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How to start career in data science as a fresher :

Updated: Jul 18, 2021


As we continue to live in a world that runs on an infinite amount of information and digital statistics, data science has continued to advance forward as one of the most encouraging career paths for the newer generation. The traditional methods of analysing large amounts of data and programming skills are insufficient to meet the demands of an economy based on analytics. The present demand of people working in the field of data mining and interpretation, the data scientists, is high. In this post we will discuss how can you start your career in Data Science as a fresher.




What is Data Science?



The field of data science mostly revolves around the extraction of information and insights from organized or unorganized data and their application in the digital world. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.


What are the pre-requisites for data science?


Becoming a data scientist is not a cakewalk. There are certain parameters that you have to fulfil for entering the world of data science. For excelling in the field, you need to know the following things :


1. Programming Languages :


Thorough knowledge of various programming languages such as Python, C/C++, Perl and Java, with Python being the most common coding language required for data science. These help in organizing unstructured data. The skills of processing the data to make it compatible with machine learning algorithms is also a prerequisite.


2. Database Management :


Learning SQL is important for database management. Most data miners use R as a common language for developing statistical software and data analysis. With such a large stack of data to work on, it is obligatory that a data scientist knows how to manage that data. DBMS consists of a group of programs that can edit, index and operate the database.


3. Mathematics


It is self-explanatory that anything related to analyzing data would surely require excellent knowledge of Mathematics. It is only through mathematical algorithms that data is processed and a model is created. A lot of mathematical tools are used in data management which includes linear algebra, probability, calculus, statistics, differential equation, regression and time series, etc.


Why a career in Data Science?


Data science is slowly becoming one of the most important fields in the market, the growth is self-explanatory. The increase in the demands of professionals who can work in data mining and interpretation in the last year was 29% and for the last decade, it has been recorded up to 344% - a dramatic upswing. In all, the supply of opportunities is high and the future of data science is promising.


Prodigies of Data Science


1. Geoffrey Hilton is called the God of Deep Learning in the field of Data Science. He is a PhD in artificial intelligence and is popularly known for his flawless work on neural nets.


2. The Co-founder of the term 'data science', Jeff Hammerbacher started Facebook's data science team, adopted Hadoop enabling the social media giant's data team to process tons of data in real-time at lightning speed.


3. The other founder of the term 'Data Science', Dhanurjay Patil is a doctorate in Applied Mathematics and currently a principal consultant to many blue-chip companies like LinkedIn, Skype, Salesforce, PayPal, eBay and Graylock Partners

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Free sources to learn Data Science :


Life is running at a very high pace, if we do not start right now, we'll have no chance at winning. Following are free sources that can help you take the first step towards a better future.


1. Google's Machine Learning Crash Course:

The course material consists of readings, exercises, and notebooks with real code implemented with Tensorflow and running on Google Colab.



2. IBM'S Machine Learning with Python :



3. Applied Data Science Module at Worldquant University:

It includes video lectures and assignments and can take 8-16 weeks.




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