Okay, hello and welcome to episode 75 the market maker podcast and a little bit different than our usual routine because Piers Curran is on holiday again and Piers, I know you're listening so enjoy yourself, but Come on. You've got to do some work at some point, but instead in his place We're gonna change things up a little bit and I'm joined by millen deep Bassie and And hey millen and millen is part of our technology team and what we're gonna do is I'm gonna quickly wrap up All of the things that have happened in markets as quick as I can because I'm just conscious of there's a lot has been happening I don't want anyone to go without and then we're gonna bring millen into the conversation and We're gonna talk a little bit about his background and his expertise Where he helps design and deliver some of our most advanced quant related training to the likes of Citadel for example And so really want to pick his brains and get into that subject matter because I know it's a real Growth area for a lot of students and future applications. So first off What's been happening in markets this week? So Mega cap tech earnings have really dominated and actually I guess summary they weren't as bad as feared Can't be said for all of them meta Facebook Continuing to fall on rough times, but let me just go through a few of them. So Amazon last night We're recording this on Friday 29th. So Thursday night their shares went up 12 percent post market We are talking about one of the biggest Companies on planet Earth going up 12 percent So that's a that's a huge move strong sales and a strong outlook lifted their shares Apple will also up 4% last night beating on revenue and profit early in the week We had Microsoft that she turned in their slowest revenue growth since 2020 at Just 12 percent year over year growth in the quarter, which is quite insane on the conference call more importantly So although their revenue growth slowed on Their outlook they said they expect revenue operating income to increase the double digit pace for fiscal 2023 and the shares rocketed after that alphabet missed on most numbers I think when you're watching those coming out earlier in the week, you're thinking who doesn't look too good But their advertising revenues came in at fifty six point two nine billion and that was above expectations There was a lot of anxiety in the market about these online advertising Stocks because of the precedent that's been set by snap and Twitter who had disastrous figures Because of marketing budgets generally just being slashed with the looming recession But Google's stranglehold on that digital spend being what is ultimately? I guess a necessity for businesses is keeping that ticking over nicely But meta as I said reported their first year-over-year revenue decline in Q2 and It's gonna get worse and so they expect a range of 26 to 28.5 billion US dollars in Q3 revenue and That was against the street estimate of over 30 billion. So they've downgraded their future outlook They cited continuation of weak advertising demand environment We experienced in the second quarter, which they believe is being driven by broader macroeconomic Uncertainty, I think I read on their their press statement and then listening to a conference call I think I heard the word tick tock more than I heard the word Facebook So that kind of is a summation of their fears. I guess at the moment Bringing earnings a little bit closer to home shell the oil major there to accelerate their share buyback program They reported record profits for the second consecutive quarter obviously Beneficent from surging oil and gas prices Francis total energies, which is their other I guess competing Big company within that space reported this week their second quarter profits that had almost tripled so whilst a Majority are suffering at this point these energy stocks have really benefited from that energy squeeze on the macro side The European gas crisis is going from bad to worse that is influencing then and Really destabilizing consumer confidence in the eurozone, which is now at a 17 months low The key factor there is flow on the Nord Stream one pipeline that critical pipeline Servicing then gas flows coming out of Russia particularly to Germany That's now been cut to just a fifth of normal capacity and there's a lot of pointing fingers between Who's blamed that lies on in that sense so other things Q2 advanced GDP in the US at fell 0.9% on an annualized rate and that came after 1.6% decline in the first three months of the year meaning two Back-to-back periods of contraction now for the US so All the different terminology aside This is a technical recession and so a lot of pressure now for what are the Fed going to do next and We had the Fed decision. They delivered back-to-back 75 basis point rate hikes, but they surprised with the dovish Hint that the Fed could slow the pace of rate hikes Actually, if you were looking at your US equity charts, I think we're up over 7% in the NASDAW 100 in the last 48 hours or so so kind of The worst case averted for any fears that might have been present for these big Megacat tech earnings in combination with the looming slowdown With the commentary from the Fed in the US has meant that then rate hikes have been paired back And in fact, European stocks are now looking at their best monthly gain since November 2020 and the NASDAW 100 It's just hit a seven-week high Talking of risk appetite if stocks are rallying so is crypto It's a mill and you'll be happy Bitcoin ether heading towards their best month since 2021 Ethos up about 72% on the month alone so Seeing of their movement there as well, but undoubtedly the biggest news of the week, of course was McDonald's raising the price of their cheeseburger. Did you see that mill? Yeah, that's all right How do you feel about that? Well, I'm at the point where I've noticed near me. Anyways, I have three McDonald's on the same road I've got to each one. They have different prices now There's one in the middle, which is the most expensive and that's now going to be like over five pounds for a meal And I don't want to go there. Okay. No, we've got an arbitrage opportunity here I reckon we should buy some at the cheap place will park out the expensive one and sell them on discount So look, we've got a business idea there. But yeah, the cheeseburger in the UK has gone up 20% And and it's the first time in 14 years. So Yeah, the cost of living crisis is real folks But look, let's let's delve into a conversation then with mill and really happy that he's taken out some time to talk to me And really it came about because at the moment, we've got our summer analyst training program happening and millen runs some really cool Simulation sessions with them that particularly around python and programming and quant trading and I jumped in and it's the complete other side of the air of markets I work in and I was in the office the other day looking at millen's screen And it made me feel it made me feel sick if i'm quite honest because it was just full of code But millen perhaps you can tell us a little bit about yourself and your background your rollout amplifier and then we can go into Perhaps a bit more of a conversation about The kind of tips and tricks about the quant space Yeah, of course, I think I found amplifier. I think back in 2018 now My dad showed me and he was like you should be a trader traders.  They live the lifestyle, you know Uh, the the stuff that used to kind of get sold around back there. So I was like, okay, you know what this sounds good I'm gonna I'm gonna check these guys out. Maybe I'll have a Ferrari or a Lambo or something by like 25, right? I was hooked on the dream at 18 And I was studying computer science at the time. Um, I roll a hallway Um, and I was like, I remember telling people I was like, I'm gonna shift, you know I don't want to work in these fine companies. I want to I want to be a trader. Um, that didn't go very well, but Uh, the point was I guess is My dad got me hooked on finance. Uh, and then from there, obviously He did the program with you guys for three weeks and Back then it was in person and uh, it was an unreal experience to still, uh, you know I still talk about it with with people today. Um, and then Things clicked in place and I started out as an intern and I used to do a lot of the the grunt work I guess and no programming back in the days I used to Little things like fixes excel. She had a formula or something here um, and then As I did more of my degree, I guess I found out where where my passions I would like java and python and things like that And then I've kind of transitioned across many roles So I think my my current title right now is like a a full stack DevOps engineer Um, which basically means I can do, you know, your front end, which is like your html your css You know making things look pretty Um, but then I can also do the back end which is all the logic and you know all all the complicated things And then on top of that most people's careers can normally confined there I now also do networking and things so I can set you up serve as I can set up all all this other stuff as well with internet Um and things and yeah, I basically do what apparently three jobs I guess it but I get the title for it. So it's worth it. Um, and then you deliver of course Yeah, yeah, so so that's the next thing my title also says i'm the fintech product lead right now, which is Basically like like aunt said, um, I create the corn oil or even algorithmic trading products now and then We go to our clients and deliver them because I want to help other people program I think it's a it's a great skill to have and You know, I think the more people that understand how code works and hopefully they don't get headaches from looking at it Um, it just makes everyone like life easier and kind of good for the future, right? could could you Have done what you're doing now without studying computer science I I don't think so. I think um It's a very difficult thing because I never touched any code until I was 18 And I think a lot of people have this misconception where if I don't do my My like programming a level for example, that's my my career gone out the window So back in a levels. I was primarily into economics. That was my my my strong subject, right? Um, and and I only went out kind of went to clearing on that day I decided I'm going to go from accounting and finance as my degree to computer science just Like that on the day because I didn't get the results I wanted for accounting. Um, and obviously I think You know kind of to get to the point where I am at. Yeah, I think I need my degree and the only reason is because a lot of uh Like probability and statistics and kind of the way you problem solve is what gets taught at uni I think that's the most valuable thing you walk away. It's not Kind of knowing how to code is the the way to look at a task and break it down And go from there, but it's not something you can't just you can I mean you can learn it online I guess it's just there's no real structure when you learn online Obviously with with a degree you just get that that kind of structure and path Yeah, that that kind of structure way of thinking you just mentioned Is we talking maths then? No, so I don't use too much maths, but it's more of like if you pose a question to me like These simple things like I want this button to do something I think a lot of people would get fixated on on the wrong thing. They might look at it and say Um, you know, I've seen in the past with some people they get focused on the end result rather than how they're going to get to the end result So I think when I say structured thinking, I mean you need to be able to Look at the task say I need to do x y z. I need to go in this order And and the problem with with with our jobs, I guess when you're in your engineers, it needs to happen now I don't have you know a week. I can't say to Manager I'll be back in a week. I'm gonna go and see what this button does You know, I need I need to look at it and say, you know off the top of my head This will take me a few days. Maybe you're you know a couple hours and I think That's that structure thinking because I need to be able to quickly say, you know, I need a E&C completed before I can do, you know, whatever whatever I need And I think that's the hardest thing that they find that a lot of people can't get is If I you know when I run the sessions is a lot of people get stuck on the How does this go to that and then go to this and it's just because they feel like there's a lot of magic in between um so It's a bit of a difficult one. I guess And is there any way in your mind to improve that way of thinking like things you can be doing like even outside of That actual task that are just general using that same memory muscle or brain muscle Yeah, I think um, well, that's where the kind of the you know, like a business analyst kind of role comes in I think one of the the lucky things of doing my my placement year with with amplifier was I had a small time where I was a business analyst and my main job was kind of You know to talk to someone like yourself and and say what do you need done? And then obviously I need to convert what you need done to what what the engineers need And being that kind of middleman translator almost from you know, just like One or two sentences to maybe a whole paragraph detailing what needs to be done is what builds that skill And I guess how do you do that yourself at home? Well, it's like if if I say to you I want to build an alga strategy the first thing I need to know is where do I get my data So googling where do you get the data reading up on it? You're putting that into maybe like a notebook and saying, okay, I can do x y z And then from there you have to pick which one I'm going to use and It's kind of just building projects from from Start to the end without kind of cheating Um rather than just saying go to a youtube video, right? Or watch this for four hours. The guy's going to teach me everything I need to know Um, I used to do that, but there's no fun, right? Because you don't have that research phase and you're looking around and figuring things out yourself Hmm Okay And then I'm just thinking about then you mentioned before just going back to computer science because I know a couple of people are I kind of thinking of that as subject To to potentially study or study further But I heard you say before about you've got a friend who was studying at a different university and he was The difference between having good algorithm skills or good programming skills What do you mean by that like how is that different? Yeah, so I think this is one of those things where when I got to uni I thought you know you get taught the same thing everywhere, but with slight differences But what I found with with my friend he went to to Loughborough Um, and the way they taught his degree and the way he's kind of come out at the end of the three years is If you had a question about algorithms or any more of a mathematical Kind of problem I would say You can ask me but it might take me a bit longer than than him Likewise when it comes to to programming basically You ask me I'll I'll get the code written and Working quicker than he does and the reason being is I think he had more mathematical modules So he learned more about like the theory and the the key understandings behind computer science like what makes things tick and um, you know But for example execution time, you know, there's a whole Maths and a bunch of formulas behind how fast or something being how do you calculate that? um And things like that But I had I think maybe two terms of kind of lessons on that Um, whereas he might have had maybe I know like for in my final year. I didn't touch a single bit of maths um, my primary kind of Modules offered to me were around information security So I have a lot of knowledge in terms of being very like safe on the internet, you know Like hacking and things like that whereas he was offered more Like it's algorithmic stuff. So His knowledge on that on that stuff varies and I think that's the One of those things when you're 18 and you pick a uni, you don't know what what are they going to teach um, and it just happened to be Raw Holloway caught a lot more programming So I think when it comes to he's come to me in the past Actually, when it comes to writing code, he'll come to me and pose a question Me like can I do this in python? Can I do this in java even though on paper? We're the same person in terms of our degree and what we achieved um I'm better at that unfortunately and he's better at the the algorah side of things and That's the tricky part is when you're kind of looking for for talent I guess is you've got a batches in computer science. You've got one Who's the better programmer? But then okay, I might be the better programmer But then I could be absolutely rubbish at my algos, which is not going to do well like a place like citadel, right? So what was the most common then is it that? Most people are specialized in an area like that rather than a generalist I mean in a typical like Citadel team then you have multiple different specialists covering them What would those areas be? Yeah, so so citadel just from a Top-level breakdown. I guess they kind of split people into investment and trading These are they still have computer science degrees But these will might be people who are on kind of the front desk any trading job, right? And they'll be good at risk management and actually reacting to markets at the same time And then another role is is your quantitative research So a quant research is pretty straightforward It's going to be someone who creates these algos and researches them and and finds all the maybe, you know discrepancies in the market through statistical analysis and models, right? So this might be someone who's really good at maths But they'll like I said, they'll still have the same background They know how to program they did computer science And then you'll have software engineers and then software engineers are those who can program And kind of create the systems for the other two to use perhaps And then you can see like everyone would have the same background almost but they've split them into kind of these three groups of People so you've got your your maths heavy So I would tell my friend for example If he got the if he understood finance a bit more you go for a qr all because right you'd excel there Myself personally, I'd probably be split between software engineering and investment and trading I'd have to kind of try both out, but um, you know other people are kind of Program more than anything I'd say go do a software engineering wrong, right? Because investment and trading is a very I guess that's the hard one to find because you need someone who can program Manage the risk and understand the finance and and react to things Like you would as a trader because you're basically managing live live code Yeah, I remember being pulled in because there was a market event that had happened and They wanted me to come in and give a quick sound bite I think when one of you guys were training with them And I remember I was talking to one girl and she was previously interning at nasa And I was just like some of these series are my mind my mind's blown now I used to work as a 16 year old in mcdonald's Yeah, I worked at toby carvery so Humble beginnings, I guess But but talking of these people then when we're talking about These kind of elite level financial institutions From your experience then because I know that's not the only one that you you've dealt with What's been your your experience at your interactions with these people because I think there is a degree of mystique around Particularly the quant side of the the industry Yeah, I think um Like obviously from my my cohort at uni I can't think of these people that would work at these companies because uh, they're they're kind of more I don't know how to describe it I couldn't pick someone from my whole cohort and say, you know, he has the skills for sister though And like you said because there is a I don't know where they find these people Uh, they're like the the talent and the way they you know, they come across is Very much like, you know, you I'll speak with peers once I was like you look at them and you think there's your average geek and Kind of um, you know, you'd expect them to work in like a fang or whatever right do it do it kind of a more compsci stereotypical job And then you speak to them and you realize these guys have immense knowledge about finance already at this such such a young age um And I think that's obviously that's why it's such a lucrative role almost right you're you're you're looking for A needle in a haystack that can kind of do something that's not I mean, I I can't I was looking at masters for example It's very hard to find a degree where they'll teach you compsci and apply that to a finance background So computational finance for example, there was only a I mean it's taught widely But there's only a few places where it actually taught well Um, and I think that's the tricky part right like I can go do a masters and compsci But it doesn't mean I'm gonna get this job at that citadel because I need that specific kind of Angle I guess and then that's very hard to find and I think the way you'll find someone like that is someone who Like myself, I guess is someone who's been interested in finance for a long time and kind of you know in their free time You know what watches the news and keeps an eye on things like markets like You know, we've got what 10 15 engineers now at the firm and I tell only myself and one other Actively keep up with markets just because we yet we have a shared interest I guess in that and the rest are just you know, they do their engineering things and And whatever interesting So the key that you're saying then is that Um, if you want to apply that technical knowledge to the financial industry You've got to still have like in any other traditional role like in global markets as an all standard trader You've just got to have that curiosity and interest in the subject matter itself as well as the technical sure I think if you like obviously if you're applying for software engineering I don't think you need it as much right because you're you're mostly going to be in a back office Um, but these lucrative Front office roles I'd say it's the skills are the same Um, which is why I think you know without bragging. I think I I do pretty well and because I did the program with you guys went back when I wanted to do finance, right? So I I took all the knowledge in and and and everything that we teach and and kind of You know, I I guess I I owe the pro if it wasn't for the the internship. I guess Uh, I wouldn't know half the things I do and I wouldn't be able to do it. Um Because I got in early. I guess when I was 18 and in my head and you had to carry on, right? Because you know well Let let let's talk a little bit about what you've got cooking in the background then because I know you've always got your fingers and a couple of pies and I know you uh You were early on the whole crypto move. You were early on the whole busting out of a lot of the game stop action when coveted hit From from uh, just general market interest point of view. I don't know nfts or just any algorithms you're working on Is there anything at the moment? that's floating your boat Uh nfts and and crypto I've kind of left behind. I think uh, I think I've told you a few times I'm kind of I burnt myself out on On that and I think it was uh the the peaks kind of you know last year around march, right? It was when I had my nft. I had my crypto hype back. I was like I was in this I'm gonna hold this for the next 10 years And then just suddenly I have nowhere one evening. I think I told you right I was like I was sitting here at 4 a.m. In the morning. I woke up and I was like I should sell my ethereum. I don't know why but I looked at the price and I was like I've been watching this for a couple days. It's not moving and since I sold it. I haven't touched Yeah, you're you're so you're the ethereum whale who got out at 4 000 or whatever it was then the drop the market Yeah, yeah, yeah, that's the thing. It's quite funny because I remember Especially I just woke up randomly in the middle of the night and I was like getting rid of this It's I have my hype has just disappeared during my sleep So have you actually run any models over the crypto data And what was that? What has that told you if any information is? Um, I had a few algos Um, they were primarily technical And the reason being is I don't want to go into the fundamental side of algos because When it was fundamental analysis, I guess in terms of news is I can't compete in speed You know, like the oil release for example All right, cool. I could probably get the number as fast as you can read it but You know, there's so many people out there who and this was one of the interesting things I guess when I spoke to a sister one of the engineers, he said they don't care about Seconds between trades. They care about like the nanoseconds between trades Um, and he because it you know to the point where you obviously, you know These these firms have their exchanges in the same building as the actual exchange when they're executing these trades. They're like Maybe across the corridor from them In terms of myself with the models, I guess I most of the strategies I've had kind of Uh, they used to work, I guess up until this year Um, you know, they they looked good on paper, but unfortunately I developed them this year So when I when I I've let them out and and I was expecting them to do things They just they just failed and I think that's that's the hard part I think a lot of people forget is you know, I well, I've been working on algos now for four years technically on and off But you know, you you need to to keep trying because and even when it does click it might work for a week And then you know markets change and and then it'll stop working even though You'd think oh, it's it's the same strat. You know, I might be looking at something like, you know Whatever you're doing and I think that's the the bit where you get frustrated you get annoyed You feel like, you know what I've wasted my time almost You know countless hours of coding and you're like, I'm just gonna shelve it So I have like on my I have a folder that has a lot of code in it and different strategies over the years and The problem is one of them might be working right now And I and I don't know because there's so many right and that that's why you have these Now it clicks in my head why they have these massive teams because you need people to Go back and look at these strats and say, okay, it used to work But what's slightly changed and obviously a one-man job on on that is quite difficult So just just a kind of general Question and I'm not expecting like a definitive answer but take like where we're at in the market at the moment where We got an inflation situation where it hasn't been this high in multiple decades Now from a data perspective, what would be interesting is to go back and look at color kind of fed rate behavior when inflation was last in in modern history at these types of Um levels in order to then calculate what might the fed do in the future How do you like from a data perspective? I can kind of see how that would make logical sense But how do you start to then overlay in like the aspect of That was 1980 we're now in 2022 the market dynamic is incredibly different now in terms of You know the dynamics of who's involved and how they're executing and managing these positional trades. So What would be your first kind of go-to then when you're looking to try and like um, I guess spec out that type of question Yeah, I guess um when when it comes to analyzing the future and things um, obviously this is when You throw in machine learning and things like that. Um, and the reason being is the machine learning You know, it's a buzzword and it gets thrown around and literally everything has machine learning these days, right? apparently But you want to take your data like you're saying and just basically just extrapolate it into thousands and you know hundreds and thousands of different outcomes. It's kind of like, you know, um When you for example in the movies, right when when doctor strange does something and Timeline goes into, you know thousands of different ones or or anything like that. It's the same thing you want to um Take this data and say, you know predict You know a million possibilities that that can happen And then the thing is then hardest part in my opinion is then you have to rank them and you have to And that's I guess your your your secret sources, right? How do you look at these a million possibilities and say? This is the best one or or you know, this is the worst one, you know I guess if you can crack that that's how you'll you'll you'll make the money and I haven't cracked that yet Uh In terms of being able to say like obviously because the problem is right you might google it and you're like, oh, no There's there's an answer right there, but it's on google. It's that answer is not going to work, right? I guess it's like like anything if you want to make a good algo even You need to it needs to be your own and and that will be your edge It's just like trading right every trader might have their own edge The same thing applies here.  They'll have have stuff that would rank things and yeah These models and and that's where the the value is I guess so that that kind of again that terminology like machine learning or ai Well, I mean Is this just fanciful to like to be talking about because I know what you mean like You get the sales team involves and all of a sudden your product has ai machine learning like Potential but what's the reality of that in your eyes in terms of where we're at at the moment and then to where it's heading over a Longer period are we like in the infancy part of it at the moment? Or is it more developed than that? No, I think we're still in the infancy like obviously If you look at nvidia, right? There are graphics cards up just now putting the power I guess of being able to actually do like lost computations in the hands of your average average joe, right? You know as we progress and I've seen this, you know, they're focused less on gaming these days and performance and focus like You know our our graphics card can also do xyz in computational power I think as time goes and and you know this computational power trickles down to your average joe Um, then I guess is is is when you know machine learning makes more sense like, you know, even with the iphones, right? Like only the the new iphones have those, you know, they obviously they added that dedicated chip just for like machine learning and things like that in your phone um Only now that because this stuff's happening I would say that machine learning makes more sense to put into a product But like you said they slap the title and let you everything and sometimes the machine like machine learning is I'd say It's misunderstood because there's different ways and different levels Like you can slap a label and I could have the smallest amount of machine learning Which is which might just say and likes coffee someone else likes coffee like that's a trend um, you know That's all machine learning is right. It's like, okay five people like coffee now and they all live in one area You know, there must be good coffee or something there Well, like that's not the machine that you think of of in your head, right? And same with AI. I think At the end of the day, I think, you know, it's an oversimplification for all AI But I probably might get some slate for this But it's just a bunch of conditional statements at the end of the day Um in terms of decision-making, right? So I could make a robot and you could ask it like five questions It'll make decisions. I can I say that that's an AI You know, you you asked it five different questions and it made a choice Obviously, then if you try and do other stuff we'll see it's it's not implemented yet. Yeah You saying that then in that kind of more narrow structured way and being a consumer of Netflix content So then at this point, how do how how I know this is a massive question but How do you then start to counteract then the designer's influence on the struck instructions that create the underlying architecture of these Of these systems. I understand these systems can grow out and then design their own pathways, but the core architecture has to come from like a you for example, and You will have Some degree of biases. No, so how how do people address? Where's that conversation at the moment because I know that's a big one um, I think that's that's a tricky one like, uh, even Yeah, I don't know like if I make an allegro like you said I will have bias because I think one way is right And and obviously you might think another way is right.  Yeah, yeah, so like Like it's a very like it's a very hard question to answer.  It needs to be unaware rather than I might be like, oh if my algo hits five stop losses I want it to now Use a tighter spread That's putting my bias into the program and then it might make the the the algo worse essentially Um, whereas I should be taking it as the algo has no emotion Every trade is should be the the same trade, right? Regardless of the previous results So, um, yeah, I guess I got two things that I read recently one was Um, just trying to recall the details, but it was Nomura put out a note about They were using AI for ESG scoring And I get that because then that was basically more about just these monumental data sets And it was generating certain pattern recognition that could then identify trends that a human would never be able to see That then similar to the other example I had was when I mean When I started using twitter for markets I mean, this was A long time ago over a decade ago. I guess and back then it was like Not a well-recognized source of information as it is today And there was lots of pop-up companies coming out and I'd have software aggregation tools that would then pick out on the hose pipe and alert then A team of analysts that would basically then add the human layer of then of the million tweets the machine's punching out 500 important ones it beams and then the human picks out the 10 Yeah, there was always like There's always the human element. There's almost like the pilot. It's almost like being in a super sophisticated aircraft Where the plane it can kind of fly itself But the human still needs to sit in it to a certain extent because of the You know the what-if scenario or is this actually the right decision for the context and things like that? So yeah, I mean that's how I've seen it develop in my career And that's what I was still reading with the mirror as well the other day Yeah, I think it's like you said that you hit the nail on the head there It's it's exactly the same still like obviously says they'll hire their investment And trading people and they do what you said like they'll they'll have the strat running But you have that that individual you can rely on and if if something you know goes haywire or It places the wrong trade you need that You know the the pilot in this case. I guess to put things back where it should be Um, and I just had a quick look at this this numeral thing that you mentioned actually. Oh, yeah I attempted this with reddit back back during the Um, I try well, there's other websites that already do this But I was like I want my own one that kind of does it for me Um, and it was just great reddit for every new post coming into wall street bets and just say You know analyze the sentiment because you know, so you have natural language processing, which is basically You look at text and you compare it to a dictionary or you know a library collection of words that will say The word bad is is negative, you know If I said the stock is good that that's a positive sentence Um, so I'd look at a headline on reddit and it might say like You know everyone uses the dollar sign for example just to to make it easier So I'd say okay in this text look for the dollar What are the what's the ticker after the dollar? It might be like dollar amd Um, then what what is the rest of the sentence and then from that I'd give it a score saying okay That was a very positive one. Um, and then I'd add to a counter saying this was mentioned once in the last 24 hours It run, you know constantly all day reading these posts and and all I'd get at the end of the day Still manually like you said right I'd log on to it. I made a web web panel for it like an admin panel I'd log on and it'd say okay He's put in this ticker was mentioned x amount of times. This this is how many were positive This is how many were negative I can click on it and view all the all the different things And the reason being is I guess back then I was like, okay You know Yeah, yeah, who's next what I want to know over the the next couple days Right, there might be something that was mentioned maybe four times and then as it starts climbing and trending and you're like You know, that's the the next one and I hopefully hop on it early The problem I ran into I guess was like I said it as a as a one-man job um I didn't have the power to To to run this thing and and the data, you know, even my computer on 24 seven for example just scraping data and analyzing it um And then I think I had so much data I just didn't know what to do with it myself because you know, it becomes too much and and that's when you know, like Big firms would have these different teams. I guess where I need some I need someone who is a data scientist essentially and takes my data and says Am I analyzing the correct way? Right? Because obviously I did it from a very very simple point of view But like so there are way more intense ways. I guess of ranking things and the problem I ran into was And this is the one that I still bugs me today because I couldn't fix it Uh was what happens if you don't put a dollar sign or You know when wall street bets kind of clocked that people were checking on the red and running these algos they started using code by um for example Like instead of gme, right? They'd write something else. Um How do I get my algos to pick up these ones because it might say that gme was mentioned five times When actually it might be 500 because they're using their their own code, right? Um, and then problems ran into okay. Well, if someone puts two spaces Between the letters g and m and e So the permutation to start building out and you need that's where you need that maintenance. I guess Yeah, and that's where machine learning kind of you want your your algos to get smarter to say, okay Look, someone's wrote gme with one space yesterday, but now they're writing it with two that's it needs to correlate that to the same I was doing it manually. I had a text file where I would add tickers Um, so if I saw something already, I'd be like, okay People are typing it like this. I'd add it to the text file and then my my my program would start looking at it Obviously, that's not sustainable. Um, so that's another one of my many shelved projects I can see I've been there and I I tried it. I guess okay. Well, look just to conclude things I guess a practical element of You get told quite a lot rightly or wrongly if you're interested in finance particularly in markets and trading And this doesn't necessarily need to be quant. This could even be just more standard investment bank sales training That you need to load a programming language. I think finance students now get told that just like vanilla You should learn programming. Yeah Now a is that true and then b If you're going to learn the language which one and then I guess See how much do you actually need to know of that language? Okay, that's fair. Um, is it true? I I think so I think Well, so I like you might learn your programming language and go into your job and you might not use it Um, but I think what it prepare you're preparing for a worst-case scenario where let's say in six years You know, we we we don't know what the landscape will be like but if you already knew now how to code and and maybe have Just just all I ask of people is just have a basic Understanding so when you're talking to an engineer, you don't feel lost perhaps, you know And and what I kind of preach I guess to our interns I guess and just anyone I meet is But learn a little bit and you never know at your job. You might come up with an idea You know, you don't have to code the idea just coming up with the idea Could lead to to you revolutionizing a company, right? But if you didn't know how to think from a Kind of what like if you don't know what can code do for you How will you come up with these ideas, right? You want to Like he said, we're always pushing forward. I guess as a um as a race um, but You know, you need to Yeah, I guess that's my point is, you know, don't think all the millen says go learn to program I never use programming for the next two or three years I'm just preparing you and kind of giving you the opportunities to say Right, this is clicks because you know, people come up to me all the time like I've got an app I'm like because you don't have an understanding of how programming works. Your app is Too ludicrous, I guess for for for me to do anyways on my own or you know You're talking a couple million in terms of development for for your your little idea um If you had a better understanding for programming, maybe you might come up with a different idea and things like that So where do you actually start then what what's like the base language you'd recommend? So I start so I think this Surprises people I actually started with java even though I don't use java anymore That was I spent a whole maybe year and a half at uni learning that um And I did javascript and python and c and everything and I think When it comes to to learning a program in language, I think I always recommend python It's not the fastest You know cc is hands down. I guess the the better one for for for finance But it's the easiest to get into right you download you go to the python website. I think it's like python.org You download the thing You open up a notepad you write code and it just works like when it comes to java and things right there There's a lot more steps involved You know the last thing you want if for someone who wants to get into programming is Have issues installing the language and getting it running right you just want to I just want to point people to this Place and say look download this you get this this little notepad window write your code and crack on um I say python, but I think the the the one thing I always emphasize is Regardless of what language you start you just want to learn the fundamentals so things like variables data types, you know data structures um, you know writing simple conditionals for example and Loops and things like that once you Understand these concepts When you go and pick up another language all you need to learn is how to write it, right? I always say to people It's the same as English uk and english American right it's the same thing at the end of the day, but they they they say aluminum and aluminium um, and at the end of the day like You know you can understand it and my point is if you learn python and Um, I think going on to your question of how much I guess you need to know Um, I I always say to people about I say 50 hours um Would probably make you very not very good like you'd get a good understanding for programming So for example, let's just say with will right I'd say when we started this whole One shenanigans I guess as a firm um, will knew You know very minimal code and it would like kind of wouldn't be able to look at like maybe three or four lines and understand What's going on? Obviously he's been doing the sims with me and and I I remember once when we were meant to go to these states Well, obviously we got denied Um, we went to have lunch and I sat down with will and maybe soon about two or three hours of me just reading line by line Each thing in our quant program and explaining to him. How does this work and and things like that and then kind of will's understanding I guess is Improved and to the point where he was even saying to me He could run the sim almost even though he's you know, he's not an engineer obviously um He doesn't even code for a living right doesn't do it every day But you know repetition and I would probably say I've spent maybe 20 to 30 hours with him just looking at the code I've got to the point where with our quantum anyways that will almost Sometimes answers the questions without even coming to me But when we ran it initially it'd be to the point where will would have his hand up every five seconds being like milling Can you come here? So I think in terms of learning I'd say go for python just the ease of getting into things Um, and then how 50 hours I'd say but so 50 hours. I'm just thinking about the maths there So you I know we're coming to the back end of summer, but you could do it as a summer project, right? Yeah, so I think um, uh The easiest way to learn programming is don't okay, so go do your your code academies, you know Your different courses online don't pay for anything ever In terms of like, you know, 15 20 quid to learn python because you can google how to learn python, right? You know engineers have helped each other out, you know, all the resources are online um but basically pick A topic that you want to do don't just go in with the idea of i'm gonna learn programming and then follow the The course I'd say Say, okay, I want to build. Let's just see a very simple Uh text analysis plot, right? You give it a sentence and it'll tell you if it was bad or good That sounds horrendous and and really hard. I guess from from say for example, you would do it and right? It's very sounds really difficult Um, but then break that down, right? Okay. I need to Get my python code to read text That's now one like subtask on its own, right? Um, okay. I want to read the text from a text file. Okay, then that's another subtask And all I would say is just do that little bit by bit, you know Like lego you're gonna start with one little little piece and and just stop building it up the next thing, you know You'll you'll have your your lego baghati or something made um But like start with that and then you google, okay, how do I read a text file in python? Like, you know, it's amazing. Everything's there Um And you start from that and the next thing you know is you've done that the next toss right now. How do I? How do I score this text? How do I determine if this text is bad or good? Then you go down a rabbit hole of that and then you're you know, I think that's the best way to program because you're not doing it for the sake of Like i'm sitting down to to learn something, right? I'm doing this because I want to achieve something but at the same time I'm going to learn from scratch Um, and I think that's the the biggest difference. I think between myself and many programmers is I spend a lot of time outside of work. I guess just Writing random stuff, right and like I said, I've got hundreds of scrapped projects and just shelf But I learned something doing all of them and and that's what kind of builds you up into to being a good programmer almost And it's just like finance. I'm sure you can agree like you've probably spent countless hours. I guess Um, just researching and reading for your own enjoyment as well at the end of the day Yeah, much as my wife hates that But look, look, we'll wrap it up there Um, Milan, thanks very much for giving up some time and sharing some of your insights and thoughts What I will do is I will put your LinkedIn profile if you don't mind into the show notes on the podcast So if anyone wants to reach out to Milan, I'm I'm sure he doesn't mind Helping that's fine. I've uh, I always tell anyone if you ever need anything Message me. Oh, yeah, I think I had someone from about two years ago. I met and she sent me Uh, an algo she did for for a dissertation or something and I was like, okay, well I had to read a bit and then I gave some feedback. Um, but like I said, my my inbox is always open If I don't reply don't take it personally. It's just quite busy Um, but eventually I I normally tend to try and get get back to people Uh, or you can always yeah message me on instagram if you're really really desperate All right Cool. All right, we'll wrap it up there. Uh, thanks everyone for listening and we'll be back as normal next week All right. Have a good weekend everyone


Welcome to Agile Roots 2010, sponsored by Version 1, Rally Software, Vario, Amirsis, Agile Alliance, and Xmission Internet. The new methodology isn't a methodology by Alistair Coburn. So who's ever had the pleasure of having a conversation with Alistair? If you haven't, I suggest at some point in your life you do. It's a very powerful experience. I'm just going to leave it at that. He's going to give his talk about methodology. He's a methodologist. Hopefully, if you're getting sleepy, I suggest you drink the energy drinks because you don't want to miss Alistair's talk. Take it away. Thank you. This hat, which I'm not going to wear, and one of the reasons I'm not going to wear it is because it's got this like a pill-shaped problem, is a kind of a ritual because this is what I wore in 2003 at the first Agile Development Conference. And last year I passed it on to Andrew when he and they started the Agile Roots. So this is kind of a limbo, but I'm not going to wear it. And by the way, Andrew, you probably need to get some pills for this hat for next year. There you go. All right. I'm going to start with some activities, so I'm kind of hoping people will come back into the room. Kay and the organizers knew that I wasn't good enough to do an 18-minute talk. For those of you who know what that refers to, or a 12-minute talk. So they gave me 40 minutes, because I'm not good enough to do an 18-minute talk, but in fact I'm so bad I really need an hour. So we're going to see if I can get this thing into 40 minutes, because I'm going to ask your help and I'm going to ask you guys to do some playing and stuff like that. Because the time's going to be of interest, I'm going to give you a little of silence to do in 90 seconds, 60 seconds, 2 minutes, 3 minutes. I'm going to ask you when I do that to just really stop fast so we can move forward. The reason is, because I actually need your help for this, that methodologies these days aren't methodologies. And so we're looking, we need a new word. The problem is that we've advanced enough in the last 15 years that what we used to call methodologies and the limitations of what we used to call methodologies, we've moved beyond that into things that aren't methodologies anymore. We just don't have a word for it, and we suffer because we call them methodologies. Scrum, the new can band stuff, the crystal stuff, are not methodologies. I just don't know what they are, and I don't find a name for it. So I'm actually going to use half of my time here to see if you guys can help me out with this. Now the first, to get you there to the kind of a state where you're kind of understanding what I'm talking about, I'm going to ask you to do a couple of different exercises. So here's what I'd like you to do. I'm going to give you three to four minutes, and I want you to work in groups of about about five, six people is good, and practice your East Coast dialect. You know, this is very, very Boston, it's very New York. Stanford is one-upping the East Coast on this little thing. So take three, four minutes and just practice your like personal jabs, a little one-upsmanship, you know, that kind of stuff.  Practice that, and I'm going to give you three, four minutes to do that. Go ahead, start. That means stop.  All right, you get the idea. So now, I want you to reverse it. Same groups. Same about length of time, but do the reverse, right? Build each other up. Ali, I love you, man, that ping-pong that was awesome. Persians are the most polite people in the world. Next to Egyptians, of course. Egyptians are awesome. Okay? Same thing, build each other up, any way you can. Ready, set, go. All right, thank you very much. I hope you get a sense that the second was very, very different in tone than the first, right? And actually, what was scary for me, there's a good part and a good news, bad news, is the first way is, though I hate to drag it up, is sort of kind of like how Hitler got into power in the 20s, right? That kind of jabbing. The second is actually, you know, your contribution to world peace, if you just did that, right? So I let that run an extra minute longer for the good stuff to the bad stuff so that I can increase, you know, world harmony just in here. So just think about that when you go back to work. If you were to do that exercise sometime with your work group, but just have a periodic, let's say, a little three minutes is a long time, right? Three minute build up session could be really interesting at work. Best comment I heard, and I won't say who it was and who she was saying it to was, your butt really does look good, she said to him. Score. All right, now the reason I bring this up is because what I gave you, it wasn't a methodology, right? Was it? Totally different behavior, completely different behavior, fairly sort of predictable kind of characteristics to the behavior. And all I did was to say, put yourself in a certain space and act that way. That's all I said. That's what the new methodologies are. That's what Agile methodologies are calling for is put yourself in a space and act a certain way. What the scrum say really says, demo or deliver every month, self-organize, inspect and adapt. That's it. That's really scrum. All the rest is decorations, you know, shoe level helpful hints for beginners, but that's really what it's saying. And Crystal says about that much or less. So it's kind of tough. The new Kanban stuff, so David Anderson has a new book about Kanbans, it's not a methodology. It's just, no, it's not a methodology. Here's the point. When I go around and I try to say what is scrum, there isn't a word. It's not a methodology. So half of this exercise, half of my session, my 40 minutes, I want to spend brainstorming with all of you as to what is a potential name. So this is going to be very experimental, like my way out on a limb here. We're going to do brainstorming with 240 people in about 15 minutes. So we're aiming for and see what we get. So stay with me. I'll give you short time frames. When I go ping-pong, like stop. In groups of three people spend two minutes nominating names for all of this, like what the exercise you just went through or things that aren't methodologies, but ask you to operate in a certain space. I'll start now. Groups of three people, write down names, potential names, brainstorming, ready, set, go. All right, just keep rolling forward. In 60 seconds in your three-person group, pick out your top two names. We're going to do single elimination term on these. Pick your top two. All right. We're going to do single elimination here. Find another three-person group. I'm going to give you 90 seconds between your two groups. Top two names you can do anywhere you want. There's a sample technique you might try. Ready, set, go. Stop talking. Make the selection. Be done. Last round of single elimination. Find another group. 90 seconds. Pick the top one. I only want one. All right. I already moved on to the next slide. There are eight steps in this. We're on step six. I'd like, need the quiet please. Need the quiet please. Quiet. Like really, thank you. All right. Each group, you're going to have one speaker. Probably somebody wrote it down. You're just a caller. Don't worry about it.  And this is where I have to take the mic off my glasses because I need my glasses. So we're going to see what happens here. Maybe it'll dangle. I don't know. Try the dangly mic version thing. All right. I'd like all the speaker from each group to stand up. I'll point to you. You call it out. I'm going to write it down.  That's how I know what I'm going to be done. I've got this little spreadsheet set up here. Scott. What? Approach. Next. Ethos. Next. Say it again. C-A-S-T. Casting. Next. Mind sprint. Next. Action. Sticky, yes. Action. Environmentality. I'll get there. Allergy. Environmentology. I got it. I don't know if my, we'll see if this thing still fits. Next. Valuology. Valu-g. It's a good thing I can type. Mono. Did you guys like connive on this culturalgy? Yes, please. Generative. I thought the word methodology was bad. Generative constraints. You know, people are supposed to like say this afterwards. Yes, please. Mind set. Mind set. Yes, please. Flowology. Flowology. Mind set. Hype. Hype. Okay, we have got a spelling. Real-time spelling correction doesn't matter about the time going by. Yes, please. I'm sorry, I have trouble hearing. Culture mob. A-L-T-E-R. Culture club. Do you guys, somebody's, one person spell it. Culture mob. Thank you. Social, what's the second word? A social accord. Anybody in the back? Diana? Excellent. I'll leave it. Brownian notion with an N? Notion. I just can't type. Brownian notion. Over here, please. Evolution. I was guessing it'd be 20 groups. We'll see how close I got. Squid. Thank you. Down here. Jabber size, excuse me, squid. Jabber size like that. Awesome. Thank you. Thank you. Creative, resourceful, agile people. Which, which I'll let you, uh, acronymize. Thank you. Somebody help me value something. Value delivery.  One more? Framework. Framework. Are we all down here, down here? Is there anybody upstairs, Scott one? You people up there just like, we have one up there? Activity. Pattern. Okay. All right. So that took, so now I'm going to try to get votes on these and now I have to muck with the Zoom stuff. We'll see if we can make this work. It barely fits. All right. So I've got some additive stuff going on there. So let's go see what the next instruction is. The next instruction says each person, no talking, write your favorite name on a card. 30 seconds and what I'll do is alphabetize those suckers. Oh, what we're going to do out of this? What are we naming? So basically it's going to be a replacement word for methodology. For the methodologies that aren't methodologies, this is a bigger thing that's just whenever we just went through in our little sample exercise. Wow, okay. Names. Something will happen out of this. Like I will run out of time, which is really cool. So let's see how I'm running out of time. I'll get to give the Ted three minute talk at the end of this. Okay. So you all have your names on a card. In your 12 person group, I'm only going to give you 60 seconds, we're going to collect the cards with the three most popular in each 12 person group. That's about how much time I can mess with. And you might, for instance, just collect the cards. All I care is the speaker's going to have three words like that and the numbers next to them. Okay. In your 60 seconds, in your 12 person group. See if you can collect those. All right. This is where we're going with that. I hope you're there. Since you guys have done all this work, I want to see if we can get some closure out of this, whatever comes. So again, we're going to do have everybody stand up. And I've got this alphabetized.  Any speaker, you know, group speaker who's got that word on the card, stand up. And when I point to you, just call out the numbers. I'll put them in here and the spreadsheet will run. Yes, please. I don't care. I'm going to do something. I'm all about self-organization, cheating, you know, so I would like a number at the end of this thing. All right. Action environmentology. Any takers? Any speakers? Just stand up. What's the number? Six. Anybody else? Going, going, gone. Next. Activity pattern. Any speakers? Yes. Two. Anybody else? One. I guess I go there. Anybody else? Going, going, gone. Approach. Speakers. One. Four. Anybody else? Going, going, gone. Brownian notion. How many? One. Anybody else? Going, going, gone. Casting. No takers. Creative resourceful Agile people. Obvious winner. I mean, this is like, so I'd be great. How many? Two. Seven. Two. Is Excel behaving itself? What? Four. Anybody else? Two. A lovely. Another. What did you do? Like buy the people all around you or something? Culture mob. Three. Anybody else? No other speakers? Like we're saving up or something? Cultureology. Three. One. Where are the takers? Ethos. Two. Five. Evolution. Any takers? Two. Thank you. Fluology. Any takers? Three. Thank you. Framework. Two. Three. One. Generative constraints. Three. Going, going, gone. Jabber size. Three. Thank you. Mindset. One. Four. Two. Two. Four. Seven. Two. You had a ten over there? Thank you. Mind sprint. Second floor's got the biggest set. Mind sprint. How many? Two. Takers? Going, going, gone. Two. Thank you. Social Accord. One. Two. Squid. Value delivery. Valueology. Three. Two. Going, going, gone. All right. See the result? Anybody else? Okay. All right. That's what I got. That was the experiment. You see the problem.  It ain't got a name with an H. So I'm going to give you now the three minute TED Talk version of where I am on this whole stuff. Methodology, when I went in 1991 to IBM, the representatives of the Big Five consulting companies got together to discuss methodology. One of them gets up there and puts this thing like on the left. This is when I say the word methodology. I mean this, right? Activities, processes, milestones, teams, roles, techniques, products, standards, tools, skills. And I was a newbie, right? So I didn't attend any. And they all go, yeah. So it was clear to me amongst those people that's what they mean with the word methodology. All the roles, all the people, everything. Everything. It's a big. You cannot be small. It's big. Write down XP with all that stuff. It's big. We've gotten away from that these days. And that's the problem is because methodology means that. Now the shift is that about, I don't know, 12 years ago, I started seeing the interplay between the ecosystem, which is the actual people who show up.  And the role definitions presuppose a certain kind of a personality. What kind of a person is a good scrumester? What kind of a person is a good project manager or BA or tester or, right? The people who show up at personalities that don't match the role, the presuppositions of the role descriptions. So they kind of bend each other around. The real people and the methodology kind of bend each other around. So the thing we talked about, we've heard some talk about culture. So methodology is, frankly, culture design. Methodology design is culture design. And you're trying to have to do that alignment like we heard in the lightning talk yesterday. You're doing culture alignment amongst the corporate culture, the group culture, the individual culture, right? So this is all stuff that was 12 years old. Trying to update crystal in the light of these, I've got very little to hang on to because I understand the complete relativity of all the stuff that we talked about. I've got kind of two things that if I were going to create a crystal two point it would look like. The first is something that someone alleged referred to yesterday. It's the oath of non- allegiance. And we're thinking of getting t-shirts made up. The badge is already there. It's not attached to crystal because if anybody comes in with crystal they have to show you the oath of non-allegiance, which means I'll tell you stuff that isn't in crystal. Like I totally don't care if the PM Bach has a good idea, we'll use it. If the CMMI crowd has got a good idea, we'll use it. I don't care. If Carpentry has a good idea, we'll use it. Even if some of these weird wacko agile people ever have a good idea, we could use it, right? Possibly. So that's the first thing is we're getting out of these. There are people who are begging to end the methodology wars, right? Good ideas from anywhere. So you can go get that as the website. The second to try to like frame what I'm talking about, I find that I put things on these three steps. People tend to hire a consultant or hire people or get started with practices and techniques. Teach me how to do that. And that's what the whole Shuhari discussion comes in that we talked about yesterday. Stuff I've been working on for the last 20 years is why does it work? What's the underlying basis for thinking any of this makes any sense? And that's theory. And that's where you get lean processes, cooperative gain, knowledge acquisition, the seven properties of successful projects. All of that fits on the theory step. And none of these steps is the correct step. You have to always move. There is no correct step. All the steps influence the other steps. You have to always keep moving. The third one, you'll hear people talk about and talk about and talk about. I just want to make it explicit. Self awareness. What are your values, your priorities? Who are the, what are the personalities? When do you do reflection? The personalities will affect the techniques you can use. The priorities will affect the techniques you can use. The theory affects the techniques you can use. So these things are all in play. So I just find that in discussions these days after Shuhari, I draw this three step. And I find I point at the different steps. Try this in a discussion you'll find you'll talk about theory. You'll talk about a technique. And you'll talk about yourselves and your values. And you'll keep your finger moving up and down those three steps. So we're looking for words around all of this. The last thing I want to give you is I've given this diagram now for a couple of years, like about five. And it says that the unit of inventory in design is the unvalidated decision.  And actually that should be users and buyers, but could be I mean buyers and sponsors. But anyway, so we talk about decisions flowing through the network, but it's missing something crucial, which was revealed to me recently when I was consulting at a company, that actually what we're talking about, we want to pay attention to, are the systems of conversations between people. It's not the decision, but rather when two people get together, two groups, they have a conflict of parameters. It's not a conflict of interest, it's a conflict of parameters about the budget, about the time, about the scope, about the quality, right? They have parameters they're working toward.  And so I've got there pairwise, although sometimes you have more than two specialties in a room. And I'm starting to look at and consult on how do we articulate the sets of discussions that take place and what are the conventions around those discussions. So I call it systems of conversations. There's not methodology, or maybe it's methodology, or maybe it's this new word, right? I don't know, but I'm desperate because this is the space we're moving into when we talk about what used to be called methodology. We need a word, but I just wanted to give you where I am on this whole story. This new X, it's a mindset, it's cultural rules like we did at the beginning. How are you allowed to talk to each other? What's the tone of voice you speak in, the structures of these conversations, and so on. If you ever want to do this brainstorming, naming exercise when you go home, that's awesome. Don't send me a single name. Just get a couple of people to agree on some interesting name. And, you know, I'll post it. I'll start using it whenever a good name shows up that I can get away with. And if it's one of these that we came up with today, I'll give the attribution to the Agile Roots Conference, where it came up. Aside from that creative thingy, right? That was a... Anyway, so when the take home, I hope you get out of this, when you go back to your places of work, don't look at the methodology. Don't look at the artifacts. Don't even look at the decisions. Look at the system of conversations that you have in place. And what are the rules, the implicit or explicit rules around how those conversations are held? That's what I've got for you today. Thank you.


Welcome to the first of a series of talks and tours about science at the University of Adelaide. So my name is Sandy Stacey. I'm the head of school of physical sciences here and if you come to the talk at 12.30 by my colleague Andrew McKinnon you'll find out the physical sciences involves chemistry, physics and earth science. We also have schools of biological science, agriculture, food and wine and animal and vet science and each school will be doing a presentation like this about a half an hour and those are listed in the booklet right in the very front. We also have a number of talks about science, some tours of our facilities and I think most people walking in were given the sheet outlining those and if not there are plenty of people afterwards who can give you that. So there's a lot going on today and then of course we've got the Big Marquis and lots of people very willing and happy to talk to you about opportunities in science and also about what life is like as a student at the University of Adelaide. So this is a really exciting time to be entering university or thinking about university or going into the job market. It's also kind of a scary time because the world is changing so so quickly. Climate change of course is a big aspect of this and so 2016 was the hottest ever recorded. So the acceleration of climate change is increasing. We've seen a lot of issues with coastal erosion in Adelaide this year and some doom and gloom predictions. A lot of work will go into that type of stuff involving all types of science from biodiversity and stabilizing dunes to the effect of hard protection on coastal systems to marine life. Lots of opportunities there. Coral bleaching has been another thing that's been in the news big time and again big challenges there but also big opportunities. And another thing that's really taking off is what's called disruptive technology. So this is technology that changes the way things work you know and the internet is a big one for us. So I'm old enough and some of the parents in the room are old enough to remember when we didn't have the internet we didn't have email. And we certainly you know didn't have these things in our pockets. So we're seeing a rise of a whole bunch of technological things but then they change society as a whole. And then on the science end there's a lot happening now with renewable energy with improved batteries. So we're starting to see that solar power isn't just transient but it can be stored. So lots of science around that lots of science around advanced materials capturing energy for example hydrogen batteries. So it's a very very rapidly changing world. And that's really important to think about because if you're a potentially university student you graduate at age 21 22 you're going to be working for at least 50 years. That's pretty scary. And the jobs that you start with will not be the jobs that you end with. So right now we're seeing this is a slide from 2014 that in Australia on average people have five careers over their lifetime. They make big changes in what they do.  So here are just some ideas of jobs that didn't exist and exist now. I asked developer being number one this is from LinkedIn by the way. So again you know we didn't used to have these things in our pockets. Now it's a huge industry and it's already starting to wane. So the last time I was at the airport half the people in the queue in front of me were checking in online with their watches. Okay so in 10 years time will you just be thinking it? Who knows. Okay so all that's going to change. Data scientist that's something that's really exploded and will probably continue to explode in different ways. So we're surrounded by big data and then how do you we make use of that. Okay so there's just some big big jobs right now that didn't exist 10 years ago. Again try to imagine what the jobs might be 10, 20, 30 years hence you can't. So what you've got to do is you've got to develop skills. And another reason for that is that there are a lot of estimates now that a lot of jobs that exist today won't exist in 10 years time because of the rise of automation. So robots are getting smarter and smarter our technology is getting much better and this is an estimate for example from Syro saying that 44% of Australian jobs are under threat of automation. And some of that is stuff that we're seeing pretty commonly starting to be pretty common driverless cars for example. Do you need taxi drivers when you can have an automated car that comes and picks you up and takes you wherever? Same thing with trucks that type of stuff lots of transport issues but also lots of things you might not think about. Eye surgery for example is starting to become automated.  But that type of stuff things that we wouldn't think of as being requiring pretty high level skills are more and more being able to be automated. And so there's already stories going out you know has the uprising already begun. There's talks about do you need accountants for example because they have to understand the tax code. Well do I need to go to a physical person with all my receipts and all my stuff to do my taxes or can a robot do it? Okay so that type of stuff. And so if you're going to be employed for the next 50 or 60 years in this environment you've got to be able to do things that robots can't do and you've got and that means you've got to be able to think. And so the skills that are so so important now and in the future are critical thinking, are problem solving, are understanding data, understanding methods, really analyzing things and coming up with stuff that's new. Because robots can do the same thing over and over and over again. They can be taught to process some level of information but they can't come up with the things that are new. Okay and that's what scientists do. And as it turns out these are exactly the skills that employers really value. So this is a survey of about a thousand employers. It was done by the Office of the Chief Scientist a year ago. The number one skill is active learning, learning on the job. And that makes sense because again a lot of the jobs don't exist now. You can't be trained for those. You've got to come in and you've got to learn. And a lot of big employers, the government, a lot of big industry run what are called graduate programs. So they take people who've graduated from uni and then they train them in their specifics for their employment. But what they do is they build on the skills they've got. So they want the students to come with the skills and then they can train them about the specifics. So the employers want being able to think on the job, learn on the job. They want critical thinking. They want problem solving. They want interpersonal skills. You've got to be able to work and play well together. And then things like time management, show up for work, that kind of stuff. And it isn't until you get to number nine that it's the occupational specific skills. So if they want somebody to work in field X, you don't have to necessarily have a lot of training in that field as long as you've got the skills. And that again is really important in a changing world. And it's the importance of STEM skills, which is the reason that the state government is putting so much money now into STEM and to science, technology, engineering and math. And so the state government absolutely believes that the future of the state is around having highly trained STEM professionals who may not necessarily work in STEM, but will work, but will use those skills. And so you've probably seen the announcement recently that the government has put in 250 million into schools to refurbish or redevelop facilities for STEM. They're also providing private schools with 250 million in loans to do exactly the same thing. And the reason for that is the estimate that 75% of jobs in the next 10 years will need STEM skills. So again, the government's putting a lot of money into that. In addition, South Australia is really positioning itself to be the science and technology state. So defense is a huge thing.  So there's going to be a lot of defense related science and technology jobs. Okay, and we don't know exactly what those will look like. Some of it you can think about some of it will change again, with the rise of automation. But there's a whole set of career paths in that area. And an indication of that this is a big international company. It's based in the UK. It's Australia offices in Brisbane. And they're pulling out of Brisbane, and they're relocating to Adelaide. Okay, and they do a lot of stuff with defense. They also do a lot of stuff with nuclear. And of course, we have no idea what's going to happen. And with the nuclear agenda in South Australia. Another thing is food. And again, the government is putting a lot of money into food and a lot of effort into supporting a food strategy for the whole Northern Corridor. And that involves everything from food technology to better ways of growing food, to work that we do at the Wake campus, which is looking at the conditions under which crops grow well and adapting them for changing conditions. Agriculture is getting much more technological. So we're using drones to monitor crop growth, for example, a lot of work being done in that. This picture on the bottom is Sundrop Farms. This is in the Port Augusta area where they're using concentrated solar to run greenhouses desalinate water. So it's all essentially a closed system powered by the sun and growing tomatoes. At the moment, probably other things in the future. So again, lots of opportunities in science, but requiring these STEM skills. But again, just just remember that science skills don't necessarily, you don't have to pursue a linear path.  And this is a picture from Geoscience, which I'm parochial because I'm a geoscientist. And so this is from the US where people who've gotten an undergraduate degree in Geoscience, and these are sectors they've gone on to work in. So the nonprofits, some stay in academia, government, industry research, but also the types of jobs people with undergraduate degrees in Geoscience pursue. Some stay in science, some go into education, some into policy, business law. So again, it's about the skills and using those skills. So if you study science at the University of Adelaide, what do you learn? And why should you study science here? One reason is our programs and the variety of programs that we offer. When you come to university, you're 18, 19 in general, 17, maybe. How can you really know what it is you want to do? You're interested in science, maybe you like physics, maybe you like math, maybe you like chemistry, biology, maybe you're not sure. Maybe and you come here, you don't really know what you want to do. The BSC gives you a lot of flexibility. So the general Bachelor of Science so that you can say, well, okay, I'm going to take some biology. I'll try a geology course. I'm going to try some, you know, a bit of physics, I'll do a bit of math, and I'll see what it is I like. And by final year, you major in something, well, you've figured it out what it is you really like to do. And I'm an American originally, as you probably got from the accent, I really liked that approach. I mean, I went to university thinking I was going to major in journalism, I ended up doing a degree in geology, PhD in geophysics, and now I'm head of a science school at university, you know, so it's that kind of path. So the BSC gives you that flexibility. We've also got the BSC advanced, which gives you a research component to it. And so this is geared more towards people who think that they want research careers, or they want to do or probably more likely to go on and do graduate work. Okay, doesn't mean that people do the general BSC can't do graduate work as well, or that people do the advanced have to go that way. But it has a research component. And it's harder to get into it requires an HR of 95. And then we also have name degrees. So there are people who really do know what they want to do. And they're really interested in wine, for example. And so they go to the weight and they study viticulture, or they're really interested in some in wildlife conservation biology, or geology, or animal science. Okay, so we have those options as well. And flexibility. So if you start and you say, Well, I'm going to do x. And then you say, Well, actually, I didn't like that, you can switch. Okay, so you're not locked in for life. So we've got that big more key outside, there'd be a lot more people to talk to you about the specifics of this. So they can tell you what you would be studying who you'd be studying with how it works. What sets the University of Adelaide apart from the other universities in South Australia is that we're very, very strong in research. So most of you would have heard about the discovery or the detection of gravitational waves recently. A team of physicists in my school contributed to that. So they developed a wavefront sensor that was built here in Adelaide. It was installed in Lego in the US. And it was part of that detection. Okay, and that same group that's a lot of work with lasers. And a lot of that work is directly relevant to defense. And so we've got a post stock in that group who's funded by defense right now. We expect that to really take off. We do in the faculty a lot of work with understanding essentially proteins and how they affect health. And so this is a picture where this turns out there's a protein that causes cataracts. And so what some colleagues of mine have done is that they developed a molecule that binds to that protein and prevents it from causing cataracts. They've tested it in mice at the moment. It works. They've gotten some money to develop it further. And the hope is eventually there will be an eye drop that you can put in and that will prevent cataracts. Okay, in animal science work at understanding how diseases develop and how various things develop in utero. So or in Ovo in this case. So this is a chicken embryo and trying to understand in this case how the gut develops. Out at the weight, we have a plant accelerator. And so the idea is that you can grow plants under a series of very, very controlled conditions. You monitor them automatically daily, and you understand what the effects of different things are on that plant growth. So for example, if the water is slightly saline, what is the effect of that on those plants? Okay, so very, you know, cutting edge research being done out there. This is my favorite picture of the lot. This is a synchrotron image of a plant. This plant is a part of a group of plants that take up metals naturally. And so the green in this picture is nickel. And so these plants can be used for bioremediation of mining sites. So that you've got tailings piles, you can grow these plants, they'll pull the nickel out, so they help remediate the site, and then you can actually mine the plants themselves for very pure nickel. Okay, so that's more research that's happening here at Adelaide. And the great thing about Adelaide is that we do this really, really good research, and then we share that with the students, so that we have what's called small group discovery, where every student works in small groups with academics who are doing cutting edge research. Okay, so you learn from the best people, and you develop the best skills by learning from the best people. We also as a university have a big push to be global. So we send students, we exchange programs, we send students overseas, we have study tours overseas, a group from biological sciences came back recently with a great video from Southeast Asia. We have short programs that are overseas as well. So it's again part of the careers of the future, it's a global world. And so we do everything we can to give our students the opportunity to get out and see the world. Employability obviously is a big issue. Again, a lot of jobs of the future, but where have our current students gone? Well, this is a snapshot from one year's set of graduates and where they've gone globally. A lot of people in Europe, Southeast Asia, some in North America. And here's a few examples. So Sarah and Peter both studied physics, theoretical physics. Sarah works for an energy company now in Melbourne, but she's basically a financial analyst there. Peter is working in financial services in Sydney. So they're using those quantitative skills that they got from physics, but applying it in finance. And as it turns out, the biggest employer of physics graduates is finance because of the skills. Jodi and Ella both studied environmental science. Jodi is actually now working as an environmental scientist and is still here at Adelaide. Ella has listed their climate change professional, but she's working for Australia Ethical Investment. So she's working for an investment firm but using her knowledge of environmental science and of climate change. Tammy did food science and she now works for Deloitte as a consultant. Here are two recent chemistry graduates we were just in touch with last week to ask them what their degree meant to them. So Patrick is working as an environmental scientist and as you can see he says, studying at the University of Adelaide helped me develop good problem solving and critical thinking skills which enabled me to gain employment in a difficult job market. And Sophie who's working as a technical officer for skincare manufacture, so fairly non-linear career. I've been able to apply the problem solving technical report writing and data handling skills that I developed during my degree to the everyday requirements of my role. So just a couple of examples again of skills. So my advice to the people in this room and you hear this over and over, pick a passion not a profession, but it's really really true because if you study what you like, you go into what you enjoy, you'll do well at it.   You don't like? So pick what you're interested in, develop your skills and I just want to end with a couple of comments from the chief scientist. So this was an address he gave the end of last month because you hear this talk about job ready and there was a very good article in the conversation recently saying that university science is not about training job ready graduates, it's about changing job capable graduates. So the difference, and this is from the chief scientist, job ready graduates might not be adaptable and run the risk of being left behind in the ebb and flow of technology driven disruption. Job capable graduates, people who are flexible, people who have skills will ride the tides as you'd want them to and then finally again from the chief scientist as long as a perfect match of discipline training to career is a social expectation we'll work very hard to fall forever short of the goal to simply pointless to pursue. What we offer instead is something worth having the capacity to adapt to change and the appetite to bring it about. We're in a world of change and as scientists you can adapt to it and also bring it about. Thank you.


So, where we have we found out energy in the nth orbit for the atom in hydro for the electron in hydrogen atom is what minus of 13.6 by n square electron volt suppose it is not hydrogen it is hydrogen like atom then what will be the energy this into z is 5 this is for hydrogen atom for hydrogen like atom. Hydrogen like atom again has a single electron only energy in the nth orbit will be 13.6 by n square into z square electron volt fine where z is the atomic number. Let us solve few questions on it the difference of 2.3 electron volt there is a difference between 2 energy levels in atom. You have to find out what frequency will be emitted if electron makes a transition from first level to the other level getting it apply Bohr's third postulate what does it say difference in energy will be equal to a photon emitted and photon's energy is as into mu mu is asked delta e which is equal to 2.3 electron volt that is getting it. So, basically mu is equal to what 2.3 into 1.6 10 minus 4 minus 19 divided by 6.6 into 10 raise to power 34 you can roughly make it 4 and you can roughly make it 24 this is 0.6. So, this is 0.6 into 10 raise to power this is how much 15 to 6 into 10 square 14 hertz fine. So, this is direct application of the Bohr's atomic model third postulate. Now, if you talk about energy levels I mean this is not part of theory I am just analyzing the energy levels of the electrons then you will see that what formula says that energy in the nth level is minus 13.6 by n square even fine. So, what is the least possible of number principle part of number n 1. So, n is 1 this is a least n for the least n energy will be minimum or maximum minimum there is a negative sign every time you forget negative sign it is minimum for n equal to 1 it is minimum. So, n equal to 1 is also called ground state you cannot go below that fine. So, least possible energy happens when n equal to 1 or when electron in its ground state.  They test you on calculation also and especially these chapters which are easy they will put some difficult calculations find out what is the third energy level how much it is minus 13.6 divided by 3 square approximately 155 1.51 electron volt e 4 will get minus of 0.85 electron volt like this you will get ok. So, if you draw energy level diagram you will have least possible energy for n equal to 1 which is minus of 13.6 electron volt fine then you have for n equal to 2 you will see that there is a big gap between n equal to 1 and n equal to 2 energy this is minus 3.4 how much is this gap 10.2 electron volt fine next energy level is minus 1.5 ok. So, energy level this is n equal to 3 that is minus 1.51 next is what minus 0.85 one thing you will notice that the difference energy is reducing ok slowly and slowly then next level is coming closer and closer. So, n equal to 5 will be further closer n equal to 6 will be more close fine.  So, after a point it does not matter what is the quantum number ok and anyways if you give it large enough energy energy tends to 0 right from this energy is going towards 0 and as soon as energy goes to 0 electron is free it is not bounded fine. So, that is what this is you can say energy level where energy is 0 and then the atom ionizes or electron leaves the atom fine. So, this is how you can see the energy diagram is now here is the thing if electron is in n equal to 4 level how many wavelengths it can emit it is n equal to 4 level. So, it can directly go to 4 to 1 one wavelength it can jump from one stable orbit to another stable orbit or it can go to 2 or it can jump to 3 or once this jumps to 3 then 3 will go to 2 and this will go to 3 or there is how many there is one more which one the electron is it n equal to 4 you cannot increase it which one this this has gone this has gone no it can go from 2 to 1. 1 2 3 or 6 are already there so 6 fine. So, you can see 6 wavelengths can be emitted if electron is at n equal to 4 excited state. In fact, when electron is at n equal to 4 it means what you can choose out of 4 you can choose any 2 transition will happen. So, basically 4 C 2 ways transition will happen right. So, if electron is in nth excited state so, it will be n C 2 number of wavelengths will be emitted. So, if you just know how many wavelengths are getting emitted you will be able to find out which excited state it is in fine. So, this will be equal to n into n minus 1 divided by 2 ok any doubt will know. See there is a mention of how you can arrive at Bohr's second postulate m v r equal to n h by 2 pi assuming electron is of deep overview wave we have done this when we studied the dual nature ok it goes like this. This is suppose electron orbit this is the electron orbit and if electron is a wave it will travel like a wave like this it travel in a way that here it will join in phase ok. So, basically the total path should be what in diesel times wavelength why because you can check from here to here 1 wavelength from there till here another wavelength from here till here 1 more wavelength from here to here 1 more fine. So, it can count number of wavelengths. So, if it has to merge here exactly in phase the total path should be in diesel times wavelength otherwise it will not be in sync with previous movement fine. So, total path is what 2 pi r this should be equal to n into lambda fine and what is lambda according to deep overview h by p which is linear momentum will become n v. So, you can get n v r is equal to n h by 2 pi. So, what D Boogley has done that coincides with the Bohr model. Now, see the theory in this chapter is very limited. So, we are done with the theory let us see a few questions that can be asked from this particular chapter. We will first touch upon the NCRT questions because that is how it should start. So, now the questions will be on entire chapter wherever what are you doing? Phone is the most precious thing nowadays you should not lose your phone everything is in the phone all the private conversation and everything your bank password.  Okay anyways energy in the ground state for a hydrogen atoms electron is minus 13.6 electron volt. What is the kinetic and potential energy in this ground state? Kinetic energy and potential energy. How many are there? I am sorry how much? Done. How much is the kinetic energy? 13.6. 13.6. How much is the potential energy? Half of this. Half of this. So, when you add kinetic energy potential energy it should come out to be this or not. Is it coming? Potential energy is double of this. Kinetic energy is minus of this. We have done this. See you are facing trouble because you are not able to accept that okay energy is negative. This is negative if you double it it decreases it goes down. Okay next question hydrogen atom which is initially at the ground state as in at n equal to 1 it is okay it jumps to n equal to 4. Find out see why it will jump because it absorbs photon right you have to find out wavelength and frequency of photon. If you do it those who are watching it online and here is from Centrum Academy we are located in Bangalore and if you want to get in touch with us this is the phone number. You guys are stuck see this you can take it like this 1 by lambda is 1.097 10 is 7 into 1 by 1 which is square? 1 square minus 1 by 4 square which is 16 okay. Once you simplify this you will get the value of lambda. How do you get frequency? Multiply this with speed of light C by lambda. So, first it is easy to get frequency you know just multiply with C that is the frequency okay. The frequency which is C by lambda is 3 into 10 is power 8 1.097 10 is power 7 into 15 by 16. So, you will be able to get the frequency from here okay. Any doubt on this? See it is all about calculation many a times in physics. So, you have to be very very good with numbers and having good with number is an art. Do not think that it is donkey's work it is not something which is respectful. It is probably the most respectful thing among mathematicians being good with numbers. And some of the highest paying jobs they only get it practice lot of calculations.