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Data Science

Data Science with Python

Beginner → Intermediate

Learn practical data analysis, visualization, and storytelling using Python — master pandas, NumPy, and Matplotlib through real projects and dashboards.

Data Science with Python: From Beginner to Analyst — Learn, Analyze, and Visualize.
Step-by-step, project-driven course to help you move from data basics to business-ready dashboards and forecasts.

Duration: 8–10 weeks · 

Format: Hands-on notebooks · 

Level: Beginner → Intermediate

Why this course

  • Practical learning path. Build confidence in Python through real-world datasets and structured projects.
  • Python-first stack. Master pandas, NumPy, Matplotlib, Seaborn, Plotly, statsmodels, and basics of scikit-learn.
  • Learn by doing. Each module includes guided notebooks, exercises, and mini-projects.
  • Portfolio-ready capstone. Create a business dashboard and predictive model (sales or churn) to showcase your skills.

Who this course is for

  • Beginners who want to learn data analysis with Python from scratch
  • Students preparing for data analytics and data science roles
  • Junior analysts ready to move from Excel to Python
  • Anyone curious about turning data into insights and stories

Learning Outcomes

By the end of the course, you’ll be able to:

  • Load, clean, and manipulate data using pandas and NumPy.
  • Perform EDA (Exploratory Data Analysis) with meaningful visualizations.
  • Apply statistical methods and simple regression for insight generation.
  • Analyze time series and forecast business metrics.
  • Build interactive dashboards to present and explain findings.
  • Complete a capstone project: predictive model + interactive business dashboard.

Course Modules

Module 0: Data Science in the Real World

Outcome:
Learners understand why each skill matters.

PHASE 1 — Core Technical Foundations (Weeks 1–2)

Module 1: Python for Data Analysis

Lab: Analyze a real business dataset (10k+ rows)

Module 2: SQL for Data Analysts

  • Relational databases
  • SELECT, WHERE, ORDER BY
  • GROUP BY & aggregations
  • JOINs (INNER, LEFT)
  • Subqueries (basic)
  • Connecting SQL to Python (pandas + SQL)

Mini Project:
Analyze a business database using SQL + Python.

PHASE 2 — Data Cleaning & Exploration (Weeks 3–4)

Module 3: Data Cleaning & Wrangling

  • Missing data strategies
  • Data type corrections
  • Duplicate handling
  • Categorical inconsistencies
  • Feature engineering
  • Data quality validation

Mini Project:
Turn a messy dataset into analysis-ready data.

Module 4: Exploratory Data Analysis (EDA)

  • Descriptive statistics
  • Distribution analysis
  • Correlation vs causation
  • Outlier detection
  • Segment-based EDA
  • Transformation techniques
  • EDA checklist framework

Lab:
Customer behavior analysis with insights.

PHASE 3 — Visualization, Communication & Statistics (Weeks 5–6)

Module 5: Data Visualization & Business Storytelling

  • Matplotlib & Seaborn
  • Business chart selection
  • Dashboard design principles
  • KPI definition
  • Executive storytelling
  • Avoiding misleading visuals

Mini Project:
Insight-driven executive dashboard.

Module 6: Statistics for Decision-Making

  • Probability intuition
  • Confidence intervals
  • Hypothesis testing
  • A/B testing
  • Bootstrapping
  • Common statistical mistakes

Lab:
Analyze experimental business data.

PHASE 4 — Predictive Modeling Foundations (Weeks 7–8)

Module 7: Regression Modeling

  • Linear & multiple regression
  • Assumptions & diagnostics
  • Residual analysis
  • Multicollinearity (VIF)
  • Regularization (Ridge, Lasso)

Mini Project:
Predict revenue or demand.

Module 8: Classification Models

  • Logistic regression
  • Decision trees
  • Random forests
  • Feature importance
  • Confusion matrix
  • Precision–recall tradeoffs
  • Imbalanced datasets

Mini Project:
Customer churn or risk prediction.

PHASE 5 — Model Validation & Forecasting (Weeks 9–10)

Module 9: Model Validation & Optimization

  • Train/test vs cross-validation
  • Bias–variance tradeoff
  • Grid vs random search
  • ROC & AUC
  • Model selection frameworks

Module 10: Time Series & Forecasting

  • Trend & seasonality
  • Rolling statistics
  • Stationarity intuition
  • Time-aware splits
  • ARIMA (conceptual)
  • Prophet overview
  • Forecast evaluation (MAPE)

Lab:
Sales or demand forecasting.

PHASE 6 — Capstone & Career Launch (Weeks 11–12)

Module 11: Capstone Project (Major Differentiator)

Choose One:

  • Sales forecasting system
  • Customer churn prediction system

Deliverables:

  • Cleaned dataset + EDA notebook
  • Validated predictive model
  • Interactive dashboard (Streamlit / Plotly)
  • GitHub repository
  • README documentation
  • 2–3 page executive business brief

Course Format & Assessment

  • Guided labs: Weekly coding notebooks.
  • Mini projects: Practical exercises after each module.
  • Peer feedback: Optional code reviews.
  • Final project: Dashboard + predictive model submission.

Prerequisites

  • No prior coding or math background required.
  • Basic computer literacy and willingness to learn by doing.

Pricing & Enrollment Options

  • Self-paced: Lifetime access + community.
  • Cohort-based (optional): Live Q&A and feedback sessions.
  • Certificate: Earn a verified certificate to showcase your achievement.

FAQ

Q: Is this course beginner-friendly?
A: Yes! It starts from Python basics and gradually builds to intermediate projects.

Q: What tools will I learn?
A: pandas, NumPy, Matplotlib, Seaborn, Plotly/Streamlit, statsmodels, and scikit-learn basics.

Q: What’s the final project?
A: A sales or churn prediction dashboard built using real-world data.

Q: How long will it take to complete?
A: Typically 8–10 weeks at 4–6 hours per week.

Ready to start your data journey?
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