CoursesData Science with Python — From Data to Decisions
Self-Paced Course

Data Science with Python — From Data to Decisions

Master data analysis, visualization, and machine learning with Python. From Pandas to scikit-learn, build real projects that solve business problems.

38 hours
27 lessons · 5 modules

About this course

Data is the new oil, but only if you know how to refine it. This course teaches you the complete data science workflow using Python — the industry standard. You'll work with real-world datasets throughout: • Analyze e-commerce sales data to find trends and predict revenue • Build a customer churn prediction model for a telecom company • Create interactive dashboards that tell compelling data stories What you'll learn: • Python for data manipulation (Pandas, NumPy) • Data visualization (Matplotlib, Seaborn, Plotly) • Statistics & probability for data science • Machine learning fundamentals (scikit-learn) • Feature engineering & model evaluation • Presenting findings to non-technical stakeholders Who this is for: • Beginners who know basic Python (or have taken our Python Fundamentals course) • Business analysts wanting to add technical skills • Anyone curious about AI/ML who wants a practical foundation • Career switchers targeting data analyst or data scientist roles What you get: • 38 hours of content across 6 modules • 12 real-world datasets to practice with • 5 portfolio-ready projects • Verified certificate on completion

Curriculum

  • Course Overview & What Data Scientists Actually DoFREE
    15 min
  • Setting Up Jupyter Notebook & AnacondaFREE
    20 min
  • Python Refresher — Lists, Dicts, Functions, Comprehensions
    30 min
  • Reference: Python Data Science Toolkit
    10 min
  • DataFrames — Loading, Exploring & Selecting Data
    35 min
  • Cleaning Messy Data — Missing Values, Duplicates, Types
    40 min
  • Merging, Joining & Concatenating DataFrames
    30 min
  • GroupBy, Pivot Tables & Aggregations
    35 min
  • NumPy Essentials — Arrays, Broadcasting & Vectorization
    28 min
  • Assignment: E-Commerce Data Cleaning Challenge
    120 min
  • Quiz: Pandas & Data Wrangling
    12 min
  • Visualization Principles — Choosing the Right Chart
    20 min
  • Matplotlib — The Foundation of Python Plotting
    35 min
  • Seaborn — Beautiful Statistical Charts
    30 min
  • Plotly — Interactive & Web-Ready Visualizations
    28 min
  • Assignment: Sales Dashboard Visualization
    150 min
  • Descriptive Statistics — Mean, Median, Mode, Variance
    25 min
  • Probability Distributions — Normal, Binomial, Poisson
    35 min
  • Hypothesis Testing — t-tests, Chi-Square, p-values
    40 min
  • Correlation vs Causation — A Critical Skill
    20 min
  • Quiz: Statistics Fundamentals
    12 min
  • What is Machine Learning? Supervised vs UnsupervisedFREE
    22 min
  • Linear Regression — Predicting Continuous Values
    35 min
  • Classification — Logistic Regression, Decision Trees, Random Forest
    45 min
  • Feature Engineering & Model Selection
    35 min
  • Model Evaluation — Accuracy, Precision, Recall, F1, ROC
    30 min
  • Assignment: Customer Churn Prediction Model
    300 min
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  • Full course access
  • Certificate on completion
  • Downloadable resources
  • Community support