UNIQUE SERIES AKTU Syllabus
BDS301

Introduction to Data Science

L-T-P2-1-0 Credits3 Session2026-27 TypeTheory
Appears in 1 semester

B.Tech Data Science · Sem 3 (Core)

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Syllabus

5 units · 40 lecture hours
Unit1

Unit 1: Introduction to Data Science

8 hrs

Introduction to Data Science Definition, scope, and evolution of Data Science, Data Science Life Cycle, Data Analytics and Business Intelligence. Real-world applications, Overview of Big Data and its challenges. Structured vs. Unstructured Data, Data types, Overview of datasets, Data Collection, Preprocessing, Analysis, Visualization, and Model Building.

Unit2

Unit 2: Exploratory Data Analysis

8 hrs

Measures of Central Tendency, Measures of Dispersion. Scatter diagram, covariance, Correlation, Statistical Analysis, Hypothesis testing: t -Test, chi- square Test, F-Test, Z-Test, ANOVA

Unit3

Unit 3: Programming in Data Science

8 hrs

Introduction to Python for Data Science Basic Syntax, Data Types, and Operators. Control Structure, Functions and Modules, Working with Data Science Libraries NumPy, Pandas, Scikit-Learn. Project Development Process, Problem Identification and Data Collection, Data Cleaning and Exploration, Model Selection and Implementation, Result Interpretation and Presentation.

Unit4

Unit 4

8 hrs

Data Wrangling, Prepossessing & Visualization Data Cleaning & Preprocessing Handling missing values, removing duplicates, and outliers, Data transformation, Data Manipulation in Python, Working with NumPy and Pandas, Filtering, Sorting, Grouping, and Aggregation. Data Visualization Techniques Basics of Matplotlib and Seaborn, Types of Graphs: Bar Charts, Histograms, Box Plots, Scatter Plots, Line Graphs, Heatmaps, and Pair Plots.

Unit5

Unit 5

8 hrs

Data Science Tools, Ethics and Applications Tools for Data Science: Introduction, Basics of Tableu, Basics of Power BI, Basics of R-Programming, Applications Ethics in Data Science, AI Agents, types and applications.

Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Explain the fundamental concepts of Data Science, its life cycle, data types, and applications in solving real-world problems.K2
  2. CO2Apply statistical methods and exploratory data analysis techniques to summarize, analyze, and interpret datasets for informed decision-making.K3
  3. CO3Develop Python programs using data science libraries to perform data analysis and implement the data science project development process.K6
  4. CO4Analyze and preprocess datasets using data wrangling techniques and create effective visualizations for data interpretation.K6
  5. CO5Evaluate the use of modern data science tools, ethical principles, AI agents, and their applications in real-world scenarios.K5

Suggested Readings

  1. "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython" by Wes McKinney (O'Reilly Media)
  2. Data Science from Scratch: First Principles with Python" by Joel Grus (O'Reilly Media).
  3. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The Frontline. O’Reilly. 2014.
  4. W. H. Inmon, "Building the Data Warehouse", 3rd edition.
  5. Anahory and Murray. Data warehousing in the real world, Pearson Education/Addison Wesley.
  6. Margaret Dunham, Data Mining: Introductory and Advanced Topics, Published by Prentice Hall.
  7. Jiawei Han, Micheline Kamber, "Data Mining: Concepts and Techniques",
  8. Morgan Kaufmann Publishers, 2002. (www.cs.sfu.ca/~han/DMbook.html). Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, & OLAP", Tata Mcgraw- Hill, 2004.

Official AKTU syllabus

This subject is on page 8, 9 of the original AKTU PDF.

Official AKTU syllabus PDF ↗