Overview

This course is designed for anyone looking to enter the exciting field of data science. Whether you’re transitioning careers, enhancing your skills, or just starting out, this program will give you everything you need to excel. By the end, you’ll have practical knowledge, hands-on experience, and a portfolio of projects to showcase your expertise.

Course Structure and Weekly Breakdown

Lesson 1: What is Data Science?

Topics Covered:

1. The role of a data scientist.
2. The data science workflow.
3. Tools and skills used in data science.

Data science is a multidisciplinary field that extracts meaningful insights from data using mathematics, programming, and business knowledge. For example, Spotify recommends songs using algorithms trained on user preferences—a clear application of data science.

Real-Life Example:
“Think of data science as solving a puzzle. For Netflix, the puzzle is: Which shows will keep users engaged? They analyze data like viewing history, ratings, and watch time to create recommendations.”

Activity:
Identify one way data science impacts your life (e.g., Google Maps, online shopping). Write a short paragraph describing its importance.

Lesson 2: Setting Up Your Environment

Topics Covered:

1. Installing Python, Jupyter Notebook, and libraries like Pandas, NumPy, and Matplotlib.
2. Using Anaconda for simplified management.

Step-by-Step Instructions:

1. Install Anaconda:
   • Download it from anaconda.com.
   • Follow the installation prompts.
2. Open Jupyter Notebook:
   • Type jupyter notebook in your terminal to start.

Activity:
Write your first Python script:

Lesson 3: Python Basics for Data Science

Topics Covered:

1. Variables, data types, and operations.
2. Conditional statements, loops, and functions.

Teaching Explanation:
Python is the foundation of data science because of its simplicity and vast libraries.

• A variable is like a box where you store information.
• A loop helps you repeat tasks without rewriting code.

Example Code:

Activity:
Create a Python program that calculates the average of a list of numbers.

Data Wrangling and Cleaning

Lesson 4: Introduction to Pandas and NumPy

Topics Covered:

1. Loading data into Python using Pandas.
2. Data manipulation with NumPy.

Teaching Explanation:
Pandas makes it easy to work with structured data, while NumPy handles numerical operations.

Example Code:

Lesson 5: Cleaning and Preprocessing Data

Topics Covered:

1. Handling missing values and duplicates.
2. Encoding categorical variables.

Teaching Explanation:
Data is rarely clean. You’ll need to handle missing or inconsistent information before analysis.

Example Code:

Data Analysis and Visualization

Lesson 6: Exploratory Data Analysis (EDA)

Topics Covered:

1. Identifying patterns in data.
2. Correlations and feature importance.

Teaching Explanation:
EDA helps you understand your dataset and identify what variables are important.

Example Code:

Lesson 7: Visualizing Data

Topics Covered:

• Using Matplotlib and Seaborn
• Creating charts like bar graphs, scatter plots, and heatmaps

Teaching Explanation:

Data visualization is crucial for understanding your data and presenting your findings. Visualizations help you to communicate complex data in an easy-to-understand way. In this lesson, we will focus on two of the most widely used Python libraries for data visualization: Matplotlib and Seaborn.

Matplotlib is a basic library for creating static, animated, and interactive plots. It’s very powerful but can require more lines of code to generate complex visualizations.

Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics. It makes it easier to generate complex visualizations with less code.

Key Visualization Types:

1. Bar Graphs: Bar charts are used to represent categorical data. The height of each bar represents the value of a particular category.
   • Example Use Case: Visualizing the monthly sales of a store, comparing the sales of different products, etc.
2. Scatter Plots: Scatter plots are used to visualize the relationship between two continuous variables. Each point represents a pair of values.
   • Example Use Case: Understanding the relationship between a house’s square footage and its price.
3. Heatmaps: Heatmaps display data in a matrix format, where individual values are represented by colors. This is useful for visualizing correlations between multiple variables.
   • Example Use Case: Analyzing correlations between different features in a dataset.

Example Code for Bar Chart (Matplotlib)

Activity:

• Task: Create a bar chart showing the sales performance of a company across different months.
• Goal: Learn how to use basic plotting techniques to visualize trends over time.

Example Code for Scatter Plot (Seaborn)

Activity:

• Task: Create a scatter plot to visualize the relationship between a house’s square footage and its price.

Example Code for Heatmap (Seaborn)

pythonCopy code:

Lesson 8: Introduction to Machine Learning

Topics Covered:

• Types of Machine Learning: Supervised vs. Unsupervised
• Understanding Regression and Classification Problems

Teaching Explanation:

Machine learning (ML) enables computers to learn from data without explicit programming. There are two main types of machine learning:

1. Supervised Learning: In supervised learning, the algorithm is trained on labeled data (data that has both the input and the output). The goal is for the model to learn the relationship between input and output so it can predict the output for new, unseen data.
   • Example: Predicting house prices based on features such as size, location, and number of rooms.
2. Unsupervised Learning: In unsupervised learning, the algorithm is given data without labels. The goal is for the model to identify patterns or relationships in the data on its own.
   • Example: Clustering customers into groups based on their purchasing behavior.

Key Machine Learning Problems:

• Regression: Predicting continuous values. For example, predicting the price of a house based on its features (size, location, etc.).
• Classification: Predicting categorical values. For example, predicting whether an email is spam or not.

Example Code for Simple Linear Regression (Supervised Learning)

Activity:

• Task: Build a simple linear regression model using the housing prices dataset. Use the model to predict prices based on size and location features.

Lesson 9: Linear Regression

Topics Covered:

• Building Regression Models in Scikit-learn

Teaching Explanation:

Linear regression is one of the simplest and most commonly used algorithms in machine learning. It works by establishing a relationship between a target variable (dependent variable) and one or more features (independent variables). The goal is to fit a line (in 2D) or a hyperplane (in higher dimensions) that minimizes the error in predictions.

Example Code for Multiple Linear Regression (Multiple Features):

Activity:

• Task: Create a multiple linear regression model with a dataset that includes multiple features like size, number of rooms, and location to predict house prices.

Lesson 10: Decision Trees and Random Forests

Topics Covered:

• What are Decision Trees?
• Understanding Random Forests

Teaching Explanation:

Decision Trees work by splitting the data at each node based on a feature that best separates the data, using measures like Gini impurity or entropy. This process continues until the data is split enough for the algorithm to make a prediction at the leaves.

Random Forests are an ensemble method that uses multiple decision trees to make predictions. By averaging the predictions of many decision trees, random forests reduce overfitting and improve the accuracy of predictions.

Example Code for Decision Tree Classification

Activity:

• Task: Train a decision tree model on the Titanic dataset to predict whether passengers survived or not.

Career Preparation and Capstone Project

Building Your Data Science Portfolio

Objective: Showcase your work to potential employers.

Tips:

1. Include 3–5 projects demonstrating data cleaning, analysis, and modeling.
2. Use GitHub to publish your code.
3. Create a personal website or LinkedIn portfolio.

Preparing for Data Science Interviews

Objective: Ace technical and behavioral questions.

Sample Questions:

1. What is the difference between bias and variance?
2. Explain the concept of overfitting.

Activity:
Practice solving problems on platforms like HackerRank or LeetCode.

Capstone Project

Objective: Apply all your knowledge to solve a real-world problem.

Example Capstone Topics:

• Predict housing prices using regression models.
• Build a recommendation system for an e-commerce website.
• Analyze stock market trends and predict future prices.

Deliverables:

1. A Jupyter Notebook with your code and findings.
2. A visual presentation of your results.