NONO Python for Data Science

AD25201:

Python for Data Science

Course Objectives:

• To impart knowledge on Python programming and how it can be used for solving problems.

• To illustrate how to handle, clean, and analyze data using Python libraries

• To make use of Python tools and open datasets for real-world data science applications.

Basics of Python: What is Python, Python Interpreter, Python language basics: Language Semantics, Data Types, Variables, Basic Functions, Operators, Flow Control Statements, Data Structures and Sequences: List, Tuple, Set, Dictionaries.

Practical:

1. Programs using conditional and looping constructs

2. Programs using different data frames like list, tuple, set and dictionary.

Functions and Files: Defining a Function, Passing Arguments, Return Values, Passing a List, Creating and Using a Class, Strings: Working with Strings, String Methods, Files: Reading from a File, Writing to a File, Exceptions, Python Libraries: Importing libraries.

Practical:

1. Programs using functions and classes.

2. Programs using strings and files

Foundations of Data Science: Introduction to Data Science- Applications of Data Science - Data Science Process: Overview, Defining Research Goals, Retrieving Data - Data Preparation: Data Wrangling- Handling Missing Data- Data Transformation, Outlier/Noise and Anomalies, Exploratory Data Analysis, Build the Model, Present Findings, Data Mining, Data Warehousing.

Practical:

1. Data Creation and Mathematical operations.

2. Graphs and Plotting.

Descriptive Analytics: Facets of Data, Types of Variables, Statistical Description of Data, Describing Data with Tables and Graphs, Describing Data with Averages, Describing Variability, Normal Distributions and Standard (z) Scores, Correlation, Scatter plots, correlation coefficient for quantitative data –computational formula for correlation coefficient, Regression, Regression line, least squares regression line.

Practical:

1. Statistical description of data without libraries

2. Generation of correlation coefficient.

3. Linear regression model.

Numpy and Pandas Libraries: Creating Arrays, attributes, Numpy Arrays objects, Basic operations (Array Join- split- search- sort), Indexing, Slicing and Iterating, Copying Arrays, Arrays shape Manipulation, Identity Array, eye function. Exploring Data using Series- Exploring Data using Data Frames, Index objects- reindex, Drop Entry, Selecting Entries- Data Alignment, Rank and Sort, Summary Statistics, Index Hierarchy.

Practical:

1. Creation of 1D, 2D, and 3D NumPy arrays

2. Array Slicing and Indexing operations

3. Reindexing, and aligning data across multiple Data Frames.

Data Visualization: Introduction to Matplotlib, Plots, making subplots, Controlling axes, Ticks, Labels and legends, Annotations and drawing on subplots, Saving plots to files, Seaborn library, Making sense of data through advanced visualization, Controlling the properties of Chart, Scatter plot, Line plot, Bar plot, Histogram, Box plot, Pair plot, Styling your plot, 3D plot of surface.

Practical:

1. Line plot, bar plot, histogram, and box plot.

2. Seaborn plots, plot styling and customization

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. Grus, J. (2019). Data science from scratch (2nd ed.). O’Reilly Media, Inc.

2. McKinney, W. (2018). Python for data analysis: Data wrangling with pandas, NumPy, and IPython. O’Reilly Media, Inc.

3. VanderPlas, J. T. (2017). Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc.

4. Thereja, R. (2022). Data science and machine learning using Python (1st ed.). Tata McGraw Hill.