Comprehensive Python Data Science Course Notes

Course Overview

• Duration: 6+ hours
• Focus: Python Data Science
• Content: Theory, demos, real-world applications, 2 detailed projects
• Goal: Provide practical experience for real-world data science
• Target Audience: Aspiring data analysts, data scientists, machine learning, or AI enthusiasts

Course Structure

Part 1: Python Data Analytics

• Introduction to Python Data Analytics: Basics in Python Programming
• Key Topics:
  • Loading data (CSV, txt, Excel, JSON, SQL)
  • Data wrangling
  • Data preprocessing
  • Sorting, filtering, and aggregation
  • Data visualization in Python

Part 2: Data Analytics and A/B Testing Theory

• Theory of Data Analytics and Data-Driven Experimentation
• Key Topics:
  • A/B testing theory, including hypothesis formulation and problem identification
  • Techniques for designing experiments
  • Statistical analysis for A/B testing
  • Making data-driven decisions for online problems

Part 3: End-to-End Portfolio Projects

• Project 1: A/B Testing Online Analytics Project
  • Detailed A/B testing implementation in Python
  • Data analysis from testing results
  • Resume-ready project
• Project 2: Superstore Data Analytics Project
  • Customer segmentation analysis
  • Revenue analysis by customer segment
  • Customer loyalty and sales analysis
  • Case study on Superstore data to implement theory into practice

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Detailed Module Breakdown

Module 1: Data Loading Techniques

• Libraries Used: pandas, numpy
• Key Functions:
  • pd.read_csv()
  • pd.read_table()
  • Load data from Excel, JSON, SQL databases
• Practical Steps:
  • Importing libraries (pandas, numpy, SQLalchemy, etc.)
  • Loading various file formats into Python
  • Understanding and dealing with missing values
  • Handling duplicates in data
  • Data inspection (shape, missing values, descriptive stats)

Module 2: Data Wrangling and Preprocessing

• Techniques:
  • Data sorting, filtering, grouping
  • Data sampling
  • Data type conversions
• Functions: groupby, aggregate, merge
• Practical Steps:
  • Sorting (ascending/descending)
  • Filtering data based on conditions
  • Grouping data to perform aggregate functions
  • Merging datasets using different join strategies (inner, outer, left, right)

Module 3: Data Visualization

• Libraries Used: matplotlib, seaborn
• Visualization Techniques:
  • Plotting line charts, scatter plots, bar graphs, histograms
• Practical Steps:
  • Creating meaningful and appealing visualizations
  • Adding labels, titles, and customizing plots
  • Comparing different data visualizations (line vs bar vs scatter)

Module 4: Descriptive Statistics and Data Analysis

• Key Concepts:
  • Mean, median, mode, variance, std deviation
  • Quantiles and percentiles
• Functions: np.mean(), pd.describe()
• Practical Steps:
  • Calculating fundamental statistics
  • Generating descriptive statistics tables
  • Analyzing different statistics to understand data distribution

Module 5: A/B Testing Theory and Implementation

• Key Concepts:
  • Business hypothesis vs statistical hypothesis
  • Metrics selection (conversion rate, click-through rate)
• Practical Steps:
  • Designing experiments
  • Power analysis, sample size calculation
  • Running A/B tests
  • Conducting statistical tests (z-test, t-test)
  • Analyzing and interpreting results
  • Practical significance vs statistical significance

Module 6: Advanced Analytical Techniques

• Projects:
  • Landing Page Experiment:
    • A/B testing implementation
    • User behavior analysis
  • Superstore Sales Analysis:
    • Customer segmentation
    • Sales performance analysis
    • Geographical sales mapping
    • Visualization of business insights

Conclusion

• Comprehensive skill set in Data Analytics using Python
• Practical applications through real-world projects
• Readiness for real-world data science and analytics roles

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