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Data Science Certification Program

Yes, it’s true “Data Science and Machine Learning are the hottest skills in demand” but wait it’s challenging too to learn. 
Today Data Science and Machine Learning are used in almost all industries, including automobile, banking, healthcare, media, telecom, and others.

As Data Science and Machine Learning practitioners, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advanced tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?

Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an in-depth understanding of the skills:- Basic to Advanced Excel, My SQL / SQL Server, Tableau, Python, R

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Instructor -Led Online & Physical Classes

Batch Start DateClass TypeUpcoming SlotsTimingsLocation
2nd October 2022Online Instructor-Led TrainingSold Out
9:15 AM to 11:15 AMOnline
9th October 2022Online Instructor-Led TrainingSeats are filling fast
weekend batch
8:00 PM to 10:00 PMOnline
16th October 2022Online Instructor-Led TrainingSAT & SUN
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9:00 AM to 11:00 AMOffline
30th October 2022Online Instructor-Led TrainingSAT & SUN
Weekend Batch
8:00 PM to 10:00 PMOnline

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INR 1,02,430/-
INR 42,000/- + 18% GST

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Course Content

For the curriculum, you will cover  in this course, click on the tab to check the detailed content

Module 1:- Getting Started with Excel

  • Introduction to Excel 2013/2016/2019/Office 365
  • Application Interface and Key Components of Excel
  • Navigating Through Excel Ribbon Tabs
  • Exploring Important Excel Options*
  • Live Session Exercise
  • Splitting data of Single Column into multiple
  • 10 Examples to use Auto-fill and Flash Fill
  • Magics of Go-To Special
  • Merge/Unmerge Cells & Wrap Text
  • Extracting Unique Values & Important Ribbon, General and Data Entry Keyboard Shortcuts

Module 2:- Formatting Essentials

  • Formatting Essentials Introduction
  • Custom Cell Number Formats
  • Custom Date/Time Formats
  • Working with Comments / Notes
  • Format Painter – A Quick way to copy ‘Formatting Attribute’
  • Paste Special
  • Table, Table Styles & Formatting
  • Freeze Panes

Module 3:- Functions & Formulas

  • Introduction to Excel Functions and Formulas
  • Basics of Functions & Formulas
  • Working with Cell References Types
  • Most Used Basics & Advanced Functions & Formulas
  • Working with Array Formulas
  • Creating Customize Formulas Step-by-Step with Live examples
  • Creating and Working with Dynamic Ranges using Function and Excel Table features
  • Formulas Debugging / Formulas Auditing
  • Types of Formula Errors / Error Handling Tricks
  • Date & Time Functions: – DATE, DAYS, TIME, NOW, WEEKNUM, WORKDAY, and WORKDAY.INTL etc.
  • Statistical Functions: – AVERAGE, COUNT, COUNTA, COUNTBLANK, MAX, MIN, LARGE etc.
  • Logical Functions: – IF, IFS, AND, OR, and IFERROR.
  • Newly Introduced Functions in Recent Version of Excel*: – CONCAT, TEXTJOIN, IFS, SWITCH, DGET, UNIQUE, FILTER etc.
  • Nested Conditions/Customize Formulas*

Module 4:- Data Analysis

  • Data Sorting
  • Data Filtering
  • Named Ranges
  • 10 different ways to use Conditional Formatting
  • 10 different use of Data Validation
  • What-If Analysis

Module 5:- Excel Charts

  • Introduction to Excel Charts
  • Exploring most commonly used Charts and Templates
  • Basics of Charts
  • Selecting Requirement based Charts
  • Working with Basic Charts:
  • Creating Customized / Advanced Charts
  • Creating Dynamic Chart
  • Working with Dynamic Interactive Charts in Excel using Drop Down
  • Working with Chart Elements, Formatting, Chart Styles, Properties etc.

Module 6:- Pivot Tables

  • Introduction to Pivot Table
  • Creating a Pivot Table
  • Use of Calculated Fields/Items
  • Pivot Table Formatting
  • Grouping Items & Summarizing data in Pivot Tables
  • Grouping and Bucketing data in Pivot Table
  • Changing/Modifying Data Sources
  • Working with Pivot Table Designs & Layouts
  • Exploring Important Pivot Table Options & Field Settings
  • Pivot Table Filters
  • Changing Pivot Table Summary Calculation
  • Use of Slicers in Pivot Table
  • Using Source Data to Convert into Infographic Summary
  • Introduction to Pivot Charts

Module 1:- Getting Started with My SQL

  • An Introduction and Overview of MySQL
  • Installation and GUI Tools
  • An Overview

Module 2:- My SQL Fundamentals

  • Introducing SELECT statement
  • Introducing WHERE clause
  • Sort result with ORDER BY
  • Using FROM to specify the source tables
  • Importance of Clause Orders
  • Data Modification tricks

Module 3:- Creating Database & Tables

  • Creating a database
  • Creating a table
  • Creating Indexes
  • Controlling column behavior with constraints
  • Using foreign key constraints
  • Creating an ID column
  • Changing a schema with ALTER
  • Introducing NULL and NOT_NULL
  • Introduction to MySQL Data Types
  • Setting up default values
  • MySQL Warnings
  • Alerting a table

Module 4:- My SQL Functions & Clause

  • Introduction to MySQL Functions
  • Aggregate Functions – COUNT, MIN, MAX, SUM, AVG, ROUND etc
  • Control Flow Functions – IF, IFNULL, NULLIF etc.

Module 5:- Multiple Tables & Joins

  • Introduction to JOINS
  • Different types of JOINS
  • JOINS and Aliases
  • Multiple Table Joins
  • Creating a simple Subselect
  • Understanding of Primary keys and Foreign keys

Module 6:- Transactions, Stored Routines & Triggers

  • Transactions & Stored Routines
  • Triggers

Bonus Modules

  • Creating a New User Login
  • Granting access to new users
  • Backup and Restore databases
  • Important Keyboard Shortcuts Guide
  • Session Study Material
  • Situational Case Studies for Best Practice and Getting Ready for Corporate World
  • 6 Months Live Support via Phone/Email/Messages

Module 1:- Getting Started with Tableau

  • Introduction to Data Visualization
  • Leading Data Visualization Tools
  • Introduction to Tableau
  • Exploring Interface and Important Key Component
  • Navigating Through Tableau Menu Tabs
  • Exploring Each Menu Tab i.e. File, Data, Worksheet, Dashboard, Story, Analysis, Map, Format, Server etc.*
  • Tableau – Design Flow
  • File Types
  • Tableau Data Types
  • Show Me
  • Data Terminology

Module 2:- Connecting to Data with Tableau Desktop

  • Introduction to Data Connection
  • Data Source Interface
  • Types of Data Connections
  • Extracting Data
  • Custom Data View
  • Joins and Unions
  • Data Blending
  • Live Connection Vs Extract
  • Field Operations
  • Basic Project Activity

Module 3:- Examining & Filtering

  • The Sheet Interface
  • Dimensions & Measures
  • Hierarchies
  • Data Granularity
  • Highlighting
  • Data Sorting
  • Grouping Data
  • Data Filtering
  • Data Source Filters
  • The Filter Shelf
  • Dimension Filters & Card Modes
  • Context Filters
  • Measure Filters
  • Creating Sets

Module 4:- Field Types & Charts

  • Utilize Auto-Generated Fields
  • Use Titles, Captions and Tooltips Effectively
  • Creating Bins
  • ToolTip
  • Basic Charts

Module 5:- Calculations in Tableau

  • What are Calculations
  • Methods to Create Calculated Field
  • Introduction to Tableau Functions
  • Operator and Syntax Conventions
  • Introduction to Table Calculations

Module 6:- Level of Detail (LOD) Expression

  • Level of Detail (LOD) Calculations
  • Live Use Cases of LOD
  • Introduction to Parameters
  • Parameters Data Type Options

Module 7:- Geographical Visualization

  • Introduction to Geographic Visualizations
  • Assigning Geographical Locations
  • Spatial Files
  • Map Types
  • Custom Geocoding
  • Background Image

Module 8:- Advanced Charts in Tableau

  • Introduction to Advanced Charts
  • Bar in Bar Chart
  • Bullet Chart
  • Pareto Chart
  • Gantt Chart
  • Hierarchy and Tree Maps
  • Box and Whisker’s Plot
  • Waterfall Chart
  • Step and Jump Lines
  • Maps on a Scatter Plot
  • Bubble Chart
  • Control Chart
  • Funnel Chart
  • Packaged Bubbles
  • Word Cloud
  • Donut Chart
  • Trendlines
  • Reference Line, Bands, and Distributions

Module 9:- Dashboard & Stories

  • Introduction to Dashboards
  • The Dashboard Interface
  • Important Dashboard Objects
  • Adding Objects to the Dashboard
  • Building a Dashboard
  • Dashboard Design and Formatting
  • Types of Actions
  • Designing Dashboard for Tablets & Mobile-Phones
  • Story Points
  • Sharing Workbook
  • Wrapping up Tableau Program

Module 1:- Installing & Running Python

  • Python 2.7 vs Python 3
  • Local Environment Setup
  • Installing Python on different platforms(Windows and Linux)
  • Python Interpreter and Python Interactive Shell
  • Python IDE(Pydev, Pycharm,VIM)

Module 2:- Python Introduction

  • Python Overview
  • History Of Python
  • CPython, Jython, PyPy
  • Python Features
  • Areas Of Application of Python
  • Understanding More About Python
  • Writing your First Python Program
  • Interactive Mode Programming
  • Script Mode Programming
  • Dir and help: Getting help from the Python interpreter

Module 3:- Python Syntax ,Keywords and Operators

  • Python Identifiers
  • Various Operators and Operators Precedence
  • Reserved Words,Lines and Indentation
  • Multi-Line Statements,Quotation in Python
  • Comments in Python,Using Blank Lines
  • Command Line Arguments
  • Python Input/Output:Using the Print Function
  • Getting Input from User
  • Python Basic Data Types And Variables
  • Binary, octal and hexadecimal numbers
  • Convert one data type to another

Module 4:- Expressions, Statements, Variables, Strings

  • Working With Numbers
  • Working With Booleans
  • Math library and its various operations
  • Working with Strings
  • String types and formatting
  • String Operations and Task
  • Program to find duplicate characters in a String.
  • Program to reverse a string
  • Program to check if String is Palindrome
  • Program to remove a newline in Python

Module 5:- Python Data Types: List,Tuples,Dictionaries

  • Python Lists, Tuples, Dictionaries
  • Accessing Values
  • Basic Operations
  • Indexing, Slicing, and Matrixes
  • Built-in Functions & Methods
  • Exercises on List, Tuples And Dictionary
  • Remove Duplicate from Lists
  • Program to find the index of an item of a tuple
  • Python program to convert a list to a tuple
  • Python program to reverse a tuple
  • Program to convert a tuple to a dictionary

Module 6:- Making Decisions – if Statements

  • The Relational Operators
  • The Logical Operators
  • Simple if Statement,if-else Statement
  • If-elif Statement
  • More Advanced If, ElIf & Else Processing

Module 7:- Loop Control

  • Introduction To while Loops
  • Count-Controlled while Loops
  • Event-Controlled while Loops
  • Using continuE,Using break
  • Introduction To for Loops
  • For loops with files,list,tuples and dictionaries

Module 8:- Iterators

  • Understanding Iterators
  • Using iter And next
  • Iterators And Dictionaries
  • Other Iterators

Module 9:- Functions And Scopes

  • Introduction To Functions – Why
  • Defining Functions
  • Calling Functions
  • Functions With Multiple Arguments
  • Predicate Functions,Recursive Functions
  • Function Objects,Generators,Decorators
  • Anonymous Functions, Higher-Order Functions
  • Scope , Global Scope, Local Scope , Nested Scope

Module 10:- Modules and Packages

  • Using Built-In Modules
  • User-Defined Modules
  • Module Namespaces
  • Installing and Uninstalling a package
  • Package vs Library vs Module

Module 11:- File I/O

  • Printing to the Screen
  • Reading Keyboard Input
  • Opening and Closing Files
  • Open Function,file Object Attributes
  • close() Method ,Read,write,seek
  • Rename,remove,
  • Mkdir,chdir,rmdir

Module 12:- Error And Exceptional Handling

  • Exception Handling, Assertions: The assert Statement
  • What is Exception, Handling an exception
  • The except Clause with No Exceptions, the try-finally Clause
  • The argument of an Exception, Raising an Exceptions
  • User-Defined Exceptions 

Module 13:- Regular Expression

  • Matching and Searching- match() and search() Functions
  • Search and Replace
  • Regular Expression Modifiers
  • Regular Expression Patterns
  • Regular Expression Quantifiers

Module 14:- Introduction to Data Science and Machine Learning

  • Matching and Searching- match() and search() Functions
  • Search and Replace
  • Regular Expression Modifiers
  • Regular Expression Patterns
  • Regular Expression Quantifiers

Module 15:- Tools & Languages available

  • Python .R
  • Python & R Differences
  • Python Distribution
  • Python tools for Data Science
  • Anaconda Installation
  • Jupiter Notebook Usage and Examples

Module 16:- Numpy

  • Introduction to Numpy. Array
  • Creation,Printing Arrays
  • Basic Operations- Indexing, Slicing and Iterating
  • Shape Manipulation – Changing
  • Shape,stacking and spliting of array
  • Random number

Module 17:- Pandas And Matplotlib


  • Introduction to Pandas
  • Importing data into Python
  • Pandas Data Frames,Indexing Data
  • Frames ,Basic Operations With Data
  • frame,Renaming Columns,Subletting and filtering a data frame.


  • Introduction,plot(),Controlling Line
  • Properties,Working with Multiple
  • scatter, hist, bar, piechart
  • subplot, titles, axis, colormap
  • Figures,Histograms


  • Plot for categorical and numerical data
  • Plot for categorical vs numerical, numerical vs numerical, categorical vs categorical
  • distplot, jointplot, boxplot, barplot, countplot, violinplot, swarmplot

Module 18:- Exploratory Data Analysis

  • Data Manipulations and Wrangling
  • Drawing Insights and Completing analysis
  • Imputing NA values

Module 1:- Getting Started With R

  • Introduction To R
  • Installation Setup
  • A quick guide to RStudio User Interface
  • RStudio’s GUI3
  • Changing the appearance in RStudio
  • Installing packages in R and using the library
  • Development Environment Overview
  • Introduction to R basics
  • Building blocks of R
  • Core programming principles
  • Fundamentals of R

Module 2:- Programming with R

  • Creating an object
  • Data types in R
  • Coercion rules in R
  • Functions and arguments
  • Conditional Statements and Loops
  • if else, for, while, repeat, break, next

Module 3:- Objects in R

  • Vectors and Vector operation
  • List and Operations
  • Factor and Operations
  • Matrices
  • Data Frame
  • Applications of R objects
  • Data Inputs and Outputs with R
  • Advanced Visualization
  • Using the script vs. using the console

Module 4:- Manipulating Data

  • Data transformation with R
  • Dplyr package
  • Sampling data with the Dplyr package
  • Select, filter, arrange, rename
  • Mutate, pipeline
  • Group by, summarize

Module 5:- Start Visualizing Data

  • Intro To Data Visualization
  • Introduction To Ggplot2
  • Coloring, Filling Color, Axis, Legend, Labelling
  • Histogram, Density
  • Bar Chart, Point Plot
  • Box And Whiskers Plot, Outliers
  • Scatterplot
  • Pie Chart

Module 6:- Projects & Assignments

  • 2 Assignments
  • 1 Projects
  • Dataset Analysis


✔️ 98 Hrs – 100 Hrs / 50 Sessions
✔️ Per Session for 2 Hours
✔️ Practice Hours

We have included real-life examples and case studies in our course. Complete written notes and code for you to read and refer

Projects for you to complete throughout the course. These provide a challenge and an opportunity for you to apply your learning.

In a set of the time period, you will be having access to getting support from our expert

After completing your course, we will provide you with a Course Completion Certificate

You will get 100% job assistance according to your CTC, Experience and Present skills

Need more details about the course, let’s connect with our experts

How, Data Science is having Best Career Opportunities?

Skills in Demand

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

Employability Increases

Companies are giving preference to those candidates who have good skills in data science and that too using Python.  Candidates who have good command in Microsoft Excel, VBA, My SQL & Power BI, Tableau, Python. Many companies heavily depend on these programs for Data Analysis along with visualization.

Job opportunities on the rise

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

Most Frequent Questions and Answers

Those who are interested in Data Science and wants to start their career as Data Scientist. Anyone who is particularly interested in big data, machine learning, and data intelligence

Of course, you can learn Python without having any coding background. We start Python learning from scratch so that we can build a strong fundamental of the application and at an advanced level you can grasp it easily. And obviously, you need a lot of practice to be a pro.

Though we have so many reasons to join us, let us highlight the main points:-

  1. After completing the course you will get 3 months of support time period extra to revise the sessions which are not cleared to you, any problem you are not able to solve that you can take our team’s help
  2. We provide video recordings if any of the reason you skip your session
  3. We have well-experienced trainers to teach our students
  4. Project-based training / unlimited examples to make you understand about the subject / Case Studies after every session / Job Support and many more

Yes, of course, you will be getting “Completion Certificate” 

  • Start at zero and become an expert whilst learning all about the inner workings of Python.
  • Learn how to write professional Python code like a professional Python developer.

  • Embrace simplicity and develop good programming habits.

  • Improve your Python code with formatters and linters

  • Extract information from existing websites using web scraping.

  • Learn to interact with REST APIs to fetch data from other web applications.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

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Data Analytics & Data Science Batches Starting from 9th of October 2022. For More Details:- Call Us Today! @ +91 99907 48956 | 91 96503 08956