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PG Certification in Data Analytics

INSTITUTE

6 Months

10th

22nd January, 2023

Live Interactive Learning

100% Job Assistance

Course description

About the Course

The Scholar's PG Certification in Data Analytics a unique program which is an excellent blend of knowledge and practice in the field of Data Analytics and its industrial applications. The program is targeted for creating qualified Data Analytics professionals.

The objective of this program is to make Data Analysts. There is a huge demand for resources skilled in Data Analysts. So it is quite obvious that existing resources along with new candidates who are interested in perusing career in this field needs to be trained. Our objective is to create a pool of talent who can meet this demand. This course is meant to sensitize students for computational statistics applications and usage as well as provide hands-on experience with solving real world data analysis issues.

Key Highlights

Designed for Fresher’s & Working Professional
90+ Hours of In Depth Learning
Covering Most Demanding Tools
25+ Case Studies & Capstone Projects
One to One with Industry Mentors
24/7 LMS Access
100% Job Assistance
Dedicated Customer Support
Building Project Portfolio
AI based Resume Building

Course description

Who Should attend & Course Pedagogy

Person from any domain who are keen on learning Data Analytics for professional development.
Software developers/ IT Professionals who are willing to move into Data Analytics.
Professors from Institutes who are willing to upgrade their skills into Analytics.
Middle and senior level executives from profit and not-for-profit organization across industries/sectors can attend the program.

Eligibility

Any Graduate with Min 50% and Above marks
No Experience is Mandatory
No Programming Background Required

Selection Process

Aptitude Test
Personal Interview with Academic Expert
Admission Letter & Enrollment

Assignments, Videos & Case Studies

Curated Course Syllabus

Module 1: SQL



- Introduction to Data Warehouse
  - What is data warehouse
  - Data warehouse architecture
  - Top down approach
  - Bottom up approach
  - Data lake architecture
  - Data lake and warehouse architecture
  - Data lake vs. Data warehouse
  - OLTP vs. OLAP
- Facts and Dimensions
  - Additive Fact
  - Factless Fact
  - Non Additive Fact
  - Semi Additive Fact
  - Role playing dimension
  - Junk dimension
  - Degenerate Dimension
  - Conformed Dimension
  - Slowly Changing Dimension
- Normal Forms
  - Normal Forms 1NF
  - Normal Forms 2NF
  - Normal Forms 3NF
  - Normal Forms 4NF
- Introduction to Constraints - Part 1
  - Naming Rules
  - Data Types
  - Default Constraint
  - Primary Key Constraints
  - Foreign Key Constraints
- Introduction to Constraints - Part 2
  - Not Null Constraint
  - Unique Constraint
- Types of SQL Commands
  - Data Definition Language
  - Data Query Language
  - Data Manipulation Language
  - Data Control Language
  - Transaction Control Language
- Operators and Clauses
  - Concatenation
  - Where Clause
  - Like Clause
  - Between and
  - Is null
  - Order by
- Single Row Function - Part 1
  - Character
  - Number
  - Date
- Single Row Function - Part 2
  - Conversion
  - General Functions
  - NVL
  - NVL2
  - COALESCE
  - Case When
  - Decode
- Multi Row Function
  - Avg
  - Count
  - Min
  - Max
  - Stddev
  - Sum
  - Variance
- Joins in SQL - Part 1
  - Different types of joins
  - Inner
  - Left Outer Join
  - Right Outer Join
  - Full Outer Join
- Joins in SQL - Part 2
  - Self-Join
  - Non Equi Join
  - Cross Joins
- Joins in SQL - Part 3
  - Self-Join
- Single Row SubQuery
  - Detailed understanding of single row subquery
- Multiple Rows SubQuery
  - Detailed understanding of multiple row subquery
  - In clause
  - Any Clause
  - All Clause
- Multiple Column Sub Queries
  - Non Pairwise
  - Pairwise
  - Correlated Sub Queries
- Exists and With Clauses
  - Detail understanding of Exists and With Clauses
- Set Operators in SQL
  - Union
  - Union All
  - Intersection
  - Minus
- Data Manipulation
  - Insert Statements
  - Update Statements
  - Delete Statements
  - Truncate
- Views in SQL
  - Simple and Complex views
  - Update through views
- Sequences, Synonyms and Index
  - Sequences and its uses
  - Synonyms and its uses
  - Index and its benefits and Drawbacks
- Alter Statements in SQL
  - How to add the constraints
  - Alter statements
  - System tables
- Multi Table Inserts
  - Unconditional Inserts
  - Conditional Inserts
  - Condition First Insert
  - Pivoting Insert
- Merge in SQL
  - Update using Merge
  - Insert using Merge
- Hierarchical Data Fetching
  - Connect by Prior
- Regular Expressions
  - REGEXP_LIKE
  - REGEXP_INSTR
  - REGEXP_SUBSTR
  - REGEXP_REPLACE
  - REGEXP_COUNT
- Analytics Functions - Part 1
  - Row Number
  - Rank
  - Dense Rank
- Analytics Functions - Part 2
  - Row Number
  - Rank
  - Dense Rank
- Materialized Views
  - Importance of Materialized Views
  - Complete on Demand
  - Complete on Commit
  - Fast on Demand
  - Explain Plan
- SQL Interview Prep - Advanced Select
  - Understanding of advanced Select
- SQL Interview Prep U+2013 Joins
  - Understanding of Joins and advanced joins

Module 2: Tableau



Tableau - An introduction
- Understanding Tableau and its products
- Tableau Public
- Tableau Desktop
- Tableau Reader
- Tableau Online
- Tableau Server
- Tableau Prep
- Tableau Mobile
Tableau - Creating first chart
- Understanding the Calculated Field concept
- If and Else statements
- Left Function
- Making data ready for visualization using Tableau Prep
Tableau - Mode of connection
- Understanding the concept of Live and Extract connections
- Data Source filters
- Union and its usage
Tableau - Data from Heterogeneous data sources
- Dive deep into the concept of Joins and how can we achieve in Tableau
- Applying the data joining using Tableau Prep
- Dive deep into the concept of Cross database joins
- Applying Cross database joins using Tableau Prep
- Understanding of Data Blending
- Understanding the concept of Primary and Secondary data sources
Tableau - Data Blending in Tableau
- Dive Deep into Data Blending
- Understanding the usage of Data Joining, Cross Database Join and Data Blending
- Solving the end-to-end problem using Data Blending
Tableau - Bins, Parameters and creating our first dashboard
- Creating the map view and understanding the idea of maps
- Creating the histogram and dynamic histogram using parameters
- Creating Donut chart
- Creating the stackbar chart
- Creating the interactive dashboard
Tableau - Dashboard and Story
- Difference between Tiled and Floating options
- Adding the buttons to download the dashboard as ppt, image and pdf
- Creating the story
- Understanding the difference between Dashboard and Story
Tableau - Time series analysis
- Understanding the concepts of Data dimension
- Difference between Discrete and continuous date field
- What is the relation between aggregation and Granularity?
- How to apply normal filters or Quick Filters
- Options in the Normal Filters or Quick Filters
- Understanding on Context Filters
Tableau - Sets and Parameters for more dynamic visualization
- Create a dynamic scatter plot using the concepts of Sets and Parameters
- Understanding the concept of sets
- Understanding the concept of Static Sets
- Understanding the concept of Dynamic Sets
- How to tag parameters with sets to make it dynamic sets
- How to tag parameters with reference lines
Tableau - Combined Sets and Formatting of the visuals
- Understanding the concept of Combined sets
- Formatting the visuals
- Adding sets as filters
Tableau - Animation in Tableau - Data Prep
- Formatting the data as per the requirement using Tableau Prep
- Formatting the data as per the requirement using Tableau Desktop
- Advantage and Disadvantage of using Tableau Desktop as data preparation tool
Tableau - Animation in Tableau - Visualization
- Creating the animated scatter plot using Animation concept
- How to animate the chart using page shelf
- Which data to choose as Primary data source
- Formatting the plot
- Sorting the secondary data source
- Adding show history option in the animation
Tableau - Level of Details (Include)
- Understanding the concept of Include Function
- Creating the visual using Include function in Tableau
Tableau - Level of Details (Include and Exclude)
- Implementing the include function on the real time use case
- Creating the visual using Include function in Tableau
- Understanding the concept of Exclude and where to use it
- Implementing the exclude function on the real time use case
- Creating the visual using exclude function in Tableau
Tableau - Level of Details (Fixed)
- Understanding the concept of Fixed and where to use it
- Implementing the Fixed function on the real time use case
- Creating the visual using Fixed function in Tableau
Tableau - Level of Details (Real time use cases)
- Customer Order Frequency
- Cohort Analysis
- Daily Profit KPI
- Percentage of Total
- New Customer Acquisition
- Average of top deals by city
- Actual vs. Target
- Value on the last day of the period
- Return Purchase by cohort
- Percentage difference from average across a range
- Relative Period Filtering
- User login frequency
- Proportional Brushing
- Annual Purchase frequency by customer cohort
- Comparative sales analysis
Tableau - Table Calculation - Data Prep Part 1
- Preparing the data using Tableau Prep
- Understanding the datetime dimension and its purpose
Tableau - Table Calculation - Data Prep Part 2
- Preparing the data using Tableau Desktop
- Pivoting using Tableau Prep
- Pivoting using Tableau Desktop
Tableau - Table Calculation - Functions Part 1
- Running Sum
- Difference
- Percentage Difference
- Understanding Table Across, Table Down
- Fixing the calculation by dimension
- Understanding the difference between Table calculation and Calculated Field
- Dive deep on the functions Lookup, ZN
Tableau - Table Calculation - Functions Part 2
- Moving Average
Creating the logic for Quality assurance in the output

Module 3: Python



Module 7: Power BI (Self Paced)



- Introduction to PowerBI and its architecture
- Import data from CSV files
- Import data from Excel files
- Import data from Web 1
- Import data from Web 2
- Import Real-time Streaming Data
- Import data from Oracle
- Import Data from Folder
- Dataflows - Introduction
- Dataflows - Create Gateway from Scratch
- Dataflows - Create Entities from CSV file
- Dataflows - Create Entities Using SQL Server
- Remove Rows
- Remove Columns
- Make first row as headers
- How to create calculate columns
- How to remove duplicates
- Unpivot columns and split columns
- Change Data type, Replace Values and Rearrange the columns
- Append Queries
- Merge Queries
- Visuals Intro
- Visuals-Bar Charts
- Visuals-Line Charts
- Visuals-Pie Chart
- Stacked bar Chart
- Clustered Column Chart
- Visuals-Area Chart and Analytics Tab Explained-0
- Visuals-Area Chart and Analytics Tab Explained-1
- Visuals-Combo Chart
- Visuals-Scatter Chart
- Visuals-Treemap Chart
- Visuals-funnel Chart
- Visuals-Card and Multi-Row Card
- Visuals-Gauge Card
- Visuals-KPIs
- Visuals-Matrix
- Visuals-Table
- Visuals-Text boxes - Shapes - Images
- Visuals-Slicers
- Visuals-Maps
- Custom Visuals - Word Cloud
- Visualization interactions
- Modeling and Relationships
- Other ways to create Relationship
- OLTP vs OLAP
- Star Schema vs Snowflake Schema
- DAX101 - Importing Data for Dax Learning
- DAX101 - Resources for Dax Learning
- DAX101 - What is Dax
- DAX101 - Dax Data Types
- DAX101 - Dax Operators and Syntax
- DAX101 - M vs Dax
- DAX101 - Create a Column
- DAX101 - Rules to Create Measures
- DAX101 - Calculated Columns vs Calculated Measures-0
- DAX101 - Calculated Columns vs Calculated Measures-1
- DAX101 - Sum()
- DAX101 - AVERAGE()-MIN()-MAX()
- DAX101 - SUMX()
- DAX101 - DIVIDE()
- DAX101 - COUNT()-COUNTROWS()
- DAX101 - CALCULATE()-0
- DAX101 - CALCULATE()-1
- DAX101 - FILTER()
- DAX101 - ALL()
- DAX101 - Time Intelligence - Create Date Table in M (important)
- DAX101 - Time Intelligence - Create Date Table in DAX
- DAX101 - Time Intelligence - SAMEPERIODLASTYEAR()
- DAX101 - Time Intelligence - TOTALYTD()
- DAX101 - Display Last Refresh Date
- DAX101 - Time Intelligence - PREVIOUSMONTH()
- DAX101 - Time Intelligence - DATEADD()
- DAX101 - Quick Measures
- PowerBI Reports
- PowerBI Workspaces
- PowerBI Datasets
- What are Dashboards
- How to create Workspace and Publish Report
- Favorite dashboards, reports, and apps in Power BI
- Subscribe to a Report or Dashboard
- Rename Workspace or Report or Dashboard
- Display Reports or Dashboards in Full screen mode
- Delete Reports or Dashboards
- Dashboard Menus
- File and View Options
- Printing Dashboard and Reports
- PATH Function
- PATHCONTAINS Function
- PATHITEM Function
- PATHITEMREVERSE Function
- PATHLENGTH Function
- RLS - Static Row Level Security
- RLS - Dynamic Row Level Security
- RLS - Organizational Hierarchy
- Sharing and Collaboration
- Sharing Dashboard
- Sharing Workspaces
- Sharing App
- Publish To Web

Program Fees

For Indian Residents

Rs. 95000 + GST*

Tenure

Deadline

Amount (INR)

1st instalment

Immediate Payment

Rs. 20000 + GST*

2nd Instalment

20 January 2023

Rs. 18750 + GST*

3rd Instalment

20 February 2023

Rs. 18750 + GST*

4th instalment

20 March 2023

Rs. 18750 + GST*

5th instalment

20 April 2023

Rs. 18750 + GST*

* Fee payable after Successful Placement

Rs. 40000 + GST*

For International Students

USD 1800

* Loan option available in 6, 9 & 12 EMIs from loan partners.
* 1% + GST* processing fees will be charged by loan partner in 6/9/12 months EMI.

100% Job Placement

Course Certificate

Data Analytics Sample Certificate

Salary Graph of Data Analyst

Tools to be Used

Program faculty members

Learn from the best

Mr Rahul Tiwary

14+ Yr Exp. Professionals | Ex Deloitte | Ex Accenture | Ex Tech Mahindra

...

Mr Rakesh Sharma

14+ Yr Experience Professionals

...

Ritu Shukla

Sr. Data Analyst ( 8 + Yr Experienced Professional)

...

Testimonials

Experience of our Alumni

SB Subhaprakash

I have attended Rahul's Tableau and Python sessions. I have to say, those sessions were real gem. Rahul's sessions were full of interactions and insightful. I would recommend everyone to attend his sessions on Tableau and Python.

Jagjit Hanspal