Data science is defined as an interdisciplinary field that uses scientific methods, processes, algorithms and systems to understand as well as extract insights from structured &(or) unstructured data.

Course Overview

Do you aspire to be employed as a Data Analyst/Business Analyst? Do you want to be renowned for analysing and interpreting complex digital data, usage statistics of a website, in order to assist businesses in their decision-making? If Yes, then you are at the right destination! Get in touch with us for 100% job oriented courses.

Course Highlights

Formulating a strategy or improving operations is key for every business. For those who seek growth in business, advance analytics focuses on analysing current data or forecasting future events and behaviours bringing revolutionary gains in organizational performance. Our curriculum for the best Business & Data Analytics course in Hyderabad primarily focuses on business & data analytics, classifications & prediction models, text analysis and visualization.

Get Hired as Business & Data Analytics with the Best Business & Data Analytics Masterclass Training Course in Hyderabad

  • In-depth understanding with a 360-degree overview of business analytics, Python, SQL and R by mastering concepts like data exploration, data visualization, different analytical techniques, data wrangling, understanding different machine learning algorithms, structured & unstructured business & data analytics and predictive analytics.
  • Leverage your Business & Data Analytics skills with a comprehensive approach to business & data analytics, data structures & visualization, regression analysis & classification, clustering & association.
  • Get enhanced exposure to real-world projects with proper hands-on training.
  • Mock exams and interviews.
  • Guidance for preparation of resume & interviews.

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Enrol Today! 8 Reasons Why We Are The No.1 Business & Data Analytics Training Institute in Hyderabad

Best Training Course

Best Business & Data Analytics Masterclass course in Hyderabad guaranteeing 100% placement assistance.


Comprehensive and holistic approach in each subject.


Regular doubt-clarification sessions, assignments, free e-books and materials for all modules.


Back-up classes for the ones you’ve missed out.

Best Opportunities

Free internship for selected candidates.

Best Exposure

Real-World project exposure.


Certification after due-completion of course.

Upskill & Upgrade

Employability skills to get ready for corporate challenges.

Learn More about the Business & Data Analytics Masterclass course

The most comprehensive and detailed Business & Data Analytics Masterclass course to facilitate your clear understanding. There will be multiple professional trainers, with the requisite expertise on the subject, who will be conducting the sessions. Each module is designed by the experts which are:

  • Well-elaborated.
  • Includes minute details aiding ‘Beginner to Expert’ transformation.
  • Career & personal development oriented approach.

Our friendly teachers who are also industry commanders provide extensive support for knowledge transfer through application-based pedagogy & real-world projects.


Python Environment Setup and Essentials

  • Introduction to Python Language
  • the advantages of Python over other programming languages
  • Python installation (Windows & Linux distribution for Anaconda Python)
  • deploying Python IDE
  • basic Python commands
  • data types
  • variables
  • keywords

Python language Basic Constructs

  • Built-in data types in Python
  • tabs and spaces indentation
  • code comment Pound # character
  • variables and names
  • Python built-in data types (Numeric, int, float, complex, list tuple, set dict)
  • containers
  • text sequence
  • exceptions
  • instances
  • classes
  • modules
  • Str(String)
  • Ellipsis Object
  • Null Object
  • Ellipsis
  • Debug
  • basic operators (comparison, arithmetic, slicing and slice operator, logical, bitwise)
  • loop and control statements (while, for, if, break, else, continue)

OOP concepts in Python and database connection

  • How to write OOP concepts program in Python
  • connecting to a database
  • classes and objects in Python
  • OOPs paradigm
  • important concepts in OOP (polymorphism, inheritance, encapsulation)
  • Python functions, return types and parameters
  • Lambda expressions
  • connecting to database and pulling the data

Operators and Keywords for Sequences

  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.

Working with missing data

  • Reading Data from CSV, Excel, JSON
  • Writing Data to CSV, Excel, JSON, HTML
  • Reading data from database and storing in data frame
  • Writing data frame to database
  • Handling PDF files - tabula-py

Python Important Libraries


  • Introduction to arrays and matrices
  • indexing of array
  • data types
  • broadcasting of array math
  • standard deviation
  • conditional probability
  • correlation and covariance


  • Pandas
  • i. Series
  • Constructing from dictionaries
  • Custom index
  • Data filtering
  • ii. Data Frames
  • Constructing from a dictionary
  • with values as lists
  • Custom indexing
  • Rearranging the columns
  • Accessing values loc(), iloc(), at()&iat()
  • Setting values
  • Sum
  • cumulative sum
  • Assigning a column to the data frame
  • Adding a new column
  • Deleting a column
  • Slicing
  • Indexing and Advanced indexing
  • Boolean indexing
  • Transposing
  • Sort by
  • Concatenate
  • Merge
  • 1. Inner join
  • 2. Outer join
  • 3. Left outer join
  • 4. Right outer join
  • 5. Merge on columns
  • Join
  • Group By- Aggregation
  • Data Munging


  • Introduction to SciPy and its functions
  • building on top of NumPy
  • cluster
  • linalg
  • signal
  • optimize
  • integrate
  • subpackages
  • SciPy with Bayes Theorem


  • How to plot graph and chart with Python
  • various aspects of line
  • scatter
  • bar
  • histogram
  • subplots


  • Math function


Scraping using BS4

Statistics and Probability


  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level & Interval
  • Degrees of freedom
  • what is pValue
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression

Linear & logistic Regression

  • Case study
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression
  • logistic regression


  • Probability
  • Naïve Bayes theorem

How to handle missing data

  • Missing Data

Reading and using different Charts

  • Box & scatter plot
  • Histograms
  • Heat map
  • correlation chart

R Programming

  • Why R and Importance of R in Analytics
  • Installation R and R-studio
  • Data Types
  • R Matrix : Create, Print, add Column, Slice
  • Operators
  • Decision Making
  • Loops
  • Lists
  • Sets
  • Vectors
  • Strings
  • Matrices
  • Arrays
  • Factors
  • Functions( Built in and user defined)
  • Data manipulation
  • Handling different types of data
  • Reshaping the data

Web scraping

  • Exploratory data analysis using R
  • Data visualization
  • Descriptive statistics using R
  • Exploratory Data analysis

SQL & Data Warehousing

Introduction to SQL

  • Data & Database etc
  • SQL Server Management Studio
  • Creating Databases : Files [MDF, LDF]
  • DDL, DML operations

Creating Tables in SQL Databases

  • Data Types for its usage
  • Single Row Inserts, Multi Row Inserts
  • Data Retrieval for analysis (select etc)
  • SELECT with WHERE Conditions
  • CHAR Versus VARCHAR Data Types

AND and OR Operators Usage

  • IN Operator and NOT IN Operator
  • LIKE and NOT LIKE Operators
  • Using Wild Card Characters
  • IS and IS NOT Operator, NULLs
  • Using DISTINCT, TOP Keywords

UPDATE Statement & Conditions

  • DELETE Statement & Conditions
  • TRUNCATE & DELETE Differences
  • Table Data / Content Modification
  • Table Structure Modifications (DDL)
  • ALTER, ADD and DROP Statements
  • Removing Tables and Databases

Schemas : Usage, Creation

  • Table Migrations across Schemas
  • Import / Export Wizard From SSMS
  • GO Statement, SQL BATCH Concept

JOINS - Table Comparisons Queries

  • INNER JOIN - Examples
  • Left Outer Joins with Example Queries
  • Right Outer Joins with Example Queries
  • FULL Outer Joins with real time scenario
  • CROSS join

Stored procedures

  • functions


  • Joins with GROUP BY and HAVING
  • Joins with Sub Queries, IS NULL
  • Date and Time Functions: Getdate()
  • Cast and Convert with Getdate()
  • Data conversion

Date & Time Styles, Data Formatting

  • DateAdd and DateDiff Functions
  • String Functions: Left and Right
  • SubString, Replace and CharIndex
  • Reverse, Len, LTrim and RTrim

Joining 3 and 4 Tables in T-SQL

  • Using Joins with Subqueries
  • Using Joins with Nested Sub Queries
  • IIF ( ) and CASE Statement Usage
  • Using IIF and CASE in Joins
  • Joins in Group By, Rollup, Cube
  • Replacing Nulls: Isnull, Coalesce

ROW_NUMBER() and RANK() Queries

  • DENSE_RANK, Sequence Identification
  • PARTITION BY and RowNumber ( )
  • CTE : Common Table Expressions
  • Using CTEs for Avoiding Self Joins
  • Using CTEs for Avoiding Subqueries
  • PIVOT versus UNPIVOT Queries

Exercise with real time data

Visualization & Dashboard

Introduction to Data Visualization

Theories of Human Perception

Basics of Data Visualization

Designing your Visualizations

Visualization Toolbox

Tableau Desktop

  • Scenario
  • Objectives
  • Application Terminology and Definitions
  • Opening and Closing Tableau
  • Data Source Page
  • Tableau Workspace
  • Files and Folders
  • Getting Started with Tableau
  • View Terminology and Definitions
  • View Sections
  • Data Terminology and Definitions
  • Data Types
  • Data Roles: Dimension vs. Measure
  • Data Roles: Continuous vs. Discrete
  • Changing Data Roles

Data Connections in the Tableau

  • Connecting to Tableau Data Server
  • What is a Join
  • Types of Joins
  • When to Use Joining
  • Enabling Right Outer Join
  • Right Outer Join and Custom SQL Enabled
  • What is Data Blending
  • When to Use Data Blending
  • Data Blending in Tableau
  • Differences Between Joining and Blending
  • Joining vs. Blending
  • Writing Custom SQL
  • Prepare your Data for Analysis

Organizing and Simplifying Data

  • Organizing and Simplifying Data
  • Objectives
  • Filtering Data
  • What is a Filter
  • Applying a Filter to a View
  • Filtering on Dimensions
  • Filtering on Dimensions Functions
  • Aggregating Measures
  • Filtering on Measures
  • Filtering on Dates
  • Quick Filters
  • Sorting of Data
  • What is Sorting
  • Sorting Data in Tableau
  • Types of Sorting
  • Creating Combined Fields
  • Combined Fields
  • Creating Groups and Defining Aliases
  • What is a Group
  • What are Aliases
  • Defining an Alias
  • Working with Sets and Combined Sets
  • Sets
  • Combined Sets
  • Working with Groups and Sets
  • Drill to Other Levels in a Hierarchy
  • Grand totals and Subtotals
  • Adding Totals
  • Adding Totals
  • How to Define Aggregations
  • Changing Aggregation Function
  • Tableau Bins
  • Bins
  • Fixed Sized Bins
  • Variable Sized Bins

Formatting and Annotations

  • Formatting and Annotations
  • Adding Caption to Views
  • Click Interaction Adding Title to View
  • Click Interaction2 Adding Captions to View
  • Using Titles Captions and Tool tips
  • Adding Tooltips to Views
  • Using Title Caption and Tooltip
  • Formatting the Axes
  • Edit Axis Option
  • Formatting Views with Labels and Annotations
  • Format Window
  • Format Mark Labels

Special Field Types

  • Date Hierarchies
  • Drilling in the Time Hierarchy
  • Pivoting Date Parts on Shelves
  • Differentiate Between Discrete and Continuous Dates
  • Using Continuous Dates
  • Using Discrete Dates
  • Working with Discrete and Continuous dates
  • What are Custom Dates
  • Creating and Using Custom Dates
  • Fiscal Year
  • Define a Date Field on a Fiscal Year
  • Relative Date Filters
  • Importing Date Dimensions in Tableau from a Cube
  • Work with Date Hierarchies on Cubes

Chart Types

  • Chart Types
  • Working with Combined Axis
  • Working with Combination Charts
  • Understanding geocoding and geographic mapping in tableau
  • Combined Axis Graph and Scatter Plot
  • Describe text and highlight tables
  • Work with Pages Shelf and Create Motion Charts
  • Heat Maps
  • Using Bins and Histograms
  • Using Histograms
  • Using Pie Charts
  • Compare Measures Using Bullet Charts
  • Using Bar in Bar Charts

Define Advanced Chart Types

  • Using Pareto Charts
  • Creating Pareto Charts
  • Using Waterfall Charts
  • Using Gantt Charts
  • Working with box plots
  • Using Sparkline Charts


  • Calculations
  • Objectives
  • Strings Date Logical and Arithmetic Calculation
  • Working with Strings Date Logical and Arithmetic Calculations
  • Using Strings Date Logical and Arithmetic Calculations
  • Working with Arithmetic Calculations
  • Aggregation Options
  • Working with Aggregation Options
  • Grand Totals and Sub-Totals
  • Quick Table Calculations
  • Creating Quick Table Calculations
  • Working with Quick Table Calculations
  • Automatic and Custom Split
  • Ad-hoc Analytics
  • LOD Calculations

Creating and using Parameters

  • Creating and using Parameters
  • Objectives
  • What is a Parameter
  • Creating a Parameter
  • Exploring Parameter Controls
  • Work with Parameters
  • Click Interaction Working with Parameters


  • Dashboards
  • Objectives
  • Build Interactive Dashboards
  • What is a Dashboard
  • Building Dashboards
  • Best practices for creating effective dashboards
  • Comprehending Best Practices
  • Creating a Dashboard and Importing Sheets
  • Interaction Exploring Dashboard Actions
  • Use of Running Actions
  • Using Dashboard Actions
  • Sharing your Work
  • How to Share your Reports
  • Exporting your Work

Machine Learning Basic's

Machine Learning

Supervised Learning

  • What is supervised Learning
  • Algorithms in Supervised Learning
  • Steps in Supervised learning
  • Regression and Classification
  • Regression Vs Classification
  • Accuracy Metrics
  • Classification
  • Decision Trees and Model Tuning
  • Pros and Cons
  • Evaluation Metrics
  • Random Forest and Model Tuning
  • Evaluation Metrics
  • Pros and Cons
  • KNN Algorithm
  • Evaluation Metrics
  • Pros and cons
  • Support Vector Machines
  • Kernel models
  • Evaluation Metrics
  • Pros and cons

Unsupervised Learning

  • Dimension Reduction
  • Principal Component Analysis
  • Singular Value Decomposition


  • K-means Clustering

Text Mining

Intro to Text Mining

  • Applications
  • Extracting text from website, URLs,PDF
  • Text Cleaning
  • Text clustering
  • Word cloud
  • N-grams
  • Sentiment Analysis
  • NLP


  • Introduction to forecasting
  • Data cleaning and manipulation
  • Time series
  • Components of Time series
  • Trend Analysis
  • Forecasting Methods
  • Smoothing methods
  • Auto regressive model
  • ETS model
  • ARIMA Model
  • Anomaly detection

Association Rules

  • Introduction
  • Metrics lift, support, Confidence and conviction
  • Apriori Model

Analytical Thinking

What is analytics

  • What is analytics
  • Linear programming (LP)
  • How to solve using Excel
  • Python library- PuLP

Analytics Using Python

  • Supply chain Analytics
  • Marketing Analytics - RFM (and/or CLV, touch upon conjoint analysis)
  • Social media Analytics – Twitter (or facebook)
  • Pricing analytics

How to build analytical thinking

  • How to view data
  • Feature Selection and Pre-processing
  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study

Business & Process Understanding

  • Connecting the dots
  • How to understand a process
  • How to take KT
  • Questions to ask to understand any process
  • Business understanding
  • Problem Statement and Analysis
  • Various approaches to solve a Business & Data Analytics Problem
  • Pros and Cons of different approaches
  • How to scale analytics

Employability Skill & Interview

  • Mock interviews
  • Group Discussions
  • Practical Examination


I’m a beginner. Is this course suitable for me?

Yes. This is a comprehensive course suitable for beginners.

Where can I apply Data Analysis in real-life?

Data Analysis is has wide range of applications in the field of technology, business, health, education, research, etc. for analysing and interpreting complex digital data, usage statistics in order to assist a business in its decision-making. Data analysis has different applications across all industries from retail, healthcare, web analytics, FMCG, manufacturing to airlines etc. analytics is also required across all functions of the company, from finance, optimisation, marketing, sales to production and even pricing.

Who else can join this course?

Any graduates (including IT or business field) can enroll for this course. Professionals can also upskill themselves for their career growth and development.

Beginner students aspiring to make it big in the field of Analytics can also enroll for this course.

What are the benefits of taking Business & Data Analytics Masterclass training?

Our best Business & Data Analytics Masterclass training helps in building a career as an IT Analyst, Data Consultant, Data Engineer, etc. Learning and successful completion of this course will help you to understand the basics, key concepts, applications and more.

What scope do I have as a Data Analyst or Engineer Professional?

A Data analyst or engineer is never obsolete and irrelevant. Various companies and corporates who seek growth in business, recruit data experts to create, develop and manage advanced analytics that focuses on forecasting future events and behaviours bringing revolutionary gains in organizational performance. With the current demand, high scope, and our best Business & Data Analytics Masterclass course in Hyderabad there is great career growth.

What kind of projects will I be a part of during my course training?

GingerBoard is offering you the most relevant, valuable and industry-oriented projects as part of all its course programs. This enables you to implement the principles and concepts that you’ve acquired in real-world industry setup. All our best training programs come with multiple projects that thoroughly test your skills, boost your learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of advanced technology, eCommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects, you will become a certified professional!

Do we have demo sessions for the course?

Definitely. We believe that every individual should get accustomed to the GingerBoard way of teaching and training. Demo sessions enable you to make concrete decisions regarding career development.

Why should I pursue a Business & Data Analytics Training course from GingerBoard Academy?

GingerBoard Academy is a leading institute that offers training courses in Data Analysis technologies by experts with the sole objective of bridging the gap between the education curriculum and the IT industry.

  • With our best Business & Data Analytics Masterclass training course in Hyderabad, excellent teaching methodologies are followed so that students & trainees can attain high-level knowledge on each and every concept.
  • Students & Trainees are guided throughout the training in such a way that they can do exploratory data analysis, find actionable insights, build relevant dashboard, improve performance of a Machine learning model and decide on what data to collect for business by the end of the Business & Data Analytics training course in Hyderabad.
  • Our industry experts deliver experiential training with practical awareness & theoretical understanding.
  • Both offline, as well as online course material covering all the topics, is given to help the trainees learn better. Technical and Analytical skills flourish through regular assessments and daily assignments.
  • The excellent learning environment, regular assistance, and expert-devised training procedures are the key features of GingerBoard Academy Business & Data Analytics Masterclass Training in Hyderabad.
  • Your careers are ever evolving; therefore, it is essential for you to need a learning solution that inspires you to create the future. With our platform, you can always match up your pace, work faster and smarter, and gain in-demand skills with the ever-changing speed & path of technology!

Is there any placement assistance after I complete my course?

GingerBoard actively provides placement assistance to all learners who have successfully completed the training. We also help you tackle job interviews through mock interviews and resume preparation part as well.

We are exclusively tied-up with numerous MNCs from around the world where you can be placed with a strengthened authentic profile. We are associated with outstanding organizations among other equally great enterprises.

How to attend the missed sessions?

GingerBoard Academy ensures that every course and industrial training you undergo is seamless and efficient in function. You can inform your trainer priorly about the classes or sessions missed, then apply for back up classes online.

What kind of job opportunities would be available post completion of my certified course?

After you’ve successfully completed the course you will be able to work under the following designations:

  • Data Analyst
  • Business Intelligence Specialist
  • Operations Analyst
  • Business Analyst
  • Data Engineer
  • Quantitative Analyst
  • Business & Data Analytics Consultant

Pursue your Dream Career with our Best Courses and expert trainers! Register Today!

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