Curriculum

Overview: This module is designed to introduce students to Python programming from scratch, with a focus on how Python is used in Data Science. By the end of this module, students will have a solid foundation in Python and be ready to dive into more advanced data science topics in subsequent modules.

• What is Python?
  • A versatile, beginner-friendly programming language used in a wide variety of fields, especially in data science and machine learning.
  • Python’s popularity in data science: Libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.
• Why Python for Data Science?
  • Easy to learn and use: Clear, readable syntax.
  • Extensive libraries and frameworks for data manipulation, analysis, and machine learning.
  • Strong community support.
• Setting up Python Environment
  • Installing Python: Anaconda distribution (recommended for data science).
  • Installing Python on different platforms: Windows, macOS, Linux.
  • Setting up Jupyter Notebooks for interactive coding.
  • PyCharm or VS Code for writing Python scripts.

• Python Syntax and Structure
  • Writing Python code in a script or interactive environment.
  • Understanding indentation and code blocks.
  • Basic commands: Print statements, comments, basic input/output.
• Data Types in Python
  • Numbers: Integers (int), Floating-point numbers (float).
  • Strings: Text data (str).
  • Boolean: True or False (bool).
  • Lists: Ordered collection of elements (list).
  • Tuples: Immutable sequence (tuple).
  • Dictionaries: Key-value pairs (dict).
  • Sets: Unordered collection of unique elements (set).
• Basic Operations
  • Arithmetic operations (+, -, *, /).
  • String manipulation: Concatenation, slicing, formatting.
  • List operations: Indexing, slicing, appending.
  • Dictionary operations: Adding/removing keys, accessing values.

• If-Else Statements
  • Writing conditional logic with if, elif, and else.
  • Understanding comparison operators (==, !=, <, >, etc.).
  • Using logical operators (and, or, not).
• Loops in Python
  • For Loop: Iterating over a sequence (e.g., list, string, range).
  • While Loop: Repeating a block of code as long as a condition is true.
  • Using break and continue to control loop flow.

• Defining Functions
  • The purpose of functions: Reusability, abstraction.
  • Function syntax: def function_name(): Arguments and return values.
  • Calling functions with arguments.
• Built-in Functions
  • Common Python built-in functions: print(), len(), type(), input().
  • Working with list comprehension for efficient loops and conditionals.

• Installing and Importing Libraries
  • Using pip and conda to install libraries.
  • Importing libraries in Python: import pandas as pd, import numpy as np, etc.
• Exploring Essential Data Science Libraries
  • Pandas: For data manipulation and analysis.
  • NumPy: For numerical operations and handling arrays.
  • Matplotlib: For basic data visualization (charts, graphs).
  • Seaborn: For statistical data visualization.

• Lists, Tuples, and Dictionaries
  • Creating, accessing, and manipulating lists, tuples, and dictionaries.
  • Common methods: .append(), .pop(), .remove(), .keys(), .values().
• Using Collections
  • Introduction to collections module: Counter, defaultdict.
  • Working with sets and handling duplicates.
  • Sorting and filtering collections using list comprehensions.

• Reading and Writing Files
  • Opening, reading, and writing files in Python.
  • Handling CSV files with Python’s built-in csv module.
  • Introduction to Pandas: Reading and writing data using pd.read_csv() and df.to_csv().
• Introduction to Pandas DataFrames
  • What is a DataFrame? An overview of how it is used to store tabular data.
  • Loading and inspecting data with Pandas (head(), tail(), info()).
  • Accessing specific rows, columns, and elements in a DataFrame.

• Data Cleaning Basics
  • Handling missing data using Pandas (isnull(), dropna(), fillna()).
  • Dropping duplicates and cleaning strings.
  • Renaming columns and other data transformations.
• Basic Data Visualization
  • Plotting simple graphs with Matplotlib: Line plots, bar charts, histograms.
  • Plotting data with Seaborn: Scatter plots, heatmaps, and pair plots.
  • Visualizing data trends: Time-series, relationships between variables.

• Mini Project: Analyzing a Simple Dataset
  • Students will use what they've learned to analyze a small dataset (e.g., sales data or customer data).
  • Tasks: Load the data, clean it, perform basic analysis, and create visualizations.
  • Presenting the findings: Using Python and Jupyter Notebooks to document the analysis.

1. What is Data Science?
   • Definition, importance, and impact of data science across various industries
   • Introduction to Kaggle and other open data platforms
2. Data Science Workflow
   • Overview of the data science lifecycle: Data collection, cleaning, analysis, and reporting
   • Project example: Introduction to analyzing a cleaned dataset

1. Loading Data from Excel
   • Using Pandas to load Excel files: pd.read_excel()
   • Exploring data in Jupyter Notebooks
   • Identifying data types, basic statistics, and summaries
2. Handling Basic Data Operations
   • Filtering and selecting specific data
   • Aggregating data by groups (e.g., finding average sales per month, grouping by categories)

1. Understanding DataFrames
   • Key features and functions of a DataFrame in Pandas
   • DataFrame methods for analyzing data (describe(), info(), head(), tail())
2. Basic Data Manipulation
   • Selecting rows and columns, slicing data
   • Sorting, filtering, and using conditional selections

1. Exploring Data Patterns
   • Generating summary statistics for numerical columns
   • Identifying distribution of categorical columns
   • Using groupby() to analyze grouped data
2. Finding Patterns and Trends
   • Calculating averages, totals, and percentages
   • Creating pivot tables to summarize data

1. Importance of Visualizing Data
   • Why visualization matters in data science
   • Types of charts and when to use them: bar, line, pie, scatter
2. Setting up Matplotlib
   • Introduction to Matplotlib library
   • Basic setup and plot customization: Titles, labels, colors, styles

1. Creating Common Visualizations
   • Bar charts: Showing categorical data
   • Line charts: Trends over time
   • Scatter plots: Examining relationships between variables
   • Histograms: Understanding distribution of numerical data
2. Advanced Visualization Techniques
   • Adding multiple data series to a plot
   • Customizing legends, annotations, and figure sizes

1. Adding Context with Seaborn
   • Introduction to Seaborn’s statistical plots: Count plots, box plots
   • Creating more detailed and aesthetically pleasing visualizations

1. Loading Data in Jupyter Notebook
   • Using Pandas to load and inspect Kaggle Excel data
   • Examining the dataset’s variables, data types, and structure
2. Setting Analysis Goals
   • Defining key questions based on data columns and project objectives
   • Identifying which columns to focus on for analysis (e.g., sales trends, seasonal patterns)

1. Descriptive Analysis
   • Calculating descriptive statistics for relevant columns
   • Identifying key metrics (e.g., highest and lowest sales, average values)
2. Trend Analysis
   • Using groupby() to track changes over time (e.g., monthly or yearly trends)
   • Visualizing trends in sales, profit, or other key metrics with line and bar charts

1. Creating a Simple Dashboard
   • Displaying key metrics and visualizations in a single notebook or PDF report
   • Annotating charts and adding insights to the report for clarity
2. Interpreting Results
   • Summarizing key findings from the data analysis
   • Suggesting potential business insights based on observed patterns (e.g., peak sales seasons, best-selling categories)