Overview

This comprehensive course guides students through the complete data analytics workflow using Python, combining programming fundamentals with advanced statistical analysis. The curriculum is structured across five interconnected modules that build upon each other, using real-world datasets to provide practical, hands-on experience. Starting with programming fundamentals, you'll learn essential Python concepts while working with real datasets like public library revenue and restaurant safety inspections. The course introduces the Jupyter Notebook environment and transitions students from spreadsheet-based analysis to powerful programmatic approaches. Students master core programming concepts including variables, functions, and control flow structures. This course helps you bridge the gap between theoretical knowledge and practical application, enabling you to become proficient in using Python for comprehensive data analysis, from basic data manipulation to advanced statistical modeling and forecasting.

Syllabus

Programming fundamentals for data analytics

This module is an introduction to Python programming, designed for beginners with no prior coding experience. You will explore the fundamental concepts and practices that underpin programming languages, with a specific focus on their application in data manipulation and analysis.

Data structures and descriptive statistics

This module introduces essential data analysis techniques using Python and the pandas library. You will learn how to import and work with data efficiently, leveraging DataFrames and Series to manipulate, filter, and analyze datasets. The module covers fundamental concepts such as vectorization for performance optimization, distinguishing between attributes and methods, and performing descriptive statistics. Additionally, you will explore data visualization techniques and segmentation methods to extract meaningful insights from structured data.

Visualization

This module focuses on data visualization using Python, covering essential tools and techniques for creating effective visuals. You will learn to generate visualizations directly from pandas DataFrames and Series, as well as use popular libraries like matplotlib and Seaborn to develop custom plots. The module explores various visualization types, from basic line graphs and bar charts to advanced distribution and categorical plots. Additionally, you will learn how to enhance readability through styling, annotations, and design choices to highlight trends, patterns, and anomalies in data.

Inferential Statistics

This module introduces statistical inference and regression modeling using Python. You will learn to construct confidence intervals, perform hypothesis testing with t-tests, and simulate data using NumPy. The module covers both simple and multiple linear regression, guiding you through model development, interpretation of key metrics (such as R-squared, p-values, and coefficients), and prediction of new data points. Additionally, you will explore methods to encode categorical variables, evaluate model performance using error metrics, and refine regression models with the help of Large Language Models (LLMs).

Time series

This module explores working with time series data in Python, focusing on DateTime objects, indexing, and visualization. You will learn to manipulate time-based data, apply descriptive statistics, and segment time series by key date features. The module covers resampling and reshaping techniques, as well as using simple and multiple linear regression to model trends and seasonality. Additionally, you will evaluate forecasting models using appropriate error metrics to assess their performance.