Description
Module 1: Introduction to Data Science
– Data science fundamentals.
– Data analysis vs. data mining vs. machine learning.
– Data science tools and environments.
Module 2: Data Collection and Preprocessing
– Data collection methods.
– Data cleaning and preprocessing.
– Data integration and transformation.
Module 3: Data Analysis and Visualization
– Exploratory data analysis (EDA).
– Data visualization with tools like Matplotlib and Seaborn.
– Communicating insights through visualizations.
Module 4: Machine Learning and Predictive Modeling
– Supervised and unsupervised learning.
– Model selection and evaluation.
– Feature engineering and selection.
Module 5: Big Data and Distributed Computing
– Handling large datasets (e.g., Hadoop, Spark).
– Scalable machine learning.
– Real-time data analysis.
