Executive Development Programme in Hands-On Prior Quantification for Data Science Projects

This course is designed for data science managers and leaders aiming to enhance their strategic decision-making through practical application of quantitative methods. Participants will gain skills in prioritizing projects effectively by leveraging advanced statistical and machine learning techniques, ensuring alignment with organizational goals.

By the end of the program, learners will be able to develop a robust framework for quantifying project impacts, allocate resources more efficiently, and make data-driven decisions that drive business success.

1. 1. Data Science Fundamentals: Learners will study the basic principles of data science, including data types, statistical measures, and data visualization techniques. They will gain foundational skills in using Python for data manipulation and analysis.
2. 2. Exploratory Data Analysis: Students will learn how to conduct exploratory data analysis (EDA) to uncover patterns and insights from data. Practical skills include using Pandas for data wrangling and Matplotlib for data visualization.
3. 3. Machine Learning Basics: This module introduces learners to the core concepts of machine learning, including supervised and unsupervised learning, and model evaluation. Practical skills include building and evaluating basic machine learning models using Scikit-learn.
4. 4. Feature Engineering: Learners will explore techniques for creating and selecting features that are most relevant to their data science projects. Practical skills include feature scaling, encoding categorical variables, and handling missing data.
5. 5. Advanced Machine Learning Models: This module covers more complex machine learning algorithms such as decision trees, random forests, and gradient boosting. Learners will gain skills in building, tuning, and optimizing these models.
6. 6. Time Series Analysis: Students will learn how to analyze and forecast time series data. Practical skills include using ARIMA models and seasonal decomposition techniques.
7. 7. Natural Language Processing (NLP): This module focuses on NLP techniques for processing and analyzing textual data. Practical skills include text preprocessing, tokenization, and sentiment analysis using NLTK and spaCy.
8. 8. Deep Learning Introduction: Learners will be introduced to deep learning concepts and neural networks. Practical skills include building and training simple neural networks using TensorFlow or PyTorch.
9. 9. Project Management for Data Science: This module covers best practices in managing data science projects, including project planning, risk management, and stakeholder communication. Practical skills include creating project plans and managing project timelines.
10. 10. Capstone Project: Learners will apply their skills to a real-world data science project, from data collection and cleaning to model building and evaluation. Practical skills include end-to-end project management and the ability to present findings to stakeholders.