Executive Development Programme in Python Data Science Projects: From Zero to Hero

This course is designed for professionals aiming to transition into data science roles or enhance their skills in Python for data analysis. Participants will gain hands-on experience with real-world projects, mastering key Python libraries such as Pandas, NumPy, and Scikit-learn. By the end, they will be proficient in data manipulation, visualization, and predictive modeling, ready to tackle complex data challenges.

Students will also learn to apply statistical methods and machine learning algorithms to solve business problems. Through project-based learning, they will develop a portfolio of projects that showcase their skills to potential employers, positioning them as data science experts capable of driving business value through data insights.

1. 1. Introduction to Python for Data Science: Learners will be introduced to Python programming basics and data science fundamentals, gaining essential skills for data manipulation and analysis. This module covers Python syntax, data structures, and basic data manipulation libraries like Pandas.
2. 2. Data Cleaning and Preparation: Learners will learn how to clean and prepare real-world data for analysis, including handling missing values, removing duplicates, and transforming data into a usable format. Practical skills include using Pandas and NumPy for data cleaning.
3. 3. Data Visualization with Matplotlib and Seaborn: This module teaches learners how to create effective visualizations using Matplotlib and Seaborn. Learners will gain skills in plotting various types of graphs, customizing visualizations, and interpreting data trends.
4. 4. Statistical Thinking and Data Analysis: Learners will explore basic statistical concepts and apply them to data analysis. This includes understanding distributions, measures of central tendency, variability, and performing hypothesis testing using Python libraries like SciPy.
5. 5. Machine Learning Fundamentals: This module introduces learners to machine learning basics, including supervised and unsupervised learning, model evaluation, and cross-validation. Practical skills include building linear regression, classification, and clustering models with Scikit-learn.
6. 6. Predictive Modeling and Regression Techniques: Learners will delve into advanced regression techniques such as multiple linear regression, polynomial regression, and regularized regression methods. Practical skills include model building, validation, and improving model performance.
7. 7. Time Series Analysis and Forecasting: This module covers time series analysis and forecasting techniques. Learners will learn how to handle temporal data, apply decomposition, and forecasting models like ARIMA and Prophet using Python.
8. 8. Text Analytics and Natural Language Processing (NLP): Learners will be introduced to NLP techniques and tools for processing and analyzing text data. Skills include text cleaning, tokenization, sentiment analysis, and building NLP models using libraries like NLTK and spaCy.
9. 9. Big Data Processing with Dask and Apache Spark: This module teaches learners how to handle large datasets using Dask and Apache Spark. Skills include parallel processing, distributed computing, and scaling data science projects to handle big data.
10. 10. Project Management and Portfolio Development: Learners will learn best practices in project management and portfolio development for data science projects. Skills include planning, execution, and presenting data science projects effectively, as well as portfolio building and career advancement strategies.