BN4111 Data Science Tools – I (Laboratory) Syllabus:

BN4111 Data Science Tools – I (Laboratory) Syllabus – Anna University PG Syllabus Regulation 2021

COURSE DESCRIPTION:

This course introduces students to the fundamentals of business analytics with a focus on using spreadsheets for data analysis. Students will learn how to collect, clean, analyze, and visualize data to make informed business decisions. Topics include data manipulation, descriptive and inferential statistics, predictive modeling, and data visualization techniques.

OBJECTIVE:

➢ To Understand the role of business analytics in decision-making processes.
➢ To Learn how to collect, clean, and manipulate data using spreadsheets.
➢ To Apply descriptive and inferential statistical techniques to analyze data.
➢ To Build predictive models using regression analysis and other techniques.
➢ To Create data visualizations to effectively communicate insights.

UNIT – I DATA MANIPULATION USING SPREAD SHEET

Sorting and filtering data, Formulas and functions for data manipulation, PivotTables and Pivot Charts for data summarization.

UNIT – II DESCRIPTIVE STATISTICS

Descriptive Statistics – Measures of central tendency and variability, Frequency distributions and histograms, Summary statistics in Excel. Inferential Statistics – Analysis of variance (ANOVA). Chi square test – Regression Analysis – Understanding regression analysis, Simple linear regression, Multiple linear regression.

UNIT – III DATA VISUALIZATION

Getting Started with Tableau, Dimensions vs. Measures, Discrete vs Continuous, Application of Discrete and Continuous Fields, Aggregation in Tableau. Working with Metadata, Filters in Tableau, Applying Analytics to the worksheet, Dashboard in Tableau, Modifications to Data Connections, Edit Data Source, Unions, Joins Data blending.

UNIT – IV DATA VISUALIZATION PLATFORMS

Introduction – Working with data – Importing from flat files, excel files, other Sources, Data Source, Loading Data , Views in Desktop, Query Editing, Transform, Clean, Shape, and Model Data Manage Data Relationship, editing a Relationship, Cross Filter Direction, Saving Work file Measures. Data Analysis Expressions – Introduction to Power Query – Introduction to Power View – Power View visualizations – Power View filtering options – Introduction to Power Map – Preparing geospatial data – Publish from data visualizations platforms – Publish Dashboard to Web.

UNIT – V INTRODUCTION TO BUSINESS ANALYTICS AND PYTHON

Overview of business analytics and its applications. Introduction to Python for data analysis. Setting up Python environment (Anaconda, Jupyter Notebooks). Introduction to python variable declaration, Keywords, Indents in Python, Python input/output operations, Python’s Built-in Data types, Conditional Statements & Loop Conditional Statements, Function in python, File Processing. Modules – Concept of modularization, Importance of modules in python, Importing modules, Built in modules ( ex: Numpy)

TOTAL : 60 PERIODS

COURSE OUTCOMES:

➢ Basic understanding of spreadsheets and familiarity with data visualization using PowerBI

REFERENCES:

1. Bharti Motwani, Data Analysis using Phython, 2020, Wiley Publications.
2. Jack A. Hyman, Microsoft Power BI For Dummies, 2023, Wiley Publications
3. David R. Anderson, et al, “An Introduction to Management Sciences: Quantitative approaches to Decision Making”, (13th edition) South-Western College Pub, 2011.
4. William J. Stevenson, Ceyhun Ozgur, “Introduction to Management Science with Spreadsheet”, TataMcGraw Hill, 2009.
5. Hansa Lysander Manohar, “Data Analysis and Business Modelling using Microsoft Excel” PHI,2017.
6. David M. Levine et al, “Statistics for Managers using MS Excel” (6th Edition) Pearson,2010.
7. Minnick, C. WebKit for Dummies. John Wiley & Sons, (2012).