The following is a presentation of TFNN. The Trader's Edge with Steve Rhodes. All toll free at 1-877-927-6648, or internationally at<PHONE_NUMBER>. The Trader's Edge. Now, Steve Rhodes. Good morning, folks. Welcome to the November 17th. The fantastic Friday edition of today's Trader's Edge show. I'm your host, Stevie Perseverance-Rhodes, who absolutely knows that each of us should always be pioneers of our future versus prisoners of our past. Hope everyone out there is having a great day. Let's make sure we have an extraordinary one. Now, the easiest way to do that is to always remember that life is happening for us, not to us. That's right. When you and I make that one little two-by-four shift, we can finally give the net reset a circumstance that life is going to toss at us. Now, today you and I are going to go check on the circumstance of these markets. We'll go figure out what those bulls and bears, what those buyers and sellers are communicating to you and I at just past 11 o'clock in the morning. I want you to know I'm absolutely grateful for your presence here, but even more important than that. And that's this. During this next 53 minutes, I am here to serve you. So feel free to pick up that phone. You can dial on in at<PHONE_NUMBER>. Now, if you've got a question but you can't dial in, you can send me an email. Send that off to Steve at tfn.com. And inside the subject heading, please put radio show question. And, of course, if you're inside our tiger's den, well, then any in every ping will do. So let's go ahead and get this show started on fabulous Friday. Of course, this is Tiger Financial News Network. I'm Steve Rhodes. Welcome to the show. Right now, we've got a mixed bag out there. The mix goes like this. Now, it's trading down 44 points. About one-tenth of a percent. Same for the S&P, which is six points. Four tenths for the Nasdaq 100. 61 points there. Russell's up by over 1 percent. A 21-point move there. A quarter percent move to the downside in the semis. That's off nine. Trendy's up 101. New York Stock Exchange up 67. Gold is flat. Trading out at 1986. Silver's off 10 cents at 2383. Light's recruit is up to $1.75. Natural gas off 12 cents to 30 Treasury. Printed out at 1504. That is down 6 ticks. Lead in the charts. Dollar-wise, the upside. It is global. SA up 10 percent. 19 bucks. Broadcom is up 10 bucks. 1 percent. Ross Stores is up 8 percent. 10 bucks. Hubble is up eight and change. That's a 3 percent move. And Palo Alto Network's up $6.66. Sounds pretty devilish to me. Nearly a 3 percent move to the upside. And to the downside, you've got Mercado. Larry Breov, 12 bucks. 13 bucks. Nearly about 1 percent. Humanity's down 8. Almost 2 percent there. Applied materials is off 8 as well. That's a 5.6 percent move. Idex Labs down 7. 1 and 5 percent. Microsoft's off nearly 2 percent. A $7 move there. Dolby Laboratories down 7 bucks. Nearly 8 percent. So we've got movers and we've got shakers. But where do we want to begin? Well, first let's begin here with a couple new profiles that are attempting to form. So you can jot these down on your pad of paper. Those new profiles are coming inside the Dow, the NQ, and the Russell 2000. Now inside the NQ, we will not have confirmation, but this is still telling us where buyers and sellers are lined up at. So that much we do know. Whether they just hold throughout the end of the day, that I don't know. But we'll certainly know come Sunday evening, come Monday morning when we're back together. But right now the new profiles inside the NQ tell us that the sellers are residing at $15,965 and the buyers are at $5,515,712. If we take a look at the Dow, the sellers are up at $35,168. The buyers are down at $34,791. Now both those profiles formed above prior profiles. That's a bullish signal as well from a profile standpoint. If we take a look at the Russell 2000, you have the same thing. However, in its case, that new profile that is attempting to form just as it was yesterday is below price. And that's a bullish message out there. When I say below price, right now we're trading at $1,800 and change. And at the top of that profile, which would be resistance typically, that would be support is $1,732 out there. So that's the first thing that we wanted to share with you. What's the second thing we want to share with you? That's a great question. Maybe we go take a look at some intraday charts out here. So let's do that. Give me a second here. We'll go over to the white background screens. Actually, let's do this. Let's start off with the daily and the weekly. Let's just change screens out here. See what other signals we've got. So we have those new profile levels inside the For Equity Future Contracts. Here you've got both the daily and the weekly. We take a look at the ES mini and the ES mini looks to me like it wants to go target at $45,66. If it closes below yesterday's low, we might have a different opinion. That different opinion could be a couple of days pullback and then $44,13 or so would be its price target to the downside. But first we've got to get below yesterday's low. We're not even near that at this stage of the game. Same thing in the NQ. Now the NQ, the level that you're watching to the downside out here, then there's no topping pattern, but we did have price run into resistance, which was at a TD9 count breakdown level. This is a daily timeframe, $15,982. So if we get it closed below yesterday's low, yesterday's low, $15,796.75, then we might see a pullback to its oscillator and change that right around the $15,509 area out there. Again, on the weekly chart for the ES and the NQ, nothing bearish about those. In fact, the ES mini says it wants to target a straight above profile out there. So there's nothing bearish about that. It's above a green oscillator and change line. In the case of the NQ, it is trading into a sell zone. And that zone is between $15,727 and $16,130. The Dow equity future contract prices, it looks like it's going to close above the top of that profile. It just needs to close above $34,473 on a weekly basis. That is bullish. And the Russell 2000 is getting in on the action. And a close today above $17, let me give you that number. $17,7370 will accomplish that task. However, it still has resistance. One more battle overhead. And that's the top of that weekly profile, which it found resistance at. That is at the $18,490 level out there. The actual high for the week has been $18,38. So, you know, within three bucks. To the daily timeframes for the Dow equity future contract. Again, it would have to close below at least yesterday's low to even signal the possibility of a couple of day pullback out there. Otherwise, it wants to go target its TD-9 count breakdown level. And that's up at the $35,357 area. And in case of the Russell 2000, we did get two consecutive closes above its TD-9 count breakout area. That's at the $1,800.9 level. You'd still love to see price close above it. It's trading right in there right now. It did close below it yesterday. It just might have been a one day wonder to the downside. We take a look at the Russell 2000. All right. So now let's go take a look at some intraday charts for the equity future contracts out here. Let's begin by looking at the NQ. What do we see out here? Well, one, we see a number of different tops. And we take a look at just the top row out here. And that would include the daily, the five hour, the four hour, and the two hour. Each of them have, well, not each of them. The daily does not have a top, but the five hour or TD-9 count top. That says the key level of resistance out here on a five hour basis is up at the $16,049.50 level. Any close above that will negate that top. It'll negate the top on the $240. It will negate the top inside the 120 minute chart. To the downside, the key support level that's been tested several times here and is held. So this would be a clue for anybody. It would be at the 15, 8, 22 mark out there. So I still say you need to see a close below yesterday's low out there to suggest there may be trouble in River City or really just to pull back to that new profile support, assuming that it confirms or completes. And that's around that 15, 7, 12 level out there. I don't really think there's anything else worthwhile to spend time on here when I take a look at these intraday charts. Let's fire up the ES mini. Go see what it is signaling to you and I out there. You may have a different message. So in a moment, those charts will be up on our screen. Of course, we're going to be going to break in about two seconds or so. Let's just see if we get this to fire up. So we're still looking at yesterday's low. I don't know if the, man, it's got a still good. Yeah, it's got a yesterday's low. Key area out there to be watching during the day, 45, 01, 75. We don't breach that. This bullish trend with price wanting to move higher should remain in effect. Steve Rhodes with TFNN. We'll be right back. Currencies, commodities and bond markets are as important as ever right now with how they're driving the volatility in equity markets across the globe, which is why it's a great time to try out Teddy Kegstad's Tiger forex report. Teddy Kegstad breaks down the forex markets every Monday using his 30 plus years of experience as a trading veteran for futures, forex stocks and options. Teddy releases his weekly Tiger forex report every Monday morning with coverage of all the major currency pairs, including the dollar index, the Euro dollar, pound dollar, dollar Swiss, dollar yen, as well as many more. And he also has weekly coverage of the crude oil market and the 30 year T bonds as they both influence forex markets tremendously. When you sign up for the Tiger forex report, you also gain instant access to Teddy's 60 minute webinar archive. He just hosted forex strategies and fundamentals. What is behind the Tiger forex report? For all the details and to start your 30 day Tiger forex report subscription today, visit the front page of TFNN.com. TFNN Educating Investors. You might think that if you want to be successful at trading in the stock market, you're going to need a crystal ball. After all, it's impossible to predict the future, right? Like any endeavor in life, before you decide it's impossible, get some advice from the experts. You might find that it's not so impossible after all. For daily market overviews that give you direction on the key indices, selective stocks and commodities, subscribe to the opening call newsletter at TFNN.com. 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At TFNN, all our newsletters come with a 30-day money back guarantee. So you have absolutely nothing to worry about. Visit TFNN.com and try Mastering Probability 30 Days Risk-Free Today. TFNN Educating Investors. Let's go take some requests that have come in. So let's start with the first one. First one coming in from Nancy. Nancy wants to take a look at Microsoft. So what do we know about Microsoft? Microsoft has got an A to B-equal CD pattern to the upside. We can draw that in there. I'll just draw in the A to B line and we'll just move that over. This is an extension. So this has done more than a one-to-one A to B-equal CD. So at day's end, if we get a bearish reversal candle, well, now you'll see it, it's made more than the one-to-one move. Now, right now what we've got going for us is a bear-sash candle. So in the case of Microsoft, Nancy, this could be confirming at day's end. Don't know whether it will or won't. It's going to be not about 11.19 in the morning, more about 4 p.m. But if it does generate and complete that bear-sash candle, or A bearish reversal candle at day's end, you'll have a sell-the-de-point pattern. Now, there's also a new profile that is forming here in Microsoft that is bullish in structure. So what we're going to get back would be towards the 365-366 area. 365-16 and 366-76, and we want to get granular on that. Resistance is up at the 374-75 level. On a weekly timeframe, you'd need a bearish reversal candle to confirm a roadspin to mitigate our top. We don't see that on a monthly basis.  Maybe what we're going to get is a short-term pullback, maybe just a couple of days. So I know you're trading some options out here. I would use this and say, okay, it looks like we might be getting ready for a little change in trend inside of Microsoft that could last for a day or two. And then you know your price targets to the downside. If we look at a 30-minute, I'm sorry, this is a 50-minute timeframe chart. Roadspin to mitigate our top. No bottoming signal. Suggest it wants to move lower. If we take a look at a 30-minute timeframe chart out here, a 30-minute timeframe chart, we've got the same pattern out there. Let's move lower. Now, 361 is the level because price is below profiles on a 30-minute basis. That price could get back to the bottom of that profiles at 365. So I'd say that is the likely target out there, Nancy, to the downside from Microsoft. Hope that information helped you out. Thanks so much for the request. G-Man inside the Tigers still wants to take a look at first solar, FSLR is the ticker symbol. As we take a look at it, what is it doing? Well, formed a nice roadspin to mitigate our bottom. We did that four or five trading sessions ago. It accomplished that task on November 10. Why did it accomplish it then? Because that was a bullish reversal candle. That was a hammer candle, and that confirmed that bottom pattern. Now price is above the top of its profile. So where is price headed to? Well, what price would like to do is get up to its TD-9 count breakdown resistance level. That's up at the 173.65 area. However, we could easily draw in a consolidation pattern. So what we're going to say here is that for first solar to make that move to 173 and change out there, you've got to see a close above the high from October 24. And that high out there, because that would basically be the top of the consolidation. And that's at 158.61. So even though everything looks really good here, G-Man, you still got to get it close above that prior swing. Otherwise, what you've got is really just a sideways type consolidation. If I draw it in rather quickly out here, it would look something like this. So that's pretty easy to identify. So that's pretty easy. I say, right, you might say, I don't know what you're talking about, Stevo. But I'm saying, right, so there's your consolidation pattern out here. And a break of that, sure you could use and say that it's a consolidation breakout. Instead, what I would do is I'd use that upper range for that price target, that 173.65 area. Is that a likely outcome? That's a great question. So I'd say, right, you're going to get a B-equal CD pattern. The reason is if you take a look at this, this is what I have to use for my C-point, the high of July 28th. I guarantee you that is more than a 0.786 retracement out there.  Instead, what I would say for first solar here and make it easier, G-Man, is at this level ever since May 12th of this year out there. So a close above 159.73, that's the number today. It'll be different, you know, as price gets there, but you can use that as a guideline. A close above that would be a bullish signal out there. Of course, that would be a bullish signal because we'd also be above the top of that consolidation on the daily timeframe. Monthly chart has a TD9 account, Roseman Dementicator top, and that has taken price back to support. The bottom that we see inside the daily. On the weekly, we really don't have a bottom, but that's okay. And so you've come back to support on that monthly chart out there. Now it's all about resistance, and we really identify where that is. So, G-Man, I know that you're short, right? You're short for solar.  So I get it, but here's the deal. If you do get a close above the, I'd say the October 24th high out there, that's what I would use, $158.61. You'd have to question the idea of being short or that might be just your longer-term position, but just anticipate that you'd see price get up towards that $173.65 level. So I hope that helps you out. At least you know what areas to watch. I would say, you know what, maybe being short isn't the best idea. We don't have that as we speak just yet. We take a look at the next request that came in. Who wants to take a look at ARKK? One of Kathy Wood's funds out there. So let's pull the ARKK fund up on our screen out there. What I did during the break was I went to see, because I actually have the, I used to have, all of the ARK charts with their holding so they could easily go back. But first let's take a look at ARKK. In the case of ARKK, what do we know? We know there's an A to B equal CD pattern to the upside as it occurred with volume. Well, the swing point would be November 8th. 18 million shares there and that 18 million shares was passed with 25 million shares. So there's an A to B equal CD to the upside. We'll draw on the A to B line out there and then we're going to go ahead and take that to A to B and we're going to move it down to the C point. And so the one to one on ARK, let's pull this down just a tad, is going to get us above its TD9 count breakdown resistance area. And that would get us in towards the 46 and change level. What we want to do ARKK is make a move to at least 45, 16. That's its TD9 count breakdown area. If we look at the weekly timeframe chart it's being supported there. The weekly actually does have a buy the D point pattern. It accomplished that two weeks ago like many of the indices did. And now you've got price trade above profile resistance. That's at 41.74. A close above that at today's end rose would be a bullish outcome. Now that's the top of its daily profile. So I'll revert back to that. That's the top of the weekly profile. Don't worry. I'll spin it out. And that has us reverting back to the daily price target area 45, 16 or higher. If we look at that monthly timeframe chart for ARK it's trying to get back inside its profile level. The counter trend rally resistance point would be 47.51. So we got 45, 16 46 and change out there. We've got 47.51 looks like ARKK wants to move to higher ground but we're not going to stop there. We're going to go take a look at at least those top eight holdings with inside ARKK and see what message they have to us. Now in order to do that I need to change screens out here. What I'm showing you is the second eight although that's not correct because I haven't updated for that eight but we're going to change our window here go back to Stevie's main screen out there. Numero uno screen and here are the eight holdings Nancy rose with inside ARKK. I may not have known that but you should go take a look at that so you can understand what those instruments are doing. I don't know what the waiting is as we speak just yet and what's this telling me here telling me I need to take a look at this. Okay we'll go back to this break we're going to go take a look at coin, roku, tesla, zoom, path, square, roblox and crisper. Those are the top eight instruments with inside ARKK we'll be right back. 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The Tiger's Den at Discord is accessible on mobile or tablets as well so it's always at your reach. To sign up today and become a part of this educational community of traders, just visit the front page of TFNN.com and hit Watch Tiger TV. That's TFNN.com and hit Watch Tiger TV. Folks, we'll take a look at the top eight weightings with inside of ARKK. Let's see, that would be 10, 18, 26, 35, 35, 43, 49, Roblox is another five, that's 54, and CRISPR that's 58, so 58% of the player index is in these eight stocks out here. So, we take a look at Coinbase. What Coinbase is dealing with here is a resistance level, that's ATD9 Cal Breakdowner, that's at 95, 95. Now, price is trading just above that, it's above profiles, above a greenhouse and change line, there's no topping signals in place out here, so Coinbase should make a run for its highs out here from back in July of this year, so that looks still bullish. Roku looks bullish here, there's no topping signal that I see, it's above profile, greenhouse and change line, Tesla did generate that cell, the D-point or Gartley cell pattern yesterday, from that bear sash candle, so price may be pulling back to test support, that'd be a 221.75, Zoom has resistance at 64.93, that's a level you'd love to see ZM close above, because that would then take us to higher highs. We take a look at PATH out here, PATH is also dealing with ATD9 Cal Breakdown resistance here, that's at 1841. It's also dealing with the TD9 account top from back here in the trading day of September 11th. Now that swing point had volume of 12 million shares, this price got into it back on November 15th, it was with 7 million shares, so you now have a test and rejection of that swing point, don't really have a top, but you do have a new profile that is formed, and it's above price, so I would watch 17.81 with regard to PATH, if price closed below that 17.40, and if price got below that, price would move back to its sell or buy zone between 17.76 and 17.02. That was on PATH. Square had formed a TD9 account top two days ago, in order for Square to get bullish, and right now it's neutral, because price is above profile, and it's above its green oscillator and change line, but in order for it to really get bullish, it needs to close above 57.27 out there, so you'd like to watch that. Roblox, no top in place here, price remains above its green oscillator and change line, it's even though it's lower than PATH, it's above price, so it needs to close above that 25.32. So in summary, you have a top inside of Square, but it's neutral, and you do have a top inside of Tesla, or the other 6 holdings, remember all 8 of these are up 58% or so of the entire index, so I would stay with ARKK. request. Let's go to our next request out here. This is coming from Dan inside the Tigers Den and Dan wants to take a look at AQST. Now I've got to switch panels here. I'm going to do that. Give me a moment and then we're going to get back to those charts. What's going to show up on your screen first is going to be Goldilocks, but if you give me a moment we'll get back to AQST out here. And so AQST, this is an instrument I think Dan you said you're hoping to hold this for years out here. Let's take a look at what it's doing as we speak right now. What it's doing right now it's taking on a resistance level. Now resistance level is where price broke down from or where it broke down from was $179. Now let me just see because I may have a little bit of a delay here AQST. There we go. I just want to see where it's trading. I've got it at $76. Yeah, $76. $75. So that's the resistance level right now in a daily timeframe that you need to deal with. Now if this does close above the swing point from the trading session of $10.17 and ideally you'd like to see it do that with more than 340,000 shares then you'd get an A to B equal CD to the upside. You have another level of resistance above $179. That's at $186. $186 happens to be the weekly TD9 breakdown resistance level. The monthly chart isn't really helping us. It's helping us from the standpoint of what? It's trading above profile. That's about the best that I can come up with there. It's trading above profile. So right now at least on an on a intra period timeframe because Dan you're in this for the long haul. I'm just going to share with you where those battles are going to be. And that first battle again is going to be at $1.79. That's already been tested rejected a couple times. And then above that $186. If you can get above $186 the next battle will be at $207. And $207 is the center of its bearish structured weekly profile. If this is only a counter trend move then that's where price would really find resistance at $207. So Dan I hope that provided you with the information that you were looking for. Thank you so much for the request. The next request is to take a look at our horizontal trading ranges. And that's for Mr. Bill in the den. Now Mr. Bill have only it takes up a huge amount of resources on my system. And usually I've got to do a bit of finagling in order to have that really kind of look right so to speak. But here we're going to give you the Dow and the S&P 500. Now make sure I'm on the right page. Yep I am. So folks what you're looking at here is you're looking at the weekly chart for the Dow. And these green horizontal lines are what we're referred to as a primary trading range by Bud Rolfs or horizontal trading range because I do things just slightly different. Bud always used his eyes. He visually was looking for the largest number of co-located opens and closes. When I say co-located it doesn't matter whether it's an open or a close. Turns out that you can actually automate that. That's what I've done here. What we can see is that the largest number of co-located opens and closes is all the way back here at 17-1-1-12. So you have 25 there above at 18-4-22 you had 11 and below at 15-8-1 you had 10. So that established our horizontal trading range. Once you have that distance then you just simply are adding to that or subtracting from that to find upside and downside resistance levels. So Mr. Bill, the next upside resistance level, we can see that that area has been tested 11 times and that has held or I can say it's held. That is where we've seen an open or a close and that would be at 35-463. And that's using the weekly time frame chart for the Dow. If we go take a look at the S&P 500, in the case of the S&P 500, let's use the daily time frames. Now these descending and rising price channels that I have out here that's something that I did a while ago but I'll leave those in place as we speak right now. And on a daily time frame, so now you understand how horizontal trading range or you have a decent idea of how horizontal trading ranges are are identified. I believe I've got a workshop on that if you are subscriber to Mastering Probability, you can go take a look at that. In the case of the S&P 500, we're taking a look at both those cases, the cash indices, both the Dow and the S&P 500. What we have is the next horizontal trading range up at 45.89. Now there have been 27 opens and closes up at that level. So that's going to be a fairly decent area of resistance. And if you can get above that, then we're likely going to head up towards this rising price channel. But more importantly, the next horizontal trading range. And Mr. Bill, that next horizontal trading range about 45.89 is up at 47.55 out there. That's not the call that we're making right now. But those are your HTRs, your primary trading ranges and their boundary lines. I hope that helps you out. And I'm going to go ahead and close these out here, just simply to free up the resources because it takes up, it takes up quite a bit out there. And I don't really need to slow the system down. So we won't slow the system down. Where we at? Apigee, Paragee, we got that coming in here pretty soon. Man, look at how natural gas, look at the natural gas. So we had Paragee come in almost two weeks ago, not exactly just yet, November the 6th out there. And what that does for reasons that I can't share, not because I can't share, because because I can't share, I have an idea, but I'm not going to share that idea because I really can't prove it. What I can prove to you is it really acts as a support or resistance level. And it is a point in time, like to the minute, to the minute, why it works. Man, I don't know, I just know that it does take look at how natural gas at 1130 on November 15 and right into resistance, the Apigee Pivot Point 327. Steve Rhodes with TFNN, we'll be right back. Currencies, commodities and bond markets are as important as ever right now with how they're driving the volatility in equity markets across the globe, which is why it's a great time to try out Teddy Kegstad's Tiger Forex report. Teddy Kegstad breaks down the Forex markets every Monday using his 30 plus years of experience as a trading veteran of futures, forex stocks and options. Teddy releases his weekly Tiger Forex report every Monday morning with coverage of all the major currency pairs, including the dollar index, the Euro dollar, pound dollar, dollar Swiss, dollar yen, as well as many more. And he also has weekly coverage of the crude oil market and the 30 year T bonds as they both influence forex markets tremendously. When you sign up for the Tiger Forex report, you also gain instant access to Teddy's 60 minute webinar archive. He just hosted forex strategies and fundamentals. What is behind the Tiger Forex report? For all the details and to start your 30 day Tiger Forex report subscription today, visit the front page of TFNN.com. TFNN Educating Investors. Everything in the universe is governed by the Fibonacci sequence. This mathematical principle is responsible for everything from the most aesthetically pleasing artwork to patterns in the stock market. To stay on top of stock patterns you can take advantage of, sign up for the Fibonacci 24 7 newsletter at TFNN.com. When you subscribe, you'll get a weekly report from veteran day trader Larry Pezzavento on stocks you need to pay attention to and you can trust Larry's analysis. After all, he's got 45 years experience as a day trader. Larry will also provide daily charts, videos and data on the key markets that he's tracking. Expect notifications from Larry on market movement you need to act on at any time. First time subscribers also get a 30 day money back guarantee. If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24 7 newsletter today. TFNN.com Educating Investors. Are China shares hot or not? If you trade China shares now may be time to take a closer look trade CHAU or CHAD directions daily CSI 300 China a share bull and bear ETFs China a shares in either direction visit direction investments.com today. An investor should consider the investment objectives risks charges and expenses of the direction shares carefully before investing. The prospectus and summary prospectus contain this and other information about direction shares to obtain a prospectus or summary prospectus. Please contact direction shares at<PHONE_NUMBER>. The prospectus or summary prospectus should be read carefully before investing. An investment in the funds is subject to risk, including the possible loss of principle. The funds are designed to be utilized only by sophisticated investors such as traders and active investors. Distributor four side fund services LLC. This program is brought to you by Vista Gold traded on the NYSE American and TSX under the symbol VGZ RF is a ticker symbol up on our screen out here. Looks like it's trying to form an A to B equal seeding the upside as it confirm that pattern with a B point out here is a trading day of November 3rd. This for snowball inside the tiger's den. So snowball on November 3rd. This generated volume of 12.9 million shares when that B point was passed. It was on November 15th. That was 9 million shares. We got lighter volume. So far see if we even what do we do yesterday 9 million shares today so far were 2 million shares. So that's that's even less than 9 million. So that does not mean that it hasn't formed an A to B equal CD upside. The preference would just simply be to take out that swing point with volume. Nonetheless, you've got a to B equal CD pattern that is present at the moment and prices trading above profiles. So let's go try to figure out where that A to B. Excuse me. Pattern will take us. That didn't copy. That's okay. So the one to one level is approximately don't use this to the T is right around the 1731 ish area out there. And you can see you've got TD 9 count breakdown resistance in 1823. So looks like that's the target area for regions financial on a weekly basis. This formed a roadsman to indicator bottom two weeks ago. Now we can also see that that red horizontal line has been resistance ever since the week of August 18th. We're seeing price close above that level. That is a bullish outcome. And we have not had two candles to consecutive candles below that bullish structured profile. So the next battle that you've got. Yes, this wants that A to B equal CD, although it hasn't passed that B point with the volume. But the next battle ground from a price standpoint is going to be 1652. And 1652 is a weekly center of its profile. Well, both buyers and sellers exist there. So that's the next resistance level that you've got. Nothing else that I can see out here for our FY. There's I take that back. Regions financial on a 30 minute timeframe. So this will be a helpful to you to understand what this is doing today. Of course, I should open up this the chart. So what we can see here is this completed a TD nine count top at 1130 and that TD nine count top high. Wow. I don't know where that cough is coming from. But if we take a look at it, the TD nine count top is 1635. You're treating it. You're trading at 1635. You've got basically 15 minutes left in this trading session. So if you see at any point in time on a 30 minute bar, you see it close above that TD nine count high. And again, that number out there is 1635. That's going to tell you that this wants to continue to move higher. You still have a top. Not until resistance is taken out. Will that change things? That top suggests that what we should see is price pulled back to the 1626 area. 1626 is its green, not certain change line. If that holds, that would be a bullish or would in this case here would turn the signal to neutral, but a price closed below that. Then what we're looking at is move back to the 1604, maybe even 1588 area. So watch that 30 minute time frame chart. That's going to give you clues as to how this is likely to trade for the rest of the day. So thank you so much for the request. No ball. Hope that helped you out. Dan inside the Tigers, they want to take a GSM. So let's get that up on our screens out here. Go see what GSM is doing. Give me a moment. GSM is Ferro Globe, and Ferro Globe right now is just consolidating. And I'd have to say it's consolidating with inside his profile between 443 and about the 496 level. 496. But there's additional resistance here, Dan. And that, oh, this is the one that you want to hold for years. I apologize. This is the one that you want to hold for years. So on a daily time frame, you've got a TD9 count bottom. On a weekly time frame, you have a TD9 count bottom. On a monthly time frame, on a monthly time frame, that's where it is the fly in your financial ointment. And you'd really love to see on a monthly basis price get back inside his profile, close above the oscillator and change sign. You'd love to see it close above 507. Might even get that. Well, we got the end of the month. It's only the, what is today, the 17th, I think? 17th, yeah. So we got a while before that. All right. So daily, weekly, everything looks pretty good. You'd love the monthly to participate. So you got that nice TD9 count rosement to indicator bottom. You got both those bottoms. Doesn't make that any better. With your real next battle, top of the profile, we already covered that. And then above that 5511, the TD9 count breakdown resistance level. If you can overcome 511, then the next move should take us to 542, 549. 542 is the top of the weekly profile. 549 is the next TD9 count breakdown level. That's assuming price can get above 511 out there. So that's what I see when I take a GSM on a intraday basis. In intraday, I'm talking about a 30-minute time frame chart. I don't see any kind of a top. Looks to me like this wants to rally further. And the reason I say that is because price is trading above a profile. It's trading above its greenhouse that are in change line. What it is dealing with though, it is dealing with a resistance from a 30-minute bar from back at 10 o'clock in the morning, 9.30 to 10, that was on November the 8th. And the volume there was 415,000 shares out there, and this was moving up with 70. So, you know, you're running into a swing point with a lot lighter volume. It's still inside that swing point. If it closes below 491, that's going to tell us this wants to move lower. And 486 is the bottom of that new profile that has formed out here. Yeah, so right now, price is above support levels. You've just got to watch support on a further move lower, knowing that from an inter-day standpoint, price is moving into a swing point with very light volume out there. But otherwise, and you're dealing with resistance on the daily time frame as well. So, I like the daily and the weekly TD9 count bottoms out there. Now it's all about the battlegrounds out here. So, I hope that helps you out, Dan, and best of luck to you. And I hope that we take a look at this. Let's, how many years you want to hold it? Five years? Five years from now? Let's re-look at this. Let's do it on, you know, just before Thanksgiving as well. And this thing will be trading at about, what, 150 bucks or so? That sounds pretty good. All right. So, let's welcome the reef. What's that? Favorite son of a... Target failed in Friday. Don't know what that's all about, Diesel, but... So, what do we want to take a look at next? Oh, I know Hector wrote, and he wants to take a look at the GDX. So, you know, for the GDX, he's asking about A to B equals CD patterns. So, for that, we're going to go where I've got that A to B equals CD tool. Much easier for me to draw it in. And I believe, Hector's first question is about a weekly time frame chart. So, let's get over and take a look at the black background screen out here. Let's just look at it for the GDX. Let's start with the daily time frame. What do we know about the daily time frame? So, the daily time frame for the GDX has an A to B equals CD to the downside. And Hector's question, I think your question, well, let me just, let me, let me do this here. Let me dress up the chart just a tad. Let me turn off these trend lines. Just kidding, I can turn those back on. It'll just make it a little bit easier for us to take a look at. Let's turn off the trend lines out here. Boom, they're gone. So, now, what we can see here, it's questionable whether this was an A to B equals CD to the downside. Why? The retracement level is 86%. So, let's not even say we even have an A to B equals CD pattern because really technically we don't. All we really have is price consolidating with inside its bearish structure daily profile. And the resistance out there is at 28.97 and support is at 27.29. And we turn that trend line back on. We can also see that price is trading in between trend line resistance and trend line support. And that is basically encompassing that daily profile. We come back to this break. Let's take a look at the weekly chart for the GDX. And then we'll close out the show. We'll be right back. The opening call newsletter is written by Basil Chapman, creator of the trading methodology known as The Chapman Wave. The Chapman Wave up-down sequence gives you an edge in identifying price turns. Finding the peaks and valleys in stock prices.  First-time subscribers also get a 30-day money back guarantee. If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. TFNN.com Educating investors. Everything in the universe is governed by the Fibonacci sequence. This mathematical principle is responsible for everything from the most aesthetically pleasing artwork to patterns in the stock market. To stay on top of stock patterns you can take advantage of, sign up for the Fibonacci 24-7 newsletter at TFNN.com. When you subscribe, you'll get a weekly report from veteran day trader Larry Pezzavento on stocks you need to pay attention to. And you can trust Larry's analysis. After all, he's got 45 years experience as a day trader. Larry will also provide daily charts, videos, and data on the key markets that he's tracking. Expect notifications from Larry on market movement you need to act on at any time. First-time subscribers also get a 30-day money back guarantee. If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24-7 newsletter today. TFNN.com Educating Investors The reality is that navigating financial markets can be risky. Markets can be chaotic and difficult to understand. Having the latest market advice can help you turn this chaos into a key for creating winning trades. At TFNN, we understand that it can be hard to find reliable market news. That's why each of our market experts offers their very own market newsletter. A must-have tool for every trader out there striving to find an edge in today's markets, TFNN newsletters cover every aspect of the markets so you can analyze the market before you trade. Try any of our great newsletters risk-free with our 30-day money back guarantee. Just visit the Newsletters tab on the front page of TFNN.com. TFNN Educating Investors The GDX, right now, what we've established is that the GDX is really trading between trend lines as well as its profile levels on the daily timeframe. Now, on a weekly basis, you've got to answer this question. The one-to-one A to B equals CD price projection was $25.43. Price back on the week of October 2nd got down to $25.62. That seems close enough to me to then call that a completed A to B equals CD because then the following week when it did, this was a three-river Morningstar candle. So that is a completed by the D point pattern. So we had a completed by the D point pattern with price finding resistance at the center of its profile, $28.97. I'd love to say that a close above $28.97 will get us up to $32.36. But we've seen a couple of different closes above that level and that hasn't come to fruition. So we won't say that. What we will say though is that price is inside the profile level and it's got support down at the lows from that week about October 2nd out there. That's down at the $25.62 area. On the monthly chart you also have price running into resistance. That resistance is the top of its profile. So that's another level to be watching. That's up at the $29.55 area. But turn on the trend there. Let me see what do we get. You really get kind of the same type of thing. You do have a rising and a falling trend line out there. Do those usurp the ones that we took a look at in the daily? The one in the daily would be the one that's basically feeding off of the April 1st high and then the high from May the 1st. And then for the larger one you're dealing with the high from August 1st all the way up to the high from April the 1st out there. So hope that helps you out to Hector and Patty. Thanks much for writing in. Let's close out the show by taking a Lightspeed crew. We're looking at January contract. That is the active contract right now. We're going to change screens with the white background screen. So on a daily basis there's need to be equal CD to downside needs a bullish reversal candle to complete that pattern. We don't have that just yet. You had a TD9 account bottom that formed earlier this morning on the five-hour timeframe chart. And price right now is trading above both profile and a source letter and change line. And to close above 7451 at 2 p.m. today is going to suggest we could see Lightspeed crew rally up to 7869 level. So folks thanks much for joining me on Monday. Please join me between eight and nine. I'm going to have to record Monday's show between eight and nine. That way I can go get granny my 96 year old grandmother bring her here for Thanksgiving. So have a fantastic Friday fabulous weekend. And I'll see you bright and early come Monday morning. Take care folks.


All right, I'm Mikaela Parker and the program coordinator for the Moore Sloan Data Science Environments. This is a partnership between two funding agencies, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, together with three universities, New York University, the University of Washington in Seattle, and the University of California, Berkeley. And this partnership sought to bring an advanced data science and academia. And it started in 2013. So I'm going to cover the highlights from the Moore Sloan Data Science Environments. You'll hear me refer to it as the MSDSEs for the last five years. The funding from the two foundations largely went to support three data science centers, one at each of the university. And so I've highlighted them in their logos on the slide. At Berkeley, it's the Institute for Data Science at NYU, the Center for Data Science at the University of Washington, it's the E Science Institute. But before I dive into some of the highlights of this partnership, I wanted to just set the stage with something that I think all of you are familiar with, and that is that data are being collected and used everywhere in our everyday lives. We are living in an age of smart homes, smart cars, smart health, smart cities. We heard of the plenary, the internet of things. But importantly for academia, we're also talking about smart discovery. Every field of inquiry at a university campus is transitioning from data poor to data rich. And that's not just the traditionally heavy data sciences like astronomy. We're seeing lots of data at the advent of the worldwide web in digital humanities, sociology, economics. It's really unlimited. But importantly, I think we need to remember that data science is not just about volume of data. Now this figure is from a paper that's five years old, but I think it largely still applies. When university researchers are asked, how much data do you work with? We're talking on the order of gigabytes typically. So this is not a lot of data necessarily, but it's also about the variety, the heterogeneity, the messiness of data that university researchers are struggling with and the velocity at which it's coming at them with all the advances with new instrumentation, new algorithms, new models that are just generating more and more types of data at increasing velocity and volumes. So it's really this challenge to bridge this gap between the university domain researcher and data science practice that was at the heart of this partnership. That is that as data increases in all forms and in all fields, even some of the very best researchers on university campuses are struggling to generate knowledge and insight from these data. So this MSDSE partnership really sought to build bridges where new advances in methods and data science practice are brought to the university researchers and enable them to generate new discoveries. And these new discoveries open up a whole new world of different questions they want to answer, which will then in turn spur new developments in data science practice. So the goal here is really a feedback loop. And this feedback loop was kind of the inspiration for the structuring of the more slow in data science environments. The efforts for this partnership were organized into what were identified as challenge areas or working groups. And these working groups were the bridges between the scientific disciplines on the left hand side and the data science methodologies. And as you can see, there's this feedback loop again. So those bridges include career paths and alternative metrics, education and training, software tools, environments and support, reproducibility and open science, working spaces and culture and ethnography and evaluation. And this last working group actually was recently renamed to data science studies and I'll come back and explain that a bit. So just a high level outline of my talk, it's mostly in two parts. As I mentioned, I'll be highlighting some of the successes and outcomes from this five year collaboration. I'm going to highlight both cross university collaborative outcomes as well as touch on some individual university achievements. But then I'm going to transition to a few key takeaways and institutional challenges. And for this section, I'm going to draw not just on the more slow in data science environments and those lessons learned, but also touch on a recent landscape survey conducted by apt associates of 20 data science centers nationwide that is now available. So I don't have it with me, but if you're interested, I can get you a copy. Also touch on the final evaluation of more slow in data science environments also conducted by apt associates. And earlier this year, there was an inaugural data science leadership summit and some of the outcomes and lessons learned. I'll touch on there as well. But first I want to nod a few unsung heroes and this is that ethnography and evaluation working group I mentioned who are now called data science studies because these folks are really, I believe at the heart of some of the successes of this program. So this working group is really tasked to understand the complex landscape within which data science is situated and identify and evaluate best practices. In a sense, they're doing the data science of data science. It's basically an ethnography meets reflective and reflexive self evaluation. And what I mean by that is our ethnographers were embedded in our programs to provide immediate feedback and of our programs on activities and that allowed these data science environments to be very flexible and adaptable. The working group also is tasked with raising awareness of ethical issues and surface best practices to the larger community. And they have their own scholarly work as well. So they publish in computational, HCI, historical and ethnographic approaches to studying the best practices and tools of the data science culture. Okay, so now I'm going to move to some highlights and successes. One that I think is really appropriate for this audience and this meeting is reproducible and open science. And New York University has had major strides in this area. They've always partnered really closely with the New York Libraries, the New York University Libraries and this year they were successful at hiring their first ever reproducibility librarian into a ten year track position. And we think this is the first ten year track position for a reproducibility librarian. They've also put a lot of time and effort into a tool called ReproZip. This is a package that allows you to essentially pack up all of your research along with any necessary data files, environmental variables, options and allows other people to reproduce your findings without having to go and dig up all the necessary dependencies and they can even run it on a completely different operating system. The next point in the reproducible and open science category is a collaborative effort, so this was a case studies book. It's available on Amazon, but it's also available completely free online. Neglected to put the URL on there, but it's something like practice of reproducible research. And this is a volume of reproducible research workflows. So it includes tools, ideas, practices for real world research projects. And the contributors to this book were all from the morse on data science environments and they were the individual postdoctoral fellows or researchers that contributed each a different chapter. And it has a real emphasis on the practical aspects to make this research reproducible, so it's a real practical handbook. Moving over to software and education, I chose an example at the intersection of the two of these at the University of California, Berkeley. They have made a lot of strides in undergraduate data science education. They launched an intro to data science called Foundations of Data Science or Data Eight course and the very first offering of this course. There were students literally sitting in the aisles and on the stairways. So there's a huge demand for foundational data science at the undergraduate level. And the course continues to see enrollment rates over 1,000 students per offering. It's the fastest growing class in campus history. But at the same time, at the University of California, Berkeley and their Institute for Data Science, they support the Jupiter team. So if you're not familiar with Jupiter notebooks, it's an open source web for creating and sharing documents that contain live code, narrative, visualizations, all packaged neatly together for sharing. And they've developed a multi-user version of this called Jupiter Hub. And Jupiter Hub is great for classrooms. So the way this works is Jupiter Hub allows you to create and deploy identical installations of all software for each user notebook. So each student has a notebook and it looks very much like the left-hand side of this slide where the student names are just in folder format. So it's a very familiar, directory-oriented layout. And then the instructor can manage authentications and the users. And it allows a lot of easy scalability, essentially, and identical installations across all of the learning tools in that classroom. And the cool thing about this is that it was so successful and it's been so widely adopted at Berkeley that the University of Washington following Berkeley's lead picked up Jupiter Hub in 2017 and is now using it in at least two of its courses. I'm gonna switch now to the University of Washington eScience Institute and focus on research support. So at the University of Washington, they have something called a Data Science Incubator Program. And you can think of this as the space between office hours and a grant proposal. So this university's institute has been around for longer than the more sun data science environment. So they already had a pretty good handle on the data science needs of their university and they came to find that most researchers, their data science challenges don't fit in a one or two hour consultation. But they're not so big that they need to write themselves, data scientists, into a grant proposal on the scale of two or three years. It's somewhere between those two engagements that most of the data science challenges at a university lie. And so they spun up what they call the Data Science Incubator Program. It's an intensive data science consultation to advance research. But importantly, it's not a throw your data over the fence and have somebody work on your problem for you. It's mostly a teach a person to fish approach. And it provides a shared environment where researchers can learn from an in-house team of data scientists, but as well as external mentors and importantly from each other. I have here, let's see if I can find the examples because I didn't want to forget them. So just as an example of the kind of cool cross pollination that can happen on the incubator program, a political science faculty member, John Wilkerson, ended up using a sequence alignment algorithm from Biology for text analytics to trace the workflow, to trace the flow of ideas through legislation. And in another example, an image processing algorithm that's used in astronomy was applied to analyze MRI data in the brain. So you can see where tools can really have cross disciplinary applications and it's at an environment like this that you get that kind of cross pollination. So this winter incubator program is, we're on the quarter system at the University of Washington. It's 10 weeks long. But importantly, it requires in-person engagement two days per week. So the person with the data science challenge proposes this project to our team. I say our because I also work at the University of Washington eScience Institute at the moment and they will sit side by side with the data scientist committing two days per week on this project. And it's that kind of side by side intensive collaborative work that really advances the project. They've had participation from faculty, graduate students, staff. And as I mentioned, the network effects here are really important. There's my two examples, biology and astronomy. And just to give you some flavor of the types of projects that are handled in the winter incubator, you can see they touch on hydrology, prostate cancer, economics with simulating the airline industry. So really the sky is the limit here. This program is so successful that the university then decided to launch a similar program in the summer called Data Science for Social Good. In this case, the projects are specifically aimed at societal good challenges and they also bring in a team of four students to work on each project. So now you're talking about a project lead who pitches the project, a data scientist and four students. The students come from a nationwide call and it's a very competitive program without very much advertising. They solicited or they managed to attract 250 applicants for just 16 spots. And these are students that come from mechanical engineering and physics. I mean, any kind of discipline you can imagine. But they all really are drawn to the idea of addressing challenges that make them feel good, that are benefit to society. So the dual goals of this program are student training. So the students who arrive have a lot of data science expertise already, that they get to learn new tools or new ways of applying the tools they already know. And also the other goal of course is to create tools that facilitate responsible data driven decision making. And just a quick highlight of some of the projects that they have hosted. Everything from permanent housing from homeless families, looking at unsafe food products, transit system questions. And the data science for social good program, many of the project proposals are coming from outside the university. City government is a really popular one. Okay, that's great. These incubator programs are very intensive and very successful, but they don't scale very well. An engagement where you're talking a one on one with a data scientist for a quarter is only going to help a handful of labs at a time. So I'm going to turn now to an option for scalable research impact. This is a program that all three of the more Sloan data science environments worked on together. It's an opportunity for community learning within domains, and they're called hack weeks. So many of you are probably familiar with the hackathon model. This is a flavor of hackathon in a way, but it has a very strong emphasis on community building and tutorials. A lot of learning. So it places a strong focus on building a culture of practice and developing resources within an existing domain specific community. And importantly, it brings together researchers from many different universities, but that are all from one domain. So these researchers already have a shared language. And in some cases, shared scientific objectives. So it's very easy to gel a community and start immediately with a learning around this kind of approach. And as I mentioned, there's a lot of peer teaching and peer learning that happens. In exit surveys from these hack weeks, 70 to 80% of participants say that they taught someone else during the hack week. So it's really powerful for fostering collaborations, learning among people from various stages of their careers and technical abilities, and catalyzing a community through a shared interest in solving computational challenges within a field. The success of these hack weeks has been so apparent that we now are seeing many others spring up. Astro hack week was the inaugural hack week from 2014. It's now in its fifth year and it was taken international because there's such a demand from international participants to have an easier travel to the conference. It took place this year in Leiden in the Netherlands. NIH recognized the power of neuro hack week and awarded that group funding to develop neuro hack week into neuro hack academy, which is now a two week program. And they just had their first iteration this summer. There are two new hack weeks, water hack week, which focuses more on freshwater and ocean hack week. And personally, I would really love to see some of the social sciences get on board with this because I think it would be a great tool for them as well. And so I put socio hack in there with a question mark because I also think that's a terrible name. Some of the outcomes include papers, software, and of course results. So now another version of this kind of broadening and scalable research impact is community learning across domains. So that was within domains. Another version would be across domains, also known as XD working groups. XD for across domain. XD working groups host workshops and they're centered more around methods. So it's an opportunity for scientists to work on similar problems, but in different domains to strengthen ties across disciplinary boundaries and work together to identify common principles, algorithms, and tools. It includes a make session, which of course is an opportunity to work together to craft solutions to shared problems or investigate research questions of shared interest. The inaugural XD workshop was image XD that was held in 2016. And there were 50 researchers from 14 different institutions. And you have listed some of the domains where these researchers come from. So you can see it's really broad, including computer vision, earth science, neuroscience, astronomy, and more. Now these XD workshops are more new. They're more recent than the hack weeks, but they're already gaining traction. We've now seen the launch of text XD and graph XD. And some of the outcomes from these shorter workshops include blueprints for open source image processing, in the case of image XD, and training sets for ML applications. So my key takeaway here is that informal, intensive, community-driven learning opportunities like hack weeks and XD workshops might quickly and effectively bring data science to campus researchers. So you might say, well, that's great. But who does all the work? And I put in there, where does the magic happen? Because that's an easier question to answer first. So here, oh, unfortunately, very washed out. But here is a view into the institutes at those three universities. All of them developed some kind of open research space with flexible furniture, adaptable configurations. A lot of emphasis put on the ability to just drop in and attend a meeting or work together side by side. And a crucial part of the development of these spaces is the idea of a neutral space. And in order to emphasize that, I'm dropping here a quote from an interview with apt associates during our final evaluation. I'll read it real quick. One thing that I think we talk a lot about and I think has been verified is that having a neutral space on campus is important. We're not viewed as part of the computer sciences department or another department in particular. There's this sort of Switzerland effect. You're outside of the departmental silos. People come here and are more likely to collaborate across disciplines than they might otherwise be if they were all going to somebody's particular department. And I think this is really a major asset of all these three institutes. But I think one that's also a lesson learned that others can take up as well is the idea of having a neutral space on campus. And so when we think about other universities, where's a good neutral space on campus? The libraries, of course. So partnership with the libraries is one of the best options, I think, for spinning up an area for data science to happen. In the case of the Moore Sloan Data Science Environments, two of the three were using renovated space that was on loan from the libraries. These are the design, as I mentioned, was around very flexible, collaborative spaces, which are also one of the key elements of the libraries. And in this case, they also tried to think of how to make it transformable and flexible. Having small and large meeting rooms, hot desks for people to come, casual seating, writable surfaces was a really important one that a lot of people wanted. And I think the East Science Institute probably took that to the extreme. They made most of their walls writable, so people just stand up and start having a conversation and start writing on the wall. And that's actually a really effective collaboration tool. People think really well when they're standing up with a pen in hand trying to explain something. OK, now I'm going to get to the harder challenge. Who does the work? Career paths for academic data scientists. I think probably many of you have heard the term data science as a team sport, and I firmly believe that. There is a lot of expertise that goes into data science challenges. But unfortunately, I think that it's more like a team of X-Men than a team sport. And the reason I say that is the challenges that face data scientists are really ever-evolving, and they themselves are ever-evolving. But unfortunately, they're also, in many cases, viewed as second-class citizens at the university. What do I mean by that? Here's another quote. This is from a research scientist at one of the more some data science environments. I'm doing all these projects, and the university is very happy to point at my work and say, isn't this really cool work? But I don't have that first-class status of a faculty member that would just grease the wheels and make everything a bit easier, including getting grants. I know that if I was an assistant professor somewhere, a lot of these doubts would just go away, just based on the title alone. So this is a challenge, right? We need to find a way to have prestigious careers at a university for data science talent, or we're not going to be able to keep them. And it's a common theme that was also expressed in the landscape survey of 20 universities. Academic labs are struggling to obtain the computational support they need. In some cases, it's obvious. This is a salary issue. Really talented data scientists can earn probably three times as much in industry. But on top of that, most academic data science positions, especially within labs, are contingent on grant funding. So you're saying to somebody who has all these data science skills, come work at a university and make less money, and we can't guarantee that money is going to be around for more than a couple of years. That's not very attractive. And on top of that, data science positions themselves are difficult to create a university, because many universities don't have that prestigious titling and career path that kind of matches what faculty are able to do. So the question is, how can we make data science more attractive in academia? So these are just some ideas I'm throwing out. One, PI status. There are universities where staff members cannot have PI status. In the case of a data scientist, that's silly. These are really talented folks, and they should be allowed to bring in their own money. This will help with the sustainability problem. Of course, we can all highlight the advantages of a university, being a more intellectual environment, and having opportunities to mentor and teach. These are actually really important to some data scientists who want to stay in academia. But then we also need to make it possible for them to mentor and teach. Competitive salaries, I put that in quotes. Of course, academia can't compete with the Googles and the Facebooks of the world, but we can certainly change our HR structure so that data scientists don't fall in the same payroll schema as somebody who's a pipette washer in a lab. We need to think differently about those pay scales. And the titling. So we heard from, I don't remember if I have a quote. No, I don't. One of the interviews from the APT Associates evaluation, there was this feeling that having a title like fellow, like data science fellow, it felt very transient and also very junior. And so that only works when you're trying to first attract data science talent to the university, but thinking in the longer term, if these are folks that want to stick around, there should be some other titling for them, maybe something like professor of practice. And I put early career mentorship here because this was a lesson learned for the Morselin data science environments. We actually successfully created some of these data scientists positions, and believe it or not, they're still with us after five years because they really enjoy the work. But in these confidential interviews, we found that we were not doing a good job about mentorship. And this quote is, this person is saying, I think there's a degree of structural change going on in the academy. Sorry, the first quote's gonna be about the titling. A degree of structural change going on in the academy, but I think that's happening very slowly. Do these kinds of positions of leadership that are not tenure track faculty get created? If not, I'll probably end up going to work for some other nonprofit open source type of place. So again, titling is important. Mentorship, mentoring of the data scientists and research scientists to help them figure out what to do strategically for themselves, their careers. It isn't something that's really addressed now. And it's hard because these are new jobs in academic research, which means we need more mentoring, not less. So again, lesson learned, but hopefully we all can take a good cue from that. So I'm gonna now, I spent a fair bit of time talking about career paths. I'm gonna now just touch on a few challenges and lessons learned that we could then use perhaps for discussion. I could dive into any one of these in excruciating detail, but I know you don't wanna hear that. So let me just gloss over a few more that arose from this five year institutional experiment. This first challenge came from the landscape study and it's about establishing a center. It was noted in that survey that the greatest challenge is not surprisingly navigating the university's political landscape and persuading faculty that a data science center would be a benefit. So the important lesson learned is to engage the university community as much and as often as possible in the design process. Some of the foundational elements that we found that were in common across data science centers nationwide are a dedicated space. I mentioned how important that is and a strong emphasis on collaboration, interdisciplinarity and community building. And to note, virtually all entities in that landscape survey are administratively based outside of any one department or school. Faculty involvement, this is really a big challenge. Faculty already have a lot on their plates with service to their departments, teaching their own research. So how do we balance engagement in a new data science center or a new data science program against all of these other demands? Some ideas, again, provide teaching releases that it worked in at least one case for the Moore Sloan data science environments. However, as great as that sounds, a lot of departments are very reluctant to release a professor from an instruction obligation. So another idea might be provide some access to discretionary funding to support some of their research while they're helping to run the data science center. Credit for software, I think this does not get discussed enough and this is something that touches faculty as well as data scientists. On the university campus in hiring committees, in tenure reviews, the time and effort and outputs that go into software development and workflows and tools that help an entire community advance science, this time and effort output are not recognized at the same level as a high impact publication in these kinds of reviews. And that really frankly needs to change because when somebody spends two years developing a tool that advances everybody's research, that needs to be recognized at least as much as a high impact publication. And paths to sustainability. This is gonna be a challenging one and I think something the plenary speaker spoke of as well. It's gonna be interesting to see where data science goes on the university campuses. Right now, there are, I would guess, probably more than 50 new data science centers and programs nationwide, but many of them are getting spun up with temporary startup funds, either from a foundation or from industry. What is gonna happen to these initiatives once that funding runs out? Who is going to continue holding up data science in the academy? I think it's important for universities to start thinking hard about data science as a service and as a core component of university research across all fields. And if that means data science becomes part of the libraries or part of research IT or maybe as the plenary speaker says, we don't need to silo and we just say support for researchers and we all go to the same place, that's great, but it is on the universities to make that happen. So moving forward, how do we start addressing these challenges? I think it's important to continue the community building for institutions. This is a shot from our last Moorestone Data Science Environments annual meeting, but as I mentioned earlier, there is a new annual meeting for Data Science Leadership, Data Science Leadership Summit. It's an opportunity for thought leaders to discuss the challenges and lessons learned as academia adapts to the data science revolution. So continuing to meet regularly to discuss these challenges and to come up with ways to advance data science on our campuses is an important step. I just wanna mention as well the Moorestone Data Science Environments Summit while it has been exclusive to those three universities, they are looking to broaden it and have other universities start participating and it's a really cool opportunity for data savvy researchers to share and learn tools and methods outside of their domains. So I think in some ways it's a really unique kind of meeting because it's not an astronomy meeting or a social sciences meeting. It's a meeting of anyone who works with data science tools and is developing methods that wants to share and learn from each other. Okay, I think I have talked long enough. So I'm going to stop there and just see if there are any questions. One thing to note, there were a lot of lessons learned coming out of five years of experimentation you can imagine. So I wanna draw your attention to a white paper that all three universities co-authored together called Creating Institutional Change in Data Science. It is available on the MSDSE website on the bottom of the first page. And there's just a highlight article in the Chronicles of Higher Ed, but the full paper is online and hopefully you can find some more nuggets of information there. But if there's anything in this talk that you have questions about, also feel free to reach out to me. My email address is right there and I'll take any questions. Otherwise, all right, thanks so much. Thank you.


QuickBooks Online. Summary balance sheet. Get ready to start moving on up with QuickBooks Online. We're gonna be using the free QuickBooks Online test drive searching in our online search engine for QuickBooks Online test drives choosing the option that has Intuit.com and the URL Intuit being the owner of QuickBooks. Support accounting instruction by clicking the link below giving you a free month membership to all of the content on our website broken out by category further broken out by course each course then organized in a logical reasonable fashion making it much more easy to find what you need than can be done on a YouTube page. We also include added resources such as Excel practice problems PDF files and more like QuickBooks backup files when applicable. So once again click the link below for a free month membership to our website and all the content on it. We're gonna be picking the United States version of the software and verifying that we're not a robot. Zooming in by holding down control up on the scroll wheel currently at 125% on the zoom in noting in the cog drop down that we're currently in the accountant view as opposed to the business view we'll try to toggle back and forth between the two views so you can see where things are located in each of them right-click it on the tab up top to duplicate it as we do every time to put our reports in the duplicated tabs right-click in the duplicated tab again to duplicate it and then tab to the middle and reports on the left and then balance sheet and we're gonna go to the tab to the right now reports on the left to open the profit and loss income statement and then close up the hamburger otherwise known as the hamburger and then change the range from 010122 tab 123122 tab and run it to refresh it tab to the left close up the hamburger scrolling to the top changing the ranging from 010122 tab 123122 tab and run it to refresh it that's the setup process that we do every time noting that the balance sheet and the income statement are the primary two financial statement reports now we want to think about another report that you might use uh if you're batching your reports together and that might be say a summary balance sheet and i just want to note that when you're presenting this information oftentimes people get overwhelmed clearly by financial statements there's a lot of numbers and whatnot so a lot of times if you're a bookkeeper and you're presenting this stuff and doing bookkeeping for a small business for example or even if you work in a corporation then what you want to do on the presentations is oftentimes present the simplest version first to draw people into the discussion and then and then break out the more detailed information so when you present your reports you might want to start off with a report that's nice clean and as simple as possible and then add your more detailed reports after that that's one way that you can think about organizing your reports and that's definitely a way that i would think about trying to do a presentation or present the financial data so that you don't just you know remove everyone from in essence their brain right at the start by presenting a whole lot of data so one way you could do that is to try to have a even a simplified balance sheet and one way you can think about doing that is just to condense all of these triangles down to what you could call like a summary balance sheet so this would be a more kind of cleaner way to see that now you could just obviously do that this way or you can there's another report that is the summary balance sheets let's just take a look at that i'm going to go to the tab to the right right click on it and duplicate the tab again and then go down to the reports and just take a look at their summary balance sheet and as we are here just note that of these reports we've got a balance sheet that's a comparison and we we basically saw how to construct those now so this is just really a report that is one that we can basically construct we've got the detailed balance sheet which has some added and then we have the summary balance sheet and then we've got the standard balance sheet so now we're going to go into their summary balance sheet and let's do the range change up top from 010122 tab 123122 tab run it and you can see this is just a condensed balance sheet so the condensing that they have done in essence is just like we saw in collapsing the triangles the triangles related to the account types now remember that this triangle up top is a financial asset category of the financial statement category these current assets are a financial statement category type of field and then these items here represent account types as opposed to individual accounts so for example if i went back to the first tab and we go to the the accountant uh account team and the chart of accounts this is under the business view we're just organizing the data with those triangles by these account types and so all of the bank accounts are grouped together accounts receivable other current assets that's basically what's going on here and you can kind of construct that if you look at just standard balance sheet by like i say minimizing all of these account types now i just want to point out that for internal use that's not really what we want because for internal use what we typically want is to have these expanded so i can look at the individual accounts so that as i do the data input with the plus button up top entering the forms i can jump to the financial statements and drill down dig down into the financial such as the checking account to see the impact of the transactions that we're putting into the system so for an internal accounting system i'm going to go back to the other page we we typically want a full standard balance sheet so that we can go back and forth between the data input and the end result the financial statements to verify that what we think is happening should happen or get a better understanding of what is going on with the transactions but when we do an external reporting you might want to start off with and lead in with something that's summarized such as this because this is a lot easier on the eyes for most people especially if you're going to start off with some kind of presentation discussing a line item you know within here and then inevitably we're going to expand if you can actually draw someone into a financial conversation about the stuff then you're going to need more data and you're going to pull out the more data which might be the more expanded balance sheet at that point and then do comparative analysis and whatnot if you just give them a big long comparative analysis balance sheet then you might just lose the conversation altogether obviously you can do other comparative type of stuff with this report as well so we can do the same kind of grouping this out by quarter and whatnot and running that so we can break it out by quarter we can do a side by side comparison just with the summary information so if I wanted to take it back to the totals run the totals and then do a a breakdown this way comparing like the prior period in this case would be the prior year percent change and dollar change run it so you can do that on the summary so in this way it's another variant so it could now you've got more detail on these changes there's nothing in the prior period here but you get the idea so now maybe that would be an easier way to present these comparative reports so that it's detailed in a horizontal analysis but has less detail you know on a vertical in the accounts because we're just looking by group so we can do that and we can of course do the vertical analysis so if i close this back out and then i do percentage of the columns and boom that might be an easier way to see kind of the vertical analysis and you can compare the vertical analysis on a summary sheet to a quarter by quarter or something like that something like this so you've got all those comparisons so now just to just to get an idea of this when you're providing these reports to someone you now have a summary balance sheet and then you can do multiple variants of the summary balance sheet for comparative purposes and then you've got the standard balance sheet which includes the accounts rather than just the account types as a grouping and you got a whole lot of variance within that so you got a lot you got to think about which reports you want to report at which cycle monthly quarterly yearly let's go back to the standard and totals and just to note that if you use this internally if i click on this account it includes multiple accounts within it so i can still drill down on it but it's not as effective because now i've got two accounts that are in that group so you could still do it like if you wanted to use this internally it's not too bad to kind of collapse these inside it's kind of nice that they have that collapse component but you're probably going to use the internal report just for the standard balance sheet that has all of the accounts within it so you can drill down on the accounts that are impacted rather than the account types so going back to the report notice that if you give this externally i would be customizing it up top and typically you're going to change i would change this to brackets personally take off the pennies because this is kind of a summary report i like to have the red numbers for the negatives and then on the headers and footers then you might call this they called it a summary balance sheet and then remove the date time report basis for external reporting possibly run it and so there we have it if i was to check it out on the printer that's what it looks like we've removed the footers it's a lot shorter of a report fits on one page which is nice closing this out if you wanted to save this for external use you can save the customize it save it if i go back to the first tab and then refresh the screen so the reports will be refreshed and then go back into the reports on the left hand side and then my custom reports there's your custom reports okay let's just see where things are located in the business view if i hit the drop down on the cog switch to the business view and we just look at where the reports are they're under the business overview and then the reports boom boom and then the chart of accounts under the business view is in the bookkeeping i believe bookkeeping down below and then the chart of accounts right there so that those where the i think those are the only real locations we went to and you could see how to get there under the two views accounting and business that is


In this field, he did a great deal of work, which included photographing many specimens for the University of Heidelberg. Because of the great contributions Reif made to Heidelberg University, he was given an honorary doctorate of parasitology in 1914.