Executive Development Programme in Data Analysis with Python: Hands-On Projects

This course is designed for executives and managers who seek to enhance their analytical skills using Python. Participants will gain hands-on experience with data manipulation, analysis, and visualization, enabling them to make data-driven decisions.

Through practical projects, learners will master key Python libraries like Pandas, NumPy, and Matplotlib, and apply statistical methods and machine learning techniques to real-world business problems. By the end, they will be equipped to leverage data insights for strategic advantage.

1. 1. Introduction to Data Analysis with Python: Learners will understand the basics of data analysis using Python, including setting up the development environment and working with Python libraries such as NumPy and Pandas. They will gain foundational skills in data manipulation and cleaning.
2. 2. Data Visualization with Matplotlib and Seaborn: This module will teach learners how to visualize data effectively using Matplotlib and Seaborn, enhancing their ability to communicate insights through visual means. By the end, they will be able to create various types of plots and customize them for different audience needs.
3. 3. Statistical Analysis and Hypothesis Testing: Learners will explore statistical methods and hypothesis testing in Python, including t-tests, chi-square tests, and ANOVA. They will gain the skills to analyze and interpret statistical data, making evidence-based decisions.
4. 4. Machine Learning Basics: This module covers fundamental concepts in machine learning, including supervised and unsupervised learning, regression, and classification. Learners will build simple models using scikit-learn and understand the basics of model evaluation.
5. 5. Advanced Machine Learning Techniques: Building on the basics, this module delves into more advanced techniques such as decision trees, random forests, and ensemble methods. Learners will apply these techniques to real-world datasets and evaluate model performance.
6. 6. Text Analysis and Natural Language Processing: Learners will learn to process and analyze text data using Python libraries such as NLTK and spaCy. They will gain skills in text preprocessing, sentiment analysis, and topic modeling.
7. 7. Time Series Analysis: This module focuses on analyzing time series data, covering techniques such as moving averages, ARIMA models, and seasonal adjustments. Learners will gain the ability to forecast future trends based on historical data.
8. 8. Data Pipeline Development: Learners will develop skills in building and managing end-to-end data pipelines, from data collection to transformation and storage. They will use tools like Apache Airflow for task orchestration and understand best practices for data pipeline management.
9. 9. Data Science Project Management: This module covers the end-to-end process of data science projects, including project planning, stakeholder communication, and documentation. Learners will learn how to manage projects effectively and deliver insights that drive business decisions.
10. 10. Capstone Project: In this final module, learners will work on a comprehensive capstone project that integrates all the skills learned throughout the programme. They will apply data analysis and Python programming to solve a real-world problem, showcasing their expertise to potential employers.