Back to Learning Paths

Data Science & Analytics

Transform data into insights. Master statistical analysis, machine learning, and data visualization to make data-driven decisions and build predictive models.

What You'll Learn

Python & Libraries

NumPy, Pandas, Scikit-learn

Machine Learning

Build predictive models

Statistics

Hypothesis testing and analysis

Data Visualization

Create compelling dashboards

Career Paths

Data ScientistData AnalystML EngineerBusiness AnalystData EngineerAnalytics Manager

Curriculum

Module 1: Python for Data Science

  • •Python fundamentals and syntax
  • •NumPy for numerical computing
  • •Pandas for data manipulation
  • •Data cleaning and preprocessing
  • •Working with CSV, JSON, and Excel files
  • •DateTime handling and time series
  • •Regular expressions for text processing
  • •Jupyter Notebooks and JupyterLab

Module 2: Statistics & Probability

  • •Descriptive statistics (mean, median, mode)
  • •Probability distributions
  • •Hypothesis testing and p-values
  • •Confidence intervals
  • •Correlation and causation
  • •A/B testing and experimentation
  • •Bayesian statistics
  • •Statistical significance

Module 3: Data Visualization

  • •Matplotlib fundamentals
  • •Seaborn for statistical plots
  • •Plotly for interactive visualizations
  • •Dashboard creation with Streamlit
  • •Tableau and Power BI basics
  • •Chart types and when to use them
  • •Color theory and accessibility
  • •Storytelling with data

Module 4: Machine Learning Fundamentals

  • •Supervised vs unsupervised learning
  • •Linear and logistic regression
  • •Decision trees and random forests
  • •K-means clustering
  • •Support Vector Machines (SVM)
  • •Model evaluation metrics
  • •Cross-validation techniques
  • •Scikit-learn library

Module 5: Advanced Machine Learning

  • •Gradient boosting (XGBoost, LightGBM)
  • •Neural networks basics
  • •Feature engineering
  • •Dimensionality reduction (PCA, t-SNE)
  • •Ensemble methods
  • •Hyperparameter tuning
  • •Handling imbalanced datasets
  • •Model deployment basics

Module 6: SQL & Databases

  • •SQL fundamentals (SELECT, JOIN, WHERE)
  • •Aggregations and GROUP BY
  • •Window functions
  • •Subqueries and CTEs
  • •Database design and normalization
  • •PostgreSQL and MySQL
  • •NoSQL databases (MongoDB)
  • •Query optimization

Module 7: Big Data & Tools

  • •Apache Spark fundamentals
  • •PySpark for big data processing
  • •Hadoop ecosystem overview
  • •Data warehousing concepts
  • •ETL pipelines
  • •Apache Airflow for orchestration
  • •Cloud data platforms (AWS, GCP, Azure)
  • •Real-time data processing

Ready to Become a Data Scientist?

Join thousands of professionals learning data science and analytics. Build the skills to extract insights from data and drive business decisions.

Get Started