How to Become a Data Scientist | Data Scientist Skills | Data Science Training | Edureka Rewind - 3

Key Takeaways

the best thing about human beings are that we always have questions and that is how we come to decision when we talk about taking a particular one my name is ayushi and I'm from edureka so in today's video I will be discussing a very hot topic that has been bothering quite a few techies engineers statisticians and business people that is how does one take a proper approach when it comes to become a data scientist or in simple terms how to become a data scientist so let me just throw some light on this topic so as I've said that human beings have lot of questions so the most relevant ones as far as data science is concerned are these the first one is why should you go for data science or why should one become a data scientist then what is the exact road map or the journey to become a data scientist next what are the different tools and techniques required given the fact we have so many in the market which ones are the best and how do they fall in the place and finally towards 10 we'll be discussing the different roles and responsibilities that one might pursue when he or she has taken up the path to become Ada scientist so let's start with the very first question that is why actually go for data science or why become data scientist now data scientist is regarded as a sexist job title of the 21st century now why well there are number of points first of all I would like to tell you that according to Harvard Business review it is considered as the high ranking professional with the training and curiosity to make discoveries in the world of Big Data therefore it comes as no surprise that data scientists are the Professionals in the big data analytics and the IIT industry apart from it this job is for people who like doing things that is different every day this job will never let you get bored now why I'm saying that because there is a challenge and it's always new to learn out of it even the data you started yesterday that might change over the next day so that creates more challenges secondly data science and analytics is not just subjected to one particular background or a field professionals from different verticals are moving to analytics by learning data science let us talk about all these one by one so first is Healthcare now have you ever imagined from where does Healthcare have so much data coming from so the data basically comes from the various sources like EMR which is an electronic medical records then we have Labs we have medical correspondence we have databases and we have many other things therefore big data and data science play a major role in the healthcare sector then we have travel industry so here again data science is highly adopted for more business opportunities then you have customer satisfaction let's say if your customer is most likely to leave a negative feedback so what you can do you can sweep in and become the helping hand to make instant yet less painful changes to their travel plans therefore with the amount of data present there is a huge opportunity to shape the future of travel and help the travel experience better which will also result in a good revenue for the organization as well next there is finance where Banks insurance companies investment companies and other Financial bodies harness large volumes of data from various sources such as your transactional retail or you can say the real-time marketing feeds then you have social media ports and existing database of customers as well so there is a large amount of data that has been generated every day so when we have just touched a handful of Industries and professionals who can learn Neuroscience but today any business that comes to your mind is Big database so the reach is endless moving ahead the third reason is increasing Trend and the salary so it's not just the job openings and the opportunities but how much it pays yes let us talk about money now so can you think of a figure of how much a data science job can fetch you well the average salary of a data scientist in US is approximately 120 000 well you can bet on it by comparing it with the salaries with any other ID jobs next let us have a look at the roadmap so first you need to start with the statistics or you can say the basic mathematics then you must know anyone programming language after that you should also be having knowledge of databases then comes the major role of a data scientist where one can Implement machine learning and some concepts of deep learning as well next in this era of Big Data you should definitely know some of the Big Data tools and Technologies and once the data is here you must know how you can ingest the data process the data or you can say clean the database also called as data ingestion and managing so after that it is very important to present your data or you can say visualize the data let's say in the form of bar graph or line chart so that a Layman can easily understand it and finally one should be capable enough to make data driven decisions to solve any problem so once you acquire all these skills congratulations you will be a data scientist next let us go into the details of all these skills one by one so the first is statistics so statistics basically deals with the mathematical area it includes your exploited data analysis a data scientist analyze different characteristics of data for example will mean median range standard deviation and we have many other things after you come to know what actually data reveals you can think of various possible outcomes in an experiment so to name some of the probability distributions we have poisson distribution we have binomial distribution and we have many more like that next you can apply various theorems and equations to manipulate the data according to your needs such as nave-based theorem or you can say linear Transformations then what you can do with that data you can basically measure how far a number is spread out from the average values that is nothing but your variance calculate by the curve analysis so these are all the basics which you need to know or it's just a general mathematics that you must be aware of so a good data scientist must be able to understand what the data is telling you and to do that you must have a basic lineal algebra and understanding of algorithm and statistic skills now once you understand statistics for data analysis you are half a data scientist now why I am saying that because you can discover from data and for that first you need to understand the data and pressure things that you will be doing on the data so this actually forms a very core part of decision making now from here you can take you to the advanced level which can be done using machine learning and deep learning but again that is an advanced level of discussion so we'll be doing that in a further slides but for now what you need to do you first need to analyze the data so that you can take meaningful decisions out of it so now let us consider the other problems that we face while we are in the flow so moving ahead with data we can't write everything on pen and paper and draw the graphs right so we need some sort of programming so next we have programming so to become a data scientist you must be well versed with the programming skills now the most commonly used programming languages a python r answers so let us discuss all of them one by one now python is an open source general purpose programming language which is extremely simple and is very easy to learn so it is a very powerful language which closely resembles your English language as well it is also portable or you can say it is picked for many platforms be it Linux platform beat Macintosh or anything next python has many libraries starting from numerical arrays to machine learning to your visualization so some of the libraries are numpy sci-fi matplotlib C born and all of these so you can perform almost any statistical operation or build any model using these libraries next let us talk about R now R is another language which is mostly preferred by data scientists so R is again an open source antisep programming as well as statistical language well one reason that it is said that R is very easy is that it is very well documented it is also cost effective and has strong statistical capabilities then we have SAS so SAS basically stands for statistical analysis system it is the most used tool in the commercial analytics market and contains various statistical functions along with a very good GUI now SARS is also considered as a fourth generation programming language so basically a fourth generation programming language is designed with a specific purpose in mind such as the development of commercial business software therefore SAS is designed to reduce the programming effort and minimize the time and cost it takes to develop a software also Python and R are not the fourth generation languages now for each of these programming languages there are various inbuilt libraries for visualizations and they are fairly nice but when you talk about business and perspective you need to have proper visualization tools and we'll be having the discussion over that in the further slides so again if you have to choose any programming language I would any day go with python but if you are coming from data analytics background R would be a better choice for you so it all depends on your need so basically you just need to be expertise in any of the programming language be it python be it r or SAS next you must have some database knowledge now with a large amount of data that has been generated every day you need to store it somewhere so you must have the basic database knowledge to store and analyze the data well nowadays every companies are using various database Management systems such as MySQL or Cassandra which is a nosql database to store the data so working with databases will surely secure your dream job to become a data scientist next and very important is machine learning and deep learning so a data scientist is incomplete without these so machine learning basically uses artificial algorithms to turn the data into value and learn without being explicitly programmed then we have deep learning so deep learning models are capable of learning to focus on the right features by themselves requiring a little guidance from the programmer so basically deep learning mimics the way that a brain works that is it learns from The Experience next deep learning is also a subfield of machine learning which is concerned with the algorithms that is inspired by the structure and the function of the brain which is called as artificial neural networks then we have various algorithms in machine learning and deep learning so talking about machine learning we have algorithms such as supervised learning unsupervised learning and reinforcement learning so you need to have good knowledge on various algorithms such as linear regression then we have logistic regression K means clustering decision tree and we have many more like that and in deep learning we have libraries such as tensorflow and Keras to implement it you can also take it forward and understand how convolutional neural network record neural networks and RPM work together next in a list is Big Data so there is a huge amount of data that has been floating around and what we do with it is all that matters right now big data and data science is not only seen in the it field but it has spread across all the reading Industries today therefore it makes it evident that for a certified data science professional sky is the limit and big data analytics has become a major role as a territory improving business decision making and providing the biggest Edge over the competitors so with big data you can perform distributed processing and you can even write map reduce codes so one should definitely know Big Data Technologies I and the most popular ones are Hadoop hdfs and Apache spark then we have data injection and managing so the process of importing transferring loading and processing the data for later use or you can say storage in a database is called as data ingestion so this involves loading data from a variety of sources so some of the data ingestion tools are Apache fume and Apache scope so if you have ever performed data analysis you might have come across feature selection before you apply it to your analytical model to the data so in general all the activity that you do on the raw data to make it clean enough to input to analytical algorithm is called as data merging so you can use any programming language for that you can use r or you can use Python packages for that as well so as a data scientist you must be able to understand what all features are important in the data set and what all features can be removed and obviously you have to remove some inconsistencies in the data set and all these things comes under the part of data managing or you can say data wrangling then we have visualization so data visualization is a very important part of a data life cycle now a good Hands-On knowledge is required on various visualization tools even you can use a programming language for that purpose but you need to understand the basics of good visualization and Reporting so you don't have to become a graphic designer but you need to be well versed in how to create reports that a complete Layman can understand it such as you can show your repository manager or maybe directly to see you so you will definitely not be showing the course to him right so it is very important to present your data in the right way so now some of the major popular visualization tools are Tableau we have click view then we have Google charts and we have many more tools in the market but these are the most popular ones used in market today so till now we have discussed the various tools and Technologies next is your problem solving or how a data scientist can solve everything now basically data driven problem solving approach is something that you need to develop and that will only come with experience so a data scientist needs to know how to productively approach a problem so the first thing he needs to understand the data or you can say explore the data you must know what data tells from it what are the Salient features of it and all those things then he needs to analyze the data properly or you can say what patterns can be drawn from the data or how can you frame a question that will help you yield the right answer so all this comes in the part of analysis then deciding what approximations make sense take the appropriate action or train the model to get the desired results afterwards you can just communicate with the co-workers perform visualizations and get the desired output but for that all you need is practice and practice if you actually want to get into this field you need to have more and more hands-on experience and for that what you can do you can go ahead and build your own projects and then you can explore various data sets you can also enter various competitions which has been organized by kaggle and many of the websites out there so a data scientist will have all the relative industry requirements and it is capable enough to do the job next let us understand the various roles and responsibilities of a data scientist so as we have discussed a data scientist is not only responsible for business analytics they're also involved in building data products and software platforms along with developing visualizations and machine learning algorithms some of the prominent data scientist job titles are data engineer then we have data architect we have data administrator then we have data analyst business analyst data analytics manager and business intelligence manager so you can choose any field so organizations who previously did mining dealt with well-behaved data but today the amount of unstructured data is far more than the structured ones and it definitely needs highly expertise data scientists to handle them so this condition is not only seen in a particular sector but it is faced across all the industries so learning and getting equipped with data science skills and technologies will not only fill the need of data scientists but it will also going to make you the IIT superheroes so to be among them a data science course is a must so anyone with the passion to become a data scientist can become one through professional data science training so get trained in data science and become the coolest profession in the IIT field now to get get in-depth knowledge on data science you can enroll for the live data science certification training by edureka with 24 7 support and the lifetime access so it provides courses on our programming as well as Python Programming now in our programming it will help you to gain expertise in machine learning algorithms like k-means clustering decision tree random forests and many more using the r language it will also help you understand the concepts such as statistics time series text Mining and an introduction to deep learning as well so throughout this data science course we will be implementing various real life use cases on media Healthcare social media and HR next it is the same course available in Python language as well so here again it will help you to gain expertise in various machine learning algorithms then it will expose you to the concepts of Statistics time series and different classes of machine learning such as your supervised learning then unsupervised learning and reinforcement learning so throughout this data science certification training you will be having 24 7 support and the lifetime accessibility to the course as well so you can go ahead and choose in any one of it now let me just summarize today's session so first of all we had a look to various facts as to why you should go for data scientist then we have talked about the roadmap or the journey to become a successful data scientist after that we have understood all the tools and techniques required to become one and discuss the various roles and responsibilities of a data scientist and finally towards the end I have talked about the trainings which are provided by edureka that is both in Python programming language as well as our programming language so don't just learn it guys Master it with edureka I hope you found the session informative well thank you so much bye