Course Outcomes: CO1 :: understand and apply Python programming fundamentals CO2 :: utilize NumPy and Pandas for efficient data manipulation, cleaning, and preparation. CO3 :: apply clear and effective data visualizations using Matplotlib and Seaborn to analyze and communicate data insights. CO4 :: execute exploratory data analysis to uncover data insights using Python CO5 :: perform statistical analysis and hypothesis testing using Python CO6 :: associate the role of machine learning in data science Through this course students should be able to
Unit I Introduction to Python for Data Science : Overview of Data Science, Basic Syntax and Data Types, Control Structures (if statements, loops), Functions and Modules.
Unit II Data Manipulation with NumPy and Pandas : Introduction to NumPy: Arrays, Operations, Data Manipulation with Pandas: Series and DataFrames, Data Cleaning and Preparation, Handling Missing Data.
Unit III Data Visualization with Matplotlib and Seaborn : Principles of Data Visualization, Creating Plots with Matplotlib, Advanced Visualization with Seaborn, Customizing Visualizations.
Unit IV Exploratory Data Analysis (EDA) : Understanding EDA and its Importance, Summary Statistics, Correlation and Covariance, Outlier Detection.
Unit V Introduction to Statistical Analysis : Descriptive and Inferential Statistics, Hypothesis Testing: Ztest, t-test, p-test, chi-squared test, variance-inflation factor(VIF), Shapiro- Wilk test, Probability Distributions: Uniform Distribution Normal Distribution Binomial Distribution Poisson Distribution, Introduction to A/B Testing.
Unit VI Exploring the role of machine learning in data science : Introduction to Machine Learning Concepts, Supervised vs. Unsupervised Learning, Understand CRISP-DM framework using Linear Regression model, Introduction to Classification Recent Trends : Generative AI and Its Applications: GPT-4 DALL-E, Synthetic Data Generation.
List of Practical's / Experiments: • Exploring and understanding Basics of Python Language • Exploring and understanding the basic concepts of Data Science and components of Python • Exploring different Control Structures and function in Python • Practical on NumPy Package • Practical to demonstrate working with Data in Python • Practical to demonstrate working with NumPy Arrays • Practical on Pandas Package • Practical on Visualization with MatPlotLib • Practical demonstration on EDA, Summary Statistics • Practical demonstration on Correlation and Covariance, Outlier Detection • Practical demonstration on Outlier Detection