Syllabus (COMPUTER APPLICATION)

Course Type: MAJ-14

Semester: 8

Course Code: BBCAMAJ14C

Course Title: Data Analytics

(L-P-Tu): 3-1-0

Credit: 4

Practical/Theory: Combined

Course Objective: Course Objectives: This course is designed to teach students how to analyze different types of data using Python. Students will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations and predict future trends from data.

Learning Outcome: Course Outcomes: On successful completion of the course, students will be able to: Understand basics of python for performing data analysis Understand the data, performing preprocessing, processing and data visualization to get insights from data. Use different python packages for mathematical, scientific applications and for web data analysis. Develop the model for data analysis and evaluate the model performance.

Syllabus:

Unit I: Theory Credit:3 (L 45)

Python Fundamentals for Data Analysis: Python data structures, Control statements, Functions, Object Oriented programming concepts, Exception handling, Implementation of user-defined Modules and Package, File handling in python. [L 7]

Introduction to Data Understanding and Preprocessing: Knowledge domains of Data Analysis, understanding structured and unstructured data, Data Analysis process, Dataset generation, Importing Dataset: Importing and Exporting Data, Basic Insights from Datasets, Cleaning and Preparing the Data: Identify and Handle Missing Values. [L 8]

Data Processing and Visualization: Data Formatting, Exploratory Data Analysis, Filtering and hierarchical indexing using Pandas. Data Visualization: Basic Visualization Tools, Specialized Visualization Tools, Seaborn Creating and Plotting Maps. [L 8]

Mathematical and Scientific applications for Data Analysis: Numpy and Scipy Package, Understanding and creating N-dimensional arrays, Basic indexing and slicing, Boolean indexing, Fancy indexing, Universal functions, Data processing using arrays, File input and output with arrays. [L 8]

Analyzing Web Data: Data wrangling, Web scrapping, Combing and merging data sets, Reshaping and pivoting, Data transformation, String Manipulation, case study for web scrapping. [L 8]

Model Development and Evaluation: Introduction to machine learning- Supervised and Unsupervised Learning, Model development, Model Visualization, Prediction and Decision Making, Model Evaluation: Over-fitting, Under-fitting and Model Selection. [L 6]

Unit II : Data Analytics Lab Credit: 1 (L 30)

Introduction: Introduction to Data Science, Exploratory Data Analysis and Data Science Process. Motivation for using Python for Data Analysis, Introduction of Python shell iPython and Jupyter Notebook.

Essential Python Libraries: NumPy, pandas, matplotlib, SciPy, scikit-learn, statsmodels.

Getting Started with Pandas: Arrays and vectorized computation, Introduction to pandas Data Structures, Essential Functionality, Summarizing and Computing Descriptive Statistics, Data Loading, Storage and File Formats. Reading and Writing Data in Text Format, Web Scraping, Binary Data Formats, Interacting with Web APIs, Interacting with Databases, Data Cleaning and Preparation, Handling Missing Data, Data Transformation, String Manipulation.

Data Wrangling: Hierarchical Indexing, Combining and Merging Data Sets Reshaping and Pivoting.

Data Visualization matplotlib: Basics of matplotlib, plotting with pandas and seaborn, other python visualization tools.

Data Aggregation and Group operations: Group by Mechanics, Data aggregation, General split-apply-combine, Pivot tables and cross tabulation.

Advanced Pandas: Categorical Data, Advanced Group By Use, Techniques for Method Chaining.

Reading References:

1. David Ascher and Mark Lutz, Learning Python, Publisher O’Reilly Media.
2. Reema Thareja, Python Programming using Problem Solving approach, Oxford University press.
3. Wes Mckinney, Python for Data Analysis, First edition, Publisher O’Reilly Media.
4. Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython. 2nd edition. O’Reilly Media.
5. C. O’Neil, R. Schutt, Doing Data Science: Straight Talk from the Frontline O’Reilly Media.
6. Allen Downey, Jeffrey Elkner ,Chris Meyers: Learning with Python, Dreamtech Press.
7. David Taieb, Data Analysis with Python: A Modern Approach, 1st Edition, Packt Publishing.