This is the age of data. And data is power! Every day, human beings generate 2.5 Exabytes of data which if used in an intelligent manner can do miracles in health, finance and various business sectors. The main aim of a Data Scientist is to analyse this digital data and come up with useful insights which help in business growth. Companies which use data intelligently are getting more profits than the companies which are not looking at data. So, the job of Data Scientist is very crucial in any business organization. Data Scientist is a rock star of IT industry as he gets highest pay in the company along with highest honour.

Educational Requirements to Become a Data Scientist



Basically, we need to have a bachelor's degree in any stream. You may have graduation in Arts, Commerce, Science or Engineering.

If you are having knowledge in Statistics or Computers, you find it easy to become a Data Scientist. Hence PG in Stats or Graduation in Computers becomes plus point. People without the knowledge of Stats or computer programming should get trained in those areas.

Some Universities in India are now offering PG in Data Science or a PG Diploma in Data Science. This course teaches you all necessary skills to become a Data Scientist. As Data Science is the most advanced subject, it may not be available in all Universities. Hence several reputed private institutions are also training people in Data Science which can be useful to anyone.

While learning Data Science, what is important for you is this: did you learn all the algorithms and applied them to solve various tasks? It is necessary to work on actual data and solve actual tasks which real Data Scientists spend their time on.

9 Skills needed to become a Data Scientist



Data Analyst and Data scientist skils Since Data science deals with understanding of theory as well as its application, we have to acquire the knowledge of several concepts and their application on data.

1. Statistics: As a Data Scientist, your primary job is to analyze and interpret large amounts of data and produce actionable insights for a company. Basic understanding of Statistics is useful to analyze the data and come up with solutions to many problems.

2. Programming Skills: When dealing with data analysis, R language will be highly useful because of its simple syntax. When coming to machine learning and AI, we need to go for Python as it has features and libraries which make our task easy. Python also has various packages like pandas for data analysis, matplotlib and seaborn for visualization, scikit learn for machine learning and nltk and opencv which are used in AI. We have to learn all these packages.

3. Machine Learning: If you are in any way connected to the tech industry, chances are you have heard of Machine Learning! It basically enables machines to learn a task from experience without programming them specifically. This is done by training the machines using various machine learning models using the data and different algorithms.

So you need to be familiar with Supervised and Unsupervised Learning algorithms in Machine Learning. When we go further to create Artificial Neural Networks, it becomes deep learning.

4. Data Management: Data plays a big part in the life of a Data Scientist (Obviously!). So, a Data Scientist needs to know about how to extract data from various sources (and most of the times this is done by a Data Engineer) and transform it into a required format for analysis. When we have to handle large volume of data, we need to use Big data technologies like Hadoop or Terra data, etc.

5. Data Wrangling: This means that cleaning and pruning the data in the warehouse before we actually use it in our programs.

6. Data Analytics: This represents analysing the data to know the relationship between various pieces of data. This solves many common questions asked by the management in a company. During the analysis phase, we need to present the data pictorially. For this purpose, you should be equipped with tools like Tableau, Power BI or Click View.

7. Data Intuition: When we observe data minutely, we can find some repeated patterns in the data. These patterns reveal relationships between various variables. This understanding is very useful for a Data Scientist to solve the problems in his job and this data intuition increases along with his experience and practice.

8. Communication Skills: While you understand the data better than anyone else, you need to translate your findings into conclusions for a non-technical team to aide in the decision making. This can also involve data storytelling! That means presenting our results in a story format so that it convinces the top level management to act.

9. Domain knowledge: This is not taught in any Data Science course. When you become a Data Scientist in a company, you need to have basic knowledge related to the operations of that company. For example, if you join a bank as a Data Scientist you are bound to know about banking transactions, deposits, fixed and recurring deposits, interest rates, balance sheets, etc. This domain knowledge can be acquired only after joining the company.

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