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Quick Facts

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

Course Overview

The Certified Business Analytics/Data Science Course Training Programme provided by ExcelR imparts skills and knowledge to the participants in various concepts like basic statistics, ensemble techniques, model validation methods, probability, linear regression, logistic regression, association rules, decision tree, neural networks, text mining and natural language processing.

This Certified Business Analytics/Data Science Course Training Programme classes help the candidate to gain hands-on experience by providing various assignments and case studies. This programme is a blend of case studies, theory and projects. Apart from the main content, value added contents are provided additionally. Basic concepts in Python, R, MySQL, Artificial intelligence, Hadoop and Spark are taught in brief.

The certification provided at the end of the Certified Business Analytics/Data Science Course Training stands as a proof for the candidate’s skills. The participant will receive a dual certificate from ExcelR and Steinbeis University. On completion of the programme, the candidate would also yield an alumni status of SGIT. This Certified Business Analytics/Data Science online programme redefines the career of the candidate in the analytics domain.

The Highlights

E-learning
Post-training support
Lifetime access to videos
Placement assistance is provided
Course curriculum designed by Steinbeis University
Classroom programme and self-paced programme available
Blended training model
6 months duration programme
Recorded sessions are also available
Alumnus status from SGIT, Steinbeis university
Certificate from Steinbeis University and IBM

Programme Offerings

Video sessions
assignments
Project works
interview preparation

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IBMSteinbeis Global Institute, Tubingen

The fee details of the Certified Business Analytics/Data Science Course Training Programme by ExcelR is given below

The cost of the live virtual classroom programme is ₹ 56999

Certified Business Analytics Data Science Course Training Program in Delhi Fees Structure

Course type	Amount in INR
Live virtual classroom programme	₹ 56999

Eligibility Criteria

Certification Qualifying Details

The participants are provided with the dual certificate from Steinbeis Akademie, Steinbeis University and ExcelR on successful completion of the Certified Business Analytics/Data Science Course Training Programme. The candidate must meet the qualification criteria provided by the university and also pass the exam by scoring more than 60% marks to receive the certificate. Only the candidates who are attending the virtual classroom programme are provided with the certificate. The candidate has to pay an additional fee for receiving the certificate.

What you will learn

Business analytics knowledgeKnowledge of Data miningKnowledge of Applied statistics

The learner will understand the concepts which are given below by pursuing the Certified Business Analytics / Data Science Course Training Programme by ExcelR.

Gain knowledge in data analytics and project life cycle
Understand concepts in probability and hypothesis testing
Learn principles of regression
The participant would also learn about the components in linear regression with examples
Understand the probability measures and their application
Learn deployment in R using Shiny and Streamlit
Get introduced to various types of linkages and clustering algorithms
Learn the regularization techniques like ridge regression and lasso regression
The classification which is done using Bayesian modelling and Naive bytes
Understand the applications of Natural language processing

Who it is for

EconomistStatisticianBusiness Analyst

The following professionals can attend this Certified Business Analytics/Data Science Course Training Programme to enhance their career opportunities

Mathematicians
Business Analyst
Economists
Six sigma consultants
Statisticians
Candidates who are working on reporting tools and Business Intelligence
Participants who are working on Data Warehouse Technologies
Freshers
Software programmers

Admission Details

The admission details for the Certified Business Analytics/Data Science Course Training Programme by ExcelR is given below

Step 1: Visit the url https://www.excelr.com/business-analytics-training-in-delhi

Step 2: The candidate has to create an account using email ID.

Step 3: The candidate has to choose the Certified Business Analytics/Data Science Course Training Programme and select either live virtual classroom or self-paced learning

Step 4: The candidate has to click “enrol now” in live virtual classroom or “Buy now” in self-paced learning

Step 5: The candidate can select their preferred batch details and enter coupon code and click “proceed” to continue the process.

Step 6: Then the personal details of the candidate has to be entered and the candidate should click “Proceed”

Step 7: After completing the payment process, the candidate can start the course.

The Syllabus

Excel: Basics to Advanced
MySQL
Tableau
Power BI

Introduction

MS office Versions(similarities and differences)
Interface(latest available version)
Row and Columns
Keyboard shortcuts for easy navigation
Data Entry(Fill series)
Find and Select
Clear Options
Ctrl+Enter
Formatting options(Font,Alignment,Clipboard(copy, paste special))

Referencing, Named ranges,Uses,Arithemetic Functions

Mathematical calculations with Cell referencing(Absolute,Relative,Mixed)
Functions with Name Range
Arithmetic functions(SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS,AVERAGE,AVERAGEIFS,MAX,MAXIFS,MIN,MINIFS)

Logical functions

Logical functions: IF,AND,OR,NESTED IFS,NOT,IFERROR
Usage of Mathematical and Logical functions nested together

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

Lookup
Vlookup
Nested vlookup
Hlookup
Index
Index with match function
Indirect
Offset

Advanced functions

Combination of Arithmatic
Logical
Lookup functions
Data Validation(with Dependent drop down)

Date and Text Functions

Date Functions: Date, Day, Month, Year, Yearfrac, Datediff, Eomonth
Text Functions: Text, Upper, Lower, Proper, Left, Right, Search, Find, Mid, Ttc, Flash Fill

Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering

Number Formatting(with shortcuts)
CTRL+T(Converting into an Excel Table)
Formatting Table
Remove Duplicate
SORT
Advanced Sort
FILTER
Advanced Filter

Data Visualization: Conditional Formatting, Charts

Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
Charts:Bar,Column,Lines,Scatter,Combo,Gantt,Waterfall,pie

Data Summarization: Pivot Report and Charts

Pivot Reports:Insert,Interface,Crosstable Reports;Filter,Pivot Charts,
Slicers:Add,Connect to multiple reports and charts
Calculated field, Calculated item

Data Summarization: Dashboard Creation, Tips and Tricks

Dashboard:Types,Getting reports and charts together, Use of Slicers.
Design and placement: Formatting of Tables,Charts,Sheets,Proper use of Colours and Shapes

Connecting to Data: Power Query, Pivot, Power Pivot within Excel

Power Query: Interface, Tabs
Connecting to data from other excel files, text files, other sources
Data Cleaning
Transforming
Loading Data into Excel Query

Connecting to Data: Power Query, Pivot, Power Pivot within Excel

Using Loaded queries
Merge and Append
Insert Power Pivot
Similarities and Differences in Pivot and Power Pivot reporting
Getting data from databases, workbooks, webpages

VBA and Macros

View Tab
Add Developer Tab
Record Macro:Name,Storage
Record Macro to Format table(Absolute Ref)
Format table of any size(Relative ref)
Play macro by button
shape
as command(in new tab)
Editing Macros
VBA:Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets
Comparing values and conditions
if statements and select cases
Repeat processes with For loops and Do While or Do Until Loops
Communicate with the end-user with message boxes and take user input with input boxes, User Form

Introduction to Mysql

Introduction to Databases
Introduction to RDBMS
Explain RDBMS through normalization
Different types of RDBMS
Software Installation(MySQL Workbench)

SQL Commands and Data Types

Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications
Data Types in SQL (Numeric, Char, Datetime)

DQL & Operators

SELECT
LIMIT
DISTINCT
WHERE AND
OR
IN
NOT IN
BETWEEN
EXIST
ISNULL
IS NOT NULL
Wild Cards
ORDER BY

Case When Then and Handling NULL Values

Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)

Group Operations & Aggregate Functions

Group By
Having Clause
COUNT
SUM
AVG
MIN
MAX
COUNT String Functions
Date & Time Function

Constraints

NOT NULL
UNIQUE
CHECK
DEFAULT
Primary key
Foreign Key (Both at column level and table level)

Joins

Inner
Left
Right
Cross
Self Joins
Full outer join

DDL

Create
Drop
Alter
Rename
Truncate
Modify
Comment

DML & TCL Commands

DML
- Insert
- Update & Delete
TCL
- Commit
- Rollback
- Savepoint
- Data Partitioning

Indexes and Views

Indexes (Different Type of Indexes)
Views in SQL

Stored Procedures

Procedure with IN Parameter
Procedure with OUT parameter
Procedure with INOUT parameter

Function, Constructs

User Define Function
Window Functions
Rank
Dense Rank
Lead
Lag
Row_number

Union, Intersect, Sub-query

Union, Union all
Intersect
Sub Queries, Multiple Query

Exception Handling

Handling Exceptions in a query
CONTINUE Handler
EXIT handler

Triggers

Triggers - Before | After DML Statement

Introduction to Tableau

What is Tableau ?
What is Data Visulaization ?
Tableau Products
Tableau Desktop Variations
Tableau File Extensions
Data Types, Dimensions, Measures, Aggregation concept
Tableau Desktop Installation
Data Source Overview
Live Vs Extract

Basic Charts & Formatting

Overview of worksheet sections
Shelves
Bar Chart, Stacked Bar Chart
Discrete & Continuous Line Charts
Symbol Map & Filled Map
Text Table, Highlight Table
Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider
Marks Card

Filters

What are Filters ?
Types of Filters
Extract, Data Source, Context, Dimension, Measure, Quick Filters
Order of operation of filters
Cascading
Apply to Worksheets

Calculations

Need for calculations
Types: Basic, LOD's, Table
Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tablea calculation functions, numerical functions, Date functions
LOD's: Examples
Table Calculations: Examples

Data Combining Techniques

What is Data Combining Techniques ?
Types
Joins, Relationships, Blending & Union

Custom Charts

Dual Axis
Combined Axis
Donut Chart
Lollipop Chart
KPI Cards (Simple)
KPI Cards (With Shape)

Groups, Bins, Hierarchies, Sets, Parameters

What are Groups ? Purpose
What are Bins ? Purpose
What are Hierarchies ? Purpose
What are Sets ? Purpose
What are Parameters ? Purpose and examples

Analytics & Dashboard

Reference Lines
Trend Line
Overview of Dashboard: Tiled Vs Floating
All Objects overview, Layout overview
Dashboard creation with formatting

Dashboard Actions & Tableau Public

Actions: Filter, Highlight, URL, Sheet, Parameter, Set
How to save the workbook to Tableau Public website ?

Power BI Introduction and Installation

Understanding Power BI Background
Installation of Power BI and check list for perfect installation
Formatting and Setting prerequisits
Understanding the difference between Power BI desktop & Power Query

The Power BI user interface, including types of data sources and visualizations

Getting familiar with the interface BI Query & Desktop
Understanding type of Visualisation
Loading data from multiple sources
Data type and the type of default chart on drag drop.
Geo location Map integration

Sample dashboard with Animation Visual

Finanical sample data in Power BI
Preparing sample dashboard as get started
Map visual Types and usages in different variation
Understanding scatter Plot chart with Play axis and the parameters

Power BI artificial intelligence Visual

Understanding the use of AI in power BI
AI analysis in power bi using chart
Q&A chat bot and the use in real life
Hirarchy tree

Power BI Visualization

Understanding Column Chart
Understanding Line Chart
Implementation of Conditional formating
Implementation of Formating techniques

Power Query Editor

Loading data from folder
Understanding Power Query in detail
Promote header, Split to limiter, Add columns, append, merge queries etc

Modelling with Power BI

Loading multiple data from different format
Understanding modelling (How to create relationship)
Connection type, Data cardinality, Filter direction
Making dashboard using new loaded data

Power Query Editor Filter Data

Power Query Custom Column & Conditional Column
Manage Parameter
Introduction to Filter and types of filter
Trend analysis, Future forecast

Customize the data in Power BI

Understanding Tool tip with information
Use and understanding of Drill Down
Visual interaction and customisation of visual interaction
Drill through function and usage
Button triggers
Bookmark and different use and implementation
Navigation buttons

Dax Expressions

Introduction to DAX
Table Dax, Calculated column, DAX measure and difference
Eg:- Calendar, Calendar auto, Summarize, Group by etc
Calculated Column
Related, Lookup value, switch, Datedif,Rankx,Date functions
Dax Measure and Quick Measure
Remove filters, Keep filters, All, Allselected, Time Intelligence Functions,Rolling average,YoY, Running total

Custom Visual

Custom visual and understanding the use of custom
Loading custom visual, Pinning visual
Loading to template for future use
Publishinhg Power Bi

Power BI Service

Introduction to app.powerbi.com
Schedule refresh
Data flow and use power bi from online
Download data as live in power point and more

Descriptive Statistics

Data Types, Measure Of central tendency, Measures of Dispersion
Graphical Techniques, Skewness & Kurtosis, Box Plot

Probability and Normal Distribution

Random Variable, Probability, Probility Distribution, Normal Distribution, SND, Expected Value

Inferential Statistics

Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval
Introduction to Hypothesis Testing
Hypothesis Testing (2 proportion test, 2 t sample t test)
Anova and Chisquare

Data cleaning and Insights

Data Cleaning(Invalid cells,Blanks,Outliers,Null values)
Imputation Techniques(Mean and Median)
Scatter Diagram
Correlation Analysis

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

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

Decision making statements,Loops,Functions

If,Ifelse,For loop,While loop,Repeat,Functions

Built in Functions in R,Joins,dplyr and ggplot2

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

Anaconda Installation,Introduction to python,Data types,Opearators

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

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

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

Sets,Set Theory,Regular Expressions,Decision making statements

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

Loops,Functions,Lambda functions,Modules

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

Pandas,Numpy,Matplotlib,Seaborn

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

Introduction to Agile

Project Definition
Difference Between Traditional & Agile Project Mgmt.
Agile Manifesto and Principles
Agile Methodology
Agile Principles
Agile Frameworks and Terminology

Agile Methodologies

Scrum
XP

Agile Analysis and Design

Product Roadmap
Product Backlog
Story Maps
Agile Modeling
Wireframes
Charting
Personas

Planning and Monitoring

Iteration and Release Planning
Progressive Elaboration
Time Boxing
Cumulative Flow Diagram
Kanban Boards
WIP Limits
Burn Charts
Retrospectives
Innovation Games

Agile Metrics and Estimations

Relative Sizing
Story Points
Wideband Delphi Technique
Planning Poker
Affinity Diagram
Ideal time
Velocity
Cycle Time
EVM
Escaped Defects

Quality

Frequent Verification and Validation
Test Driven Development
Definition of Done
Continues Integration
Feedback Techniques
Incremental Delivery
Continuous Improvement

Value Based Prioritization

Customer Valued Prioritization
Compliance
Relative Prioritization
Value Stream Mapping
Minimum Marketable Feature

Risk Management

Risk Adjusted backlog
Risk Burn down charts
Risk based spike

Agile Communications

Team Space
Information Radiator
Agile Tooling
Daily Stand-ups
Osmotic Communication

Introduction to ChatGPT and AI

What is ChatGPT?
The history of ChatGPT
Applications of ChatGPT
ChatGPT vs other chatbot platforms
Industries using ChatGPT
The benefits and limitations of ChatGPT
Future developments in ChatGPT technology
Ethical considerations related to ChatGPT and

Types of AI and Chatgpt architecture

What is AI?
Types of AI
What is Machine Learning?
Neural Networks
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Robotics and AI

ChatGPT Functionalities and Applications

How does ChatGPT work?
ChatGPT Functionalities
Drafting emails and professional communication
Automating content creation
Resume and Cover letter creation
Research and information gathering
Brainstorming ideas and creative problem solving
Best Practices for Using ChatGPT

ChatGPT Prompt Engineering

What is Prompt Engineering?
Types of Prompts
Crafting Effective Prompts
Using ChatGPT to generate prompt

Business Statistics
Fundamentals of R
Fundamentals of Python
SAS(Self Paced)
Agile
ChatGPT

Steinbeis University, Berlin Frequently Asked Questions (FAQ's)

1: What does instructor led online training mean?

Instructor-led online training is a training mode where the instructors and participants will log in at the same timing and the sessions will be held online. These sessions provide better interaction between the trainer and the participant.

2: Are recorded sessions available?

Yes, both live training and recorded sessions are available. Recorded sessions are provided for the candidate to access the course if they missed watching a session.

3: Will the candidate receive a certificate on course completion?

Yes, Certified Business Analytics/Data Science Course completion certificate is provided on successful completion of the course. The candidate has to score 60% marks in final exam to avail certificate.

4: What does jumbo pass mean?

Jumbo pass is an initiative taken by ExcelR to offer access to the candidate to attend unlimited batches for one year. The candidate can attend unlimited classes of their own choice.

5: What is the duration of the programme?

The duration of the program is 6 months. The candidate is provided with live sessions, assignments and projects that would help them to gain more knowledge.

6: Who can attend this course?

Professionals who are working in Data warehouse technologies and business intelligence, Statisticians, Mathematicians, Economists, Software programmers, Fresher, Business analysts , and Six sigma consultants can attend this course.

7: Are placement assistance provided?

Yes, placement assistance is provided through various interview preparation sessions. The learner will undergo the training if he/she has completed the course.

8: Are value added courses available?

Yes, value added courses are given as a part of the curriculum. The basic concepts in Python, R, MySQL, Hadoop, Artificial intelligence, Tableau and Spark are taught to the candidate.

9: Whom can the candidate contact for queries?

The candidate can chat with the course guidance team or call them via 1800-212-2120 or chat on WhatsApp via +91 91082 38354

10: Can a fresher get a job in business analytics?

Yes, the freshers can find more jobs as business analysts. This course helps the candidate to gain conceptual knowledge in the business analytics domain that would help them to get better career opportunities.