How to Become a Data Scientist in 2023: Step by Step guide

How to Become a Data Scientist - Data science course is one of the trendiest professions in the world right now. Working as a data scientist in India can be intellectually challenging, and analytically gratifying, and put candidates at the vanguard of recent advancements in technology. Data scientists are the nervous system of an organization as it plays an important role in determining the decisions taken by the organization. With the upsurge in the trend of Data Scientists, candidates can expect a salary of INR 2,00,000 to INR 15,00,000 per annum.

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To become a Data Scientist in India, candidates have to earn a bachelor’s degree and have skills in relevant programming languages such as SQL, Python, R, and SAS. To know more about the step step procedure to become a Data Scientist in 2023, read the article below.

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What is a Data Scientist?

A Data Scientist is a white-collar professional who is involved in decision making and improving operations utilizing their data science skills. They are specialists in the field of background in statistics, mathematics, and computer science. Candidates who want to become a data scientist are required to possess strong mathematical and analytical skills. The main job of a Data Scientist is to utilize proficiency to interpret large sets of data and locate patterns or trends. Further, Data Scientists also develop unique ways to accumulate and stockpile data.

What Does a Data Scientist Do?

The responsibilities of a data scientist are to demarcate the queries their team may be questioning and extrapolate out ways to respond to those inquiries utilizing data. Some of the job roles of a Data scientist include -

• Locate trends and patterns in data sets to unveil insights
• Employ machine learning strategies to enhance the grade of product offerings or data
• Envision data models and algorithms to predict outcomes
• Arrange data tools like R, Python, R, SQL, and SAS in data analysis
• Disseminate recommendations to different teams and senior members of the organization
• Dwell on top of inventions in the data science field

Why Become a Data Scientist?

One of the main reasons why candidates are opting for a data scientist profession is its trends and scope in India as well as abroad. Data Scientist is a gratifying career with out of box salaries, a stimulating environment, and intellectually challenging. According to reports, the demand for data scientists is reportedly to increase by 45% in the next 10 years. Many Indian organizations are sponsoring laboriously in Data Science as they concede the competitive benefit that it can deliver.

The Government of India (GOI) is supporting careers in Data Scientists in India, funding government infrastructure, and industries for promoting adaptations in data-driven approaches. Some of the top recruiters of Data Scientists in India are Microsoft, LinkedIn, Flipkart, Ola, Amazon, Airtel, and TCS. Taking the example of Data Scientist Careers in Flipkart, they are approaching data science to enhance their consumer targeting and product suggestions. On the other hand, Ola has been using data science strategies to optimize its pricing forecast and demand prognosis. Based on reports from Glassdoor, the average data scientist salary in India is INR 11,07,640 per annum which is higher than the IT professionals in top MNCs. It has been evaluated that Data Scientists with an experience of 4 to 9 years of experience can earn INR 13,00,000 yearly and 10 to 19 years of experience can expect INR 16,00,000 per annum.

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Step by Step Guide on How to Become a Data Scientist?

Candidates who are ready to become a data scientist in India can follow the steps mentioned below for a slick growth.

Step 1 - Graduate in a Relevant Field

The first and most important step to becoming a data scientist that companies look for as the primary requirement is to have a bachelor’s degree in data science, computer science, or statistics.

Step 2: Know Suitable Programming Languages

Along with a bachelor's degree in a relevant field, candidates are also required to possess some appropriate programming languages that will form the base of their career. Knowing these programming languages will help the candidates to handle these chunks of data on a daily basis. Some of the top and most important languages include Python, SQL, R, and SAS.

Step 3: Acquire Certain Skills

There are certain skills that are very important for a candidate to possess while constructing a career in data science.

• Data Visualization - As the profession of data science requires handling statistical data on a regular basis, candidates must have a very strong foundation in data visualization which can be done through Tableau, PowerBI, and Excel.
• Machine learning - Integrating machine learning into daily work as a data scientist signifies constantly enhancing the grade of the data they collect and potentially being capable to forecast the consequences of forthcoming datasets.
• Big Data Analytics - Handling big data comes under a huge responsibility of a data scientist as many of the recruiters demand for their company. Some of the software frameworks employed to process big data contain Apache Spark and Hadoop.
• Communication - The most important thing in working with any organization is they should be able to communicate properly. Without effective communication, candidates will not be able to convey their thoughts.

Step 4: Obtaining Data Science Certifications

Certifications can boost a candidate’s resume as well as help elevate their skills. Some of the top online data science certifications have been listed in the table below.

Qualifications and Eligibility Required for Data Scientist

The basic eligibility criteria to become a data scientist is that the candidate must have acquired a bachelor’s degree in data science, statistics, and mathematics. Candidates must score at least 50% marks in their graduation from a renowned institute. Apart from the basic eligibility, candidates must have acquired strong mathematical and analytical skills. Besides, candidates should also possess programming language skills such as Python, SQL, R, and SAS along with data visualization tools such as Tableau, PowerBI, and Excel.

Data Analyst vs Data Scientist: What’s the Difference?

The difference between data analyst and data scientist has been evaluated in the table below.

Skills Required to Become A Data Scientist

Candidates who wish to become data scientists have to possess these skills as mentioned below to be successful. Some of the skills required to become a data scientist have been listed in the pointers below.

1. Increase database knowledge which is needed to store and interpret data using tools. These tools include MySQL, Oracle, Microsoft, Database, SQL Server, and Teradata.
2. Discover probability, statistics, and mathematical analysis. Dealing with Mathematical analysis means dealing with limitations and corresponding theories, such as integration, differentiation, infinite series, measure, and analytic functions.
3. Mastering at least one programming language is mandatory. Programming tools like Python, R, and SAS are very essential when performing analytics on data. R is a free software environment for statistical computing and graphics, which sustains most Machine Learning algorithms for Data Analytics such as association, regression, and clustering. Some of the Python libraries are NumPy and SciPy which are used in Data Science. SAS can manage, alter, and recover data from a combination of sources as well as conduct statistical play-by-play on the data.
4. Understand Data Wrangling that concerns manipulating, cleaning, and organizing data. Some of the popular tools for data wrangling tool include Python, R, Flume, and Scoop.
5. Master the concepts of Machine Learning. Machine Learning can be accomplished through various algorithms such as Naive Bayes, Regressions, K Means Clustering, SVM, KNN, and Decision Tree algorithms, etc.
6. Having an operating understanding of Big Data tools such as Hadoop, Talend, Apache Spark, and Tableau.
7. Data visualization incorporates separate data sets and achieves a graphical display of the results using charts, diagrams, and graphs.

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