Data Scientist Roadmap[2024]

This data science career roadmap provides a structured path to master the critical concepts and skills needed for success. Remember, data science is dynamic, so staying current with trends and technologies is key. Gaining real-world experience through projects and internships can boost your skills and credibility as a data scientist. Follow this roadmap, continuously learn, and adapt to advancements for a rewarding data science journey

1) Mathematics

Math skills are very important as they help us understand various machine-learning algorithms that play an important role in Data Science.

• Part 1:
  • Linear Algebra
  • Analytic Geometry
  • Matrix
  • Vector Calculus
  • Optimization
• Part 2:
  • Regression
  • Dimensionality Reduction
  • Density Estimation
  • Classification

2) Probability

Probability is also significant to statistics, and it is considered a prerequisite for mastering machine learning.

• Introduction to Probability
• 1D Random Variable
• The function of One Random Variable
• Joint Probability Distribution
• Discrete Distribution
  • Binomial (Python | R)
  • Bernoulli
  • Geometric etc
• Continuous Distribution
  • Uniform
  • Exponential
  • Gamma
• Normal Distribution (Python | R)

3) Statistics

Understanding Statistics is very significant as this is a part of Data analysis. 

• Introduction to Statistics
• Data Description
• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing (Python | R)
• ANOVA (Python | R)
• Reliability Engineering
• Stochastic Process
• Computer Simulation
• Design of Experiments
• Simple Linear Regression
• Correlation
• Multiple Regression (Python | R)
• Nonparametric Statistics
  • Sign Test
  • The Wilcoxon Signed-Rank Test (R)
  • The Wilcoxon Rank Sum Test
  • The Kruskal-Wallis Test (R)
• Statistical Quality Control
• Basics of Graphs

4) Programming

One needs to have a good grasp of programming concepts such as Data structures and Algorithms. The programming languages used are Python, R, Java, Scala. C++ is also useful in some places where performance is very important.

• Python:
  • Python Basics
    • List
    • Set
    • Tuples
    • Dictionary
    • Function, etc.
  • NumPy
  • Pandas
  • Matplotlib/Seaborn, etc.
• R:
  • R Basics
    • Vector
    • List
    • Data Frame
    • Matrix
    • Array
    • Function, etc.
  • dplyr
  • ggplot2
  • Tidyr
  • Shiny, etc.
• DataBase:
  • SQL
  • MongoDB
• Other:
  • Data Structure
    • Time Complexity
  • Web Scraping (Python | R)
  • Linux
  • Git

5) Machine Learning

ML is one of the most vital parts of data science and the hottest subject of research among researchers so each year new advancements are made in this. One at least needs to understand the basic algorithms of Supervised and Unsupervised Learning. There are multiple libraries available in Python and R for implementing 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

6) Deep Learning

Deep Learning uses TensorFlow and Keras to build and train neural networks for structured data.

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

7) Feature Engineering

In Feature Engineering discover the most effective way to improve your models.

• Baseline Model
• Categorical Encodings
• Feature Generation
• Feature Selection

8) Natural Language Processing

In NLP distinguish yourself by learning to work with text data.

• Text Classification
• Word Vectors

9) Data Visualization Tools

Make great data visualizations. A great way to see the power of coding!

• Excel VBA
• BI (Business Intelligence):
  • Tableau
  • Power BI
  • Qlik View
  • Qlik Sense

10) Deployment

The last part is doing the deployment. Definitely, whether you are fresher or 5+ years of experience, or 10+ years of experience, deployment is necessary. Because deployment will definitely give you a fact is that you worked a lot.  

• Microsoft Azure
• Heroku
• Google Cloud Platform
• Flask
• DJango

11) Other Points to Learn

• Domain Knowledge
• Communication Skill
• Reinforcement Learning
• Different Case Studies:
  • Data Science at Netflix
  • Data Science at Flipkart
  • Project on Credit Card Fraud Detection
  • Project on Movie Recommendation, etc.

12) Keep Practicing

“Practice makes a man perfect” which tells the importance of continuous practice in any subject to learn anything. 

So keep practicing and improving your knowledge day by day. Below is a complete diagrammatical representation of the Data Scientist Roadmap.

Welcome to your comprehensive Data Science Roadmap! If you’ve ever wondered, about “Steps or Path to Become a Data Scientist”, you’re in the right place. This guide is perfect for Data Science for Beginners and seasoned professionals alike, covering everything from mastering Python for Data Science and R for Data Science, to understanding the importance of Data Cleaning and Data Visualization.

We’ll delve into the essential Data Science Tools and how they’re used in real-world applications, including Machine Learning and AI in Data Science. You’ll also learn about the role of Statistics for Data Science and get hands-on with Real-world Data Science Projects. In this rapidly evolving field, Continuous Learning in Data Science is key. So, we’ll keep you updated with the latest Data Science Trends to help you stay ahead in your Data Science Career. Let’s embark on this exciting journey together.

Join our “Complete Machine Learning & Data Science Program“ to master data analysis, machine learning algorithms, and real-world projects. Get expert guidance and kickstart your career in data science today!