25BBWHS532: Data Analytics Using Python

This course will introduce fundamental concepts of Data Analytics Using Python.

Class Schedule

  • Batch: 24W11
    Day & Time: Monday: 12:00 – 13:00 (ECL-7) | Tuesday: 10:00 – 11:00 (ECL-7) | Thursday: 12:00 – 13:00 (ECL-7)

Tutorial Schedule

  • Batch: 24W11
    Day & Time: Wednesday: 10:00 – 11:00 | (ECL-7)

Instructors

  • Mr. Sandeep Kumar Patel (SKP) (Course Coordinator)

Syllabus

This is a 3-1-0 (L–T–P)

  • Unit 1: Introduction to Data Analytics
    Definition and scope of data analytics; Importance of data in decision-making; Types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. Business analytics and business value: A comparative case study. (4 Hours)
  • Unit 2: Fundamentals of Python Programming
    Setting up the Python environment; Python basics: syntax, data types, control structures, Lists, Tuples, Dictionaries, and Functions. (8 Hours)
  • Unit 3: Introduction to NumPy and Data Manipulation using Pandas
    Understanding NumPy arrays and operations; Array indexing and slicing. Introduction to Pandas: DataFrames and Series; Importing data from CSV, Excel, and other formats; Basic data exploration and inspection; Data cleaning techniques: handling missing values and duplicates; Data type conversions; Filtering and sorting data. (8 Hours)
  • Unit 4: Data Exploration and Visualization
    Data visualization using Matplotlib and Seaborn; Creating basic plots: line, bar, scatter, and histogram; Customizing visualizations: titles, labels, and colors; Advanced visualizations with Seaborn: box plots and pair plots. (6 Hours)
  • Unit 5: Predictive Analytics and Machine Learning Basics
    Overview of predictive analytics and its role; Time series data: time series visualization and decomposition; Basic forecasting methods: moving averages and ARIMA. Introduction to regression analysis: Building a linear regression model with Scikit-learn and evaluating regression models. Introduction to classification problems: Logistic regression and decision trees; Evaluating classification models using accuracy and confusion matrix. (9 Hours)
  • Unit 6: Applications of Data Analytics in Business and Ethics in Data Analytics
    Case studies in Marketing, Finance, and Operations; The role of data analytics in strategic decision-making. Ethics in Data Analytics: Data privacy and security concerns; Ethical considerations in analytics; Data governance frameworks and practices; Future trends: AI in analytics and big data technologies; Career pathways in data analytics.
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Text Books

  1. Wes McKinney, Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3rd Edition, Shroff/O'Reilly, 2022.

Reference Books

  1. Sandhya Arora and Latesh Malik, Data Science and Analytics using Python, 1st Edition, University Press, 2023.
  2. Bharti Motwani, Data Analytics using Python, 2nd Edition, Wiley, 2020.

Lecture Schedule

Topic Slides
Data Analytics Using python Marking Scheme Preview Download
Unit 1: Introduction to Data Analytics Preview Download