1. Introduction to Python for Data Analytics
o Python installation and environment setup (Anaconda, Jupyter Notebook)
o Basic Python syntax and data types
• Introduction to Data Analytics:
o Understanding the role of data analytics in decision-making
o Key libraries and tools for data analytics in Python
2. Python Basics for Data Analysis
o Lists, tuples, dictionaries, and sets
o Data manipulation and operations
o Conditional statements (if, elif, else)
o Defining and using functions
o Importing and using modules
3. Data Manipulation with Pandas
• Introduction to Pandas:
o Series and DataFrames: creation, manipulation, and operations
o Indexing, selecting, and filtering data
o Handling missing data (imputation, removal)
o Data transformation (normalization, standardization)
• Data Aggregation and Grouping:
o GroupBy operations and aggregation functions
o Pivot tables and cross-tabulations
• Introduction to Visualization Libraries:
o Overview of Matplotlib, Seaborn, and Plotly
o Creating line plots, bar charts, histograms, scatter plots
o Customizing plots: titles, labels, legends
• Advanced Visualizations:
o Heatmaps, pair plots, violin plots
o Interactive visualizations with Plotly
5. Stats for Data Analytics
6. Statistical Analysis and Hypothesis Testing
• Introduction to Statistics:
o Probability distributions (normal, binomial, Poisson)
o Sampling and estimation
o Formulating and testing hypotheses
o T-tests, chi-square tests, ANOVA
• Correlation and Regression Analysis:
o Pearson and Spearman correlation coefficients
o Linear regression: fitting models and interpreting results
o Working with date and time data
o Time series decomposition (trend, seasonality, residuals)
• Time Series Forecasting:
o Moving averages, exponential smoothing
o ARIMA models and forecasting with statsmodels
8. Machine Learning Basics
• Introduction to Machine Learning:
9. Data Pipelines and Automation
• Data Pipeline Concepts:
o Creating data pipelines using Python
o Automating data processing tasks
o Fetching data from APIs (requests library)
10. Case Studies and Real-world Applications
o Analysing financial data, marketing data, or healthcare data
o A comprehensive project involving data collection, cleaning, analysis, and visualization
