SRI VENKATESWARA COLLEGE OF ENGINEERING AND TECHNOLOGY (AUTONOMOUS)
L T P C
2 0 2 4
15CMB08 INTRODUCTION TO BUSINESS ANALYTICS
To develop an understanding of basic concepts of business analytics and its importance in managerial decision making.
To enable the students to make use of
Ms-excel work sheets for business problem solving.
To outline the processes needed to develop, report, and analyze business data.
To introduce the students to R- programming and its applications.
After completion of the course the students will be able to:-
Apply the tools and techniques of Business Analytics to solve business problems and improve the quality of decision making.
Illustrate the practices for scalable and reliable Excel based reporting and analysis solutions.
Compare the strengths and weaknesses of alternative strategies for collecting, analyzing and interpreting data.
Read, analyze and interpret the data using
UNIT - I :
MS-EXCEL: Basics of MS Excel- Overview of Toolbars- Gridlines- VLookup- Index- Formatting Cells- Summation – Autofill- Formatting Text – Conditional Formatting.
UNIT - II: ADVANCED TECHNIQUES: Functions- Array
Formulae-Tables- Filters-Sorting- Totals-Subtotals; Managing windows- Multiple windows- Splitting windows; Tables-Range.
UNIT - III : TOOLS: Freezing
panes-Linking data with MS-Access –Toolpack-Goal Seek- Pivot table-Import Multiple relation model-Charting- Macros – Chart – Advanced chart techniques: Break-even lines and waterfall charts - Pictures in chart columns Crystal report – Forms
UNIT - IV : INTRODUCTION TO THE R LANGUAGE: - SAS versus R - R, S, and
S-plus - Obtaining and managing R -Objects- types of objects – classes; creating and accessing objects; Arithmetic and matrix operations; Introduction to functions.
MORE DETAILS ON WORKING WITH R - Reading and writing data - R libraries
-Functions and R programming- the if-statement – looping for repeat- while-writing functions-function arguments and options.
UNIT - V : GRAPHICS: Basic plotting - Manipulating the plotting window
-Advanced plotting using lattice - library - Saving plots. Standard statistical models in R :- Model formulae and model options - Output and extraction from fitted models- - Models considered - Linear regression: lm()– Logistic regression: glm().
1.Jordan Goldneior. Advanced Excel Essentials.
2.John walkenbach. Excel 2013 Bible.
3.John walkenbach. Excel 2013 Power programming with VBA.
4.John chambers. Software for Data Analysis: programming with R(statistics and computing).
1.Conrad colberg. Productive Analytics : Micro Excel.
2.Bob umlas. Excel outside the box
3.Nina Zumel. Practical Data science with R.