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 DoFREE15 min
- Setting Up Jupyter Notebook & AnacondaFREE20 min
- Python Refresher — Lists, Dicts, Functions, Comprehensions30 min
- Reference: Python Data Science Toolkit10 min
- DataFrames — Loading, Exploring & Selecting Data35 min
- Cleaning Messy Data — Missing Values, Duplicates, Types40 min
- Merging, Joining & Concatenating DataFrames30 min
- GroupBy, Pivot Tables & Aggregations35 min
- NumPy Essentials — Arrays, Broadcasting & Vectorization28 min
- Assignment: E-Commerce Data Cleaning Challenge120 min
- Quiz: Pandas & Data Wrangling12 min
- Visualization Principles — Choosing the Right Chart20 min
- Matplotlib — The Foundation of Python Plotting35 min
- Seaborn — Beautiful Statistical Charts30 min
- Plotly — Interactive & Web-Ready Visualizations28 min
- Assignment: Sales Dashboard Visualization150 min
- Descriptive Statistics — Mean, Median, Mode, Variance25 min
- Probability Distributions — Normal, Binomial, Poisson35 min
- Hypothesis Testing — t-tests, Chi-Square, p-values40 min
- Correlation vs Causation — A Critical Skill20 min
- Quiz: Statistics Fundamentals12 min
- What is Machine Learning? Supervised vs UnsupervisedFREE22 min
- Linear Regression — Predicting Continuous Values35 min
- Classification — Logistic Regression, Decision Trees, Random Forest45 min
- Feature Engineering & Model Selection35 min
- Model Evaluation — Accuracy, Precision, Recall, F1, ROC30 min
- Assignment: Customer Churn Prediction Model300 min
PKR 15,000PKR 25,000
Save PKR 10,000Lifetime access — learn at your own pace
You'll create an account during enrollment
- Full course access
- Certificate on completion
- Downloadable resources
- Community support