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What are the pre-requisites to start a career in Data Science?

A data scientist is a highly demandable profession these days. As per NASSCOM report, there will be a rise in the demand for data scientist jobs by 40% in the year 2020. Data analytic industry is expected to have a market share of more than USD 15 billion by 2022. Data scientists are highly qualified persons and qualifications for same range from Bachelors degree to PhD.  The key role played by a data scientist is to gather, analyze and interpret the data that requires high skill sets. A data scientist should have a good academic and technical language which helps in understanding the data and course in a better way, however, academic excellence is not the only criteria to become a data scientist. One who is interested in learning the things and understanding the logic may excel in this field.

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The pre-requisites required to start a career in data science can be categorized into two factors.

1. Non- technical or fundamentals skills.

2. Technical skills

Non-technical skills are those which you learn during your academic period. These are the basics that include math, and statistics. It is considered that statistics is the core of the data science learning and one should be aware of the fundamentals of same. It is further subdivided into-

1.    Descriptive statistics

2.    Inferential statistics

Descriptive statistics are more descriptive in nature and is normally represented in the forms of graphs. Type of Descriptive statistics are-

1.    Normal Distribution

2.    Central Tendency

3.    Kurtosis

4.    Variability

Inferential statistics focuses on the conclusion part and provides an explanation related to the data or statistics.

One should also be good at math for analyzing and understanding the vast data. You should be well versed with basic mathematics like linear algebra, calculus, and probability.

We will now focus on the technical skills one should have to understand data science. Technical skills are the one which is not taught directly into your academic curriculum, however, need to be acquired separately to widen your knowledge. Some of them are like:

Ø  Excel- Excel is considered to be a good start for the beginners, it teaches you how to short the data, work on it, creating the charts and tables, filtering the data and most important analyzing the data. It also involves representing the data in the form of pivot tables.

Ø  Python- Python has gained its popularity in these few years due to its ease if codding and understanding of language. It is considered to be one of the easiest languages one can learn without having prior knowledge of programming before. Python has the vast presence of libraries and packages which makes it easier for the user.

Ø  R- It is one of the required languages one should know if you are entering into the data science field. It is also one of the easiest languages but not as easy as Python is. The beginner's mat find it difficult to understand and learn, however, practice makes nothing impossible. R also contains large libraries database and it is one of the best statistical tools in use.

Ø  SQL- Known as a structured query language is one of the oldest database query language being in use. It is one of the most important languages one should learn to become a data scientist. SQL is used to mine data from the database, which contains a vast amount of data structured in the manner of rows and columns of a table.

Other than this one should have good communication skills to understand and interpret the data given by your clients. It is important that you must be able to convey your ideas and logic to your superior and to your clients. An understanding of business models and its functions are also mandatory, that helps in identifying the actual need of your clients and its solutions. For more information on data science I would recommend the Best Data Science Institute