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hello everybody and welcome to this webinar where we're going to be taking a look into how data science can enable digital transformation in your organization we'll be exploring what data science is and how it's influencing better decision making in businesses as a result of the pandemic every organization is experiencing a move towards a more digital world and as part of that shift recognizing that the power of data is becoming increasingly crucial to any business worldwide and that's why today's session will be relevant to every one of us i'm sally and i'm the sales director here at ilx and i'm joined by alice from our marketing team and also armando who is a data science strategist as well as a lead trainer at simplyland and has 20 years of technology industry experience we've prepared today's session so it's relevant to those of you that are new to data science and also for those of you that are more experienced but before i hand you over to armando i just wanted to run through a couple of house keeping notes it won't take long and also share a little bit of information on ielts and simply learn partnership just in case you're new to us okay so first of all um this uh recording is this session is recorded so if you need to nip out or you want to forward this recording to a colleague we'll be emailing you the recording tomorrow so look out for that in your inbox we've scheduled one hour for this session so we're allowing 10 to 15 minutes for questions and answers at the end of the session please if you can mute yourself so there's no disturbance on the line and that'll be much appreciated and if you're not already doing so please follow us on any of these social media platforms that you see on the screen we're constantly posting really good stuff whether it's a podcast a blog a white paper or a webinar and um and if you have got questions that you want to ping over to us as we're going through the presentation you'll see there's a chat box to the right so please uh submit your questions alice can collate those at the end and we can go through as many of those as we possibly can and then last but not least we are a learning and development business improvement business and we are really hoping that today's session will inspire you to go back and do something different back in the workplace so we encourage you to take notes and turn those notes to action okay so it's probably helpful for you to understand a little bit about the ilx and the simply learn partnership ilex is an international accredited learning provider and we're a specialist in improving capability in project management service management and agile disciplines and simply learn our leading global online edutech business and it's through our partnership where we can provide online learning solutions that cover a wide range of technical disciplines so whether or not it's data science ai project management big data cloud computing i.t service management to name a few uh we can support you on those needs and right now more than ever upskilling is seen as a must so businesses can move forward and really embrace this digital economy so whether or not whether you're looking to uh develop a team of highly skilled data scientists or whether or not we're probably a more outcome-centric highly engaging learning program would be more appropriate or whether or not you're looking for a light touch introduction to data science as an example we can cover all of those needs and it's our combined 40 years experience that's proven that our approach improves learning outcomes it really engages the learners and drives better performance so hopefully that gives you a sort of a sense of our organizations and our approach to learning and upskilling i think now is probably a good time to hand you over to armando who's going to talk data science but thank you for listening excellent sally thank you very much and it's a pleasure being here and try to you know get uh get all of all of you and all our attendees excited about data science and especially mentioning why it's so important and not so important it's fundamental for every business in this new digital era okay so as uh salome mentioned i'm mr armando i've been a data strategist and data scientist i'm a co-founder of uber technologies we will provide their products to mid-size customers this is what we'll be exploring today i will walk you through not only through what data science is but why data science is so relevant not only to organizations but also to individuals who are looking to boost their careers why data science is so important in all these detailed transformation buzz that i'm pretty sure that everyone has heard of uh but the question is where does data fit into all these and where does a data scientist fit into all these so i will take a dive into that and afterwards once we set the context and that we understand the reason why data science is so important then we'll take a deeper dive into what are some of these data science trends talk about internet of things edge edge computing um quantum computing and we will then uh start wrapping it up with a few use cases real use cases of companies are leveraging on data science to boost their businesses and disrupt the industry and finally how you can upscal upskill your uh your talent in data science so let's start with you know what is data science and data science is precisely that it's a science and in any science the objective is to observe um understand patterns come up with a theory and communicate some some results now the difference is that uh data science today leverages a lot of data so every organization now is ingesting tons of data we are familiar with software and crm systems erps and mobile apps so we are ingesting a lot of data and at the very end we want to find what are some underlying reasons on why there are behaviors whether in customers in products and prices so that we can make better informed decisions so data science is a process it's a process of understanding what are the business problems that we can resolve with data collect all the right information all the right data organize it and very important communicate it a data scientist is uh i know some people say that data scientists are statisticians or are more similar to several developers or even some to say that are more similar to mathematicians but in reality i think that a data scientist is more like a storyteller someone that can link a business problem to the underlying patterns to drive actions and then communicate the story on how you go to the business problem to the solution through data so visualization is extremely important now how do we get all the way here and why data science has been such a pause especially recently science as you know has been around for centuries you have stories of visionaries like taiko prey who was a danish astronomer and his biggest contribution was data so he would go every night to his observatory and just log you know positions of stars and celestial bodies and that was his main contribution because years later other physicists mathematicians astronomers they will take a look into that data that he would have collected through uh through um uh through years and years as a matter of fact all his um adult uh adult life he will be recording that manually but today we don't have to manually record how long a phone call takes wouldn't have to record how manually how much someone buys wouldn't have to record the distance between a transportation from point movement point of transportation um device moving from point a to point b all that now is recorded through electronic devices pretty much through digitalization now digitalization is also not new right so we have been with computers for a while now but the problem is that computers before were very expensive you know in 1950s just to lease five megabytes of storage will cost the company around thirty thousand dollars a month take a look at that five megabytes of storage thirty thousand dollars a month with ibm today you can have terabytes of data in the thumb drive for i know 100 bucks maybe four dollars or i don't know 30 euros so storage has become way more accessible um not only storage has become more accessible worse to compute i don't know if you know this but um intel's ascii red super computer that was developed in uh around the mid 90s uh though had a cost of or like 46 million dollars has a similar compute power than a playstation 4 that you can get by 300 so not only we have grown our capacity to store data but also how to process it so that you don't necessarily need a huge data center to start doing data science you can start doing data science in your laptops which is amazing which is really incredible and not only that the amount of data that humankind has been producing has also been growing exponentially just to give you a little bit of perspective on the last sentences about these 175 zetabytes of information you know we already know what's a bit well zero one a byte that is eight bits pretty much like a letter we're gonna have a kilobyte which are uh which is kind of like 180 to maybe 200 words a megabyte that would be a book of like 500 pages we have a gigabyte that is if you have um around um 10 yards or books and shelf um there'll be one gigabyte and in a in a large um university um a hundred gigabytes will be enough to store um all the academic journals now terabytes hold around 3.5 3.5 million um images of 300 kilobytes or you can store in one terabyte 500 hours of good quality video now that's terabytes we're still missing petabytes xbox and cell bytes um it is said that the human brain has an estimated capacity of 1.25 terabytes so that's a lot of information one terabyte if you have text you would have 20 million cabinets full of text it's like 500 billion pages and that's a petabyte now we have an exabyte which can store about 000 petabytes or 1 million terabytes or 1 billion gigabytes can contain 18 000 times all the information of all the books ever written so if you take all the books ever written multiply them 18 000 times that is one exabyte okay now this is just to start figuring out how much data we're producing now one exabyte is uh one cetabyte is one around 1 000 exabytes so it is said that from some researchers that if you can record all human texts or all human speech ever since we started speaking we can store all that speech in 42 zettabytes of storage so we have all these data and the problem is that we don't have enough people to process and understand that data that is the reason why this been arise you look on linkedin and you look at all the data science positions and by understanding why we have so many data science positions in the market you can have about two things one is career growth but otherwise another perspective is companies are looking for data scientists the question is is your company looking for data scientists because if it's not there's something that may be a little bit off in terms of your data strategy so we have a lot more data out there than when we can analyze and when the one we can use to drive insights and that is the reason why data science has becoming so relevant um now what is data science so important digital economy so we've already heard about this boss of digital transformation the italic digital economy and digital organizations so let's first understand what is a digital organization a digital organization is an organization that can do something different or doing something differently by leveraging on digital technologies okay now what are these all technologies digital technologies is the mix between your operations technologies and information technologies operation technologies is capturing observations okay in your environment in your devices is capturing all those observations capturing when a user logs in capture when you get a purchase capture when someone clicks something so it's capturing all that all those changes of state and then information technology is using and present that information but when you have a big data when you have a lot of data you need to have a way to translate all that data into insights and actions that can help you do things very differently or do things different meaning to improve your performance by improving efficiency effectiveness effectiveness and even throughput and that is the reason why we're building now our strategies around digital economy because we need data and data is extremely valuable look at the largest companies today look at the alibaba's the tencent the googles the netflix their business the facebooks the business is data that is the reason why we are living in this digital economy so the question is for you are you really getting on board with this technology at digital economy and the way to get on board of that is by performing transformations so idea transformation is is it's performing these quick shifts from traditional ways of doing things to leverage our digital technologies leveraging data to do something different to enable your business to make better decisions um because you already have the observations that can prove why something behaves the way it behaves you can also describe the way that your business is performing in a more holistic and rounded way it's a lot easier to keep track of all your operations whether your your customers behavior your operations uh your transactions and also can can give you perspective and context understand the history understand the context so that you can start driving and making difference in your organizations there are some examples and pretty much that some of you already familiar with like like marketing and target marketing uh hyper personalized content but we have tons of use cases like in e-commerce right in e-commerce we can we can do recommender systems so that based on similar products what are other products that can be bundled in or based on behavior of other consumers what you can position to the rest of your consumers you can analyze your transactions to perform forecasts and to um and to plan for budgets uh to to improve your investments and also you can understand the underlying reasons on why your consumers are behaving the way they're behaving because when you're once you understand the online reasons then you can start taking actions to improve your business in logistics very similar story look at amazon in the warehouses they have these little robots that they will sort and dispatch different packages and they use iot so all those devices are interconnected and by using reinforcement learning they they define the best possible routes to stock and to rearrange or accommodate all different packages so that the packages are ready to be dispatched they're the they're the closest to the dispatch point and that helps um to uh to improve not only the the time to dispatch but also square footage because then you will you'll not need that amount of storage and you don't have to have a bigger warehouse because you're able to move inventory around you can also make projections on inventory so at the very end we do have a lot of use cases on data science and again data science is critical for data transformation it's cornerstone to thrive in this dlc economy now let's look at some trends in data science so first uh as i mentioned before if you look into linkedin or glassdoor for you know any job searching search platform or engine you will see that there's been a rise in demand of data analysts on data engineers data scientists even business analysts because the data scientists in large organizations they really don't work alone okay because there's a lot of data there's a lot of requests from the business and because business they understand how important data science is they bring the people on board that can support all the data science process so let's start from the very beginning the business problem because what we're going to be doing is trying to figure out how to solve business problems by leveraging data and this is when the business analyst comes into play the business analyst can help translate the business requirements or business needs into data requirements things like hey you know something we need to improve our margin in our sales process we need to reduce our set ours our sales cycles or we want to make sure that we target only valid opportunities so that's the business analyst so the business analysts will will capture those requirements and will typically communicate with the data science team then you have the data engineer the data engineer is the individual that will help bring all those data sources together such that the data can be ready for consumption by the data scientists and the data analysts the data scientist is the one who will be taking all the data i'll start using mathematical statistical tools to find patterns underlying the data understanding reasons forecasting and we'll put together a series of hypotheses that will later will be tested by data in conjunction with data analysts so the variant can be presented in the form of dashboards and visualizations but you can totally see how the axis around all these is data and how the data scientists play such an important role in driving insights so we can do forecasts and understand patterns that will help drive better business decisions so before we get into these new developments related to data science when we look into new businesses and when we start analyzing why data has become so relevant it's because every time that we capture you know a transaction we make an observation of a phone call or an incident i mean a resume so in hr organizations you have resumes you have interviews those data points are just snapshots of reality at a given point in time they're not assumptions they're just things that happen there was a phone call there was this resume this interview happened so we are really trying to make sense of reality so we take all those different observations and we'll start building theories around them so we are not starting from theory you know and trying to map it to realities all the way around we're taking all those data points things that we know that actually happened to start creating statistical and mathematical models that will help us predict the future right it's like having this crystal ball this magical crystal ball now if on top of that you add how fast we have been evolving as society the last 10 years even five years you would see that there's been a growth in iot in internet of things you have all these devices connected um that can give you a great snapshot of how the environment looks like and iot comes all the way from agriculture where you have sensors that will track humidity and um and ph of soil and wind and temperature that can automate when you need to you know trigger sprinklers or what's a good time to crop you also have iot inside data centers when you have all your servers and network and even your components being tracked in terms of performance so that if something fails you know when it fails and you can even predict when someone may actually help may actually fail we had this very big customer that using iot and data science they automated almost a full data center for uh with self-healing capabilities so that the only situation where someone will go inside physically inside the data center is if they would know if they would have a part to replace and they would know where to replace it everything else was self-healing by using iot and this is a trend that will keep on spiking and growing now you have wearable devices um and even when it comes to new technologies you see that there is these new brain computer interfaces when you can control you know games with your brain and if you wonder how was that even possible well data science was behind it so as more of these devices start being you know um becoming mainstream the more relevant data science is going to be we now look at event-driven architecture and software development to capture all those events remember that when i told you about what is a digital digital technology combination between operations technology information technology well from operations technology when would you want to capture all those observations across all your environment well now in software there are event driven architectures that will allow you to capture all those events all those observations so that you can understand patterns in how your consumers behave understand how they interact with your system so that you can even improve maybe user experience you can improve your cross-selling capability the cross-link capabilities even offering new products doing targeted marketing there's also been a rise in natural language understanding natural language for us is the way that right now we're communicating you see a slide with text and i am speaking to you but natural language is not that natural to computers but now with advanced like bird and gpt3 you know we can actually have a conversation with a computer uh not the computer is conscious about the conversation but it feels natural to us so we can have a chat conversation with a computer and you can actually see that in most platforms right now as an organization you want to make sure that you provide to your service consumers this omnichannel support so that there are multiple chances to reach you out and one of those channels can be chatbots and chatbots rely on data science so that they can understand text and you can feel like you're having a conversation we also have image processing right for object recognition that's happening across the board from from self-driving cars to defense um where you can have even in um in social media right that's what facebook is doing is they do image processing so they can capture all the pictures where you're at or maybe capture pictures whether where a product is showing right and get a context so image processing is also becoming more and more relevant and i have machine learning operations machine learning operations data science doesn't work alone when you want to roll it out into your daily operations you definitely need to roll out all your insights and your models and your algorithms and embed them into the business so that requires to embed data science into your operational interoperational function in your organization and we find machine learning operations sitting there robotic process automation and the wordpress automation can be simple when you code specific rules like hey someone selects yellow over these super simple but as decisions become more and more complicated we cannot hardcode the rules we need robotic process automation to be powered by data science so that complex decisions can be made and can be automated and when autonomous devices we have like a combination of all these right when you look at self-driving cars there's a combination between image processing iot brain driven architectures machine learning operations and even broader process automation and autonomous devices there's this race because organizations they see how how profitable how successful autonomous devices investments are going to be but this also raises some new topics to discuss that we have never discussed before which is which are things like our rights as digital citizens what's going to happen to our data what's going on to our customers data and then you have regulations like um like um like gdpr um gpdr like uh hipaa like uh sspa where organizations see how data and the use of data can get out of control so that's the reason why we need more professionals that really understand the implications of using data to the benefit of the human of humankind so rights of digital citizens it's also becoming part of the conversation same as ai ethics how do we avoid bias because even the fact that data science has been proved to um to really boost our performance as humanity by discovering new vaccines and new ways to do things it can also enhance our biases so potential future applications you will find you know these constant intelligence discontinues um metadata it's the data associated to the data so if you take a picture the picture for us is an observation but there's some metadata associated to that picture like when was it taken where was it taken uh the red the the device which was taken from and all that metadata creates more data for us to make analysis and also capture um capture insights something that is right now very very popular is edge computing and hyper personalization each computing means instead of running the compute in your servers you can run compute closer to the consumer maybe in their cell phones or in their computers running very complex decision making algorithms can be very compute expensive but when we have a data science model or machine learning model that was created by the scientists edge computing becomes out of reach of our hands which will make the overall experience for your users and consumers a lot better because they will be able to process a lot of information in their devices just look at your iphone or like pixel you'll think or people think that most of the data goes to apple or google it's processed there and then sent back to your device well to be quite honest that doesn't happen most of the time most of the time the actual predictions and the voice recognition happens in the device without you having to communicate with google or apple and then we have hyper personalization i know this is one of the one of the most evident applications of data science you go to amazon and do you know that in some situations amazon they will know what you're going to buy before you know it because it's hyper personalized they know your patterns and it's important for us to understand how our consumers behave so that we can anticipate their needs so that when they need something we already prepared because we understand their context and we can personalize the support and the delivery for all the different consumers then we'll continue to digitize analog information to better analysis and something that will happen in the next few years is leveraging on quantum computing for artificial intelligence and machine learning meaning that as a data scientist you will recommend specific models but as we'll be moving forward there are a lot of things you need to fine-tune as a data scientist to have an ideal model with one computing that fine-tuning will happen so fast and so cheap that will be that the the growth of data science will be exponential even exponential to what it is today so there are some things to keep an eye on now there are definitely benefits a lot of examples that you should be already familiar with netflix is one of them netflix for the ones that did that don't know netflix they crowd source the first algorithm so they open a competition say okay whoever provides the best recommender system you know they will they would the algorithm would brought on board into netflix netflix disrupted the whole entertainment industry by leveraging on digital technologies that is digital transformation so netflix was born as a digital organization because they are able to perform something very different to the rest of the industrial entertainment industry by leveraging on data so they have data and they have data scientists that will analyze and try to understand what is a movie that you may like or documentary or series that you may like so you can see that data science is not only really applicable it's profitable and for the ones that are more in you know i want to cut an impact in humankind data science is being used to address things like poverty um hunger vaccines amongst other aspects that are causing huge social impact and then you also have youtube right um youtube youtube is super interesting because they have a mix of everything first they use a lot of image recognition and video recognition so that they can automatically filter censored content so uh don't think that there's people all the time and the only thing that they do is to filter and censor content so youtube they actually have algorithms that will point out what are some possible videos that have unapproved content automatically they can also understand which videos are shown to whom so that they can start embedding marketing materials like targeted marketing and also trying to understand what your preferences so that they can show you more videos like the ones that you like you know you go to my youtube channel and you will see like all sorts of like data science astronomy kind of videos uh you go to my wife she's an artist and they'll be like all about art and trends and on in visual arts so this customization happens because there's data science in the backend you have other applications like face recognition cameras government powered by government logistics and even dating dating apps there is believe it or not a lot more of data science and machine learning affecting your lives than what you even think data science is all over the question is are you part of it or not i'm not part of it as a consumer but part of it as someone creating a transformation in your organizations as part of this you know revolution digital era let me bring you one amazing case study so there was this uh data set collected for uh about 15 000 employees and the question was why are they leaving and before i make this switch i just want to emphasize on something what i'm explaining here in this slide is all external so we actually see how companies are benefiting from from capturing consumers and capturing business but data science does not only apply to capture business it also helps to understand why things happen inside our organizations like in this situation for hr organizations we know that it's a painful process every time that an employee leaves especially if you have a good employee so it is extremely relevant for organizations to understand how to retain talent and there was this study um on why employees leave so we have this data set about the 15 000 employees 15 000 observations and there are multiple columns right for every employee there was information about their over satisfaction level the last evaluation projects completed time spent if they were of course still in the company or not um salary they've been promoted and so forth they've had accidents or not so what's this data set and using this data set the question was how do we retain employees and what where are they leaving because we cannot make any decisions to retain employees if we don't understand why they're living in the first place and again that's where data science comes into play a data scientist jumped in and said okay let's make sure that we understand the data we can do some exploration to try to find out what are some patterns how data is related we'll do a detailed analysis and then we'll start performing machine learning to do clustering and then to model the data so that we can predict if an employee might leave or not at the very end of this project if an employee would fill out a specific survey or they would or the of the organization would capture all this information employee you would get a score with the likelihood of that individual staying or living and the overall performance of the algorithm was really high so the best algorithms are able to predict if an employee will stay or leave with some like 99.95 of accuracy which is huge so when the data scientists with data scientists team started analyzing the situation this is kind of what they showed to executives right they say okay first there are top factors why employees leave and the fact that job satisfaction is important in reality what is affecting the most is the number of of projects that they have assigned so some people that would think that it would be promotions or salary the main reason why employees leave but the funny thing it was not they were not complaining about salaries they were not complaining promotions and by the way don't don't imagine you'll think that this company was paying tons of money to the employees average organization but after making the analysis they found out that it's not a matter of investing maybe in recognition or in salaries people were living because they were feeling burned out so once once a data scientist was able to understand the threshold of that burnout it's easier to embed an algorithm into it into an hr system such that a manager when a manager assigns work to an employee they can get an alarm saying hey if you continue to sign workload you will have 60 chances for this employee living in six months imagine how useful something like this could be to any hr organization and there are even some additional aspects right like um not only if employees will leave but how do you select the rest the best resumes for hiring so all these solutions can be powered by data science now i know that data science sounds like super super amazing and uh but then the question is how do i take this to my executives how do i get buy-in for my executives so that we can start our data science initiatives and there are very tangible ways to measure return on investment because data science and overall will help you with three things whether to improve efficiency effectiveness or productivity effectiveness meaning that what you promised to deliver meets the standards of quality efficiency that you are able to maximize the use of your resources or productivity so that you can improve your throughput of work and you can put that in the context of revenue on time to market on team efficiency on utilization of of of hardware resources logistics or even to be competitive to gain more markets so mission return of investment data science is really not that hard you have a baseline you have a benchmark and if you can hypothetically increase the performance how much savings or how much revenue we get and that's how you put together a return on investment analysis okay now why now just in case it's not clear right now why it's so important for you to get on board in data science well because you want to make sure that you prepare weather yourselves because this will be the skill of the future so um and also to make sure that your organization can accomplish its vision a lot sooner and better and in a way that it's way more competitive in these very competitive landscape delivery today so make sure that you define business objectives that's the most important step what are the things that you want to improve what are the things that you want to do different how can you how are you planning to bring a detailed experience to your stakeholders whether internal or external whether consumers or internal customers now make sure that not only you define your business problems you define your objectives what you want to perform how do you want to transform to get on board in this digital economy but make sure that you understand what are the capabilities that you have currently today and your team capabilities in terms of data and also in terms of skills so perform this gap analysis this is your current state your snapshot this is your end state having a full data science team that will boost your organization's performance so what is it that we need to go from here to here and then you can start figuring out smaller initiatives that will be packaged in data products that you can implement and later leverage to you know improve performance now where to begin right so um where to begin again there are different approaches but uh we'll give a recommendation on what is the best path for you to begin to get on board of these you know data science um data science opportunity or these uh new data science driven world right so um data scientists and sometimes may require advanced degrees but with um common libraries being used um with enough mathematics and high school statistics you can actually start getting on board with data science skills are required for data scientists will be some level of math calculus algebra algebra and calculus are the most important statistics distributions probability um and i think that's pretty much the gist of how deep you will go in terms of mathematics and statistics of course the more the more understanding the better and programming you cannot do data science in an excel spreadsheet so you need more powerful tools like python or r to start analyzing huge amount of data and a very important skill storytelling because data scientists besides being scientists they are storytellers you need to communicate your story the reasons why the benefits advantages not everyone will speak your language your data science language not everyone will be as savvy as you in mathematics and in statistics should to figure out how to drive a message that will resonate with everyone in words to drive elegant communications to be able to position a very powerful message in simple words and also another and on path is going through data engineering now data engineering he refers more on how we will be ingesting the data how we'll be sourcing extracting information from multiple sources transforming so that it's consistent lowering it for distribution and consumption so they work a lot with cubes warehouses architectures and of course you're also familiar with programming languages maybe not python maybe not r but maybe sql okay and this is where ios comes to play because ilx offers all that ilx offers a comprehensive path to become data scientists in the engineering for data and to get into data analytics and business analytics and by the way don't be spooked for the fact that you require mathematics and statistics ilx in partnership with simple learn provide the tools and paths so that you know what is it that you require so you can board and you can start your data science career now it can be costly to recruit and hire data science of data scientists and data engineers but there's a different path instead of hiring data scientists and engineering why not upscaling your skills and broaden the perspective of your teams power them so that because they have the understanding on the business now they can they just need to get on board with what is data science how to drive inserts from data and you already have people that know the business because there's this huge gap you have great data scientists no data science but they don't know the business you have people in the business that know the business but not the science but you can be both you can be that unicorn that understand the business and can drive better solutions and insights with data science okay with ilx in partnership with simple learn and third trainings you don't only get the top-notch state-of-the-art content but you get multiple ways of engagement you have um you have live presentations you have projects you have initiatives uh you you will be shared with data sets with proven trainers that have experience like business real life experience in data science that can address business problems right and this is just kind of a sample solution to track record of the path to become a data scientist and if you can see from the track record not only is comprehensive by providing tools like understanding python and programming languages like r again in python but also to get into statistics all the way through understanding machine learning so you can have this holistic well-rounded profile and these powered by top quality trainers by labs by live projects by reference material and very important by the engagement of hundreds of people that are putting all their effort to keep the material up to date and relevant for the most updated or most relevant and current problems and technologies problems that we face and technologies that we can leverage so today we covered the story of data science what's relevant what's important for data for transformation how data driven decisions have become so relevant to drive performance what's the evolution data science rule applications and how to develop your personal skills and how to move your organization to become a digital one so i would like to pass over to sally well thank you so much armando that was a real whistle stop tour on data science so thank you um so to all of our listeners uh you know if you are interested in getting on board with data science um you know today was very much a sort of a taster an introduction but please do get in touch with us if you go to our website you can see all the details on the screen you can have a look at all of the different learning tracks learning programs pertaining to data science and other technical curriculum areas as well if you would like to talk to us then please contact us on the contact us email address or the telephone number that's on the screen i'd just like to conclude by saying thank you to you armando um thank you to all of our listeners and we have got um seven or so minutes left um so i'm going to part you over to alice and alice hopefully you have some questions if you haven't pinged over a question please do we still have time and hopefully in the next seven minutes we'll answer as many of those as we possibly can thank you cool thanks sally um a couple of questions i'm just going to start with um some of the more kind of uh direct ones so for those who are new to data science armando um which would you say which training course would you recommend for a beginner um i'll recommend so if you look at the at the track path this pretty much explains where to get started so it started with what is data science for then a limited statistics and get to get started with you know programming languages going through data science machine learning and then visualization so i think that this slide kind of gives a sneak peek on where to get started brilliant thank you um a bunch more questions come through now um someone uh has commented earlier on that edge computing looks like it's backtracking somewhat from cloud computing um so they're not quite sure how to understand why this partial reversal might be taking place okay because cloud computing you will be using it for things like training your models and training algorithms and doing the analysis but once you have trained the model you don't want to leverage on that compute and those servers to do the actual predictions so once the model is trained that requires a lot of compute the model can be installing device so that you make your resources utilization more efficient cab intensive in your data center not that intensive on device brilliant thank you next question someone's asked what are the biggest challenges or barriers the organization organizations sorry face in adopting a data-driven approach the biggest challenge is bridging the gap between the knowledge of a data scientist and the business problems i've seen organizations hiring phds in data science and then the phds are just crashing the bellies trying to find out what to do and then the business they're struggling with a lot of problems that they don't know can be solved with data science so the biggest challenge that i've seen is having people in the business understanding data science and getting data science um a little bit of data science training or education thank you um is uh powerhou sorry is power bi used in data science someone said we've only talked about python and our programming yes so the way that a data scientist will communicate findings vary um there are tools like tableau power bi even some pythagorean tools like bokeh unplugly so those are visualization tools that data scientists will use to present the results or even to present predictions amazing thanks um next question uh what are the industries that have been most impacted by applying data science um i i think all of them but i think the first ones were marketing so i think marketing is where we saw the boom in um in data science and artificial intelligence you know social social media and now logistics but i think at this point of time it's every industry logistics consumer products uh transportation i think that today if an organization is not already leveraging on data science it's sooner or later well they will not be competitive enough and will be taken over by companies that are leveraging on data science cool thank you um and another question we've talked about this the slide you've got up at the moment the sample solution talks about a learning path what exactly do we mean by a data science learning path essentially i yeah i can maybe answer that so it's it's really a combination of courses and all of the online learning content is accessed and consumed online either through the ilx simply learn portal or through your own lms but rather than thinking of just coming on one course a big bang course it's it's literally you are you are attending live virtual classes which are instructor-led and you're also doing your own self-directed learning through the e-learning modules as well as you go so you go through a learning journey which comprises of all for example of these courses here and then there is a sort of almost like an end point where we release a certificate when you have completed the learning track yeah i think the completion rate is 85 and you've applied the learning to the various sort of projects and labs that are part of that learning program hopefully that explains yeah no that's great thank you sally um that's all the questions currently um just to reiterate that we will be emailing out a recording of this webinar tomorrow in an email um or anyone who missed that at the start uh yeah those are all questions um thank you everyone who submitted them and thank you again armando i'll hand back over to sally yes so i don't think there's any more anything more that i want to say other than thank you everybody uh for listening and you know once again you know if you do require any further information from us or any support or want to have a conversation or are interested in getting on board with data science and up skilling within your organization then please reach out to us and we'd be more than happy to help you and thanks everyone for your time today thank you armando and enjoy the rest of your afternoon and evening that's goodbye from us here at alex and simply then bye thank you