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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 (Lite — Functional Level)


Module Goal: By the end of this module, learners can query a real database confidently, pull data into Python, and combine SQL with pandas — enough to work as a junior data analyst without needing a separate SQL course yet.

💡 This module covers SQL at a functional analyst level. If you want to go deeper into advanced SQL, database design, and query optimization — stay tuned for our dedicated SQL for Analysts course.”

Topics:

  1. Why SQL for Data Analysts?
    • ∙ SQL vs Excel vs Python — when to use what
    • ∙ How companies store data (tables, databases, schemas — conceptual only)
    • Setting up SQLite + converting Superstore CSV to a database
  2. Your First Queries — SELECT, WHERE, ORDER BY
    • SELECT specific columns
    • DISTINCT and LIMIT
    • Filtering with WHERE (=, >, <, BETWEEN, LIKE)
    • Sorting with ORDER BY
  3. Aggregations — Summarising Data
    • COUNT, SUM, AVG, MIN, MAX
    • GROUP BY
    • Filtering groups with HAVING
    • Difference between WHERE and HAVING
  4. Combining Tables — JOINs
    • What is a JOIN and why it exists
    • INNER JOIN
    • LEFT JOIN
    • Handling NULLs that appear after a JOIN
  5. Subqueries — Queries Inside Queries (Basic)
    • Subquery in WHERE clause
    • Subquery in FROM clause
    • When to use a subquery vs a JOIN (conceptual — not exhaustive)
  6. SQL Meets Python
    • Loading SQLite into Python with sqlite3
    • Running queries with pd.read_sql()
    • When to query in SQL vs filter in pandas
    • Exporting query results to a DataFrame for further analysis

Lab: 10 Guided Queries on the Superstore Database

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

Want to master SQL fully? Check out our SQL for Analysts course

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

Module 3: Data Cleaning & Wrangling

Module Goal: By the end of this module, learners can take a raw, messy dataset and produce a clean, analysis-ready file with documented decisions — a skill that separates someone who can work with textbook data from someone who can handle data the way it actually arrives in the real world.

  1. Understanding Messy Data
    • What makes data messy and why it matters
    • The real cost of dirty data in analysis
    • Four types of data problems: structural, content, completeness, consistency
    • Your first-look checklist: df.info(), df.describe(), df.head(), df.shape, df.dtypes
    • Building a habit of inspecting before touching
  2. Handling Missing Values
    • Detecting missing data: isnull(), isna().sum(), heatmap visualisation
    • Why data goes missing: three types explained simply (MCAR, MAR, MNAR)
    • Drop strategy: when it is safe and when it loses critical data
    • Fill strategy: mean, median, mode, forward fill, backward fill, constant
    • Flagging missing values as their own category
    • dropna(), fillna(), ffill(), bfill()
  3. Fixing Data Types
    • Why wrong data types silently break calculations
    • Spotting type problems with df.dtypes and df.info()
    • String to datetime: pd.to_datetime() with format handling
    • Object to numeric: pd.to_numeric() with errors=’coerce’
    • Converting to category dtype for memory and performance
    • Extracting date parts: year, month, day, quarter, day of week
  4. Removing Duplicates and Fixing Inconsistencies
    • Detecting duplicates: duplicated(), value_counts()
    • Removing duplicates safely: drop_duplicates() with subset and keep parameters
    • Standardising text: .str.strip(), .str.lower(), .str.upper(), .str.title()
    • Fixing inconsistent category labels: .replace() and .map()
    • Handling whitespace, extra spaces, and encoding issues
  5. Feature Engineering
    • What feature engineering is and why it matters for analysis
    • Creating new columns from existing ones
    • Date-based features: days since order, tenure, month, quarter, day of week
    • Binning numeric columns: pd.cut() for equal width, pd.qcut() for equal frequency
    • Derived metrics: profit margin, revenue per unit, order size
    • Flagging outliers as binary indicator columns
  6. Data Quality Validation
    • Why validation before analysis prevents wrong conclusions
    • Range checks: do numeric values fall within expected bounds
    • Cross-column validation: logical consistency between related columns
    • Referential integrity: do IDs in one table exist in another
    • Writing a structured data quality report in pandas
    • Building a reusable cleaning pipeline using functions

Mini Project — Clean a Messy Dataset End to End

  • Start with a deliberately messy real-world dataset
  • Apply all six topic skills in sequence
  • Document every cleaning decision with inline comments
  • Deliver a clean CSV, a cleaning log, and a GitHub repository

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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