Foundations of Data Science using Python

IT25201: 

Foundations of Data Science Using Python

Course Objective:

• To equip students with a strong foundational understanding of data science concepts.

• To collect, clean, manipulate, and analyse data using Python libraries

• To perform data operations and derive insights from real-world datasets.

Python Language Basics and Data Structures: Python Language Basics - Scalar Types - Control Flow. Data Structures and Sequences: Tuple - List - Built-in Sequence Functions - dict - set- List, Set, and Dict Comprehensions. Functions: Namespaces, Scope, and Local Functions - Returning Multiple Values - Functions Are Objects - Files and the Operating System.

Practical:

1. Programs using Data Frames

2. Programs using functions and files.

Numpy Basics: The NumPy ndarray: A Multidimensional Array Object - Universal Functions: Fast Element-Wise Array Functions - Array-Oriented Programming with Arrays - File Input and Output with Arrays - Linear Algebra - Pseudorandom Number Generation.

Practical:

1.Programs using numpy

2.Programs to solve linear algebra problems with numpy functions

Pandas Basics: Introduction to pandas Data Structures –Loading and Understanding Data- Data aggregation for computing Descriptive Statistics- Data Cleaning and Preprocessing

Practical:

1. Programs using numpy

2. Solving linear algebra problems

Data Loading, Storage, and File Formats: Reading and Writing Data in Text Format - Binary Data Formats - Interacting with Web APIs - Interacting with Databases

Practical:

1. Data and Databases

2. Web APIs

Data Exploration: Data Transformation - String Manipulation. Data Wrangling: Hierarchical Indexing - Combining and Merging Datasets - Reshaping and Pivoting.

Practical:

1. String manipulations

2. Data wrangling

Data Wrangling: Data Aggregation and Group Operations: GroupBy Mechanics - Data Aggregation - Apply: General split-apply-combine - Pivot Tables and Cross-Tabulation - Date and Time Data Types.

Practical:

1. Data aggregation operations

2. Handle time series data

Data Visualization: Introduction to Data Visualization- Visualizing categorical data, visualizing time series data, Visualizing multiple variables -Visualizing Distribution &Relationships -Multivariate and Time Series Visualization exploration

Practical:

1. Visualization of Different kinds of Data

2. Distribution Analysis

Weightage: Continuous Assessment: 40%, End Semester Examinations: 60%

Assessment Methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment: 50%

References:

1. McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, NumPy, and IPython (Modules I–V). O’Reilly Media.

2. Mukhiya, S. K., & Ahmed, U. (2020). Hands-on exploratory data analysis with Python. Packt Publishing.

3. VanderPlas, J. (2017). Python data science handbook: Essential tools for working with data. O’Reilly Media.

4. Cielen, D., Meysman, A. D. B., & Ali, M. (2016). Introducing data science. Manning Publications.

5. Ward, M. O., Grinstein, G., & Keim, D. (2015). Interactive data visualization: Foundations, techniques, and applications. A. K. Peters/CRC Press.