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IntermediateDatabase & Data

Data Scientist Roadmap

Master data analysis, machine learning, and statistical modeling

Duration
10-12 months
Job Demand
very high
Phases
6
Views
2135

About This Roadmap

Data Science is one of the most sought-after careers in tech, combining statistics, programming, and domain expertise to extract insights from data. As a data scientist, you will collect, clean, analyze, and visualize data to help organizations make data-driven decisions. This comprehensive roadmap guides you through mastering Python programming, statistical analysis, machine learning algorithms, deep learning, and big data technologies. You will learn to work with popular libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Data scientists are crucial in industries like finance, healthcare, e-commerce, and technology, helping companies optimize operations, predict trends, and build intelligent systems. The role requires strong analytical thinking, programming skills, statistical knowledge, and the ability to communicate complex findings to non-technical stakeholders.

Prerequisites

  • Basic programming knowledge
  • High school mathematics
  • Curiosity about data
  • Problem-solving mindset

What You'll Learn

Master Python for data science
Perform statistical analysis
Build machine learning models
Create data visualizations
Work with big data
Deploy ML models
Communicate insights

Complete Learning Path

6-8 weeks

1
Python Basics

Variables, data types, control flow, functions, OOP

2
NumPy

Arrays, mathematical operations, broadcasting

3
Pandas

DataFrames, data manipulation, cleaning, merging

4
Jupyter Notebooks

Interactive development environment

8-10 weeks

1
Descriptive Statistics

Mean, median, mode, variance, standard deviation

2
Probability

Probability distributions, Bayes theorem, hypothesis testing

3
Inferential Statistics

Confidence intervals, p-values, A/B testing

4
Linear Algebra

Vectors, matrices, eigenvalues for ML

4-6 weeks

1
Matplotlib

Basic plotting, customization, subplots

2
Seaborn

Statistical visualizations, heatmaps

3
Plotly

Interactive visualizations, dashboards

4
Tableau/Power BI

Business intelligence tools

10-12 weeks

1
Supervised Learning

Linear/logistic regression, decision trees, random forests, SVM

2
Unsupervised Learning

K-means, hierarchical clustering, PCA

3
Model Evaluation

Cross-validation, confusion matrix, ROC curves

4
Feature Engineering

Feature selection, scaling, encoding

8-10 weeks

1
Neural Networks

Perceptrons, backpropagation, activation functions

2
TensorFlow/PyTorch

Deep learning frameworks

3
CNNs

Computer vision, image classification

4
NLP

Text processing, sentiment analysis, transformers

6-8 weeks

1
SQL

Database querying, joins, aggregations

2
Spark

Distributed computing, PySpark

3
Model Deployment

Flask/FastAPI, Docker, cloud deployment

4
MLOps

Model versioning, monitoring, CI/CD

Tools & Technologies

PythonPandasNumPyScikit-learnTensorFlowPyTorchMatplotlibSeabornJupyterSQLSparkDocker

Career Opportunities

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • AI Engineer
  • Research Scientist
  • Data Science Manager

Expected Salary (India)

₹5-12 LPA (Entry)
₹12-25 LPA (Mid)
₹25-50 LPA (Senior)
₹50L+ (Lead)

Companies Hiring

GoogleAmazonMicrosoftMetaFlipkartSwiggyZomatoOlaUberNetflixLinkedInAdobe

Ready to Start?

Follow this roadmap step-by-step and track your progress.

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