Executive Development Programme in Data Science with Python: Hands-On Machine Learning

This course is designed for mid-to-senior level executives and managers seeking to leverage data science to drive strategic decisions. Participants will gain practical skills in Python programming and machine learning, enabling them to interpret data and build predictive models.

You will walk away with the ability to implement basic to intermediate machine learning techniques, collaborate effectively with data scientists, and make informed decisions based on data-driven insights.

1. 1: Introduction to Data Science with Python: Learners will be introduced to the Python programming environment and essential libraries for data science. They will gain foundational skills in using Python for data manipulation and visualization.
2. 2: Data Wrangling and Preparation: This module covers techniques for cleaning and preparing data for analysis. Learners will gain practical skills in handling missing data, transforming variables, and merging datasets.
3. 3: Exploratory Data Analysis (EDA): Learners will study methods for exploring and summarizing data to uncover patterns, trends, and insights. They will gain experience in visualizing data using Python libraries and interpreting results.
4. 4: Statistical Foundations for Data Science: This module provides a solid grounding in statistical concepts and methods used in data science. Learners will learn how to apply statistical tests and measures to data analysis.
5. 5: Machine Learning Fundamentals: Learners will be introduced to the basic principles of machine learning, including supervised and unsupervised learning. They will gain hands-on experience with simple machine learning models.
6. 6: Linear Regression and Evaluation Metrics: This module focuses on linear regression models and their evaluation. Learners will learn how to fit linear models, interpret coefficients, and assess model performance.
7. 7: Classification Techniques: Learners will explore various classification algorithms, including logistic regression, decision trees, and random forests. They will gain practical skills in building and evaluating classification models.
8. 8: Unsupervised Learning and Clustering: This module covers unsupervised learning techniques, including clustering algorithms such as K-means and hierarchical clustering. Learners will gain experience in identifying patterns and structures in data without labeled responses.
9. 9: Time Series Analysis: Learners will study methods for analyzing time series data and forecasting future values. They will gain skills in handling temporal data and applying appropriate models.
10. 10: Advanced Machine Learning Techniques: This module introduces more advanced machine learning topics, including deep learning and neural networks. Learners will gain practical experience in building and training complex models for real-world applications.