Data Science Roadmap: A Comprehensive Guide

Last updated on August 5th, 2024 at 10:34 am

Data science, in recent years, has become one of the most popular fields of study in the globe. With the exponential growth of data, the demand for data scientists is expanding across industries. As per the report by the US Bureau of Labor Statistics, data scientist jobs are projected to have 36 per cent growth between the years 2021 and 2031. Therefore, aspiring IT professionals, who want a reliable career, should consider data science as their main area of study. However, it could be challenging to learn a new field. Hence, creating and applying a solid roadmap can help mitigate this hassle. So, let’s start with our data science roadmap.

What is Data Science?

Data science is a multidisciplinary field of study that uses scientific methods, processes, systems and algorithms to extract insights and knowledge from structured and unstructured data. It incorporates several disciplines, such as statistics, data analysis, machine learning and visualisation to discover hidden patterns, trends and correlations in data. Data science plays a vital role in decision-making, strategic planning and problem-solving across companies, driving a revolution and aiding organisations in making data-centric decisions.

Data Science Roadmap

This data science roadmap provides an organised path to learning the important concepts and skills required for success in the field of data science. So, let’s dive into this!

Mathematics: Math skills are required to understand several machine-learning algorithms that are crucial in data science. These include arithmetic, algebra and geometry. Additionally, learning mathematical notation and symbols, commonly used in data science, is important. So, learn the following mathematical concepts to start your data science journey.

Linear Algebra
Matrix
Calculus
Optimisation
Probability Theory

Statistics: It is essential to understand statistics as this is a part of data analysis and helps you collect, analyse, interpret and present data. It is a key element of data science as it enables us to draw significant insights from data and make well-informed decisions. So, the following are a few concepts that you must learn:
Basics of Statistics
Hypotheses Testing
Sampling Distribution
Regression Analysis
Correlation
Computer Simulation
Basics of Graphs

Programming Skills: Programming skills are crucial for data scientists to analyse, employ and visualise data. So, developing programming skills with an emphasis on data science is important. Also, learning programming languages, such as Python, r, Java, Scale and C+, is useful for better performance.

Python:

Basics of Python
Numpy
Pandas
Matplotlib/Seaborn, etc.

R Basics
dplyr
ggplot2
Tidyr
Shiny, etc.

DataBase:

SQL
MongoDB

Other:

Data Structure
Web Scraping (Python | R)
Linux
Git

Machine Learning (ML): Machine learning is among the most crucial parts of data science. So, it is important for data scientists to understand the basic algorithms of Supervised and Unsupervised Learning. Various libraries are available in Python and R for applying these algorithms.

Introduction:

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forests (Python | R)
scikit-learn

Intermediate:

Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation (R)
XGBoost (Python | R)
Data Leakage

Deep Learning: TensorFlow and Keras are used in deep learning to develop and train neural networks for structured data.

TensorFlow
Keras
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

Natural Language Processing (NLP): Natural Language Processing (NLP) is a type of machine learning technology that allows computers to understand and operate human language. In NLP, you need to learn to work with text data.

Text Classification
Word Vectors

Feature Engineering: In Feature Engineering, you need to learn techniques to discover the most effective way to improve your models.

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

Data Visualization Tools: Learn to create great data visualisations. It is an excellent way to see the power of coding.

Excel VBA
BI (Business Intelligence):

Tableau
Power BI
Qlik View
Qlik Sense

Deployment: Whether you are fresher or have over 5 years of experience or 10 years of experience, deployment is an important element for data science. Because it will definitely provide you with the fact that you worked so much.

Microsoft Azure
Heroku
Google Cloud Platform
Flask
DJango

Other Points to Learn: There are some other points that you must learn as a part of your data science journey. They include:

Domain Knowledge
Communication Skill
Reinforcement Learning
Different Case Studies

How to Become a Data Scientist?

To become a successful data scientist, you need to follow the following steps:

Get a bachelor’s degree in the field of data science

Learn programming skills required
Enhance your related skills
Get a data science certification
Do internships as they are a great way to learn practical skills the job demands
Master in data science tools
Start your career in data science.

Data Scientist Salary in India

The average salary for a data scientist is Rs. 7,08,012 annually. Freshers can start their careers with a salary of around Rs. 5,77,893, while experienced professionals can expect about Rs. 19,44,566.

Conclusion

The demand for data scientists is growing, offering impressive salaries and great work opportunities. The data science roadmap includes important areas, including mathematics, programming skills, machine learning, deep learning, natural language processing, data visualisation tools and deployment.

Want to transform your career in data science? Then, enrol in a data science course - Postgraduate Program in Data Science and Analytics offered by Imarticus Learning. This program is suitable for graduates and IT professionals who want to enhance a successful data science and analytics career.