Data Science & Analytics Masterclass

Level: Beginner to Intermediate
Duration: 10 Weeks (or Self-paced)
Format: Video Lectures, PDFs, Quizzes, Hands-on Labs, Capstone Project
Tools Used: Python, Excel, Power BI, SQL, Pandas, Jupyter, Scikit-learn, Tableau (optional)

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Course Objective

Equip learners with the practical and theoretical skills to collect, analyze, visualize, and derive insights from data using industry-standard tools and techniques.

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MODULE BREAKDOWN

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Module 1: Introduction to Data Science & Analytics

Topics Covered:

• What is Data Science?

• Difference between Data Science, Data Analytics, Machine Learning & AI

• The Data Science Lifecycle (CRISP-DM)

• Roles: Data Analyst vs Scientist vs Engineer

• Real-world applications in different sectors

Outcome:

Understand the purpose and scope of data science, and how it’s applied in businesses.

Exercise:

• Write a short summary of how data is transforming a field you’re interested in.

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Module 2: Data Collection & Data Types

Topics Covered:

• Structured vs Unstructured Data

• Data Sources (files, APIs, databases, web scraping)

• Introduction to datasets (CSV, Excel, JSON, SQL)

• Web scraping basics with Python (BeautifulSoup)

• Ethical data collection & GDPR basics

Outcome:

Learn how to gather clean and relevant data for analysis.

Exercise:

• Collect a public dataset (e.g., COVID-19, movies, jobs) and describe its features.

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Module 3: Python for Data Science (Core Programming Skills)

Topics Covered:

• Python Basics (variables, lists, loops, functions)

• NumPy: Arrays & numerical computing

• Pandas: Series, DataFrames, filtering, merging

• Matplotlib & Seaborn: Basic data visualization

• Jupyter Notebooks for workflow

Outcome:

Gain hands-on skills using Python to manipulate and explore data.

Exercise:

• Load and explore a dataset in Pandas (e.g., Titanic, Iris)

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Module 4: Data Wrangling & Cleaning

Topics Covered:

• Handling missing values

• Removing duplicates

• Data formatting and type conversion

• Feature scaling and encoding

• Outlier detection

• Dealing with messy or inconsistent data

Outcome:

Clean and prepare real-world data for analysis or modeling.

Exercise:

• Clean a messy CSV dataset and prepare it for analysis

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Module 5: Exploratory Data Analysis (EDA)

Topics Covered:

• Understanding distributions, central tendency, and variability

• Correlation and covariance

• Grouping and aggregation

• Crosstabs and Pivot Tables

• Visualizing patterns and trends using plots

Outcome:

Use EDA techniques to generate insights and hypotheses from data.

Exercise:

• Perform EDA on a customer sales or health dataset using Seaborn

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Module 6: Statistics for Data Science

Topics Covered:

• Descriptive statistics

• Probability basics

• Inferential statistics: Confidence intervals, hypothesis testing

• Normal distribution, z-score, p-value

• Central Limit Theorem

Outcome:

Interpret and use statistical tools to validate findings.

Exercise:

• Conduct a hypothesis test on a sample dataset (e.g., average salary between genders)

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Module 7: Data Visualization & Dashboarding

Topics Covered:

• Principles of data storytelling

• Visual chart types (bar, pie, histograms, line, box)

• Power BI: Connecting data, building dashboards

• Tableau (optional)

• Python Dashboards with Plotly (for intermediate users)

Outcome:

Build and share interactive dashboards for decision-making.

Exercise:

• Create a Power BI dashboard showing sales, trends, and filters

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Module 8: SQL for Data Analysis

Topics Covered:

• Basics of relational databases

• SELECT, WHERE, JOIN, GROUP BY, ORDER BY

• Subqueries and Nested Queries

• SQL Window Functions (Intro)

• Using SQL in Excel and Python (via SQLite or MySQL)

Outcome:

Write SQL queries to extract insights from relational databases.

Exercise:

• Analyze employee data from an SQL table using groupings and joins

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Module 9: Intro to Machine Learning (Optional for Advanced Users)

Topics Covered:

• Supervised vs Unsupervised Learning

• Classification vs Regression

• Scikit-learn pipeline: model fitting and evaluation

• Model metrics: accuracy, confusion matrix, R2-score

• Overfitting & Cross-validation

Outcome:

Apply basic machine learning models to solve real-world problems.

Exercise:

• Build a simple decision tree to predict loan approval

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Module 10: Capstone Project & Portfolio Development

Topics Covered:

• Choose a real dataset and perform full analysis:

  • Data cleaning

  • EDA

  • Visualization

  • Dashboard or Model (optional)

• Writing insights and recommendations

• Portfolio & GitHub tips

Outcome:

Apply everything you’ve learned in a real project and present findings.

Project Ideas:

• E-commerce customer behavior analysis

• COVID-19 data tracker

• Sales & marketing dashboard for a company

• Housing price prediction model

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Final Assessment & Certification

• Final Quiz (Multiple choice + mini coding tasks)

• Capstone Project Presentation

• Certificate of Completion: “Certified Data Analyst & Junior Data Scientist”

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BONUS MATERIALS

• Data Science Cheatsheets (Pandas, NumPy, Seaborn, SQL)

• Sample Datasets for Practice

• Resume & LinkedIn Optimization Tips for Data Jobs

• Guide to Freelance & Remote Data Analytics Work

• Weekly Challenges & Group Discussions (if cohort-based)

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TEACHING METHODOLOGY

• Bite-sized video lessons (10–15 mins)

• Downloadable lecture notes

• Hands-on notebooks (Google Colab & Jupyter)

• Real-world data case studies

• Community support (Discord, Forum, or WhatsApp)

• Live Q&A (optional weekly)

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TARGET AUDIENCE

• Students exploring data careers

• Professionals transitioning into data roles

• Entrepreneurs seeking data-driven decision skills

• Researchers and project leaders