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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IBMBelhaven University, Mississippi

Total Admission Fee - ₹ 75,012 + GST
No Cost EMI Starts at - ₹ 4,999 (Per Month)

The Syllabus

Learn how to do Business analysis, data transformation and other important analysis functions.
Basic fundamental of Python and learn how to do analytics using the same.

2.1 Introduction to Business Analysis

Business Analysis overview
Who is a Business Analyst?
Introduction to governance body (IIBA)
Business Analysis body of knowledge
Knowledge areas of BABOK
SDLC & requirement analysis

2.2 Business Analysis Concepts and Knowledge Areas

Business Analysis core concepts and knowledge areas
Planning and monitoring
Business Analysis approach and governance
Business Analysis information management
Elicitation and collaboration

2.3 Requirement Lifecycle Management and Strategy Analysis

Trace, maintain, & prioritize requirements
Assess requirement changes and approval
Analyze current state
Define future state
Assess risks
Define change strategy

2.4 Requirement Analysis, Design Definition, Model Requirements, and Solution Evaluation

Specify model requirements
Verify and validate requirements
Define requirement architecture and design options
Measure and analyze solution performance
Assess solution and enterprise limitations
Recommend actions to increase solution values
Data model, UI model, user stories

2.5 Agile Business Perspectives and Creating UML

Introduction to business Agile perspective
Business Intelligence perspective
Information Technology perspective
Business architecture perspective
Business Process Management perspective

2.6 Understand BA Role on the Agile Team

Write effective user stories and acceptance criteria
Develop backlogs into great products
BA role in new age Business Analysis

2.7 Underlying Competencies of a Business Analyst & Microsoft Visio

Business Analyst underlying competencies
BA Skills
Soft skills, leadership and personal development
UML diagrams using Microsoft Visio
Types of flowcharts, cross-functional flowcharts

3.1 Data Science and Analytics Fundamentals

Introduction to data science and analytics
Use cases in data science and analytics
Data science life cycle
Data Analytics: Types
AI and Machine Learning: Overview

3.2 CBDA: Introduction

CBDA: Overview
What is Business Data Analytics?
Eligibility criteria
Six Business Data Analytics domains

3.3 Business Analysis and Data Analytics

Business analysis in data analytics projects
Comparison between business analysis and data analytics

3.4 Business Data Analytics Domains: Introduction

Domain 1: Identifying the Research Questions

Defining business problems or opportunities
Identifying and understanding stakeholders
Current state assessment
Defining the future state
Formulation of research questions
Planning the Business Data Analytics approach
Selection of techniques for the identification of research questions

Domain 2: Source Data

Data collection planning
Determination of datasets
Data collection
Data validation

Domain 3: Data Analysis

Developing a Data Analysis plan
Data preparation
Data exploration
Performing data analysis
Assessment of the analytics and system approach

Domain 4: Interpreting and Reporting Results

Validating the understanding of stakeholders
Planning for stakeholder communication
Determining the communication requirements of the stakeholders
Deriving insights from data
Documentation and communication of findings from the completed analysis
Selection of techniques for interpreting and reporting results

Domain 5: Using Results to Influence Business Decision-making

Action recommendation
Implementation plan development
Change management
Guiding the organizational-level strategy for Business Data Analytics
Organizational strategy
Talent strategy
Data strategy

3.5 Techniques

Business simulation
Business visualization
Concept modeling
Data dictionary, flow diagrams, mapping, and storytelling
Decision modeling and analysis
Descriptive and inferential statistics
Extract, Transform, and Load (ETL)
Exploratory data analysis
Hypothesis formulation and testing
Interface analysis
Optimization
Problem shaping and reframing
Stakeholder list, map, and personas
Survey and questionnaire
Technical visualizations
The big idea
3-minute story

4.1 Introduction to Agile Scrum

Agile Scrum overview
Agile project principles
Sprint and Increment in Agile Scrum
Scrum theory
Other Agile Frameworks

4.2 Scrum Team and Artifacts and Events

Scrum team and Scrum Master
Scrum roles
Scrum flow
Scrum artifacts: product backlog
Artifacts transparency
Scrum events: sprint planning and retrospective
Agile estimating, planning, monitoring, and control

4.3 Project Management Using Jira

Introduction to Project Management in Jira
Jira issue types
Scrum & Kanban boards
Customizing board settings
Release and backlog management
Jira reports

5.1 Class diagram
5.2 Component diagram
5.3 Composite structure diagram
5.4 Deployment diagram
5.5 Object diagram
5.6 Package diagram
5.7 Profile diagram
5.8 Activity diagram
5.9 State machine diagram
5.10 Use case diagram
5.11 Communication diagram
5.12 Interaction overview diagram
5.13 Sequence diagram
5.14 Timing diagram

6.1 Communication skills
6.2 Leadership skills
6.3 Problem-solving skills
6.4 Business knowledge
6.5 IT knowledge

7.1 Introduction to Data Visualization and Power of Tableau

What is data visualization?
Comparison and benefits against reading raw numbers
Real use cases from various business domains
Examples using Tableau
Installing Tableau, Tableau interface, connecting to DataSource, Tableau data types
Data preparation

7.2 Architecture of Tableau

Installation of Tableau Desktop
Architecture of Tableau
Tableau interface
How to start with Tableau
The ways to share and export the work done in Tableau

Hands-on Exercise:

Play with Tableau desktop, learn about the interface, share and export existing works

7.3 Working with Metadata and Data Blending

Connection to Excel
Cubes and PDFs
Management of metadata and extracts
Data preparation
Joins and Union

Hands-on Exercise:

Connect to Excel sheet to import data, use metadata and extracts, manage NULL values, clean up data before using, perform the join techniques, execute data blending from multiple sources

7.4 Creation of Sets

Mark, highlight, sort, group, and use sets
Constant sets
Computed sets, bins, etc.

Hands-on Exercise:

Use marks to create and edit sets, highlight the desired items, make groups, apply to sort on results, make hierarchies among the created sets

7.5 Working with Filters

Filters
Filtering continuous dates, dimensions, and measures
Interactive filters, marks card, and hierarchies
How to create folders in Tableau
Sorting in Tableau, types of sorting, filtering in Tableau, Types of filters
Filtering the order of operations

Hands-on Exercise:

Use the data set by date/dimensions/measures to add a filter, Use an interactive filter to view the data, Customize/remove filters to view the result

7.6 Organizing Data and Visual Analytics

Using formatting pane
Formatting data using labels and tooltips
Edit axes and annotations
K-means cluster analysis
Trend and reference lines
Visual analytics in Tableau

Hands-on Exercise:

Apply labels and tooltips to graphs, annotations, edit axes’ attributes, set the reference line, perform k-means cluster analysis on the given dataset

7.7 Working with Mapping

Working on coordinate points
Plotting longitude and latitude
Editing unrecognized locations
Customizing geocoding, polygon maps, WMS: web mapping services
Working on the background image, including add image
Plotting points on images and generating coordinates from them
Map visualization, custom territories, map box, WMS map
Create map projects in Tableau
Creating dual axes maps, and editing locations

Hands-on Exercise:

Plot longitude and latitude on a geo map, edit locations on the geo map, custom geocoding, use images of the map and plot points, find coordinates, create a polygon map, use WMS

7.8 Working with Calculations and Expressions

Calculation syntax and functions in Tableau
Various types of calculations
LOD expressions, including concept and syntax
Aggregation and replication with LOD expressions
Nested LOD expressions
Levels of details
Quick table calculations
The creation of calculated fields
Predefined calculations
How to validate

7.9 Working with Parameters

Creating parameters
Parameters in calculations
Using parameters with filters
Column selection parameters
Chart selection parameters
Parameters in the filter session
Parameters in calculated fields
Parameters in the reference line

Hands-on Exercise:

Creating new parameters to apply on a filter, passing parameters to filters to select columns, passing parameters to filters to select charts

7.10 Charts and Graphs

Dual axes graph
Histograms, single and dual axes, box plot
Charts
Maps
Market basket analysis (MBA)
Using show me, text table, and highlighted table

Hands-on Exercise:

Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset, perform market basket analysis (MBA) on the same dataset

7.11 Dashboards and Stories

What is a dashboard?
Building and formatting a dashboard
Best practices for making dashboards
Creating stories
Adding annotations
Tableau joins, Types of joins, Tableau field types
Saving as well as a publishing data source

Hands-on Exercise:

Create a Tableau dashboard view, include legends, objects, and filters, make the dashboard interactive, use visual effects, annotations, and descriptions to create and edit a story

7.12 Tableau Prep

Introduction to Tableau Prep
Getting deeper insights into the data with visualizations
Making data preparation simpler and accessible
Integrating Tableau Prep with Tableau analytical workflow
Data preparation to analysis with Tableau Prep

8.1 Introduction to MS Excel & Data Extraction
8.2 Referencing in formulas
8.3 Name range
8.4 Logical functions
8.5 Conditional formatting
8.6 Advanced-level validation
8.7 Important formulas in Excel
8.8 Dynamic table
8.9 Data sorting, filtering
8.10 Chart creation techniques
8.11 Pivot tables in Excel
8.12 Data and file security
8.13 VBA Macros
8.14 Best practices of dashboards visualization
8.15 Creating dashboards
8.16 Creation of interactive components

9.1 Introduction to SQL

Various types of databases
Structured Query Language
Distinction between client-server and file server databases
Understanding SQL Server Management Studio
SQL Table basics
Data types and functions
Transaction-SQL
Authentication for Windows
Data control language
Identification of keywords in T-SQL

9.2 Database Normalization and Entity Relationship Model

Data anomalies
What is normalization?
Types of normalization forms
Entity-relationship model

9.3 SQL Operators

Introduction to relational databases
Fundamental concepts of relational rows, tables, and columns
Several operators (such as logical and relational), constraints, domains, indexes, stored procedures, primary and foreign keys
Understanding group functions, the unique key

9.4 Working with SQL: Join, Tables, and Variables

Advanced concepts of SQL tables
SQL functions
Operators & queries
Table creation
Data retrieval from tables
Combining rows from tables
Deploying operators
Temporary table creation
Set operator rules
Table variables

9.5 Deep Dive into SQL Functions

SQL functions
Scalar and aggregate functions
Inline SQL functions
General and duplicate functions

9.6 Working with Subqueries

SQL subqueries and rules
Statements and operators with which subqueries can be used
Modify subqueries
Types of subqueries
Methods to create and view subqueries

9.7 SQL Views, Functions, and Stored Procedures

Learning SQL views
Methods of working with views
Stored procedures and their key benefits

You will be working on multiple projects on different domains like retail, finance, banking and learn how Machine Learning is used to solve todays world problems.

11.1 Python Environment Setup and Essentials

Introduction to Python
Features
Python installation – Windows, Mac & Linux distribution for Anaconda Python
Deploying Python IDE
Basic Python commands, data types, variables, keywords, etc.

Hands-on Exercise

Installing Python Anaconda for Windows, Linux, and Mac

11.2 Python language Basic Constructs

Built-in data types in Python
Learn classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug
Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
Loop and control statements

Hands-on Exercise

Write your first Python program, Write a Python function (with and without parameters), use Lambda expression, write a class, create a member function and a variable, create an object, write a for loop

11.3 OOP concepts in Python

Write OOP concepts program in Python
Connecting to a database
Classes and objects in Python
OOPs paradigm
Python functions, return types, and parameters
Lambda expressions

Hands-on Exercise

Creating an application that helps to check balance, deposit money and withdraw the money using the concepts of OOPS.

11.4 Database Connection

Database and need of database
Installing MySQL on Windows
Database connection using Python

Hands-on Exercise

Demo on Database Connection using python and pulling the data.

11.5 NumPy for Mathematical Computing

Arrays and matrices
Broadcasting of array math
Indexing of the array
Standard deviation, conditional probability, correlation, and covariance

Hands-on Exercise

How to import the NumPy module, creating an array using an ND-array, calculating standard deviation on an array of numbers, calculating correlation between two variables.