December 20, 2023

How to become a Data Scientist in 2024?

Are you interested in working as a data scientist in 2024? Then you are at the right place. ! In this blog post we present to you the roadmap to learn data science in the simplest way possible and land your dream job.

In the year 2017, the main headlines of a leading newspaper , The Economist read : “The world’s most valuable resource is no longer oil, but data.”  As businesses are becoming technology oriented, data has become really, really important. This has led to an increase in the need  of skilled data scientists who can help these companies make sense of their data. 

In 2012 a report published by Harvard Business Review called data science the “sexiest job of the 21st century”. Even more than a decade later the demand for these professionals has grown exponentially creating lots of new job opportunities in the market. According to a recent report by Nasscom the need of these professionals will increase up to 1 million by 2026. 

Whether you are a professional looking for a career switch or a college student wanting to build their career in this domain, this blog post will provide a comprehensive guide for aspirants in 2024. 

Who is a Data Scientist? 

A data scientist is an expert who uses data and statistical analysis to uncover valuable insights from large, complex datasets. They should have technical skills like Python and R, along with a strong grasp of statistics, math, and machine learning. Data scientists aim to assist organizations in making data-driven decisions, refining processes, and enhancing outcomes across fields like finance, healthcare, marketing, and technology. 

Career Opportunities and Salaries for Data Scientists

The data scientist profile commands a lucrative salary package. As per  Glassdoor , the average salary for these professionals is ₹ 14 LPA which is  28% more than other IT professionals.  A senior data scientist with an experience of 5-7 years can earn  ₹ 27 LPA on an average. The principal data scientists are usually the top earners with an average salary of ₹48 LPA.  

Some of the top companies hiring for these positions include:

How to become a Data Scientist : Roadmap

At this point, you're probably excited  about starting on your path to becoming a Data Scientist. Thinking  where to begin? Here, we outline ten  essential steps to start your journey to becoming a Data Scientist from scratch.

Step 1: Get a Bachelor's  Degree 

To initiate your journey as a data scientist, it's important  to hold a degree in a relevant field like computer science, statistics, mathematics or engineering. A good foundation in these subjects is essential for effectively working with data and deriving meaningful insights. While a  Masters or Ph.D. degree  is not mandatory for all data science roles, it might be necessary for advanced positions or academic research.

Step 2: Learn a Programming Language 

There are several programming languages used in data science but Python and R are popular languages.

1. Python for Data Science 

If you're getting into data science and haven't picked a programming language, starting with Python is a good idea because of the ease of its use.

Python basics to be learned include:

Important  Python basics includes data structures (lists, dicts), types (int, str) for effective data handling. Also get to know about Functions that offer reusable code blocks, loops (for, while) and conditionals (if, else) control flow, and algorithms which are a guide for solving problems step-by-step. Even if you are a beginner don't skip  the concept of Object-Oriented Programming and basic regular expressions. 

Python Libraries for data science:

  • NumPy: For vectorization of linear algebra and mathematical functions
  • Pandas : For data manipulation 
  • Seaborn and Matplotlib : For data visualization 
  • SciPy: For statistical analysis 
  • Scikit Learn : Machine learning library 

Learning Resources:

  1. Free Intro to Python Course | Udacity
  2. Python for Everybody Specialization | Coursera
  3. Corey Schafer - YouTube
  4. "Python for Data Analysis" by Wes McKinney-Book 

2. R for Data Science 

Additionally you can also learn R programming language.  

Key R basics to study include vectors, ordered collections of homogeneous elements; lists, ordered and heterogeneous; data frames, two-dimensional tables storing data; matrices, homogeneous two-dimensional arrays; arrays, multi-dimensional structures with homogeneous elements; and functions, sets of instructions performing specific tasks. Learning about  these concepts is essential for doing well in R programming.

Learning Resources:
  1. R programming complete certification training|Udemy
  2. Introduction to R Programming Course by DataCamp
  3. R Programming Tutorial - Learn the Basics of Statistical Computing

3. Database management & SQL

Relational databases enable data scientists to store structured data quickly and efficiently. When gathering and organizing data, SQL often becomes the preferred tool for this task.Learn the  fundamentals of SQL and advanced querying techniques . Important SQL concepts to be learned:

  • Introduction to SQL and Basic Querying
  • Advanced Querying (Joins, Subqueries)
  • Aggregation Functions and Grouping Data
  • Modifying Databases with Insert, Update, and Delete Statements
Learning Resources : 

Step 3 : Learn Mathematics and Statistics 

Once you know Python, it's important to learn basic statistics and math. This will help you understand the data science algorithms and assist with data analysis. 

1. Math concepts:

Key math concepts include linear algebra for vectors and matrices, calculus for rates of change, multivariable calculus for multiple variables crucial in machine learning, matrix and analytic geometry for data transformations and understanding relationships, and regression/classification for predicting outcomes and labeling in data science modeling.

2. Statistical and Probability  Concepts: 

Important  statistical and probability concepts include inferential statistics (hypothesis testing, confidence intervals), descriptive statistics (central tendency, variability), and basic probability (conditional probability, random variables), forming essential tools for data analysis and decision-making. 

Resources to learn these concepts: 

Step 4: Learn Data Collection , Data Wrangling and Data Exploration 

1. Data collection :

This means gathering important information for analysis from different places like databases, web scraping, and APIs. Python's Pandas library helps collect data from these sources in various ways.

Learning Resources: 

2. Data Wrangling:

After gathering data, the next step is Data Wrangling. This involves getting the data ready and transforming it to make analysis easier. It includes tasks like cleaning, preparing, and feature engineering. Using Pandas and NumPy libraries provides the methods and functions necessary for Data Wrangling and manipulation.

Learning Resources:

2. Exploratory Data Analysis:

This involves  finding  patterns and outliers in data with the help of statistical analysis. Data scientists must be good at using libraries like Matplotlib and Seaborn to visualize and explore data.

Learning Resources:

Step 5: Understanding Business Acumen,Data Visualization, and Mastering Storytelling

1. Data Visualization : You should know how to make graphs and charts with the help of python libraries like Matplotlib and Seaborn can help with this.

2. Storytelling: Data scientists should be able to communicate their results and insights to non-technical stakeholders effectively. It is important to learn business intelligence tools such as Tableau and Power BI to make dashboards to tell your story. 

3. Business acumen: As you explore and analyze data, make sure to ask the right questions. This helps businesses reach their goals.

Learning Resources 
  1. Data Visualization with Python Course (IBM) | Coursera
  2. Exploratory Data Analysis in Python Course | DataCamp
  3. Power BI Tutorial - GeeksforGeeks
  4. Python Plotting With Matplotlib (Guide) – Real Python
  5. "Storytelling with Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic

Step 6: Learn Machine Learning Fundamentals and AI

Machine learning is very important in data science. Knowing how machine learning works is really important for making predictions and building models with data. Practice machine learning using pythons scikit learn library. Basic machine learning concepts to be learned include supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and advanced  techniques like feature selection, and model evaluation (confusion matrix, accuracy, F1 score).

Learning resources
  1. Machine Learning Specialization [3 courses] (Stanford) | Coursera
  2. Channel Intro - Applied Machine Learning
  3. Machine Learning - YouTube
  4. Machine Learning Crash Course | Google Developers
  5. AWS documentation
  6. GCP documentation
  7. Youtube: Krish Naik

Step 7 : Learn Deep learning 

It's a sub part of Machine Learning that uses Neural Networks to model data. Neural Networks are basically math models imitating the human brain. Deep Learning helps Data Scientists handle complex data like images and texts. To be a Data Scientist, it's important to understand algorithms  like Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Autoencoders, etc.You can deep dive into more advanced topics later on such as natural language processing(NLP), Large Language Models(LLM) and Computer Vision. 

Learning Resources:
  1. Deep Learning Crash Course for Beginners
  2. Deep learning specialization by Coursera 
  3. Practical Deep Learning for Coders by
  4. Natural Language Processing Specialization [4 courses] (DeepLearning.AI) | Coursera
  5. Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Step 8: Big Data Tools and Technologies and Model Deployment 

1. Big Data Tools and Technologies 

To excel in the field of data science, it's  important to master handling large datasets. Learn big data tools such as Hadoop, MapReduce, Apache Spark, and NoSQL databases, providing a comprehensive understanding. Additionally, advanced SQL querying techniques  to address intricate problems.

Learning Resources:

2. Model deployment 

The last step is deploying. Whether you're new or have a lot of  years of experience, deploying is important. It shows that you've put in a lot of effort. Learn to use cloud platforms such as Microsoft Azure , Google cloud platform , Django and Flask. 

Learning Resources:
  1. Python Django Tutorial for Beginners
  2. Tutorial: Deploy a model - Azure Machine Learning
  3. ML Model Deployment With Flask On Heroku | How To Deploy Machine Learning Model With Flask | Edureka

Step 9 : Gain practical experience and make projects 

Getting hands-on experience is a crucial part of becoming a data scientist. It allows you to apply what you've learned in school or online courses to real-world situations.

Here are articles that offer data science projects ideas along with websites providing open source datasets:

  1. Top 10 Machine Learning Projects and Ideas For Beginners
  2. Top 100+ Machine Learning Projects for 2023 [with Source Code]
  3. 25 Machine Learning Projects for All Levels | DataCamp
  4. 90+ Data Science Projects You Can Try with Python
  5. 21 Data Science Projects Ideas-Alphaa AI
  6. 10 Data Science Project Ideas for your Resume- Super AI
  7. Kaggle
  8. GitHub

Step 10:  Interview preparation and job applications

Getting a job in data science is what everyone's aiming for, and there are plenty of opportunities due to high demand. However, interviews can be tough, but there are courses and common questions to help you prepare and handle the pressure. 

Interview preparation resources: 
  1. 250+ Data Science Interview Questions- Just Prepare Before The Interview🔥 🔥 🔥 🔥
  2. Data Science Career Guide - Interview Preparation | Udemy
  3. 4 courses for Data Enthusiast Interview Preparation- Alphaa AI
To apply for jobs you can use the following platforms:
  1. Glassdoor
  2. Internshala
  3. Naukri 
  4. Indeed


In summary, becoming a data scientist requires education, experience, and ongoing learning. Start by building a foundation in math, stats, and computer science—pursue a degree in data science or a related field. Gain hands-on experience through internships or projects. For continuous growth, stay updated on industry trends, take online courses, and collaborate with others. With dedication, anyone can make a significant impact as a successful data scientist.

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