks. Mostly I'll be just saying what are the frameworks out there, because there is not enough time to cover details about each of them. And then some more examples of visualizing structured data using the Grammar of Graphics, again, where we go from one dimension up to six dimensions. And then the interesting part because of which all of you are here, visualizing unstructured data. So what do we really do in text data, image data, and also audio data? How do you really visualize something which you hear, right? So which you can't see, so, and then the final words. Slides and code are here, as I've also shown it before. So feel free to access it, and you can even open the notebooks, check it out now, later, and so on. So yeah, a bit about me, basically I'm a data scientist at Intel, and I do a couple of other things like writing, doing some research, publishing articles, papers, and also mentoring and training people around data science. So understanding the what and why of data visualization. So the idea is storytelling with visualization where you are building up a nice story with regard to data which you have, using the right visuals and using a combination of these two, you build a nice story, and how do you do that? A combination of art and science. As I said, data science typically is not just science, it's art, and you need a combination of art and science for visualization. So what is data visualization? Basically it's a technique to build a visual from data to convey the right information, that's the key definition. And why do we need it? So data aggregation, summarization, and visualization, typically the core pillars in any data science or analytics pipeline and be it from the traditional BI days as a lot of you have been there, and till now when we are moving into the age of AI, it's still there, right? It's adopted widely by the organizations and even if you like build a model, in the end you have to visualize the results so people can see it. So why do we need data visualization? Like, why is it important? So as you can see here, we as humans are wired in such a way with our eyes and brain that we kind of try to find out patterns in everything which we see and interact with daily, right? Like, basically if I'm traveling from my home to my work, I will try to find out a pattern like, which is the shortest route or which is an interesting route in which I might take less time, things like this. So basically we try to find out patterns and building stories and narratives from them on a day to day basis. And from the business context, basically you shouldn't really be building a model directly with the data. You should try to understand what is your data, what are your features, what is important, what may not be important so that you don't end up blindly building a model which can lead to some cascading adverse effects. We'll show an example of this later. And the idea is to enable better business insights to the right visuals. Some general interesting statistics. So 90% of the information is basically transmitted as visual information in the human brain. And as you can see, we process visuals 60,000 times faster than text. So obviously if you have a visual, it's very easy to see it. As you can see, we basically can process and observe visual and understand it and comprehend it within a fraction of a second. So that's why obviously, better than reading a bunch of text, as you can see this visual, you process it immediately. And 65% of humans are visual learners. We love to see things. So now an interesting thing. So what do you think is common in all these images? Yeah, they have points definitely. Yeah, exactly. So the summary statistics of all of these is interestingly the same. But as you can see, the data is widely different. So that's the reason why we need data visualization. Obviously, this is an interesting adaptation from the classic visualization, which a lot of you might be knowing the ANSCOMs quadrature. As you can see, four different types of visualizations, but the summary statistics is pretty much the same. So this basically tells us that don't model your data blindly. Definitely visualize some aspects of it before building a model. So why effective data visualization? Why do we need to build effective visualization? So the answer is pretty simple. We need to focus on abstracting out unnecessary data, noise and clutter and show the information in the right way with simplistic visualizations instead of building out fancy visuals. And obviously, the way to do this is to leverage the concept of grammar of graphics to depict the right information using clean and concise visuals. And as you can see, typically some famous sayings, a picture is worth a thousand words and John Tukey, the founder of the box plot, which we often use day in and day out, tells us that the greatest value of a picture is when it forces us to notice what we never expected to see. So not only seeing the expected, but also the unexpected. So quick question, who is John Snow? Game of Thrones? Okay, so we'll be talking about the other John Snow today. Famous physician who built a nice visualization actually in 1854. So, yes, exactly. A lot of you might be knowing it's very famous. So visualizing the Broad Street cholera outbreak, obviously, we didn't have any fancy tools, no frameworks, no dashboards or whatever right then. So as you can see here, if you check out this visualization, these bar charts are basically like a dot plot. These are the number of deaths due to cholera occurring across this area of Broad Street in the city of Westminster, London, England. So over 600 deaths happened in 1854, and people were not knowing why so many people are dying, right? Because it was, I mean, you didn't have good healthcare back then, and so on. So the physician John Snow identified the source of the outbreak as this water pump, as you can see. So how he found this out, obviously, he narrowed it down based on, as you can see, the frequency of deaths are increasing closer to the pump. And what he saw was the quality of the water, which the pump was dispensing, it was connected with some sewer lines. So due to the sewage water leaking with the actual drinking water, people were ending up getting cholera. So an interesting visualization, and back in 1854, basically a source of motivation for us to do even better with all the nice tools we have. Another interesting visualization again around the same time.  So she had a very deep seated interest in statistics and also nursing, obviously. And she developed a polar area diagram. So the visualization she built was actually showing the causes of death due to basically for military personnel in the hospital she was working at. So as you can see, this is a visualization, not a very simplistic one, but she built this in 1854. And as you can see, nowadays a lot of you might be seeing this visualizations with frameworks like D3JS and so on, but nothing was there back then. But she still did it by hand. So that forms a source of motivation. Basically, these are the total number of deaths, the blue wedges. And these are caused based on preventable diseases.  And the red proportions show the ones where the soldiers were actually wounded. And the black ones are basically the proportion where they died due to unknown causes. So a pretty interesting visualization even back in 1854. So let's talk about effective multidimensional data visualization. How do we visualize data in a structured way using the grammar of graphics? As you know, grammar is basically a set of fixed rules and structural rules which we use to define or give structure and semantics, syntax and semantics to any language like the English language and so on. And the grammar of graphics is a framework which follows a layered and a structured approach where using some specific layers, some semantics, you can build a nice visualization instead of doing some random trials and errors. So what is this layered approach in the grammar of graphics? This is exactly what it is. So as you can see, the lowest layer is the data, which is basically our data sets. The next part is the aesthetics. So what is the aesthetics? It's things like what goes on the x-axis, what goes on the y-axis? Do we need to show some variable with colors, sizes, proportions, things like that? Scale, if you need to scale some values, if you need to represent multiple values using some scale, geometric objects. So what kind of a plot it will be? Will it be geometric points like a scatter plot, which we saw earlier, or a line chart, things like that, or a bar chart? A statistics, this is optional if you want to showcase some statistical measures in the plot like a confidence interval, quantiles, so on and so forth. Facets is very important, perhaps one of the most interesting things because of which we're able to do or go to a reasonable proportion of higher number of dimensions where you can basically create subplots based on multiple dimensions, each dimension again is a feature. And the coordinate system, typically whether you want to go for a Cartesian system or a polar system, and so on. So a quick example of the grammar of graphics. So I'm using the plot 9 library. So ggplot2, as you folks might know, very popular library in R. There is an analogy in Python that's known as ggplot, but it's not actively maintained anymore. And due to pandas doing a lot of changes recently, if you try to do some specific things like try to plot the statistics, it throws an error. Plot 9 is actively updated, so feel free to check it out. So this is an interesting data set. A lot of you may have seen it, a basic car data set. You have the actual cars, the miles per gallon, number of cylinders, some other interesting things like whether it's a V shaped or a square shaped engine, whether transmission is automatic or manual number of gears, and so on. So a simple plot, you start building with data, aesthetics, scale, and geoms. So hopefully you can see the code. If not, feel free to check out the notebook also. You mentioned the name of the data set. The aesthetics basically my x and the y axis. Geom point basically tells us we are doing a scatter plot. And the next one is a theme basically. So how do we move higher? We leverage color and size here for visualizing up to four dimensions. So this is typically a three dimensional plot where we typically use color. And we can visualize what's happening. As you can see, you can build a nice inference here with regard to the number of gears and the weight and the miles per gallon. Now if you want to introduce a number of cylinders, we basically introduce it with the size here. Size equals CYL, if you can see it. And we can visualize both with the factor, the number of gears as well as the cylinders. So as you can see here, typically number of cylinders are higher, as well as the number of gears are lower, but the weight of the engine is higher. So you can build out some insights like this. And if you want to visualize some data aesthetics and statistics, what you can do is you can also mention what kind of a statistical measure you want like in this case I'm plotting a linear model with regard to each of the aspects of weight and miles per gallon. So with facets, you can actually visualize up to five or even six dimensions here. So in this case, we are visualizing four dimensions. So the difference here is instead of using the size, we are just putting a facet around it. So basically subplots, that's the gist of it. So you have your two dimensions, your third dimension is the color. The fourth dimension, basically the number of cylinders is used as a facet. And now if you want to move to 5D, what do you do? Typically, put in the size also because it's difficult to fake a notion of depth. Which we'll talk about later with another data set. So you have your facets here where 0 or 1 is basically whether it's automatic or manual, the transmission. Your miles per gallon and weight is here. Your size is based on the number of cylinders. And the factor is basically the gear, which is the color. And as you can see here that with automatic transmission, you typically have cars with higher number of gears. So how do you go up to 6D? Basically visualize with multiple facets here. So on the X, we put the transmission. And on the Y, we put the number of carburetors, I think. And as you can see here, you get some interesting insights. Like higher number of gears basically falls under automatic transmission. And also the size is huge because the number of cylinders is more. And here, number of cylinders is less typically. So this was a quick introduction into the grammar of graphics. I don't encourage using this library in general because as you all might be using, you use more of Matplotlib or Seaborn if you use Python. So I'll show an example around that shortly. Quick glance at the popular tools and frameworks. General purpose, Python and R. So these are the popular data visualization tools and frameworks. Combination of open source and commercial. So you have D3JS, Tableau. Again, it's very difficult to put everything on a sheet. So I picked some of the most popular ones and even some which we use. So Excel definitely one of the most oldest ones out there, right? And you have some interesting JavaScript based libraries including fusion charts, high charts, leaflet. There's Data Rapper, Plotlib, pretty much a generic framework you can use almost anywhere, right, even with Python and R. And Kibana, another interesting one. How many of you have used the ELK Stack Elasticsearch Logstash Kibana? Excellent, so we use the Elasticsearch Python Kibana. Typically we use Logstash if it's very straightforward for more complex data enrichment we use Python. But visualizations, you all must know it's quite interesting and often easy to build some visualizations. But definitely a lot of work has to be done, right? It's pretty limiting in some areas, so they are doing interesting advancements. How many of you have used TimeLeon in Kibana, okay, nice. So yeah, most stuff is coming out, so that's another interesting tool. Python data visualization frameworks pretty much, Matplotlib, Tandus, Seaborn, Boke, PyGal, Plotlib. Any other interesting frameworks which maybe I have missed out? Altair, yeah, I thought I would add it. I didn't get a nice logo for it, so good one. How many of you have used the Tidyverse in R? It's really amazing, right? So that's an excellent, I would say a combination of libraries. So GGplot to definitely one of the core libraries showcasing the grammar of graphics by Hadley Wickham, so definitely a good one. There is Lattice, there is Base R. GGruff is a nice one. It actually enables your GGplot to plots to be interactive. So definitely check it out if you haven't used it. Then there's TauCharts and Plotly. So visualizing structured data, basically another example using actual libraries, which we use, Dane and Dayout, Matplotlib, Seaborn. Start with the data as always. What's this data? You can check it out in the repository also. It's the wine data set, wine quality data set, a pretty common data set from the UCI machine learning repository. You have different physical chemical properties of wines, so things like acidity, residual sugar, types of wine, basically white and red wine samples. And the quality of the wine, like higher the number, the better the quality of the wine. And these are some examples. The complete description is in the notebook, so don't worry about it. You can check it out anytime. Things, what are the properties of different wines? So what can you start by doing? You can always do some basic descriptive statistics. Hopefully the code is visible. So you have basically two data frames which I had created and I'm visualizing some basic statistics. And as you can see things, interesting things like residual sugar typically is lower in red wine as compared to white wine. So hold on to that thought. We'll come and check it out in some visualizations also. But basically we start like this, right? We do some basic descriptive stats. And then we move on to start our visualization process. So one thing you can do is if you have a pandas data frame, just call a HIST and check out the numeric variables typically which would be showcased as histograms here. And you can see some visualizations as to what it looks like as a univariate analysis. And ways to visualize one dimensional data, pretty standard techniques, right? If you have a continuous numerical attribute, you can do a histogram. Those are basic annotations in case anyone is wondering. I'll maybe mention this once and we'll go over it quickly for the next one. So this mentions the size of the figure typically in matplotlib. This is like a super title as it's known as if you have multiple subplots, you can have one title for it. These are basically spacings between the title and the plots. Here I mentioned I just want one plot and x label, y label. I'm also showcasing the mean there as you can see by computing the mean and annotating it in the plot. And this is the gist of it. So this is the main thing. I'm doing a histogram on this axis. And I'm doing it on the sulfates. So I'm showing the distribution of sulfates. So histogram is one thing which you can do. You can also visualize a kernel density plot using C bond. So there is KDE plot which you can use and visualize the distribution. What do you do for categorical data? Bar charts, typically the most standard technique. So you can use bar charts and check out the frequency. Pie charts, maybe if you have three or four categories, wouldn't encourage if there are more because you see how difficult it becomes to interpret. So not effective. Going to 2D, what you can start with, obviously, visualizing feature correlations. So do a wines.corr. You get the correlation matrix and put it in a C bond heat map, and you get this nice looking visualization. Can anyone tell why the density and alcohol are negatively correlated? Yes. So higher the alcohol content, because density is measured based on water. And as you know, alcohol, if you put it in water, it will basically float. So it's inversely related. So higher the alcohol content, the density becomes lower. So these are interesting things you can always check out. Next part of visualizing 2D data, pretty much standard techniques. If you have two numeric attributes, you can visualize it using a scatter plot. If you use C bond, you can do a joint plot search. So I've shown both type of examples. So you can see what you can do with map plot lib as well as C bond. And obviously, you can check for correlations directly if there are any. If there are not, it's not there. And you can always do a pairwise plot. Not sure if the code is visible, but it's using C bond pair plot. And you basically specify the column names which you want. If you don't specify it, we'll do it for everything. And you can visualize correlations, check out what's happening, and so on. Visualizing two categorical attributes. So in this case, visualizing the quality of the wines and the type of the wine, red and white. So as you can see here, we have used a phasing technique, two subplots basically. And as you can see, basically a pane with map plot lib, not because of the huge lines of code, but more because we have to specify the x and the y values, like extract it out and specify. So this is a big pane. I'm sure a lot of you may have faced it with bar charts when you're doing with map plot lib. So what is there a better alternative? Yes, there is one line of code. You get everything. And you can compare it also. So use a count plot, specify the x-axis quality, use the hue. So we are using the color aesthetic here. Remember from the grammar of graphics, data is my wines as a data frame. And you can specify a palette because I wanted to customize the color.  But just one line of code specified, you're done. So mix of categorical and numeric attributes. So again, if I have a numeric attribute, basically continuous numeric data, that's what I'm talking about. And a categorical attribute, like in this case, the type of wine, a red or a white wine, what can you do? So basically histogram distributions. And this is using a faceting technique. So you can also do this with c-borne. But what I will show, I'll come to it in the next file. But as you can see here, this is the core code. So we set up two subplots. So what this is saying is basically one row. And two means there will be two columns. That's what. So if I had four plots, I would do two, two, and then one, two, three, and four. That's how you build subplots. And here, this is the main aspect. So I'm plotting the sulfates. And here, I'm plotting the sulfates again for the white wine. So you have to separate out your data and do this. So is there a better way? Yes, there is. Use a facet grid combined with a distribution plot. So in this case, facet grid helps us in specifying the hue, again, using the grammar of graphics, where I'm saying that the color should be based on the wine type. Use a disk plot. It's basically a histogram. And what am I plotting on sulfates? KDE false means I'm not doing kernel density estimation. I am not showing a density plot, but the histogram directly. So using just two lines of code, besides the annotations and the other stuff, you can also do this. What about more number of categories? So here, we have just red and white. But if you have a lot of categories, what you can do is visualize using a box plot or a violin plot, where your x-axis is pretty much the categories and your y-axis is the numeric variable. You can visualize the distribution also using a violin plot. Or in this case, check out the quadiles, the overall outliers, and so on, using a box plot. So visualizing 3D, we have three numeric attributes. Is this effective? Not really, because as I said, here we are faking the notion of depth. I mean, we are introducing a third dimension as you can see here in the z-axis using alcohol. But it's still a 2D plot, so it becomes difficult to interpret as to what is really happening in this case. So what you can do is you can bin one of the numeric attributes as a categorical attribute. So because at the end of the day, you are still stuck with the 2D screen. And we as humans, we cannot visualize so many dimensions together. So use one of the attributes as a categorical attribute. So in this case, that is residual sugar. So what do these numbers mean? It is basically the quantiles. So the 0th percentile, 25th percentile, 50th percentile, and 75th percentile. So those are the ranges, and it's trying to plot it. Obviously, the color one shows some more indication, as you can see, where the residual sugar is typically there. Like higher residual sugar is pretty much here, as you can see, but it's still not that interpretable, especially the left-hand side plot. So you can introduce phase setting here. So now the problem is if you're using a phase set grid with matplotlib scatter, interestingly, in the previous plot, if you saw that C-bond automatically did it, like if you check this out, we just specified the continuous numeric variable there as residual sugar, and it automatically found out the quantiles. But if you try to do that here, your kernel might crash, because matplotlib is not that intelligent. So that's why you can use this interesting function, pandas, q-cut, a lot of you might be knowing, where you pass in the continuous variable, you specify the quantile list of the bins you want, and it will automatically bin the data for you and convert it into a kind of a categorical bins. And then you can put it here, and as you can see here, this gives us an interesting aspect. So actually, I used quantiles on both alcohol levels, as well as residual sugar, just to show it. But you can see here that, overall, the residual sugar level, based on here, if you check this out, the alcohol levels are pretty much, if the residual sugar is higher, the alcohol levels, or basically the fixed acidity in general, is pretty much low. And for the other ones in the lower ones, it's more spread out. So you can get insights like this, and even play around with other variables, and put this in and check out what's going on. So visualizing categorical attributes, if you have, I think, three categorical attributes, in this case, what you can do. So basically, you can use a combination here, with regard to the wine sample type, whether a red or a white wine. What is the quality of the wine? The higher, the better, or the more pricier wine. And these are the alcohol levels. So as you can see, higher the alcohol level in general, the wine qualities are also higher. Like in this case, lower quality wines are having a much higher frequency, as compared to the ones where the alcohol level is higher. So the gist is higher the alcohol level, the more expensive the wine is. Visualizing mixed attributes, so combination of numeric and categorical data. So if you look at it here, as you can see, you pretty much have a scatter plot, and in that case, a density plot, and you can visualize one of the categorical attributes using the hue, as before using the grammar of graphics. Some other examples here. So using a box plot for more categories, and the alcohol level is on the x-axis, and you can see, basically what we saw earlier, that higher the wine quality, the alcohol level consistently keeps increasing. Same with the wine quality class. And for the red and the white wines, it's pretty much not differing that much. So, yeah. Visualizing four dimensions. As you can see here, that pretty much some parts are interpretable, like the red wine, the residual sugar, which we talked about earlier, is lower as compared to the white wine, but other than that, it becomes difficult. So use a combination of the different aesthetics from the grammar of graphics again. So you have the residual sugar levels, and as you can see, higher the residual sugar, you can clearly see now that red wine samples are very less as compared to white wine samples, and also fixed acidity, you can check out like lower the residual sugar levels, higher the fixed acidity, and also more the content of the red wines. So red wines have a higher fixed acidity in general. Some other examples, basically, you can check this out like alcohol, volatile acidity, wine quality class, and also the type of the wine. So as you can see, higher quality wines have a higher alcohol level based on that concentration you see on the top. So, yeah. Visualizing 5D, so in this case, again the notion of depth, color, and now size. So it becomes more complex, as you can see, right? As we start trying to go higher and higher. In this case, we used matte-plot lip for this one. So one way is obviously start with faceting and sizes. So here we already have the hue determined by the wine quality class. The size you can see here is the total sulphur dioxide. So higher the sulphur dioxide, the bubble size will be basically higher. But as you can see, it becomes pretty hard to interpret it as the number of dimensions start increasing here. So here we are using a facet grid with the same things again, the column is the wine type, the color is the wine type, sorry, the color is the quality label, the wine quality class, and the columns are basically, or the facets are basically the type of the wine, red and the white. But the size becomes pretty hard to interpret. So what you can do here is do a multi-faceting instead of taking the notion of size. So what we do here is for the sulphur dioxide, which I said, I again created a binning for it because that is what C bond does internally. And then I use this as another facet basically. So considering a facet grid, we have now, not sure if you can see this, but these are all the things for white wine. This is for the red wine and sulphur dioxide levels basically are increasing based on the quartile. So as you can see, as we have seen there that with the highest level of sulphur dioxide there, the red wines pretty much are decreasing. So red wines typically have a lower sulphur dioxide level. And for white wines, it's pretty much not really varying that much. But as you can see here, alcohol levels as we saw, it's consistently higher in that case. So you can get insights like this by using multi-faceting. Size becomes very difficult, but in case of 6D, as you can see here, we pretty much introduce a notion of shape. So that makes things even worse. So as you can see, it's not really interpretable at all. So what you can do here is use colour, size and multi-facets because that is what you're limited to in the end. You have an X and a Y axis and that is the farthest you can break. So as you can see here, mixed attributes, colour, size, multi-facets. So our size is basically what we did with the total sulphur dioxide again. And our other facets are basically the wine type, the quality of the wine. So red wine and white wine and the quality of the wine and the colour is another categorical attribute. And our two numeric attributes are basically residual sugar and in this case, alcohol levels. So again, as you see, the notion of size becomes more and more difficult to interpret and that is always a challenge in general. Now for the fun stuff, visualizing unstructured data, I want to show that notebook towards the end. So let's see if we get enough time. How much time we are having around? Okay, cool. So visualizing unstructured data. So what do we do with unstructured data? It becomes more difficult, right? Text data, image data and audio data. So starting off with some basic exploratory analysis. This is the Bible corpus. You can get it from NLTK, from the corpora there. And these are basically the different lines of the Bible corpus, some of the basic stuff which we do. Check out the length of the sentences. So as you can see here, typically a length of a sentence, the maximum length or where the majority of the distribution is there is between 60 to 70 characters. You can even split each of the sentences and see what's the typical word length. So for the sentence length in terms of words. So you can also visualize that distribution like typically 13 to 15 words are occurring the most. Next, what we can do is visualize language structure which we can use later for other things like POS tagging, building custom POS taggers or feature extraction. That is why we do this. So shallow parsing code is already there in the notebooks, so don't worry about how to do this. All the code is there, you can check it out. So shallow parsing is basically also known as chunking where we extract out the phrase, different phrases of any sentence. So as you can see here, the typical form here is NP, VP, NP, which means a noun phrase followed by a verb phrase and then again a noun phrase. And inside each noun phrase, you typically have things like a proper noun and then different, these are known as parts of speech tags. So pretty much if you just search POS tags, you will get the full list and what it means. Constituously parsing is another parsing where typically you break a sentence into substructures as you can see in this case, like you break a sentence into a hierarchical structure or a hierarchy and you can visualize the dependencies and what's happening with regard to a sentence. The next form of parsing which we can use to visualize what is actually happening in a sentence and also very useful for things like finding out dependencies, doing named entity recognition, things like these is basically dependency parsing where you can find out what are the main dependencies occurring between different entities in any sentence or basically multiple sentences and so on. Now how do I visualize text data? I mean actually like there is so much unstructured data if you follow traditional models like bag-of-words or TFID of things like these. Each feature is basically a word, right? So what do you do there? I mean you can visualize things like the frequency of n-grams, build out a n-gram cloud or a bar chart or whatever, but I mean word clouds are cool to look at but they are pretty much useless. So when you are building stuff with text data, what you can do is embeddings as today morning the talk was there, right? They are so important. So visualizing dependencies, semantics, you can use embeddings. The code for this is also there in the notebook. If you are interested in building your own word embedding, you keep hearing skip-gram method. Maybe you have used jensim.word2veg but how is it really working? Unfortunately, we won't be able to cover that but check out the code. It's there built in Keras so you can use it and even understand what's going on behind the scenes. Briefly, continuous bag-of-words model, what it does is you have all the context or the surrounding words and you have to guess what is the target word or basically the center word. The opposite is for skip-gram. You basically have a target word. You have to guess what are the context words. How it's implemented is slightly different. Do check it out and what do we get from this? So you feed in a corpus of data and essentially that's why it's known as word2veg. So literally a word gets converted into a fixed length vector or an embedding and then you can do a lot of fun stuff, right? Like we heard today the king, queen, man, woman stuff so you can start visualizing entities which might be semantically similar to each other. In this case, this is from the Bible corpus so some interesting things as you can see here like famine, pestilence, diseases coming close to each other. This is Noah and these are basically the sons of Noah so being clustered together. Jesus being clustered together there as you can see and also things like God, Christ, gospel. So some interesting stuff in general which you can start finding out and seeing that what entities are occurring close to each other. Check out the synonyms and semantically again not syntactically but why do we need all this, right? So considering machine learning, one of the great things is you can visualize documents based on the similarity once you have the document vector. So just like imagine it as a clustering problem but you're not really doing clustering. So what you can do is in this case, the full code is again, there is an example. We are just showing some specific samples so you understand why we are doing this. So you have some simple unstructured text documents and a basic category as to what the category, what's it all about? Like whether some are talking about food, some animals and so on. So what we do is we put this through a word-to-vec model. In this case, I have just used 10 features for basically the length of the vector. So each word gets converted into a fixed 10-dimensional vector. So what can you do now? Convert the document into a vector, right? So there is doc-to-vec. What you can do in this case is take the word vectors and just average them, that is one strategy. And for each document, now you have a nice vector. Now what you can do, just take a PCA of this and just visualize it. So then, as you can see here, the documents which are related to animals are kind of close to each other, the ones which are related to weather close to each other. So this is exactly like actually doing clustering, right? So you can start visualizing dependencies, you can start visualizing connections, things like that with word embeddings. Definitely very useful and a lot of you must have used it but these are the ways you can actually start visualizing and seeing what's actually happening with word embeddings. Image data pretty much all of you have been, or those of you who work with computer vision use cases must have been using it. So visualizing image data definitely a bit difficult because especially if you consider a grayscale image, you are kind of good with two dimensions but otherwise it becomes an n-dimensional tensor. So like a 3D image has three channels. So it's like considering an image of size 200 cross 200 pixels, it becomes a n cross 200 cross 200. So n is three channels in this case, red, green and blue and each image is basically just a 2D matrix at the end of the day. So as you can see here, you have some image data, you can just load it up to see what it's out there and then you can separate out the different channels using a technique like this to see how much of the red is contributing the blue and the green and what are some techniques to visualize image data from which we can extract features. So we can start with traditional image processing techniques. Those who have been working in computer vision will know this maybe even better than me. So you can start with checking out the image intensity distribution. So in this case, you just plot a histogram with regard to what is the overall intensity distribution with regard to the pixels and then what you can do edge detection. So check out the edges with regard to the images and extract those out as features which can go further into your model. Again, we are not really visualizing just for the sake of it but we want to actually use these features in the end. A more interesting thing is the HOG known as Histogram of Oriented Gradients. So here like SK Image is a nice open source library. You can use that it has Histogram of Gradients and what does this give us? I mean, this is an image it's cool to look at but what is this? So it basically counts the occurrences of the gradient orientation in basically specific localized portions and then you get this nice feature descriptor. So what is this? A flattened feature vector for each image which you can feed into a classifier or a model and so on. But now things have definitely changed because you can visualize image data using convolutional neural networks as there was a talk even in the morning about the importance of deep learning. So pretty much if you are building a facial recognition system you have a layered hierarchy in this case. So again, this is a dense fully connected layer but ignoring that like in the lower layers it learns more localized representations like the eyebrow or the edge or whatever the eye and so on. And as you go higher up the feature representations become more and more complex. So at the end like as you can rightly see it kind of recognizes the full face. So feature representations are great because as even the Spark talk which was there you saw the importance of transfer learning, right? So you use a pre-trained model which was trained on the ImageNet corpus of a thousand images and you can just feed a new image to it and even check out at each level what is happening in the network. Visualize it because if there are misclassifications you need to always go back and check what's happening in the network. So that is the most important thing. So the next part I have an interesting thing which we recently did. So can you see what you hear? That's the main thing, right? So how do you visualize audio data? You just hear music day in and day out or different things. So this is the urban sound data set as you can see. So what we have done is we have visualized the data. How do you visualize audio data? The first thing you can do is basically build a wave or an amplitude plot where you can see the amplitude of the different sound. So the urban sound 8K data set is basically 8,000 samples of 10 classes like street music, engine idling, a gunshot, siren and so on and the problem is can you build an audio classifier at the end of the day? So as you can see gunshot pretty much in the beginning there is a huge sound and then it's idle, right? So based on the pitch you can understand what really happens. So how do you visualize audio data? This is one way but the most effective thing is to use a spectrogram. If you check the definition of spectrogram it's literally a visual representation of audio data. So using spectrograms what you can do is you pretty much have an image now, right? So using this image, think of the possibilities. You can use any pre-trained image model and you can start classifying sound and that is exactly what we did. We used the spectrograms. We used spectrogram based on the harmonic and the percussive components as you can see here with this code. The library is Librosa if anyone is interested, open source Python library and this code you can pretty much it's extensible. You can use it on any audio samples and what was the use case we did? So audio classification. So you have this audio basically like a player. So this plays the music. It's just a representation.  So using this what you can do is you have this nice, think of it like an RGB image which you get from actual audio data and now you can just leverage a pre-trained model, right? Use a VGG and inception a resnet and train your model using this data and you can basically these are the results if you see for 10 images, this was trained with VGG 16. So overall accuracy close to 90% is what we got. I trained it for around 50 epochs mostly. Let's quickly check it out. Then I'll go to the final words. So this is the sound data as you guys can see, I'll zoom it for you. Is it visible? So we just get the sound data, we use Librosa to sample the data and then this is exactly the same thing I showed you, right, the spectrograms and so on and then what we do is we build this feature engineering model. This is exactly doing that diagram which I showed you, building that RGB image or you can think of it as RGB, it's not an RGB image and we are getting this nice 64 cross 64 cross 3 feature sets and then what we do is once we have this leverage VGG 16 as a feature extractor, we are not doing any fine tuning here so maybe that might improve the performance even more. I didn't test that and then extract the features, this is just bottleneck features. So you are just putting the image, getting out the features and that is your input data, fit a dense layer at the end of the day, that is what we do and then we predict it. So that is the training, let me go to that quickly. So these are bottleneck features which I stored which comes out from the VGG model. What do I do next? I put it four dense layers as you can see. So this is the architecture. So the features from the VGG go into this dense net and we train this model for 50 epochs. Code is there, you can check it out and you can even reuse it because it's there. It will be coming out in my new book around transfer learning. So you can check it out. The code will be there on GitHub. I haven't put it yet so you folks are the first ones to see it. So as you can see here, this is running for 50 epochs. This is exactly the same thing and how does it do on the test data set? That's the main thing, right? So that was just validation. So on the test data set we get similar scores as you can see 89% overall considering precision recall and the F1 score. And this is basically the confusion matrix as you can see diagonal is better and you can see here street music gets confused with children playing because it's outdoor. You can understand the thing, right? So you can start understanding what's going on behind the scenes but this is an interesting case study where audio data you converted in the form of images and that is exactly what the first talk today was about, right? Transfer the knowledge from one domain to another. So these are the ways where you can not only just build the model blindly but visualize the data, check out the amplitudes, the spectrograms and then build the model. So quickly the final words maybe I'll try to wrap this up in one or maximum two minutes. I know I'm running out of time. So what if you have a lot of features? Use domain expertise, use modeling to get the most important features, feature selection, dimension reduction techniques. We talked about it using PCA, SVD, T-stochastic neighbor embedding. That's what we did to visualize the word embeddings. You remember the Bible ones. Always remember to scale the features, remove the outliers as needed always because very important and if people keep pestering you about how are you going to visualize so many features? Just show them this file and that should take care of everything. So if you want to visualize 17 dimensions, visualize three and say 17 over and over again. That's how everyone does it. Jeffrey Hinton has said that. So some promotions towards the end. So what we use in our work, we are using the Intel distribution for Python. Many of you might be already using it like Intel, Math, Cornell libraries come by default with Anaconda now. So how you can get this, it's pretty much free to use. Just do a conda, config, add channels, Intel. Anyways, they're in the slides and you can get this. It typically runs faster on Intel architecture systems if you are using that. And last but not the least, some references, you can check this out. From where I, so these are some of my articles where similar code is there, which I've reused today. The paper, that paper is around the grammar of graphics and the code is already there, which you can get from here. And if you're interested in collaborating, research, writing stuff, reach out to me. If you're interested in writing for towards data science or even collaborating with me, my LinkedIn is here, GitHub is here. So that concludes the talk, open for questions. Oh, we have time? Okay, you can reach out to me, I'm here. So, yeah, it's okay. I'll be there, so feel free to reach out.