Here’s a list focused on Data Analysis with Python, combining programming concepts with data analysis techniques:
1. Introduction to Data Analysis
- What is Data Analysis?
- Importance of Data Analysis
- Overview of Data Analysis Process
- Types of Data (Structured, Unstructured, Semi-structured)
- Role of Python in Data Analysis
2. Setting Up the Environment
- Installing Python and Anaconda
- Introduction to Jupyter Notebooks
- Python IDEs for Data Analysis (e.g., Spyder, VS Code)
- Working with Virtual Environments
3. Python Basics for Data Analysis
- Python Syntax and Basics
- Variables and Data Types
- Control Structures (Loops, Conditionals)
- Functions and Modules
- Importing and Exporting Data (CSV, Excel, JSON)
4. Introduction to Pandas
- Introduction to the Pandas Library
- DataFrames and Series
- Reading and Writing Data with Pandas
- Indexing and Selecting Data
- Data Manipulation (Adding/Removing Columns, Filtering, Sorting)
- Handling Missing Data
5. Data Cleaning and Preprocessing
- Importance of Data Cleaning
- Handling Missing Values
- Data Transformation and Normalization
- Removing Duplicates
- Handling Outliers
- Working with Dates and Times
- Data Type Conversion
6. Exploratory Data Analysis (EDA)
- Understanding EDA
- Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Data Visualization for EDA
- Histograms, Bar Charts, and Box Plots
- Scatter Plots and Pair Plots
- Correlation Matrices
- Identifying Patterns and Trends
- Feature Engineering and Selection
7. Data Visualization
- Introduction to Data Visualization
- Using Matplotlib for Basic Visualizations
- Advanced Visualizations with Seaborn
- Creating Interactive Plots with Plotly
- Customizing Plots (Titles, Labels, Colors, Themes)
- Visualization Best Practices
8. Working with Large Datasets
- Techniques for Handling Large Data
- Working with SQL Databases in Python
- Dask for Parallel Computing
- Optimizing Pandas Performance
- Memory Management
9. Statistical Analysis
- Introduction to Statistics for Data Analysis
- Probability Distributions
- Hypothesis Testing
- ANOVA (Analysis of Variance)
- Chi-Square Tests
- Correlation and Causation
- Time Series Analysis
10. Introduction to Machine Learning for Data Analysis
- Understanding Machine Learning Basics
- Supervised vs. Unsupervised Learning
- Implementing Basic Models in Python (Linear Regression, KNN, Decision Trees)
- Evaluating Model Performance (Accuracy, Precision, Recall, F1-Score)
- Feature Scaling and Encoding
- Cross-Validation Techniques
11. Data Analysis Projects
- Beginner-Level Projects
- Sales Data Analysis
- Exploratory Analysis on Titanic Dataset
- Intermediate-Level Projects
- Customer Segmentation Analysis
- Predictive Modeling on Housing Prices
- Advanced-Level Projects
- Time Series Forecasting
- Sentiment Analysis on Social Media Data
- Case Studies and Real-World Applications
12. Data Ethics and Privacy
- Understanding Data Ethics
- Data Privacy Concerns
- Anonymization and De-identification Techniques
- Ethical Considerations in Data Analysis
- Bias and Fairness in Data Analysis
13. Automation and Reporting
- Automating Data Analysis Tasks with Python
- Generating Automated Reports with Pandas and Jupyter Notebooks
- Using Python for Dashboarding (Plotly Dash, Bokeh)
- Integrating Data Analysis with Business Intelligence Tools
14. Final Capstone Project
- Defining the Project Scope
- Data Collection and Preparation
- Conducting Comprehensive Data Analysis
- Presenting Findings (Reports, Visualizations, Dashboards)
- Reflecting on Insights and Business Impact
This chart covers everything from the basics of data analysis with Python to more advanced topics like machine learning and large-scale data handling. It can serve as a roadmap for building a comprehensive course or self-study guide.
