How to Become a Data Scientist in 7 Steps

Data is the world’s largest commodity. Everything from marketing, product planning to business decision-making is driven by data science. Dubbed by Harvard Business School, Data Science is the hottest career choice of this century. Learn how to become a data scientist with Intellipaat’s step-by-step roadmap.

If you want to know how to become a Data Scientist, this blog is all you need.

• Introduction to Data Science
• Who is a Data Scientist?
• Why Become a Data Scientist?
• What Does a Data Scientist Do?
• Data Scientist Job Description
• Data Science Qualifications and Eligibility Required
• Skills Required to Become Data Scientist
• Difference Between Data Scientist and Data Analyst
• How to Become a Data Scientist: 7 Steps Guide
• Data Science Tools
• Data Scientist Jobs Available Right Now
• Data Scientist Salary (India & USA)
• Career in Data Science
• Data Science at Work
• Conclusion
• FAQs

Check out this video by Intellipaat on How to Become a Data Scientist:

Introduction to Data Science

Have you ever wondered how Instagram recommends the videos that you often like? How does Uber find a driver close to your location? Or how Amazon recommends the products that you want to buy. Well, data science is the driving force behind all these possibilities.

With generative AI revolution, the demand for data related job-roles is at an all time high. According to the Economic Forum, data science and AI together will produce 70 million new jobs. 

According to Forbes, on a daily basis 2.5 quintillion bytes of data are being generated. And over the last two years alone, 90% of the data in the world was generated. Considering these facts, the number of job-roles predicted for data science seems appropriate. Because in order to process these huge amounts of data, companies are going to need data scientists.

That’s why if you are trying to figure out how to become a data scientist, you’re on the right track of making your career future-proof.

Who is a Data Scientist?

Data scientists work around data and figure out the critical insights out of it, which could help business owners make data-driven business decisions. They carry out a range of data-related tasks, such as preprocessing the data, and performing exploratory data analysis to look into key business KPIs. After figuring out insights, they do the predictive modeling for future events using machine learning techniques.

Reporting and data visualization tools are yet another important factor that data scientists work on. These tools are used to display the insights generated through data mining that will further enable one to make informed decisions that are customer-oriented and consider potential revenue opportunities, among many others.

The Data Scientist role is often a subset of many other traditional job profiles such as statistician, computer professional, mathematician, and scientist.

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Why Become a Data Scientist?

Right now, in Feb 2024, the number of data scientist jobs around the globe stands at 2.7 million, but guess what is projected to happen by 2028? The US Bureau of Labor Statistics predicts that by 2028, there will be 11.5 million data science jobs. 

At the beginning of 2024, CNBC came up with a report that claims the job postings for data science and AI since the inception of generative AI are up by 306%. 

It’s not just about job opportunities, according to Glassdoor, there are some data scientists who are earning salaries above a whopping 70 lakh rupees in India, compared to US$190,000 in the USA. 

Data science is ranked 4th in the whole array of tech jobs, and it is 6th best among the STEM jobs, according to the analysis by The U.S. News & World Ranking Report. 

The impressive number of job-roles, demand and supply gap, and lucrative salaries are some of the great prospects that make data science a great career choice. Also, there are lots of learning resources available, so professionals from both tech and non-tech backgrounds can succeed in this field.

What Does a Data Scientist Do?

Data Science is the art of churning numbers and understanding what story data has in store. Thus, in a business setting, a data scientist is ideally responsible with setting up data management mechanisms, converting data into analyzable format, and applying statistical modeling to draw patterns and insights out of it. 

Let’s break down these tasks further to understand what exactly a data scientist does:

1. Data Collection: Data scientists scrape data from websites and social media platforms. They also extract it from databases using SQL or big data tools.

2. Data Cleaning: Before analysis, data has to be converted into an analyzable format. That’s where data cleaning comes into play. During this process, data scientists remove any errors and inconsistencies.

3. Data Analytics: Using statistical and ML techniques, data scientists try to find patterns in data. For continuous monitoring, they also develop automation that would inform businesses about trends in the data.

4. Data Visualization: Once the analysis is done, data analysts convert their findings into a visualization to help stakeholders understand the key insights.

5. Model Building: Data scientists further create predictive models that can be used to make predictions or recommendations based on the data.

Moving further, let’s break down the job description of a data scientist to understand what skill-related requirements businesses have for this role.

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Data Scientist Job Description

A data scientist is critically supposed to make value out of data that the organization has collected. Though this may sound like a clear definition for this job-role, that’s not the ground truth. A data scientist can be assigned varied tasks, from creating data pipelines to exploratory data analysis, or even working on ML projects and AI projects, etc.

That’s why, to figure out the important roles of a data scientist job, we went through a couple of data scientist job posts available on LinkedIn. Here’s what a job description for a data scientist position at Microsoft looks like:

Here’s yet another example of data analyst job responsibilities for Harman International:

However, while you can see a difference in job-role definition, certain critical responsibilities remain the same. They are as follows: 

• Data collection, cleaning and preprocessing
• Creating data pipelines
• Perform exploratory data analysis
• Predictive analytics
• Develop and apply statistical models
• Build and deploy machine learning models
• Evaluate and communicate insights to stakeholders in the form of presentations and dashboards

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Data Scientist Qualifications and Eligibility Required

Generally, there is no specific educational requirement for you to become a data scientist. Even if you are from a non-tech, business, or finance background, you can become a data scientist. It’s just you will need a minimum bachelor’s degree along with strong skills such as statistics, machine learning, SQL, deep learning, etc.

We have put out a comprehensive overview of data scientist skills below.

Skills Required to Become a Data Scientist

There is a blend of technical and soft skills required for one to become a proficient data scientist. And on the journey of how to become a data scientist, it is critical that you understand these referred skills and master them first-hand.

Following are the technical attributes and set of soft skills a data scientist must possess:

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Difference Between Data Scientist and Data Analyst

The professionals across data scientist and data analyst domains both work with data, but they have different roles and responsibilities, which are elaborated further:

Learn the essential skills for a career in data science with our comprehensive Data Science Learning Path blog

How to Become a Data Scientist: 7 Steps Guide

To become successful in a data scientist career, follow these steps:

Step 1: Revise Statistics, Probability, and Linear Algebra Concepts

While in college, you must have learned the important mathematical concepts such as statistics, probability, and linear algebra. These very same concepts are going to act as a fundamental block when you deal with data. Hence, it is important that you revise them. 

If you don’t have a mathematics background, don’t worry! You can still learn these skills on your own. Many resources like blogs, video tutorials, and documents are available. Alongside normal distribution and descriptive statistics, below are some concepts you will have to master:

Step 2: Learn Programming Language 

If you’re looking forward to figuring out a solution to your query about how to become a data scientist, you must first understand that you will need to master a programming language. The most prominent programming languages used in data science space are Python, R, and SQL.

If you are already familiar with a programming language, picking up another new language will be an easy task. If you are from a non-tech background, then you must refer to online documents, tutorials, and YouTube videos to master these languages.

Step 3: Learn More About Databases

In the previous topic, we mentioned SQL as a must-have for data scientist roles, and it’s worth mentioning again. At the beginning of every data process, data is in its raw form. To convert it into an analyzable format, big data processing is necessary. 

Once that is done, the data gets converted into relational databases. And to be able to operate on this data, you definitely need some knowledge about databases. Learn concepts such as ER Diagrams, database programming, database schema design, normalization, etc. This will allow you to swiftly work around databases. 

Step 4: Master the Skill of Working Around Big Data

While telling you the importance of learning databases, we told you that for every data science process, big data processing is the starting point. Most often, as a data scientist, you will be dealing with large data sets. Thus, it is a challenging task to collect, maintain, and process these data sets.

That’s where knowledge of big data tools is critical. You may need to learn the big data tools from AWS, Azure, or GCP. If not, mastering the Apache Spark, Hadoop or Cassandra frameworks will be more important.

Step 5: Learn Machine Learning

Data science is all about figuring out key insights from the raw data. This can only be done by examining data at a very detailed level and understanding the complex behaviors and trends. This is where machine learning comes into play.

Without machine learning, tasks such as predictive analysis and descriptive analysis cannot be done. You will have to spend a good amount of time learning the important ML algorithms. Starting from data classification, regression, clustering to ensemble learning and dimensionality reduction, you will have to learn every concept under the umbrella of machine learning.

Step 6: Learn Data Wrangling and Data Visualization

These days, almost any kind of data can be a valuable source of information. The challenge lies in making sense of it, but before even that, you need to get the data to work on, right? The process of getting the data and preparing it for analysis is data wrangling, in a nutshell.

Alteryx, Talend, and OpenRefine are the most commonly used wrangling tools. Besides that, pandas in Python is another go-to choice. You will also have to learn the skill of data visualization using libraries like Matplotlib, seaborn, Plotly, etc.

Step 7: Solve Data Science Problems

While mastering technical skills, it is very critical to do hands-on practice. Projects are the best gateways to achieve that. It is recommended that you visit multiple data science platforms, such as Kaggle, UCI Machine Learning Repo, OpenML, etc. Get the datasets from there, understand the problem, and figure out how the solution can be approached.

Doing this will provide you with the know-how of how exactly the data science process works. And how you can grab insights out of data, by using appropriate techniques.

This learning schema is your answer to how to become a data scientist. You need to learn the above technologies one by one. This could be a bit challenging, which is why professional certifications are significant if you’re someone getting started with data science from a beginner’s standpoint.

Check out these Data Scientist Interview Questions and stay prepared for your next interview.

Data Science Tools

We have created a comprehensive guide on top data science tools that are relevant and important right now. Give this blog a read to know more about these tools. However, we have listed some important tools below:

Data Scientist Jobs Available Right Now

Right now, if you just Google and browse ‘data scientist jobs in India’, you will find out that just on LinkedIn, there are 93,000+ jobs available.

In the United States, the number is even better, 1,68,000 jobs.

And this is just LinkedIn. If you consider other job boards such as Naukri.com or Indeed, the numbers would be even larger. That is why you should understand how to become a Python developer and start your preparation right away!

Learn about the difference between Data Engineer and Data Scientist in our blog on Data Engineer vs Data Scientist!

Data Scientist Salary (India & USA)

Here’s a detailed reference of data scientist salaries in India and the USA, based on insights from different sources:

Data Scientist Salary in India

• According to Glassdoor, the salary of an entry-level data scientist in India lies between ₹6 lakh to ₹22 lakh.

• The average salary for data scientists is ₹14 lakh, and with more than 8 years of experience on the back, these professionals could easily earn above ₹30 lakh.

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Data Scientist Salary in USA

• According to Glassdoor, the salary of an entry-level data scientist in the USA lies between $100,000 and $200,000.

• The average pay for data science professionals is $156,000. Fortunately, as you get experience in this field, you can easily expect a salary above $250,000 as a senior data scientist.

Career in Data Science

As already mentioned above, many data scientists begin their careers first as data analyst, and then enter the field of data science via job changes or an internal promotion. Once experienced, these professionals can then look for senior roles in this profession. More experts who have management skills can go for director-level and executive-level roles.

Here are a few ways one can stay active in the world of data science:

1. Follow Data Science groups and influencers

Joining Data Science groups is an effective way to stay updated and maintain relationships with fellow enthusiasts. Furthermore, attending meetups from time-to-time is also a great way to expand your network. Many professionals and big players have turned to social media to share Data Science-related know-how. Make sure to follow these accounts for regular updates.

2. Build a public portfolio of Data Science projects

As a beginner, you can start building a simple portfolio of interesting data science projects you have taken up and showcase them on platforms such as GitHub. Not only will you be creating a personal brand from scratch, but it will also ensure future growth in your career path.

3. Go for an Online Training Course and Earn a Certification

The most effective and the least costly way to boost your skills is by enrolling in courses that develop hot skills such as Python, R, Tableau, SQL, and Machine Learning.

4. Target employers

Every business, big or small, generates data. Not every company might be able to afford a full-sized team of data science experts. They need capable professionals who can do the work for them. Your job is to define your target employers and make a list of organizations that appeal to you. Once you start following them and stay updated over social media, you will get a clear picture of what you should be doing as a Data Scientist if you want to join their team.

Learn about the difference between Data Engineer and Data Scientist in our blog on Data Engineer vs Data Scientist!

Data Science at Work

Since you are clear about the demand and lucrative compensation data science offers, the only factor that needs to be addressed now is job satisfaction. This field of technology has created an immense impact. From healthcare and E-commerce to government service optimization, data science has revolutionized the entire process. 

Here are certain examples of data science at work:

1. Data Science in Healthcare: Deep learning algorithms detect and figure out medical conditions based on scans like MRI, X-ray, and CT scans. This removes human error and provides better detection of medical concerns.
2. Data Science in E-Commerce: E-commerce giants use data science to determine customer lifetime value. The tool like recommendation engines, and actions like promotional emails and ad campaigns are all driven by the way customers behave on their websites.
3. Internet Search: Search engines like Google, Bing, Safari, and Yahoo all use data science to comprehend how users behave and their search habits. Based on the results and interests, these search engines come up with content you may like.

Conclusion

In this blog post on ‘How to Become a Data Scientist’ we discovered how demanding the career in data science is and why you should discover this as a career opportunity. We covered the skills, job description, and career outlook for data scientists. We also helped you with the step-by-step roadmap, which can help you become a data scientist if followed appropriately.

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