Webinar: Data Science for Beginners - How to Get Started

Key Takeaways

hi everyone my name is Ilya and I'm the co-founder of 365 data science I hope you're excited because we'll be talking about some really important data science stuff in this video and if you're here chances are you already know a thing or two about the topic which makes this even more exciting for me but first things first for those of you who don't know 365 data science yet we are an online training platform for aspiring data scientists you might have seen our name tossed around websites like KT Nuggets big data made simple or towards data science comm but even if you haven't that's okay what we're going to talk about here is not tailored to professional data scientists anyways it's for anyone who's interested in what data science is why it's the number one job in the u.s. right now actually four years in a row and how you can make a career as a data scientist who's paid that oh so lovely six-figure salary so if you're a professional you really won't benefit from watching this video but if you're a beginner or a motivated quick learner who wants to learn more about how to make it in this field stick around also we'll talk a little about the best online training for aspiring data scientists at the end and we'll share a special youtube coupon for those of you who want to sign up at a substantial discount so yeah stick around there's plenty awesome stuff coming all right moving on now we didn't become data scientists overnight and we're not offering you a magical solution that will get you hired tomorrow we read the textbooks did tons of practice read some more books the familiar cycle but all of us really started thinking like data scientists once we understood what data science is sounds like the easiest thing right yeah but you'd be surprised how tricky it is to put all the buzzwords and tech terms together and have them make sense anyway what we extracted from our experience are three knowledge pillars three data science pillars around which all your learning and career drive need to be organized and that's what we'll talk about right now these three pieces of information that help you make sense of the field and the many opportunities it offers the terms and data science how they fit together and the timeline of data science processes all right let's get started as I said my name is Ilya and I am the co-founder of 365 data science I am also responsible for all things mathematics statistics machine and deep learning related here and 365 data science I am co-hosting this with my colleague Simona she teaches our programming and stats and I'm going to pass the mic to her for a minute hey everyone it's pretty awesome to be here and talk to you about data science because there are definitely some confusing things in the data science world and misconceptions flying around see we feel like data science terms and related buzzwords are being tossed around by people from all walks of life who have taken an interest to the topic this is not to say that they don't know what they're talking about on the contrary but they often use the terms of data science assuming everyone knows and understands where these terms come from and how they fit into the bigger picture what I mean is often the people discussing data science are not teachers and this creates a massive amount of uncertainty or confusion for the beginner data scientists there's also a lot of step-by-step guides out there about how to become a data scientist but nobody really delves into what data science is how and why it works the way it does okay everyone so data science can you give a single comprehensive definition of what data science is try it I believe it will prove extremely difficult to come up with something that doesn't invite a hundred follow-up questions that's the thing about data science it's a universally recognizable term that is in desperate need of dissemination from my experience I believe this is a term that escapes any single complete definition which makes it difficult to use especially if the goal is to use it correctly most articles and publications use the term freely with the assumption that it is universally understood however data science its methods goals and applications evolve with time and technology twenty-five years ago data science referred to gathering and cleaning data sets then applying statistical methods to that data in 2018 data science has grown to a field that encompasses data analysis predictive analytics data mining business intelligence machine learning and so much more right in fact because no one definition fits the bill seamlessly it's up to those who do data science to define it here's the plan we will define the key processes in data science and get to a complete description of the field why because if you want to be a competitive job applicant you need to understand how various data science activities fit into the big picture you also need to learn about the timing of the different data processing analyses as well as who carries them out and let's not forget how does that make sense if you want to work in medicine you will learn how the human body functions and then decide whether you want to be a pediatrician a nurse and oncologist etc that's what we're about to do here but for data science let's start with a picture this is data science in a nutshell but we're not interested in just the nutshell this infographic contains the most concise representation of data science terms that we know of and ultimately all the things you need to have a solid grasp of before entering the field of data science I mean that was our grand idea when we designed it right you are absolutely right Simona by the way if you guys want to download the infographic in this entire presentation there's a link in the description that will take you to our download page or you can click the widget on the screen to the same effect okay let's get started by talking about data before anything else there is always data data is the foundation of data science in the context of data science there are two types of data traditional and big data you've heard of both I don't doubt that traditional data is data that is structured and stored in databases which can be managed from one computer it is in a table format containing numeric or text values big data on the other hand is bigger than traditional data and not in the trivial sense it isn't simply represented by numbers and text but also by images audio mobile data and so on in addition big data has high velocity what does that mean it means that it's retrieved and computed in real time finally think about its volume big data is measured in tera petaa and exabytes and it is often distributed across a network of computers why because it is very very big so to get some perspective where does it all come from well traditional data may come from sources like basic customer records of a retail store or historical stock price information in the world of finance big data however is all around us a consistently growing number of companies and industries use and generate big data consider online communities for example this might seem like an anecdotal example but really think about it Facebook Google and LinkedIn they generate massive amounts of user data Facebook alone for example collects information about the pictures places posts demographics products used audio and video shared of all its 2.2 billion users in fact right now digital data in the world amounts to 3.2 zeta bytes that's 3.2 times 10 to the power of 21 and collectively 90% of all the data we have since the beginning of time has been collected in the last two years that's a scary amount of big data gathering and it's only going to grow right now imagine the following scenario we're already a data science profession and you are working for a telecommunications company a superior member of staff tells you one of two things a we need to consider client satisfaction in the next quarter so we can predict churn rate oversee the process and come up with some numbers or B we have an enormous amount of customer data from the previous quarter can you oversee the analysis and deliver an approximation of churn rates for the next quarter can you pinpoint the difference between a and B I'll give you ten seconds you the difference is that in the first case you do not have data you would need to gather it this data can come from surveys like asking people how much they like or dislike a product on a scale of one to ten okay so you have surveyed all these people and their responses have been sent to you is this data ready to be analyzed not exactly this is called raw data because you still haven't done any processing on it it is untouched data that cannot be analyzed straight away awesome now let me introduce a new term pre-processing this is what we can think of as preliminary data science pre-processing is an absolutely crucial group of operations that convert raw data into a format that is more understandable and hence useful for further processing plus it fixes the mistakes that occurred during the gathering phase if you've ever worked with data before then you know these happen constantly like when we're thinking about customer data it's unrealistically easy to have a person register as 932 years old called United Kingdom from Kevin Smith as their country obviously those data entries are incorrect and therefore must be handled before proceeding to any type of analysis right yeah absolutely that's why there are tons of pre-processing practices in place I'll tell you about some of the more common ones first is class labeling your observations this consists of arranging data by category or labeling data points to the correct data type for example numerical or categorical number of goods sold daily would be numerical you can manipulate this information mathematically and a person's profession or place of birth is categorical because no mathematical operations can be done on this information alright just keep in mind that with big data the classes are extremely varied therefore instead of numerical vs. categorical the labels will be text digital image data did video data digital audio data and so on okay then there is data cleansing or scrubbing these are techniques for dealing with inconsistent data like misspelled categories and missing values you know the lot people sharing their name and occupation but omitting their age or gender data shuffling is another interesting one imagine shuffling a deck of cards it ensures that your data set is free from unwanted patterns caused by problematic data collection like what like if the first 100 observations in your data from the first 100 people who have visited your website this data isn't randomized and it's likely to reflect just the behavior of those 100 people when your website was still under construction in a word data shuffling prevents patterns due to sampling to emerge and finally consider data masking this is primarily a Big Data specific technique and no wonder when collecting data on a mass scale you can accidentally put your hands onto a lot of sensitive information but you need to urgently hide from yourself masking aims to ensure that any confidential information in the data remains private without hindering the analysis and extraction of insight essentially the process involves concealing the original data with random and false data allowing the scientists to conduct their analyses without compromising private details and let's not forget all of this is just at the very beginning of doing data science pre-processing your data to make it usable is laying the groundwork all right let's assume your databases are clean and organized at this point so let's get into the real deal now before we begin I want to make sure we are on the same page here there are two ways of looking at data right one with the intent to explain behavior that has already happened and you have gathered data for it and two to use the data you already have in order to predict future behavior that has not yet happened you need to be very clear on this distinction because it can be what tilts the scale one way or another when you're deliberating which data science path is best for you there is also a temporal relationship between the two ways of looking at data basically before data science jumps into predictive analytics it must look at the patterns of behavior the past provides right it must analyze them to draw insight which will then inform the direction in which forecasting should go yep and there's a name for the data science that focuses on explaining the past that's business intelligence think about it this way business intelligence provides data-driven answers to questions like how many units were sold in which region were the most goods sold which type of goods sold where how did the email marketing perform last quarter in terms of click-through rates and revenue generated how does that compare to the performance in the same quarter of last year although business intelligence does not have data science in the title it is part of data science and not in any trivial sense I'm sure you're beginning to get that feeling already right so what do you do as a business intelligence analyst of course data science can be applied to measure business performance but in order for the business intelligence analysts to achieve that they must use specific data handling techniques let's review some of them right so the starting point of all data science is data for bi analysts that consists of things like monthly revenue customer volume sales volume and so on and as soon as they get their hands on that data they must go through three fundamental operations first extract meaningful metrics from the dataset like average quarterly revenue per new customer second identify the key performance indicators you know only those metrics that will clearly show how the business is doing and third analyze the data to extract insights from it think about it for a second why is business intelligence an important data science stepping stone well consider this the company you're working for is running a marketing campaign and you have received the data you examine it and identify one of the metrics it indicates all the traffic to a page on your website then you think about what a KPI could be in this case and you realize that a KPI would show the volume of the traffic to the same page but only if generated from users who have clicked on a link in your ad campaign to get there this way you can check if the ads you're positioning are in fact working and driving customers to click on a link in turn this will determine whether you should continue to spend on ads or not of course this is not where the business intelligence analyst responsibilities conclude I want to keep in mind this next thing I will tell you really hold on to it data science is about telling a story I will say that again data science is about telling a story and crunching the numbers is just the introduction to the story so apart from handling strictly numerical information data science and specifically business intelligence is about visualizing the findings and creating easily digestible images supported only by the most relevant numbers after all all levels of management should be able to understand the insights from the data and inform their decision making and this is in the hands of the business intelligence analyst business intelligence analysts create dashboards and reports accompanied by graphs diagrams maps and other comparable visualizations to present the findings most relevant to the current business objectives that's all super interesting but what are the directly actionable results of these analyses what line of work would need a bi analyst in other words where would you come in as a bi analyst let's try using our analytical brains and try to answer that together if you are a hotel manager would you keep the prices of rooms constant all year round probably not if you want to attract visitors when the tourist season is not in bloom and if you want to capitalize on it when it is and how would you inform your strategic decisions to lower or room prices with bi insight data science is constantly applied to inform price optimisation techniques what bi analysts can do is extract information in real time think about booking sites and then compare this to historical z' update the dashboards and reflect the necessary price change instantaneously the exciting bottom line is bi allows you to adjust your strategy to pass data as soon as it is available all right here's some more food for thought think about inventory of any sort really which is better over supply or under supply if you choose secret option number three neither you are correct over and under supply can cause massive problems in a business too much inventory and you lose money you have already invested too little inventory and you lose money you could have potentially gained and implementing effective inventory management means supplying enough stock to meet demand with the minimal amount of waste and cost and the million-dollar question how do you ensure your inventory management is effective data science and business intelligence are invaluable for handling over and under supply a bi analyst can carry out in-depth analyses of past sales transactions to identify seasonality patterns and the times of the year with the highest sales this can then inform inventory managers and results in the implementation of effective inventory management techniques the meet demands at minimum cost easy once the bi reports and dashboards have been prepared and insights extracted from them this information can become the basis for predicting future values and that's where it becomes truly awesome but the accuracy of your forecasts will differ based on the methods and techniques you decide to apply and this is where the more popular data science concepts come out to play examples of such techniques are neural networks deep learning time series and random forests but let's backtrack a little justice there is a distinction between traditional and big data there is also a distinction between traditional methods in predictive analytics and machine learning judging by the way of position these can you guess what the basis of this distinction is that's right it's the type of data that methods operate on traditional invites traditional analytics like regression cluster and factor analysis whereas machine learning is far better equipped to handle big data as you can imagine machine learning steps on the shoulders of classical statistical forecasting in fact people in the data science industry refer to some of these methods as machine learning - but when I talk about machine learning I am referring to newer smarter better methods like deep learning just something to keep that in mind right now what's the statistical knowledge you need for traditional analytics in data science most often data science employs one of these five analyses linear regression logistic regression cluster analysis factor analysis time series analysis ok let's do a little exercise I will describe what each analyses does but I won't tell you its name your task is to try and match the methodology with the correct name think of this as a self test how well do you know your way around the fundamentals of statistics it'll give you a relative idea of whether you're prepared for practical interview questions for a data science position all right this method is used for quantifying causal relationships among the different variables included in the analysis you will use this if you need to assess the relationship between house prices the size of the house and the year they're built the model calculates coefficients with which you can predict the price of a new house if you have the rest of the relevant information available is this cluster or factor analysis or is it time series analysis if you set it to regression you are correct but which type this of course is linear regression there is a line which governs the relationship between the size and the price okay this exploratory data science technique is applied when the observations in the data form groups according to some criteria it takes into account that some observations show similarities and facilitates the discovery of new significant predictors ones that were not part of the original conceptualization of the data if your house data looks like this this analysis would identify these groups small expensive houses in the city center big cheap houses in the suburbs and big expensive houses and good neighborhoods this of course is cluster analysis next one if clustering is about grouping observations together this analysis is about grouping features together data science resorts to using it to reduce the dimensionality of a problem let me explain if you have a questionnaire with 100 questions and each 10 questions are trying to determine a single general attitude this analysis will identify the 10 factors did you guess this correctly this is factor analysis once factor analysis identifies some factors they can be used for a regression that will deliver a more interpretable prediction I hope you're not surprised a lot of the techniques in data science are integrated like this which is why a solid fundamental understanding of statistics is genuinely a must we're left with two statistical methods time series analysis and logistic regression that's almost too easy but let's do it anyways this is a popular method for following the development of specific values over time it is widely used in economics and finance because there sub J mater stock prices and sales volume variables that are typically plotted against time I'm sure you notice that I said the word time about 16 times this description is of course time series analysis and finally logistic regression since not all relationships between variables can be expressed as linear data science makes use of methods like logistic regression to create nonlinear models logistic regression operates with zeros and ones for instance think about the process of hiring new staff companies apply logistic regression algorithms to filter job candidates during their screening process if the algorithm estimates that the probability that a prospective candidate will perform well in the company within a year is above 50% it would return one or a successful application otherwise it will return zero and that candidate will not be called in for an interview does that make sense I'm pretty sure things are starting to click already just keep this in mind linear and logistic regression cluster and factor analysis and time series are at the core of the traditional methods for predictive analytics in data science a lot of the smart advanced machine learning methods for data handling are heavily grounded in this statistical theory so if you're interested in data science please make sure to cover these foundations well it'll be a complete game changer this is also one of the reasons why our program starts from scratch explaining math and statistical fundamentals first before studying more advanced topics okay now that you know what traditional data science does you are definitely trying to think of the practical applications and where it fits in the world or at least you are now the application of these analyses is extremely broad data science is finding a way into an increasingly large number of industries and it's getting incredibly creative but use your experience and forecasting sales are still the big names here think about it when companies launch an product they often designed surveys that we measure the attitudes of customers towards that product that's a crucial marketing step after the bi team has generated their dashboards the data scientists can spread their wings they can analyze the results by grouping the observations by segments for example sales regions and then analyzing each segment separately to extract meaningful predictive coefficients after all calculations are complete the team reaches the conclusion that the product needs slight but significantly different adjustments in each segment to maximize customer satisfaction only then can everyone be happy and isn't that the goal what about forecasting sales volume this type of analysis invites time series onto the scene you have sales data that's been gathered until a certain date and you want to know what is likely to happen in the next sales period or a year ahead you apply mathematical and statistical models and run multiple simulations these simulations are what provides you with future scenarios this is at the core of data science because based on these scenarios the company can make better predictions and implement adequate strategies don't worry if this sounds a little vague at this point data science is massive and sometimes you need the bird's-eye view to stay on track all right we are now at the end of the line we've looked at data explanatory dashboards from the BI team and predictive analytics using classical statistical approaches data science is coming into shape but it's missing one powerful methodology isn't it in fact that's what most people consider true data science machine learning but just as it doesn't make sense to learn how to run before you can walk approaching machine learning needs to be done with caution do you understand the basics if you've been paying attention you have a good idea at this point so machine learning is the state-of-the-art approach to doing data science and rightly so the main advantage machine learning has over any of the traditional data science techniques is the fact that added resides the algorithm you have heard of it before I am sure these are the directions a computer uses to find a model that fits the data as well as possible the difference between machine learning and traditional data science methods is that in machine learning we do not give the computer instructions on how to find the model it takes the algorithm or forms a trial-and-error like process to find it out on its own unlike in traditional data science human involvement is minimized in fact machine learning especially deep learning algorithms are so complicated that humans cannot genuinely understand what is happening inside even though we are the ones that design them isn't that fascinating but let's get back to the algorithm because that's the core a machine learning algorithm is like a trial and error process but the special thing about it is that each consecutive trial is at least as good as the previous one that opens the door to much more accurate predictions and models but bear in mind that in order to learn well the machine has to go through hundreds of thousands of trial and errors with the air is decreasing throughout then once the training is complete the machine will be able to apply the complex computational model it has learned to novel data and still produce highly reliable predictions this is where the power of machine learning lies and the reason why it's considered the epitome of data science ok to give you a better idea of how machine learning works there were three major types of machine learning supervised unsupervised and reinforcement learning right if you've been following data science trends you should have at least heard about them imagine having some data a lot of it actually big data consisting of video files and images and you have labeled your data these are videos of cats these of dogs and third are animals that are neither cats nor dogs in supervised learning you want to have labeled data just like the one you have right now the machine gets the data and that data is associated with a correct answer if the machines performance does not get that correct answer an optimization algorithm adjust the computational process and the computer does another trial of course keep in mind that typically the machine does this on a batch of say 1,000 data points at once machine learning is powerful that's the takeaway here and I will probably say it at least once more support vector machines deep neural networks random force models and Bayesian networks are all instances of supervised learning all right let's get back to the data again but this time imagine it's not just big it's too big and you cannot label it or you are too pressured for resources to take the time to do that or you don't know what the labels are at all in this case data science resorts to using unsupervised learning this consists of giving the Machine unlabeled data and asking it to extract insights from it this often results in the data being divided in a certain way according to its properties to use a term we just discussed it is clustered see how traditional methods spill into machine learning the really cool thing about unsupervised learning is that it is extremely effective for discovering patterns in data especially things that humans using traditional analysis techniques would miss in our case this could be a whole new animal group we've missed like pictures and videos of alligators which we would have otherwise labeled neither cats nor dogs actually data science often makes use of super and unsupervised learning together with unsupervised learning labeling the data and supervised learning finding the best model to fit that data but we can discuss this another time there's a more pertinent question here where is machine learning applied in the world of data science and business think about fraud detection with machine learning specifically supervised learning banks can take passed data label the transactions is either legitimate or fraudulent and train their models to detect fraudulent activity when these models detect even the slightest probability of theft they flag the transactions and prevent the fraud in real-time pretty impressive isn't it and in terms of corporate ml is indispensable for client retention insights with machine learning algorithms corporate organizations can know which customers are unlikely to purchase goods from them it's again follow the pattern kind of activity and this means the store can offer discounts and a personal touch in a very efficient way minimizing marketing costs and maximizing profits when talking about this technique one name always comes to mind the e-commerce giant Amazon all right let's quickly do a recap data science is a slippery term that encompasses everything from handling data traditional or big to explain patterns and predict behavior data science is done through traditional methods like regression and cluster analysis or through unorthodox machine learning techniques it is a vast field but right now you are one step closer to understanding how all encompassing and intertwined with human life it is and we will give you one better did you notice we haven't said anything about programming languages and the software data science uses that's because we wanted to run you through the ABCs of data science first and then discuss the most useful tools for each data science subfield okay so we can split data science tools into two categories programming languages and so we're knowing a programming language enables the data scientists to devise programs that can execute specific operations so the biggest advantage programming languages have over software is that they offer massive flexibility as you can imagine capable programmers can write code that lets them do two data pretty much anything that strikes their fancy our python and matlab combined with SQL cover most of the tools used when working with traditional data bi and conventional data science in fact are and python are the two most popular languages across all data science sub disciplines we actually carried out our own extensive research looking into 1001 data scientist and their backgrounds and 53% of them knew our and/or a Python yes the overlap is huge why are they so popular well their biggest advantage is that they can manipulate data and are integrated within multiple data and data science software platforms they are not just suitable for mathematical and statistical computations they are adaptable SQL is king however when it comes to working with relational database management systems because it was specifically created for that purpose SQL shines brightest when working with traditional historical data for example when preparing a bi analysis what about big data big data and data science is handled with the help of our and Python of course but people working in this area are often proficient in other languages like Java or skaila these two definitely come in handy when combining data from multiple sources which is not a rare occurrence trust me right let's talk about software for a minute a lot of software's used in data science especially in corporate because it is just that a tool adjusted for the specific business needs of a company yeah for example Excel is definitely a household name and a tool applicable to more than one category traditional data bi and data science similarly SPSS is a very famous tool for working with traditional day and applying statistical analysis tensorflow on the other hand is software library designed for working with big data and designing machine learning algorithms it was developed by Google for internal use became public in 2015 and is generally the leader for working with and deploying neural networks you've also heard of Apache Hadoop Hitachi HBase and MongoDB I don't doubt that these are two big data centered software on the visualization side of the court especially having business intelligence reports and dashboards in mind tableau is unparalleled in terms of ease of use versatility and looks alright that about covers it this is data science in 30 minutes so let's do a quick recap everybody we talked about data where you get it from and what type of pre-processing operations you must be comfortable with before the real data science begins if there is one takeaway here it is that data is the foundation of anything data science does if you cannot understand how to work with raw data you will not be able to get into the more sophisticated analyses so action point number one if you're thinking of starting on the data scientist path learn how to handle data we also looked at business intelligence and saw the clear cut line between predictive data analytics and explanatory analysis business intelligence focuses on explaining past business behavior these analyses are the stepping stone for predicting how businesses will perform in the future learn the past to know the future makes sense right and this leads to action point number two be comfortable with extracting insight from past data first and only then proceed to making forecasts about your company's business performance in the future we mentioned dashboards and presentations to data science is about telling a story and that story includes both numbers and visual okay to be an effective storyteller you must visualize the insights you draw from your data otherwise if your audience is not number savvy the point you're trying to get across won't make any impact and your work will go unnoticed in most cases that's not ideal for me at least so action point number three learn data visualization tableau ggplot2 matplotlib Seabourn any software would do the trick then we talked about the traditional methods of predictive analytics right regressions factor and cluster analysis time series the bread-and-butter of forecasting a lot of the more advanced approaches to data science like machine and deep learning rest on the theory at the core of these methodologies hate to sound like a conspiracy theorist really but the beautiful marvelous thing about data science is that everything is connected and honestly I believe this is one of the main reasons why it's difficult to find your way around as a beginner does that make sense okay cool action point number four as far as statistics is concerned cover the foundations first before playing with the big guns like neural networks email algorithms and so on and only once the foundations are covered can we talk about and we did the state of the art approach to data science machine learning these are the newest smartest best methods for predictive analytics including deep learning neural networks k-means clustering and reinforcement learning mastering machine learning is definitely the number one skill you need to start a career in data science but it is also the most challenging data science subfield actionpoint number 5 build a portfolio of ML projects even if it takes you some time practice will be your best teacher and you have something to show for during your interviews absolutely ok we also looked at programming languages there is a ton of research that indicates the data scientists on average can comfortably work with two languages in most cases that's some combination between our Python and SQL I guess the action point here is simple when it comes to programming languages learn one or two but learn them well finally have fun data science is awesome and exciting and opens up a world of possibilities really so don't stress out too much and enjoy your journey okay I'm super glad we cleared all of that up and if you're really serious about developing some data science skills we can definitely give you some guidance for sure I for one hope you feel like the time you just spent learning about data science was worth it not only because you now have a clear data science path you can follow if you choose to develop your data scientist skills but also because we are a motivated team and we are going to show off something we're super proud of and you get to be a part of it you'll see how all right it's called the 365 data science online program and it says it all in the name this is the comprehensive data science curriculum my co-founders and I set out to develop so others wouldn't have to invest as much time energy and resources as we did when we were growing our own data science skill sets it's a pretty awesome program that's actually something our students say too which is great because it totally validates everything we are trying to achieve which is creating an accessible and transparent fast-track into data science about our our and statistics training Matthew says that while other courses have been somewhat helpful with us he managed to really get into doing statistics with our and actually had fun doing it and rajeev was absolutely thrilled with our machine learning portion of the program he says it's both in-depth and logical unlike a lot of the resources you can find scatter online that's exactly what we are aiming to do so that's spectacular he also says that building models from scratch helped him learn a lot and to certainly see the real-world business applications of ML Annabelle on the other hand is already a data science grad student and she finds our statistics training flawless and super intuitive she says it's essential especially if you're going into machine learning in fact she would recommend the course to anyone in the data science field which is absolutely fantastic and makes us really really happy to hear that now the program includes a lot more than these courses and we've created it with the entire professional journey in mind so we've designed trainings for all of the skills that step-by-step build up the data scientist including the fundamentals of mathematics probability statistics tableau SQL our Python all the way to machine learning yes and this way when you start growing your skillset you will be able to do it systematically in a structured practice rich environment and if your motivation is at the level we think it is you'll complete the training in a couple of months instead of years in fact I'm pretty sure that most of you guys are already highly motivated and you're here because you've been trying to teach yourself on your own and find the right way through the labyrinth of resources available online right so how do you get to be a part of this well we've created a secret coupon for only those of you who are with us in this presentation because that's our way of saying thanks and reaching out there's a link to the side of the screen that says subscribe now if you click that link it'll take you to a subscription page where you will be able to enroll in the 365 data science online program with 75% taken off the standard non webinar price this offer is only available to the first 100 people who sign up so make sure to claim yours before the coupons are all gone we know that's a massive discount but we decided to do it so we can make this gateway into data science and accessible reality we kind of feel like you're being here talking to us online is a pretty good sign that you are serious about your future in data science so this way all of you that enroll will have access to a massive amount of resources and you will be able to learn a lot as well as see if it makes sense for you to continue on the data science path what we really hope will happen though is that you will realize how awesome and full of opportunities data science is and you will let us help you become a more effective professional so to recap as our amazing webinar audience you get a free coupon to begin the 365 data science online program at 75% off the discount will only be applied to the first 100 of you who enroll in the training which means that once the coupons are gone they're gone clicking on the subscribe now widget next to the screen will claim the coupon so claim yours soon and after that we'll see you in class alright everyone thanks for watching we are super glad data science terminology is no longer confusing in your minds and just a reminder if you want to get all the materials we used in the video the infographic and the presentation there's a link in the description that will take you to our website where you can download them from or you can click on the widget on the screen now thanks and good luck yeah we had a lot of fun good luck everybody