Course Curriculum

Module 1: Introduction to Data Science

• What is Data Science?
• Applications of Data Science in industries
• Data Science workflow and process

Module 2: Python for Data Science

• Python basics (variables, loops, functions)
• Data structures: lists, tuples, dictionaries, sets
• Libraries: NumPy, Pandas

Module 3: Data Collection & Cleaning

• Data collection methods and sources
• Handling missing data
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)

Module 4: Data Visualization

• Introduction to data visualization
• Plotting with Matplotlib and Seaborn
• Charts: line, bar, histogram, scatter, boxplots
• Creating dashboards and summary reports

Module 5: Statistics & Probability

• Descriptive statistics: mean, median, mode, variance, standard deviation
• Probability basics and distributions
• Correlation and covariance

Module 6: Introduction to Machine Learning

• What is machine learning?
• Supervised vs unsupervised learning
• Building a simple regression and classification model
• Evaluating model performance

Module 7: Final Project

• Analyze a real-world dataset
• Perform data cleaning, visualization, and basic modeling
• Present insights in a clear and actionable way

Skills Covered

• Python programming for data analysis
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Data visualization (Matplotlib, Seaborn)
• Basic statistics and probability
• Introduction to machine learning
• Data storytelling and reporting