Business Analytics Trainer

Duration: Hours

Training Mode: Online


Business analytics refers to taking in and processing historical business data. Analyzing that data to identify trends, patterns, and root causes. Locus IT has decade long industry experience in “Business analytics” consulting, staffing & training services.

Business Analytics Training Topics to be covered :

  1. Business Statistics
  2. Excel: Basics to Advanced
  3. MySQL
  4. Tableau
  5. Power BI
  6. SAS
  7. R Basics
  8. Python Basics
  9. Agile

Business Statistics

1. Descriptive Statistics

a). Data Types, Measure Of central Tendency, Measures of Dispersion

b). Graphical Techniques, Skewness & Kurtosis, Box Plot

2. Probability and Normal Distribution

a). Random Variable, Probability, Probility Distribution, Normal Distribution, SND, Expected Value

3. Inferential Statistics

a). Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence Interval

b). Introduction to Hypothesis Testing

c). Hypothesis Testing (2 Proportion test, 2 t sample t test)

d). Anova and Chisquare

4. Data cleaning and Insights

a). Data Cleaning(Invalid cells, Blanks, Outliers, Null values)

b). Imputation Techniques(Mean and Median)

c). Scatter Diagram

d). Correlation Analysis

Advanced Excel tools with Business Analytics

1. Introduction to Excel:Quantum of Excel and Basics

a). Workbook,Types of workbooks and their uses(XLSX, XLS, CSV, XLSM and XLSB)

b). Common uses of Excel

c). Cell,Row,Column,Range/Array,Name box

d). Formatting of cells(Wrap Text,Number,Text,Cell formatting ,commenting,etc)

e). Ribbon,Formula bar,Status bar

f). Basic operators(+,-,/,*,%,>,<,>=,<=,( ),{ },[ ],&,’ ‘, “” “”,!)

2 .Intorduction to Functions:Commonly used Excel Functions

a). What is syntax,arguments(Optional,Mandatory)Navigations using keyboard,shortcuts

b). Sum,Average,Max,Min,Product

c). CountBlank,CountA,CountIF,If,Now,Today

d). Cut,Copy,Paste,Paste Special

3. Anchoring data:Referencing,Named ranges and its uses

a). Absolute,Relative,Mixed referencing

b). Name Manager,Named ranges,Creating Tables

c). Create functions using named ranges AND/OR referencing

4. Referring data from different tables:Various types of Lookup,Nested IF

a). Lookup,Vlookup,Nested Vlookup,Hlookup,Index,Index with Match function

b). If,If with combination of AND/OR(multiple ways to get the output),IFERROR

5. Referring data from different tables:Advanced functions


6. Data Handling:Data cleaning,Data type identification,Data restrictions




d). Data validation,Depended drop down,Protecting cell,Array,range,sheet,Workbook

7. Data Handling:Formatting and Filtering

a). Conditional formatting(Icon sets/Highlighted color sets/Data bars/custom formatting),Sort,Advanced Sort,Filtering

8. Data Summerization:Advanced functions,Charts

a). Sum,Average,Max,Min with IF and IF’S,CountIF’S

b). Various types of Charts

9. Data Summerization : Pivots,Preparing the Dashboard

a). Pivot table,Slicers,Pivot charts,Calculated field,Calculated item, ADD/REMOVE/CHANGE data into the pivot table,Refreshing pivot data

b). Dashboard creation

10. Power query,power pivot

a). Cleaning data,extracting data from multiple sources

b). Transforming data,imputation techniques.Getting data from CSV files,databases,workbooks,webpages

11. Power query,power pivot,Use case discussion:Data Preparation,Project Summarization

a). Consolidating data from multiple sources,merging data from different workbooks/worksheets,relationships.

b). Use Data handling steps taught in the previous session,Use Data summarization techniques,Populate output in Excel,Combining multiple functions

12. Intro to Automation:Macros(Recorded /VBA)

a). How VBA works,Record a sample macro(Recording macros,Absolute mode,relative mode,different methods of executing macros)

13. VBA

a). If constructs,Select construct,User defined functions,input box, message box,procedures,automatic macros, methods to cleanu


1. Introduction to Databases, Software Installation

a). Introduction to RDBMS,Explain RDBMS through normalization,Different types of RDBMS

2. Types of SQL Commands; Data Types in SQL

a). DDL,DML,DQL,DCL,TCL,Datatypes:int,float,char,varchar,date,date&time,UTC

3. DDL and DML and TCL commands

a). DDL:Create,Drop,Rename,Alter,Truncate,DML:Insert,Update,Delete,TCL:Commit,rollback,savepoint

4. Database Constraints

a). Domain Constraint,Key Constraint,Referential Integrity Constraint,Primary key,Foreign Key

5. Operators in SQL


6. Grouping operations

a). Select query,Order by,Group by,Having Classes,Aggregating functions

7. Ranking functions,Analytical functions

a). Rank,Dense rank,row number,percentile rank,lead and lag functions

8. Joining Tables

a). Inner,Left,Right,Cross,Self Joins,Full outer join, Interview Scenarios

9. Views,Triggers

a). Simple views,Complex views,Different types of triggers

10. Introduction to subqueries, different types of subqueries

a). Explaintion of subqueries with interview scenarios

11. Indexing,Sequence Objects

a). B.Tree Index,Hash Index,Unique index,Advantage of Index,Creation of Sequence on primary key column

12. Stored procedures

a). Parameters in stored procedures,Exception handling in stored procedures

Tableau with Business Analytics

1. Intro to Tableau Tool

a). What is data,Types of Data(Structured,Unstructured,Semi Structured),Visualization basics,Different visualizaion tools,Popularity of the tools, Licencing Cost,Different products of Tableau,Installation (student id), ,Connecting to Static files,Mysql

2. Data pane window

a). Live Vs Extract,Data source window,Navigating to work sheet,Data pane,Analytics pane,Dimensions,Measures,Auto generated fields,Data visualization window explaination,Data source window operations

3. Groups, Sets, Parameters

a). Hierarchy(In built hierarchy,Manual),Grouping ,Sets,Parameter with filters and Parameter with Sets,Usage of meausre names and Measure Values

4. Filters in Tableau

a). Dual axis,Blended axis,Dimension filters,Measure filters(Record level filters,summary level filters),Date filters,Cascading Filters,Context filters,Data source filters,Extract filters,

5. Calculated fields

a). Quick table calculations,Introduction to calculated fields,string calculated fields,Number calculated fields,date calculated fields,logical calculated fields,ZN Function

6. Data Blending and Joins

a). Mixing up of all calculated fields,Conditional Formatting in Tableau, Data blending,Data joins,Unions,Relationships,Basic Charts and use cases, Introduction to Show me,Development of In built charts part1,

7. Charts in Tableau

a). Development of inbuilt charts part2,Customized graphs(Donut, Waterfall,Bump,Barometer,Butterfly,Gauge meter,Basic Funnel, Advanced Funnel,Word cloud,Gantt Bar),Animated Chart

8. Reference lines,Bands,Distributions

a). Arbitary formatting,Explaination of Marks Card,Reference lines, Reference Bands,Reference Distribution

9. LOD’s, Intro to Dashboard,Story

a). Forecasting,Introduction to Dashboard,Story board interfaces,LOD’s(Fixed,Include,Exclude)

10. Creating a Dashboard

a). Creating of a Basic Dashboard with both Tiled,Floating layouts, Explaination of objects in the Dashboard interface,Action filters on Dashboards

11. Creating a Advanced Dashboard

a). Advanced level dashboard(Drill down dashboards),Designing of Basic Story board

12. Tableau public server

a). Publishing Dashboards on Tableau public server,Exposure to the websites which consists of real time data,Interview cracking resources,introduction to Tableau certification

Basics of R in Business Analytics

1. Introduction to R,Installation of Rstudio,Data Types in R

a).Data types(Numeric,Char,Logical,Complex,Vector,List,Matrix,Factor,Array,Dataframe),Relational operators,Logical operators

2. Decision making statements,Loops,Functions

a). If,Ifelse,For loop,While loop,Repeat,Functions

3. Built in Functions in R,Joins,dplyr and ggplot2

a). Merging dataframes,Analyzing Iris Dataset using apply functions,dplyr package(Filter,Sel,Arrange),Data visualization using ggplot2,Scatterplot,Histogram,Boxplot

Basics of Python in Business Analytics

1. Anaconda Installation,Introduction to python, Data types, Opearators

a). Variables,data types(integer, Boolean, Float, List, tuple, string), Opearators in python

2. Data types Contd,Slicing the data, Inbuilt functions in python

a). Dictionaries, Sequence methods, Concatenate, Repetition, len,min, max functions, Index position, Addition and deletion of elements, Reverse, Sorting

3. Sets,Set Theory,Regular Expressions,Decision making statements

a). Sets, re module(findall, search, split, match), if, elifGetting input from user, Identity Operators

4. Loops,Functions,Lambda functions, Modules

a). For, While loops, Functions, Lambda functions, Math module, Calender module, Date & time module

5. Pandas,Numpy, Matplotlib, Seaborn

a). Data frame creation using different methods,Using Pandas anlysis on Universities,Salary data sets, Visualization using Matplotlib and Seaborn,Numpy introduction

Power BI

1. Power BI Introduction

a). Introduction to Power BI Desktop

b). Getting data (Excel and RDBMS, Web, SharePoint)

c). Naming for Q&A

d). Direct Query vs Import data

2. Modelling with Power BI

a). Introduction to Modelling

b). Set up and Manager relationships

c). Cardinality and Cross Filtering

d). Creating Hierarchy in the model

e). Default Summarization and sort by

f). Creating Calculated columns

g). Creating measures and quick measures

3. Power BI Desktop Visualizations

a). Creating visuals

b). Colour and Conditional Formatting

c). Setting sort order

d). Scatter and bubble charts and play axis

e). Tool tips

f). Slicers, Timeline Slicers and sync Slicers

g). Cross Filtering and Highlighting

h). Visual, Page and Report level filters

i). Drill down/up

j). Hierarchies

k). Constant Lines

l). Tables, Matrix and Table Conditional Formatting

m). KPI’s, Cards and Gauges

n). Map Visualizations

o). Custom visuals

4. DAX Expressions

a). Introduction to Dax (how to write Dax and basic functions in Power BI)

b). Important Dax used in Power BI along with its applications

c). Introduction to Dax (how to write Dax and basic functions in Power BI)

d). how to create Calculated columns and measures in Power BI and difference in its application

e). Scenarios with Questions on Dax & explanation

f). Creating date Dimension in Power BI using Calendar functions and its importance

5. Publishing and Sharing

a). Sharing options

b). Publish from Power BI Desktop

c). Publish reports to Web

d). Sharing reports and Dashboards

e). Workspaces

f). Apps

g). Printing, PDF’s and exports

h). Row level Security

i). Exporting data from Visualizations Refreshing Datasets

j). Understanding data refresh

k). Gateways


1. Introduction to Agile

a). Project Definition

b). Difference Between Traditional & Agile Project Mgmt.

c). Agile Manifesto and Principles

d). Agile Methodology

e). Agile Principles

f). Agile Frameworks and Terminology

2. Agile Methodologies

a). Scrum

b). XP

3. Agile Analysis and Design

a). Product Roadmap

b). Product Backlog

c). Story Maps

d). Agile Modeling

e). Wireframes

f). Charting

g). Personas

4. Planning and Monitoring

a). Iteration and Release Planning

b). Progressive Elaboration

c). Time Boxing

d). Cumulative Flow Diagram

e). Kanban Boards

f). WIP Limits

g). Burn Charts

h). Retrospectives

i). Innovation Games

5. Agile Metrics and Estimations

a). Relative Sizing

b). Story Points

c). Wideband Delphi Technique

d). Planning Poker

e). Affinity Diagram

f). Ideal time

g). Velocity

h). Cycle Time

i). EVM

j). Escaped Defects

6. Quality

a). Frequent Verification and Validation

b). Test Driven Development

c). Definition of Done

d). Continues Integration

e). Feedback Techniques

f). Incremental Delivery

g). Continuous Improvement

7. Value Based Prioritization

a). Customer Valued Prioritization

b). Compliance

c). Relative Prioritization

d). Value Stream Mapping

e). Minimum Marketable Feature

8. Risk Management

a). Risk Adjusted backlog

b). Risk Burn down charts

c). Risk based spike

9. Agile Communications

a). Team Space

b). Information Radiator

c). Agile Tooling

d). Daily Stand-ups

e). Osmotic Communication

For more inputs on Business Analytics Training  you can connect here.
Contact the L&D Specialist at Locus IT.

Locus Academy has more than a decade experience in delivering the training on Business Analytics for corporates across the globe. The participants for the training on Business Analytics are extremely satisfied and are able to implement the learnings in their on going projects.


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Business analytics refers to: Taking in and processing historical business data. Analyzing that data to identify trends, patterns, and root causes. Types of analytics such as descriptive analytics,  predictive analytics and prescriptive analytics.