Data Science Tutorial For Beginners

Data Science Tutorial For Beginners Data Science Tutorial Learn Data Science from Scratch! 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Data Scientist Mast ers Program (12 Blogs) Become a Certified Professional AWS Global Infrastructure Data Science Introduction What Is Data Science? A Beginner's Guide To Data ScienceData Science Tutorial â€" Learn Data Science from Scratch!What are the Best Books for Data Science?Top 15 Hot Artificial Intelligence TechnologiesTop 8 Data Science Tools Everyone Should KnowTop 10 Data Analytics Tools You Need To Know In 20205 Data Science Projects â€" Data Science Projects For PracticeTop 10 Data Science ApplicationsWho is a Data Scientist?SQL For Data Science: One stop Solution for Beginners Statistical Inference All You Need To Know About Statistics And ProbabilityA Complete Guide To Math And Statistics For Data ScienceIntroduction To Markov Chains With Examples â€" Markov Chains With PythonWhat is Fuzzy Logic in AI and What are its Applications?How To Implement Bayesian Networks In Python? â€" Bayesian Networks Explained With ExamplesAll You Need To Know About Principal Component Analysis (PCA) Python for Data Science â€" How to Implement Python Libraries Machine Learning What is Machine Learning? 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Last updated on May 22,2019 52K Views Hemant Sh arma11 Comments Bookmark 2 / 10 Blog from Data Science Introduction Become a Certified Professional Want to start your career as a Data Scientist, but dont know where to start? You are at the right place! Hey Guys, welcome to this awesome Data Science Tutorial blog, it will give you a kick start into data science world.To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by Edureka with 24/7 support and lifetime access. Lets look at what we will be learning today:Why Data Science?What is Data Science?Who is a Data Scientist?Job TrendsHow to solve a problem in Data Science?Data Science ComponentsData Scientist Job RolesWhy Data Science?Its been said that Data Scientist is the Sexiest Job of the 21st century. Why? Because over the past few years, companies have been storing their data. And this being done by each and every company, it has suddenly led to data explosion. Data has become the most abundant thing today.But, what will yo u do with this data? Lets understand this using an example:Say, you have a company which makes mobile phones. You released your first product, and it became a massive hit. Every technology has a life, right? So, now its time to come up with something new. But you dont know what should be innovated, so as to meet the expectations of the users, who are eagerly waiting for your next release?Somebody, in your company comes up with an idea of using the user generated feedback and pick things which we feel users are expecting in the next release.Comes in Data Science, you apply various data mining techniques like sentiment analysis etc and get the desired results.Its not only this, you can make better decisions, you can reduce your production costs by coming out with efficient ways, and give your customers what they actually want!With this, there are countless benefits that Data Science can result in, and hence it has become absolutely necessary for your company to have a Data Science Tea m.Requirements like these led to Data Science as a subject today, and hence we are writing this blog on Data Science Tutorial for you. :)Data Science Tutorial: What is Data Science?The term Data Science has emerged recently with the evolution of mathematical statisticsand data analysis. The journey has been amazing, we have accomplished so much today in the field of Data Science.In the next few years, we will be able to predict the future as claimed by researchersfrom MIT. They already have reached a milestone in predicting the future, with their awesome research. They can now predict what will happen in the next scene of a movie, with their machine! How? Well it might be a little complex for you to understand as of now, but dont worry by the end of this blog, you shall have an answer to that as well.Coming back, we were talking about Data Science, it is also known as data driven science, which makes use of scientific methods, processes and systems to extract knowledge or insights f rom data in various forms, i.e either structured or unstructured.What are these methods and processes, is what we are going to discuss in this Data Science Tutorial today.Moving forward, who does all this brain storming, or who practices Data Science? A Data Scientist.Who is a Data Scientist?As you can see in the image, a Data Scientist is the master of all trades! He should be proficient in maths, he should be acingthe Business field, and should have great Computer Science skills as well. Scared? Dont be. Though you need to be good in all these fields, but even if you arent, youre not alone! There is no such thing as a complete data scientist. If we talkabout working in a corporate environment, the work is distributed among teams, wherein each team has their own expertise. But the thing is, you should be proficientin atleast one of these fields. Also,even if these skills are new to you, chill! It may take time, but these skills can be developed, and believe me it would be worth the time you will be investing. Why? Well, lets look at the job trends.Data Scientist Job TrendsWell, the graphsays it all, not only there are lot of job openingsfor a data scientist, but the jobs are well-paid too! And no, our blog will not cover the salary figures, go google!Well, we now know, learning data science actually makes sense, not only because it is very useful, but also you have a great career in it in the near future.Lets start our journey in learning data science now and begin with,How to solve a problem in Data Science?So now, lets discuss how should one approach a problem and solve it with data science. Problems in Data Science are solved using Algorithms. But, the biggest thing to judge is which algorithm to use and when to use it?Basically there are 5 kinds of problems which you can face in data science.Lets address each of these questions and the associated algorithms one by one:Is this A or B?With this question, we are referring to problemswhich have a categorical answer, as in problemswhich have a fixed solution, the answer could either be a yes or a no, 1 or 0, interested, maybe or not interested.For Example:Q. What will you have, Tea or Coffee?Here, you cannot say you would want a coke! Since the question only offers tea or coffee, and hence you may answer one of these only.When we have only two type of answers i.e yes or no, 1 or 0, it is called 2 Class Classification. With more than two options, it is called Multi Class Classification.Concluding, whenever you come across questions, the answer to which is categorical, in Data Science you will be solving these problems using Classification Algorithms.The next problem in this Data Science Tutorial, that you may come across, maybe something like this,Is this weird?Questions like these deal with patterns and can be solved using Anomaly Detection algorithms.For Example:Try associatingthe problem is this weird? to this diagram,What is weird in the above pattern? The red guy, isnt it?Whenever t here is a break in pattern, the algorithm flags that particular event for us to review. A real world application of this algorithm has been implemented byCredit Card companies where in, any unusual transaction by a user is flagged for review. Hence implementing security and reducing humans effort on surveillance.Lets look at the next problem in this Data Science Tutorial, dont be scared, deals with maths!How much or How many?Those of you, who dont like maths, be relieved! Regression algorithms are here!So, whenever there is aproblem which may ask for figures or numerical values, we solve it using Regression Algorithms.For Example:What will be the temperature for tomorrow? Since we expect a numeric value in the response to this problem, we will solve it using Regression Algorithms.Moving along in this Data Science Tutorial, lets discuss the next algorithm,How is this organised?Say you have some data, now you dont have any idea, how to make sense out of this data. Hence the questio n, how is this organised?Well, you can solve it using clustering algorithms. How do they solve these problems? Lets see:Clustering algorithms group the data in terms of characteristics which are common. For example in the above diagram, the dots are organised based on colors. Similarly, be it any data, clustering algorithms try to apprehend what is common between them and hence clusters them together.The next and final kind of problem in this Data Science Tutorial, that you may encounter is,What should I do next?Whenever you encounter a problem, wherein your computer has to make a decision based on the training that you have given it, it involves Reinforcement Algorithms.For Example:Your temperature control system, when it has to decide whether it should lower the temperature of the room, or increase it.How do these algorithms work?These algorithms are based on human psychology. We like being appreciated right? Computers implement these algorithms, and expect being appreciatedwhen b eing trained. How? Lets see.Rather than teaching the computer what to do, you let it decide what to do, and at the end of that action, you give either a positive or a negative feedback. Hence, rather than defining what is right and what is wrong in your system, you let your system decide what to do, and in the end give a feedback.Its just like training your dog. You cannot control what your dog does, right? But you can scold him when he does wrong. Similarly, maybe patting him on the back when he does what is expected.Lets apply this understanding in the example above, imagine you are training the temperature control system, so whenever the no. of people in the room increase, there has to be an action taken by the system. Either lower the temperature or increase it. Since our system doesnt understand anything, it takes a random decision, lets suppose, itincreases the temperature. Therefore, you give a negative feedback. With this, the computer understands whenever the number of peop leincrease in the room,never increase the temperature. Similarly for other actions, you shall give feedback. With each feedback your system is learning and hence becomes more accurate in its next decision, this type of learning is called Reinforcement Learning.Now, the algorithms that we learnt above in this Data Science Tutorial involve a common learning practice. We are making the machine learn right?What is Machine Learning?It is a type of Artificial Intelligence that makes the computers capable of learning on their own i.e without explicitly being programmed. With machine learning, machines can update their own code, whenever they come across a new situation.Concluding in this Data Science Tutorial, we now know Data Science is backed by Machine Learning and its algorithms for its analysis. How we do the analysis, where do we do it. Data Science further has some components which aids us in addressing all these questions.Before that let me answer how MIT can predict the future, be cause I think you guys might be able to relate it now. So, researchers in MIT trained their model with movies and the computers learnt how humans respond, or how do they act before doing an action.For example, when you are about shake hands with someone you take your hand out of your pocket, or maybe lean in on the person. Basically there is a pre action attached to every thing we do. The computer with the help of movies was trained on these pre actions. And by observing more and more movies, their computers were then able to predict what the characters next action could be. Easy aint it? Let me throw one more question at you then in this Data Science Tutorial! Which algorithm of Machine Learning they must have implemented in this?Data Science Components1. DatasetsWhat will you analyze on? Data, right? You need a lot of data which can be analyzed, this data is fed to your algorithms or analytical tools. You get this data from various researches conducted in the past.2. R StudioR is an open source programming language and software environment for statistical computing and graphics that is supported by the R foundation. The R language is used in an IDE called R Studio.Why is it used?Programming and Statistical Language Apart from being used as a statistical language , it can also be used a programming language for analytical purposes.Data Analysis and VisualizationApart from being one of the most dominant analytics tools, R also is one of the most popular tools used for data visualization.Simple and Easy to LearnR is a simple and easy to learn, read writeFree and Open SourceR is an example of aFLOSS(Free/Libre and Open Source Software) which means one can freely distribute copies of this software, read its source code, modify it, etc.R Studio was sufficient for analysis, until our datasets became huge, also unstructured at the same time. This type of data was called Big Data.3. Big DataBig data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.Now to tame this data, we had to come up with a tool, because no traditional software could handle this kind of data, and hence we came up with Hadoop.4. HadoopHadoop is a framework which helps us to store and process large datasets in parallel and in a distribution fashion.Lets focus on the store and process part of Hadoop.StoreThe storage part in Hadoop is handled by HDFS i.e Hadoop Distributed File System. It provides high availability across a distributed ecosystem. The way it function is like this, it breaks the incoming information into chunks, and distributes them to different nodes in a cluster, allowing distributed storage.ProcessMapReduce is the heart of Hadoop processing. The algorithms do two important tasks, map and reduce. The mappers break the task into smaller tasks which are processed parallely. Once, all the mappers do their share of work, they aggre gate their results, and then these results are reduced to a simpler value by the Reduce process. To learnmore on Hadoop you can go through our Hadoop Tutorial blog series.If we use Hadoop as our storage in Data Science it becomes difficult to process the input with R Studio, due to its inability to perform well in distributed environment, hence we have Spark R.5. Spark RIt is an R package, that provides a lightweight way of using Apache Spark with R. Why will you use it over tradition R applications? Because, it provides a distributed data frame implementation that supports operation like selection, filtering, aggregation etc but on large datasets.Take a breather now ! We are done with the technical part in this Data Science Tutorial, lets look at it from your job perspective now. I think you would have googled the salaries by now for a data scientist, but still, lets discuss the job roles which are available for you as a data scientist.Data Scientist Job RolesSome of the prominent Data Scientist job titles are:Data ScientistData EngineerData ArchitectData AdministratorData AnalystBusiness AnalystData/Analytics ManagerBusiness Intelligence ManagerThe Payscale.com chart in this Data Science Tutorial below shows the average Data Scientist salary by skills in the USA and India.The time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities that come your way. This brings us to the end of Data Sciencetutorial blog. I hope this blog was informative and added value to you.Now is the time toenter the Data Science world and become a successful Data Scientist.Edureka has a specially curated Data Science coursewhich helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes. Youll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning as well. New batches for this course are starting soon!!Got a question for us in Data Science Tutorial? 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