PG Program in Business Analysis and Product Management

• 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

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

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

• 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

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

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

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

• 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

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

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

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

• 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

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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

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

• 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

• 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

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

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

• 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.

• 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

• 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

• 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.

• 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.

• 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.

• Introduction to SciPy
• Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, 
• SciPy with Bayes Theorem

Hands-on Exercise

• Importing of SciPy, applying the Bayes theorem on the given dataset

• How to plot graph and chart with Python
• Various aspects of line, scatter, bar, histogram, 3D, the API of Matplotlib, subplots

Hands-on Exercise

• Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram

• Introduction to Python data frames
• Importing data from JSON, CSV, Excel, SQL database, NumPy array to data frame
• Various data operations 

Hands-on Exercise 

• Working on importing data from JSON files, selecting record by a group, applying filter on top, viewing records