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Data Science Certification Program

Data Science Certification Program

Data Science Certification programs 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, and R Language. 

(8K+ Satisfied Learners)
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To become a data scientist, or to get any job in data science, it is a good idea to get a data science certification. A certification (or certificate) will provide you with the necessary knowledge and skills to succeed as a data scientist
What are the Benefits of being a Data Scientist?
  • Machine learning, deep learning, and artificial intelligence application and implementation.
  • Mathematical and statistical knowledge.
  • Well-versed in data visualization, data analytics, data cleaning, and big data.
  • Good communication skill.
  • Excellent organizational skills.
The interviewer will test everything that you have mentioned in your skillset. Therefore, if you choose to go ahead with a data science certification, make sure that you keep up with your classes and gain the right skills.Your certificate won’t get you the job, skills will

Instructor -Led Online & Physical Classes

Batch Start DateClass TypeUpcoming SlotsTimingsLocation
29th Jan 2023OnlineSold Out
SAT & SUN (5.5 WEEKS)
7:30 PM to 9:30 PMOnline
12th Feb 2023OnlineFilling Fast
SAT & SUN
Weekend Batch
4:30 PM to 6:30 PMOnline
26th Feb 2023OnlineSAT & SUN
Weekend Batch
10:00 AM to 12:00 PMOnline
12th Mar 2023OnlineSAT & SUN
Weekend Batch
8:00 PM to 10:00 PMOnline
26th Mar 2023OnlineSAT & SUN
Weekend Batch
10:00 AM to 12:00 PMOnline

Start Your Course Now

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
  • Magic 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 Customized 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
  • Text Functions: – CLEAN, CONCATENATE, LEFT, RIGHT, MID, LEN, FIND, SEARCH, SUBSTITUTE, and TEXT, etc.
  • Date & Time Functions: – DATE, DAYS, TIME, NOW, WEEKNUM, WORKDAY, and WORKDAY.INTL etc.
  • Math & Trig Functions: – INT, MOD, ROUND, ROUNDDOWN, SUMIF, SUMIFS, SUMPRODUCT etc.
  • Statistical Functions: – AVERAGE, COUNT, COUNTA, COUNTBLANK, MAX, MIN, LARGE etc.
  • Logical Functions: – IF, IFS, AND, OR, and IFERROR.
  • Lookup & Reference Functions: – FORMULATEXT, VLOOKUP, HLOOKUP, INDEX, MATCH, INDIRECT, and OFFSET
  • 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 the 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
  • String Functions – CONCAT, SUBSTRING, REPLACE, REVERSE, LENGTH, UPPER, LCASE etc.
  • Aggregate Functions – COUNT, MIN, MAX, SUM, AVG, ROUND etc
  • Date/Time Functions – CURDATE, CURTIME, CURRENT_DATE, LOCALTIME 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:- 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

Module 1: Getting Started with Python Core

✓ Need of Programming with an Example
✓ Why Programming
✓ Advantages of Programming
✓ Different Programming Languages
✓ Introduction to Python
❑ A Brief History of Python
❑ Why Python
✓ Installing Python
✓ Creating Python File using IDLE
✓ Write your first Program in Python
✓ How to execute Python Program
✓ Identifier
❑ Rules for Naming Identifiers
✓ Variables
✓ Operator
❑ Operator Types
✓ Q&A

Module 2: Datatypes in Python

✓ Introduction to Python Data Types
✓ Strings
❑ Introduction to Python ‘String’ data type
❑ String Properties
❑ String built-in functions
❑ Programming with Strings
❑ String Formatting
✓ Lists and Tuples
❑ Introduction to Python ‘List’ data type
❑ List Properties
❑ List built-in functions
❑ Programming with Lists
❑ List Comprehension
❑ Introduction to Python ‘tuple’ data types
❑ Tuples as Read only lists
✓ Dictionary and Sets
❑ Introduction to Python ‘Dictionary’ data type
❑ Creating a dictionary
❑ Dictionary built-in functions
❑ Introduction to Python ‘set’ data types
❑ Set and Set properties
❑ Set built-in functions
✓ Q&A

Module 3: Conditional & Control Statements in Python

✓ Introduction to Conditional Statements
❑ Types of Conditional Statements
o If….Statement
o If….Else Statement
o Elif…. Statement
✓ Introduction to Loops
❑ Types of Loops in Python
o While….Loop
o For….Loop
o Nested Loop
✓ Introduction to Loop Control Statements
❑ Loop Control Statements Keywords
o Break Statement
o Continue Statement
o Pass Statement
✓ Q&A

Module 4: Functions in Python

✓ Introduction to Python Functions
✓ User Defined Functions
❑ Functions definition and return statement
❑ Calling a Function
❑ Parameters and Arguments
❑ Required Arguments
❑ Default Argument
✓ Variable Scope in Function
❑ Local Scope
❑ Global Scope
❑ Enclosing Scope
❑ Built-in Scope
✓ Modules and Packages
❑ Importing Module (from, import statement)
✓ Anonymous functions (Lambda)
✓ Q&A

Module 5: Exception Handling & OOPs Concepts

✓ Getting started working with Files
❑ File Objects and Modes of file operations
❑ Reading, Writing, and use of ‘With’ Keyword
❑ Read(), Readline(), Readlines(), Write(), Writeline()
✓ Introduction to Exception Handling
❑ Understanding Exceptions
❑ Handling An Exceptions
❑ Try, Except, Else, and Finalizing
❑ Raising Exceptions with: Raise, Assert
✓ Introduction to Object Orientated Programming (OOPs)
❑ Why OOPs
❑ Difference Between POPs and OOPs
❑ OOPs Concepts
❑ Python OOP Vs Other OOPs
❑ Class and Objects
❑ Relation Between Class and Objects
❑ Creating a Class
❑ Attributes
✓ Built-In Class Attributes
✓ Class Variable and Instance Variable
❑ Constructor and Destructor
❑ Multiple Constructors
❑ Abstraction
❑ Inheritance
✓ Inheritance Types
❑ Overloading
❑ Overriding
❑ Data Hiding
✓ Q&A

Module 6: Database Connectivity & Regular Expressions

✓ Introduction to Regular Expressions
❑ What are Regular Expressions
❑ Regular Expressions Operations
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❑ Search Function
❑ Match Function
❑ Modifiers
❑ Patterns
✓ Database Connectivity
❑ Introduction to Database Connectivity
❑ Connections
❑ Executing Queries
❑ Transactions
✓ Q&A

Module 7: Data Manipulation & Data Visualization

✓ What is Data Manipulation
✓ Introductions to Pandas
✓ Data Manipulation with Pandas
✓ Data Structures & Series
✓ Data Frame
✓ Missing Values
✓ Data Operations
✓ Data Standardizations
✓ Pandas File Read (CSV, Excel, SQL) and Write Support
✓ Data Acquisition (Import & Export)
✓ Introduction to Data Visualization using Matplotlib
❑ Installing Matplotlib
❑ Plotting in Matplotlib
❑ Creating First Plot with Matplotlib
❑ Creating Column/Line/Scatter Plots
✓ Wrapping Python Core Program
✓ Q&

Highlights

✔️ 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

Let’s Get Our Expert’s Guidance



    How, Data Science is having Best Career Opportunities?

    Skills in Demand

    Data science is a rapidly growing, highly sought-after career path for skilled professionals. To be successful in this field, data scientists must have more than just the traditional skills of analyzing large quantities of data and programming; they must also understand the entire data science life cycle and possess an adequate level of adaptability to maximize returns at each stage of the process.

    Employability Increases

    Companies are increasingly prioritizing candidates with strong skills in data science, specifically Python. Professionals who possess advanced knowledge of automation using Python, Machine Learning, Power BI, and Tableau are highly desirable as many organizations rely heavily on these programs for data analysis and visualization.

    Job opportunities on the rise

    Data Science has been ranked the top job on Glassdoor and boasts an impressive average salary of $120,000 in the United States according to Indeed! Data Science is a highly rewarding career path that enables one to solve some of the world’s most fascinating 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!

    Student Reviews


    Had great experience learning at ATH. Joined in January 2022 for 3 months program for Excel, My SQL and power BI....learned from Anil Sir and Nayan Sir...both great teachers...will definitely recommend all to join this program. Also, in job interview it helps when you are trained in multiple skills.
    Anubhav Mahajan
    Machine Learning Engineer


    I was struggling with advance excel when I find out about this platform and I decided to take Data Science course from here and my overall experience was quite amazing I am glad i found out about this platform in just few weeks i got to learn so much and I would recommend this to everyone.
    Dolly Bhardwaj
    Data Architect and Administrators
    Hi folks , i have completed my training part that is Data Science course with ATH in a budget . big thanks to Anil dhawan sir who helped me to clear every party MY sql , and also to Mr. Nayan who taught me POWER BI DESKTOP. At the end of training and hard work which i given to myself is endless. I am delighted to share that , I got placed with "KPMG" as a Business IT Analyst role.
    ATH student review
    Chandan Chauhan
    Business IT Analyst

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    Data Analytics & Data Science Batches Starting from 12th of February 2023. For More Details:- Call Us Today! @ +91 99907 48956 | 91 96503 08956