How to Become a Data Scientist - Salary, Courses, Career, Skills And College

17 February,2023  |  By Brainwonders

Overview of Data Scientist

Data Scientists are the brains behind some of today's most innovative applications. Combining data, technology and business intelligence, they are able to develop powerful insights that shape the future of our world.

Data Science is a rapidly growing field, requiring a unique blend of skills to be successful. Data Scientists must be experts in mathematics, programming, statistics and business acumen. They must be able to interpret data, analyse patterns and trends, and draw meaningful conclusions from their analyses.

No two projects are the same - Data Scientists need to be creative problem-solvers, always ready to find new solutions for challenging problems. They must develop an understanding of how their work can be used in different contexts and how it affects the people it serves.

At the heart of it all, what really sets a Data Scientist apart is their passion for learning and experimentation. By constantly pushing boundaries and seeking out opportunities to improve their craft, they ensure that their projects stay relevant, impactful and game-changing.

What does a data scientist do?

Data scientists are the people who make sense of data. They uncover patterns, trends, and correlations through complex analysis techniques that help organisations make better decisions. A data scientist’s job is to take large amounts of data and find the answers to difficult questions that can have a huge impact on businesses. 

They use machine learning algorithms, statistical modelling, and data visualisation tools to interpret data from surveys, customer transactions, web clicks, sensor readings and social media posts. By analysing large datasets, data scientists can uncover insights that provide valuable information for making business decisions. 

Data scientists work in areas like computer science, finance, marketing and healthcare. They look at data with an eye to understanding it and turning it into actionable insights. With their skills and expertise, they can help businesses identify new opportunities while reducing risk.

What is the difference between a data analyst and data scientist?

Data analysts and data scientists both work with data and use it to make informed decisions. However, they have two very different roles. 

Data analysts have a more narrow focus. They usually specialise in one particular area of data, such as market research or finance. Their job is to pull useful information out of the data and suggest decisions based on that information. 

On the other hand, data scientists have a much broader range of skills. They are experts in all areas of data, often specialising in multiple fields. They use techniques such as machine learning to identify trends and patterns in data. They also use statistics and predictive modelling to drive informed decisions, which can then be used to develop new products or services.

How to become a data scientist

Getting started can seem daunting, but with the right resources and experience you’ll be able to become a successful data scientist in no time. Here’s how:

1.  Get A Bachelor’s Degree: An academic background isn't always necessary to land a job in data science, but it certainly doesn't hurt. Earning a degree related to the field – such as data science, statistics, or computer science – can give potential employers peace of mind that you have the skills and knowledge needed for the role. Having one of these degrees on your resume can help you stand out from other applicants and make reaching the top of the 'Yes' pile that much easier!

2. Improve your existing skills:Taking an online course or joining a bootcamp can be the perfect way to do it. Here are some of the key competencies you'll need to master:

A) Programming Languages: Mastery of popular programming languages is essential for any data scientist. These include 

• Python 
• R
• SQL
• SAS

Each of these offers different strengths for you to use when organising, analysing, and transforming data. Through practice, you'll become more confident in your ability to work with this valuable information.

B) Data visualisation: Visualising data is an essential skill for any aspiring data scientist. With the right tools, you can quickly and easily create charts, tables and graphs that allow you to identify patterns, trends, and conclusions. 

Tableau, PowerBI and Excel are among the most popular and effective pieces of software that make this possible. Take the time to get familiar with them - doing so will give your data visualisations the professional and impactful touch they deserve.

C) Machine learning: As a data scientist, you can take your work to a whole new level by incorporating machine learning and deep learning into it. With the right course, you can easily get started with the fundamentals of these technologies – from gathering higher-quality data to potentially predicting outcomes of future datasets. It's an exciting world, and machine learning and deep learning can help you explore it!

D) Big Data: If you're a job seeker, having experience working with large data sets is becoming increasingly important. Different software frameworks help make sense of the data, like Hadoop and Apache Spark. Both are powerful tools used to process and analyse large volumes of information quickly and accurately. Having some experience working with these sorts of systems can give you an edge over other applicants and help you land the job you've been eyeing!

E) Communication: For data scientists, being able to communicate the results of their work is just as important as their technical skills. Being able to share ideas through both verbal and written means is a key strength that employers look for when hiring. Being able to explain complex ideas in simple terms makes a data scientist more effective in their role, and also ensures their findings can be widely understood and applied.

3. Get Hands-on Experience: Working on real-world projects is essential for gaining the skills needed to become a data scientist. Find relevant internships and apprenticeships, or participate in open source projects and hackathons to gain practical experience. This will help you build your portfolio and demonstrate your proficiency in the field.

4. Prepare For Data Science Interviews: As an experienced data analytics professional, you may feel ready to take the plunge into data science. To make sure you ace your interview, it's wise to prepare answers to potential technical and behavioural questions. When practising your replies, it can help to speak them out loud. You may want to draw on examples from your work and academic history to demonstrate your confidence in your skills. With a little research and preparation, you can be on your way to a successful data science career.

Roles and Responsibilities of a Data Scientist

1. Data Scientists are the professionals responsible for interpreting and analysing data to solve complex problems. 
2. They are highly skilled professionals who use data analysis techniques and technology to uncover insights, trends, and patterns in datasets that can be used to identify opportunities or improve processes.
3. Data Scientists strive to make data-driven decisions by gathering the right information, finding meaningful insights, and developing strategies that support organisational goals.
4. They must also stay up-to-date with the latest industry trends, technologies, and tools to ensure accuracy in their analyses.
5. Their work also includes identifying problematic areas and providing actionable solutions.

Characteristics of an Effective Data Scientist

1. Data Scientists are a critical asset to any organisation as they make decisions that drive business growth and propel innovation. To be effective, they must possess a unique blend of technical skills, creative problem-solving abilities, and an understanding of the larger organisational context.
2. The technical skills required for a data scientist include strong programming capabilities and deep knowledge in statistical analysis, machine learning, and artificial intelligence. They must have the ability to work with large datasets, visualise their findings, deliver insights at scale, and develop production-level machine learning models. 
3. Creative problem solving is also essential for a successful data scientist. They should be able to think outside the box to identify new opportunities and approaches, which can help their organisation succeed. They must be able to structure data and make connections between seemingly unrelated elements. They should also be comfortable exploring unknown territory and embracing ambiguity.
4. Finally, data scientists must understand the broader organisational context in which their work exists. They need to know how their organisation makes money, what objectives it wants to achieve, and how to present their findings in a way that resonates with stakeholders. This requires an understanding of the different roles within an organisation and how to engage them effectively. 

In summary, effective data scientists possess the technical skills necessary for handling large datasets and the ability to creatively identify business opportunities. They must also understand the organisational context in order to deliver meaningful insights that drive lasting value.

Best colleges For Data Science 

1. IIT Delhi

2. IIM Calcutta

3.  IIT Roorkee

4. IIT Madras

5. Jain University Bangalore

6. Chandigarh University

7. BITS Pilani

8. Annamalai University

Data Scientist Salary In India And Growth

On average, data scientists can expect to make an annual salary of ₹ 10 Lakhs in India. Salaries range anywhere from ₹ 3.8 Lakhs to ₹26.0 Lakhs, giving data scientists plenty of room to grow their careers and income.

Data scientists and mathematical science occupations are expected to grow by an amazing 31 percent and statisticians by 33 percent from 2020 to 2030, according to the US Bureau of Labor Statistics. This growth rate is way above the average 8 percent growth rate for all other jobs.

The reason for this high demand is the rise of big data and its increasing importance for businesses and organisations.

Are you feeling lost in your career and unsure of what path to take? Brainwonders counselling team can provide the answers you need. With their industry experience, expert insight, and tailored advice, our counsellors can help you make the best decisions for your future. Don't let confusion stand in your way. Take a step forward and book a session with the Braindwonders counselling team today!

Frequently asked questions about Data Scientist In India

1. What is a Data Scientist? 

Ans: Data Scientist is someone who specialises in analysing large sets of data to uncover patterns and insights that can be used to make better decisions. They use various techniques such as machine learning, statistical modelling, and artificial intelligence to gain insights from the data.

2. What does a Data Scientist do in India? 

Ans: Data Scientists in India typically work with organisations to develop strategies that help them make smarter decisions based on data-driven insights. This could include developing predictive models, creating dashboards and visualisations, designing experiments and A/B tests, and more. 

3. How much do Data Scientists get paid in India? 

Ans: The average salary for Data Scientists in India ranges from 6-10 lakhs INR per annum depending on experience and skill level. They can even earn a salary of 26 lakhs INR depending upon the expertise and relevant experience that you provide to an organisation.

4. What educational qualifications are required to become a Data Scientist in India? 

Ans: Most Data Scientists have at least a bachelor's degree in computer science or related field. Additionally, many employers require Data Scientists to have advanced degrees such as a master's or doctorate in a relevant field. 

5. What challenges do Data Scientists face in India? 

Ans: Data Scientists in India often face challenges such as finding quality datasets, dealing with outdated technology, lack of resources and funding, and lack of access to experts and mentors.