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Exclusive Data Science using Python Language Training from Experts
with AI ML

Data Science for Artificial Intelligence and Machine Learning Training

  • Complete Online and Classroom Training Program
  • 200 Hrs
  • Learn from Expert Trainer
  • Fully Live Training
  • Flexible Batch Timing
Enroll Now

We are the best IT Training and Placement Institute in Pune. We provide Data Science Training for freshers as well as for working professionals. Our experts teach knowledge about the python language which is base of Data Science. We consult our students for career opportunities and support for long time.

This course is designed to become efficient Data Engineer to see yourself as one of the best IT professional Data Scientist in the IT industry. Also to develop your skills in Python, Database and Data Science for ML and AI for the applications.
Before going to Data Science, let’s first understand

What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
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.

What Does a Data Scientist Do?

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

This course will,

  • Help you to learn what is Python Language
  • How to write code for project
  • Help you to learn what is all the modules designed for Data Science
  • Help you to learn Tableau in  Data Science
  • Help you to learn Database in  Data Science
  • Help you to learn Machine Learning with Python
  • Help you to build your own project

We are eager to see you on desired position by learning and implementing this skills.

Wish you all the best for your career!!!

Data Science with Python Language With Artificial Intelligence And Machine Learning Syllabus

Introduction to Data Science

  • Why Data Science?
  • What is Data Science?
  • Components of Data Science
  • Life Cycle of Data Science
  • Tasks of Data Scientist
  • Data Science in different sectors
  • Data Science tools

====================================================

The syllabus is divided into 4 modules where you will learn about various Data Science skills using Python programming language. In the first module, Basic and advanced Python will be covered. In the second module, you will learn about Data Analytics frameworks using Numpy, Pandas, etc. In the third module, we will be covering data visualization which is necessary for data science. In the last modules, Machine Learning and its various algorithms will be covered.

Module 1 

Python (Basic & Advanced)

Python Basic

Introduction to Python

  • Overview of Python
    • Introduction of Python
    • Installation of python
    • Feature of python
    • History of python
    • Installing Python and code editor IDE
    • Introduction to the anaconda distribution platform
    • Introduction to Jupytrt Notebook and how to work on it
    • Introduction to google collaboratory
    • Running python programs using terminal
    • Variables
    • Data type
    • Working on Integers, Floats, Booleans in Python
    • Playing with Strings in Python
    • Working on Lists
      • Creating lists
      • List functions
      • List Methods
      • Nested Lists
    • Working on Tuples
      • Creating Tuple
      • Tuple Functions
      • Tuple Methods
    • Working on Sets
      • Creating Sets
      • Set Functions
      • Set Methods
      • Frozen Sets
    • Working on Dictionary
      • Creating Dictionaries
      • Dictionary Functions
      • Dictionary Methods
    • Operators
    • Arithmetic Operator
    • Relational operators
    • Assignment operators
    • Logical operators
    • Special operators
    • Bitwise Operators
    • Control Structures
      • Conditional Statements
      • IF STATEMENT
      • IF / ELSE Statement
      • IF/ElIF/ELSE Statement
      • Nested IF/ELSE Statements
    • Expressions and Statements
    • Indentations
    • Writing Comments
    • Iterations and Iterables
    • Loops
      • For Loop
      • While Loop
      • Range and xrange Functions
      • Break, Continue, and Pass
    • Python Functions
      • Introduction to Python function
      • Types of Python Functions
      • Built-in Python Functions
      • User-defined Python function
      • Defining function
      • Calling Function
      • Parameters and arguments
      • Positional Arguments
      • Default Arguments
      • Keyword Arguments
      • Arbitrary Arguments
      • Docstrings
      • Recursive Functions
      • Lambda Functions
    • Object Oriented Programming
      • Objects
      • Classes
      • Creating empty class
      • Creating class and defining attributes
      • Class methods and attributes
      • Constructors
      • Generators
      • Decorators
      • Instance and class variables
      • Inheritance
      • Polymorphism
    • File Handling
      • Reading and writing files
      • Opening and closing files
      • Deleting Testing and debugging Files
      • Errors and Exception Handling
    • Modules and Packages
      • Importing Modules
      • Creating and importing packages
      • Namespaces
    • Testing and Debugging
      • Debugging with print statement
      • Debugging with the debugger
      • Testing with unit Test module
    • Database connectivity

====================================================

Module 2

Data Analytics Training with Tableau and Power Bi

Module 1

Basic Excel Course Syllabus Introduction to Computers

  • Font formatting
  • Number formatting
  • Table formatting
  • Conditional formatting
  • Hide/ Unhide
  • Sort/filter
  • Paste special
  • Find and select

Insert

  • Illustrations
  • Charts
  • Tex

Basic Functions

  • Sum/Average/Count/Max/Min
  • Basic Text/date/time/lookup/information functions
  • Name manager
  • Formula Auditing

Data

  • Import from web
  • Import from text
  • Text to columns
  • Remove duplicates
  • Grouping and ungrouping

Review

  • Proofing
  • Comments
  • Protection

Views

  • Types of views
  • Zoom
  • Windows

Developer

  • Enable developer
  • Using checkbox/option buttons

============================================

Module 2

Advanced Excel Course Syllabus

Excel 2007 & 2010,2013, 2016 Quick Overview

  • Difference between Excel2003, 2007 and 2010, 2013, 2016
  • Use of Excel, its boundaries & features

Basic Formula

  • Formulae that Add/ Subtract/ Multiply/ Divide
  • BODMAS / Formula Error Checking
  • The Sum Function

Absolute Referencing

  • Problems with Absolute /Relative Cell Referencing
  • Creating Absolute /Mixed References

LOOKUP Functions

  • The VLOOKUP/ HLOOKUP Functions

Pivot Tables

  • Creating,Formatting Simple Pivot Tables
  • Page Field in a Pivot Table
  • Formatting a Pivot Table
  • Creating/ Modifying a Pivot Chart

Logical Functions

  • IFs and Nested IF Functions
  • Using AND/OR/ NOT Functions

Statistical Functions

  • Using The SUM IF / COUNT IF Functions
  • Using The AVERAGE/COUNT/LARGER/SMALLER Functions

Pivot Tables–Advance

  • Adding new calculated Fields/Items
  • Changing the Summary Function
  • Consolidate Pivot table

LOOKUP Functions–Advance

  • MATCH with VLOOKUP Functions
  • INDEX& MATCH Functions
  • OFFSET/INDIRECT functions

Logical Functions–Advance

  • If Loop and Nested IF Loop Functions
  • Using IF/IS ERROR Functions

Chart Data Techniques

  • The Chart Wizard
  • Chart Types
  • Adding Title/Legends/ Labels
  • Printing Charts
  • Adding Data to a Chart
  • Formatting/Renaming/Deleting Data Series
  • Changing the Order of Data Series

Date / Time Functions

  • Using The Today
  • Now& Date Functions
  • Using The Dated if/ Network days/Eomonth Functions
  • Using The Week num Functions
  • Using The E date/Network days.Intl/Weekdays.Intl Functions

Text Functions Using

  • The Mid/Search/Left/Right Functions
  • Using The Trim/Clean/Upper/Lower Functions
  • Using The Substitute/Text Functions
  • Using The Trim/Clean/Proper/Dollar Function

Validations

  • Input Messages/Error Alerts/Drop-Down Lists
  • Conditional Formatting

Advanced Filters

  • Extracting Records with Advanced Filter
  • Using Formula sin Criteria

Advanced Sorting

  • Sorting by Top to Bottom/Left to Right
  • Creating/Deleting Custom List
  • Sort by using Custom List

Hyper/Data Linking

  • Hyper linking data,within sheet/workbook
  • Linking&Updating links between workbooks&application

Math & Trigonometry Functions

  • Using SUM PRODUCT Functions
  • Using FLOOR/CEILING/MROUND/MOD/QUOTIENT Functions

Summarizing Data

  • Creating Subtotals/Nested Subtotals
  • SUBTOTALS Formula

Outlining

  • Creating/Working with an Automatic/Manual Outline
  • Grouping/Un grouping

Consolidation

  • Consolidating Data with Identical/Different Layout

Using Auditing Tools

  • Displaying/Removing Dependent&Precedent Arrows
  • Evaluate Formula–StepIN/StepOut

Custom Views

  • Creating Custom Views
  • Displaying Custom Views
  • Deleting Custom Views

Sharing and Protecting Workbooks

  • Sharing Workbooks&Tracking Changes
  • Protecting sheets/workbooks/Files

Importing & Exporting Data

  • Importing Data from Database/ TextFiles/Web
  • Exporting Data
  • Changing External Data Range

====================================================

Module 3

SQL

 Introduction of SQL

SQL Statements

  • DDL
  • DML
  • DQL
  • DCL

SQL CONSTRINTS

  • UNIQUE
  • PRIMARY KEY
  • NULL
  • NOT NULL
  • CHECK
  • DEFAULT
  • FOREIGN KEY

SQL OPERTORS

  • AND
  • OR
  • LIKE
  • IN
  • BETWEEN
  • IS

SQL FUNCTIONS

  • MIN()
  • MAX()
  • COUNT()
  • SUM()
  • AVG()
  • LENGTH()
  • REVERSE()
  • REPLACE()
  • INSTR()
  • SUBSTR()
  • TRIM()
  • ROUND()
  • ABS()
  • SQRT()
  • MOD()

SQL CLAUSE

  • WHERE
  • GROUP BY
  • ORDER BY
  • HAVING

SUB QUERIS

JOINS

  • inner join
  • outer join
  • left join
  • right join

====================================================

Module 4

TABLEAU

Understanding Data

  • What is data Where to find data
  • Foundations for building Data Visualizations

Creating Your First visualization

  • Getting started with Tableau Software Using Data file formats
  • Connecting your Data to Tableau
  • Creating basic charts(line,bar charts,Tree maps) Using the Show me panel

Tableau Calculations

  • Overview of SUM, AVR, and Aggregate features
  • Creating custom calculations and fields
  • Applying new data calculations to your visualization

Formatting

  • Visualizations
  • Formatting Tools and Menus
  • Formatting specific parts of the view
  • Editing and Formatting Axes

Manipulating Data in Tableau

  • Cleaning- up the data with the Data Interpreter Structuring your data
  • Sorting and filtering Tableau data
  • Pivoting Tableau data

Advanced Visualization Tools

  • Using Filters
  •  Using the Detail panel Using the Size panels Customizing filters
  • Using and Customizing tooltips Formatting your data with colors

Creating Dashboards & Stories

  • Using Storytelling
  • Creating your first dashboard and Story Design for different displays
  • Adding interactivity to your Dashboard

Distributing & Publishing Your Visualization

  • Tableau file types
  • Publishing to Tableau
  • Online Sharing your visualization Printing and exporting

====================================================

Module 5

POWER BI

Power BI Introduction

  • Data Visualization,Reporting
  • Business Intelligence(BI),Traditional BI,Self-Serviced BI
  • Cloud Based BI,On Premise BI
  • Power BI Products
  • Power BI Desktop(Power Query,Power Pivot,Power View)
  • Flow of Working Power Bi Desktop
  • Power Bi Report Server,PowerBI Service,PowerBI Mobile Flow-of Working Power Bi/Power BI Architecture
  • A Brief History of Power Bi
  • Data Transformation,Benefits of Data Transformation
  • Shape or Transform Data using Power Query

Power Query

  • Overview of Power Query/Query Editor, Query Editor User Interface The
  • Ribbon(Home,Transform, Add Column,View Tabs)
  • The Queries Pane,The Data View/Results Pane,The Query Settings Pane,Formula Bar
  • Saving the Work
  • Datatypes,Changing the Data type of a Column Filter in Power Query
  • Auto Filter/Basic Filtering
  • Filter a Column using Text Filters
  • Filter a Column using Number Filters
  • Filter a Column using Date Filters
  • Filter Multiple Columns
  • Remove Columns/Remove Other Columns
  • Name/Rename a Column
  • Reorder Columns or Sort Columns
  • Add Column/Custom Column Split
  • Columns
  • Merge Columns
  • PIVOT,UNPIVOT Columns
  • Transpose Columns
  • Header Row or Use First Row as Headers
  • Keep Top Rows,Keep Bottom Rows Keep
  • Range of Rows
  • Keep Duplicates,Keep Errors
  • Remove Top Rows, Remove Bottom Rows,Remove Alternative Rows
  • Remove Duplicates,Remove Blank Rows,Remove Errors
  • Group Rows/Group By

Visualizations

    • Visualizing Data,Why Visualizations
    • Visualization types,Create and Format Bar and Column Charts
    • Create and Format Stacked Bar Chart Stacked Column Chart Create
    • and Format Clustered Bar Chart,Clustered Column Chart
    • Create and Format 100% Stacked Bar Chart,100% Stacked Column Chart Createand
    • Format Pie and Donut Charts
    • Create and Format Scatter Charts
    • Create and Format Table Visual,Matrix Visualization
    • Line and Area Charts
    • Create and Format Line Chart,Area Chart,Stacked Area Chart
    • Combo Charts
    • Create and Format Line and Stacked Column Chart,Line and Clustered Column Chart
    • Create and Format Ribbon Chart,Waterfall Chart,Funnel Chart

Power Bi Service

  • Power Bi Service Introduction,PowerBI Cloud Architecture
  • Creating Power Bi Service Account, SIGN IN to Power Bi Service Account
  • Publishing Reports to the Power Bi service,Import/Getting the Report to PBI Service My
  • Workspace/App Workspaces Tabs
  • DATASETS,WORKBOOKS,REPORTS,DASHBOARDS
  • Working with Datasets,Creating Reports in Cloud using Published Datasets

Creating Dashboards

  • P in Visuals and Pin LIVE Report Pages to Dashboard
  • Advantages of Dashboards
  • Interacting with Dashboards
  • Formatting Dashboard
  • Sharing Dashboard

====================================================

Module 3

Mathematics

  • Calculus
  • Vector Algebra
  • Matrices Algebra
  • Introduction to Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Probability Theory
  • Random Variables
  • Discrete & Continuous Distribution
  • Joint Distribution
  • Central Limit Theorem
  • Hypothesis Testing
  • Statistics Interview Questions

====================================================

Module 4

Machine Learning

  • Introduction to ML
  • Supervised and Unsupervised Machine Learning
  • Linear Regression & Linear Regression Implementation
  • Bias Variance Tradeoff
  • Cross Validation and Hyper Parameter Tuning
  • Logistic Regression
  • Decision Trees and Ensembles of Decision Trees
  • K-Nearest Neighbours
  • Naive Bayes Classifier
  • Support Vector Machine
  • Principal Component Analysis
  • K-Means Clustering
  • Introduction to Natural Language Processing
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Movie Similarities
  • Improving the Results of Movie Similarities
  • Making Movie Recommendations to People
  • Improve the recommender’s results
  • Machine Learning in Healthcare
  • Machine Learning in Fraud Risk Analytics
  • Machine Learning in Healthcare
  • Machine Learning Interview Questions

====================================================

Module 5

Artificial Intelligence (AI)

  • Decision Trees and Ensembles of Decision Trees
  • K-Nearest Neighbours
  • K-Means Clustering
  • Introduction to Natural Language Processing
  • Artificial Intelligence in Healthcare
  • Artificial Intelligence in Fraud Risk Analytics
  • Artificial Intelligence in Healthcare
  • Artificial Intelligence Interview Questions
  • Project: Project will be given as per latest topic.

====================================================

 

Project: Project will be given as per latest topic.

Wish you all the best for your career!!!

You will get certification after successful course completion.

Students who are looking for job after completion of the course

  1. Should be Graduate(From IT /Computer Science) ,ME, BE, M Tech. with Min 50% Passing Criteria
  2. Qualifications Include: BE/ME/MTech(ALL BRANCHES), BCA/MCA,BCS/MCS,BSC(IT/CS) ,PG(IT/CS)
  3. No coding background necessary.
  4. Knowledge of basic coding is preferred.
  5. Anyone who wants boost knowledge and switch the job.
  6. Anyone who is having Laptop/ Computer.
  7. Anyone who wants to switch jobs to Data Science.
  8. Basic knowledge of English is mandatory.

  1. Live Interactive Sessions will take you to the industry based learning and activities fulfill the need of Industry.
  2. Hands-on project knowledge
  3. Notes provided by expert trainer
  4. There are career opportunities available like Data Engineer, Data Scientist, Tableau Developer and Project Engineer etc.
  5. Focus on basic concepts of Python makes you professional Data Scientist
  6. A beginner also go for this training and explore may opportunities
Nitya,

Good Experience about learning Data Science.  There were very less Students in my batch which helped me to understand the course easily which I have taken. The batch timings were flexible. The professor and staff are very helpful and co-operative. Would recommend to join this institute if you want to gain knowledge in Data Science.

Megha,

the training was great. Teaching faculty is very nice to give practical with each and every concept. I was not having a single knowledge about coding but that after joining Puneri Pattern, I have great knowledge about Data Science and now able to writing codes by own way. I refer to all if you want to practically grow your knowledge then 100% join to Puneri Pattern. Thank you giving me training.  Also service very nice provided and friendly nature of all faculty.

Kindly scroll down to see FAQs.

+ About

This course is designed to become efficient Data Engineer to see yourself as one of the best IT professional Data Scientist in the IT industry. Also to develop your skills in Python, Database and Data Science for ML and AI for the applications.
Before going to Data Science, let’s first understand

What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
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.

What Does a Data Scientist Do?

Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

This course will,

  • Help you to learn what is Python Language
  • How to write code for project
  • Help you to learn what is all the modules designed for Data Science
  • Help you to learn Tableau in  Data Science
  • Help you to learn Database in  Data Science
  • Help you to learn Machine Learning with Python
  • Help you to build your own project

We are eager to see you on desired position by learning and implementing this skills.

Wish you all the best for your career!!!

+ Syllabus

Data Science with Python Language With Artificial Intelligence And Machine Learning Syllabus

Introduction to Data Science

  • Why Data Science?
  • What is Data Science?
  • Components of Data Science
  • Life Cycle of Data Science
  • Tasks of Data Scientist
  • Data Science in different sectors
  • Data Science tools

====================================================

The syllabus is divided into 4 modules where you will learn about various Data Science skills using Python programming language. In the first module, Basic and advanced Python will be covered. In the second module, you will learn about Data Analytics frameworks using Numpy, Pandas, etc. In the third module, we will be covering data visualization which is necessary for data science. In the last modules, Machine Learning and its various algorithms will be covered.

Module 1 

Python (Basic & Advanced)

Python Basic

Introduction to Python

  • Overview of Python
    • Introduction of Python
    • Installation of python
    • Feature of python
    • History of python
    • Installing Python and code editor IDE
    • Introduction to the anaconda distribution platform
    • Introduction to Jupytrt Notebook and how to work on it
    • Introduction to google collaboratory
    • Running python programs using terminal
    • Variables
    • Data type
    • Working on Integers, Floats, Booleans in Python
    • Playing with Strings in Python
    • Working on Lists
      • Creating lists
      • List functions
      • List Methods
      • Nested Lists
    • Working on Tuples
      • Creating Tuple
      • Tuple Functions
      • Tuple Methods
    • Working on Sets
      • Creating Sets
      • Set Functions
      • Set Methods
      • Frozen Sets
    • Working on Dictionary
      • Creating Dictionaries
      • Dictionary Functions
      • Dictionary Methods
    • Operators
    • Arithmetic Operator
    • Relational operators
    • Assignment operators
    • Logical operators
    • Special operators
    • Bitwise Operators
    • Control Structures
      • Conditional Statements
      • IF STATEMENT
      • IF / ELSE Statement
      • IF/ElIF/ELSE Statement
      • Nested IF/ELSE Statements
    • Expressions and Statements
    • Indentations
    • Writing Comments
    • Iterations and Iterables
    • Loops
      • For Loop
      • While Loop
      • Range and xrange Functions
      • Break, Continue, and Pass
    • Python Functions
      • Introduction to Python function
      • Types of Python Functions
      • Built-in Python Functions
      • User-defined Python function
      • Defining function
      • Calling Function
      • Parameters and arguments
      • Positional Arguments
      • Default Arguments
      • Keyword Arguments
      • Arbitrary Arguments
      • Docstrings
      • Recursive Functions
      • Lambda Functions
    • Object Oriented Programming
      • Objects
      • Classes
      • Creating empty class
      • Creating class and defining attributes
      • Class methods and attributes
      • Constructors
      • Generators
      • Decorators
      • Instance and class variables
      • Inheritance
      • Polymorphism
    • File Handling
      • Reading and writing files
      • Opening and closing files
      • Deleting Testing and debugging Files
      • Errors and Exception Handling
    • Modules and Packages
      • Importing Modules
      • Creating and importing packages
      • Namespaces
    • Testing and Debugging
      • Debugging with print statement
      • Debugging with the debugger
      • Testing with unit Test module
    • Database connectivity

====================================================

Module 2

Data Analytics Training with Tableau and Power Bi

Module 1

Basic Excel Course Syllabus Introduction to Computers

  • Font formatting
  • Number formatting
  • Table formatting
  • Conditional formatting
  • Hide/ Unhide
  • Sort/filter
  • Paste special
  • Find and select

Insert

  • Illustrations
  • Charts
  • Tex

Basic Functions

  • Sum/Average/Count/Max/Min
  • Basic Text/date/time/lookup/information functions
  • Name manager
  • Formula Auditing

Data

  • Import from web
  • Import from text
  • Text to columns
  • Remove duplicates
  • Grouping and ungrouping

Review

  • Proofing
  • Comments
  • Protection

Views

  • Types of views
  • Zoom
  • Windows

Developer

  • Enable developer
  • Using checkbox/option buttons

============================================

Module 2

Advanced Excel Course Syllabus

Excel 2007 & 2010,2013, 2016 Quick Overview

  • Difference between Excel2003, 2007 and 2010, 2013, 2016
  • Use of Excel, its boundaries & features

Basic Formula

  • Formulae that Add/ Subtract/ Multiply/ Divide
  • BODMAS / Formula Error Checking
  • The Sum Function

Absolute Referencing

  • Problems with Absolute /Relative Cell Referencing
  • Creating Absolute /Mixed References

LOOKUP Functions

  • The VLOOKUP/ HLOOKUP Functions

Pivot Tables

  • Creating,Formatting Simple Pivot Tables
  • Page Field in a Pivot Table
  • Formatting a Pivot Table
  • Creating/ Modifying a Pivot Chart

Logical Functions

  • IFs and Nested IF Functions
  • Using AND/OR/ NOT Functions

Statistical Functions

  • Using The SUM IF / COUNT IF Functions
  • Using The AVERAGE/COUNT/LARGER/SMALLER Functions

Pivot Tables–Advance

  • Adding new calculated Fields/Items
  • Changing the Summary Function
  • Consolidate Pivot table

LOOKUP Functions–Advance

  • MATCH with VLOOKUP Functions
  • INDEX& MATCH Functions
  • OFFSET/INDIRECT functions

Logical Functions–Advance

  • If Loop and Nested IF Loop Functions
  • Using IF/IS ERROR Functions

Chart Data Techniques

  • The Chart Wizard
  • Chart Types
  • Adding Title/Legends/ Labels
  • Printing Charts
  • Adding Data to a Chart
  • Formatting/Renaming/Deleting Data Series
  • Changing the Order of Data Series

Date / Time Functions

  • Using The Today
  • Now& Date Functions
  • Using The Dated if/ Network days/Eomonth Functions
  • Using The Week num Functions
  • Using The E date/Network days.Intl/Weekdays.Intl Functions

Text Functions Using

  • The Mid/Search/Left/Right Functions
  • Using The Trim/Clean/Upper/Lower Functions
  • Using The Substitute/Text Functions
  • Using The Trim/Clean/Proper/Dollar Function

Validations

  • Input Messages/Error Alerts/Drop-Down Lists
  • Conditional Formatting

Advanced Filters

  • Extracting Records with Advanced Filter
  • Using Formula sin Criteria

Advanced Sorting

  • Sorting by Top to Bottom/Left to Right
  • Creating/Deleting Custom List
  • Sort by using Custom List

Hyper/Data Linking

  • Hyper linking data,within sheet/workbook
  • Linking&Updating links between workbooks&application

Math & Trigonometry Functions

  • Using SUM PRODUCT Functions
  • Using FLOOR/CEILING/MROUND/MOD/QUOTIENT Functions

Summarizing Data

  • Creating Subtotals/Nested Subtotals
  • SUBTOTALS Formula

Outlining

  • Creating/Working with an Automatic/Manual Outline
  • Grouping/Un grouping

Consolidation

  • Consolidating Data with Identical/Different Layout

Using Auditing Tools

  • Displaying/Removing Dependent&Precedent Arrows
  • Evaluate Formula–StepIN/StepOut

Custom Views

  • Creating Custom Views
  • Displaying Custom Views
  • Deleting Custom Views

Sharing and Protecting Workbooks

  • Sharing Workbooks&Tracking Changes
  • Protecting sheets/workbooks/Files

Importing & Exporting Data

  • Importing Data from Database/ TextFiles/Web
  • Exporting Data
  • Changing External Data Range

====================================================

Module 3

SQL

 Introduction of SQL

SQL Statements

  • DDL
  • DML
  • DQL
  • DCL

SQL CONSTRINTS

  • UNIQUE
  • PRIMARY KEY
  • NULL
  • NOT NULL
  • CHECK
  • DEFAULT
  • FOREIGN KEY

SQL OPERTORS

  • AND
  • OR
  • LIKE
  • IN
  • BETWEEN
  • IS

SQL FUNCTIONS

  • MIN()
  • MAX()
  • COUNT()
  • SUM()
  • AVG()
  • LENGTH()
  • REVERSE()
  • REPLACE()
  • INSTR()
  • SUBSTR()
  • TRIM()
  • ROUND()
  • ABS()
  • SQRT()
  • MOD()

SQL CLAUSE

  • WHERE
  • GROUP BY
  • ORDER BY
  • HAVING

SUB QUERIS

JOINS

  • inner join
  • outer join
  • left join
  • right join

====================================================

Module 4

TABLEAU

Understanding Data

  • What is data Where to find data
  • Foundations for building Data Visualizations

Creating Your First visualization

  • Getting started with Tableau Software Using Data file formats
  • Connecting your Data to Tableau
  • Creating basic charts(line,bar charts,Tree maps) Using the Show me panel

Tableau Calculations

  • Overview of SUM, AVR, and Aggregate features
  • Creating custom calculations and fields
  • Applying new data calculations to your visualization

Formatting

  • Visualizations
  • Formatting Tools and Menus
  • Formatting specific parts of the view
  • Editing and Formatting Axes

Manipulating Data in Tableau

  • Cleaning- up the data with the Data Interpreter Structuring your data
  • Sorting and filtering Tableau data
  • Pivoting Tableau data

Advanced Visualization Tools

  • Using Filters
  •  Using the Detail panel Using the Size panels Customizing filters
  • Using and Customizing tooltips Formatting your data with colors

Creating Dashboards & Stories

  • Using Storytelling
  • Creating your first dashboard and Story Design for different displays
  • Adding interactivity to your Dashboard

Distributing & Publishing Your Visualization

  • Tableau file types
  • Publishing to Tableau
  • Online Sharing your visualization Printing and exporting

====================================================

Module 5

POWER BI

Power BI Introduction

  • Data Visualization,Reporting
  • Business Intelligence(BI),Traditional BI,Self-Serviced BI
  • Cloud Based BI,On Premise BI
  • Power BI Products
  • Power BI Desktop(Power Query,Power Pivot,Power View)
  • Flow of Working Power Bi Desktop
  • Power Bi Report Server,PowerBI Service,PowerBI Mobile Flow-of Working Power Bi/Power BI Architecture
  • A Brief History of Power Bi
  • Data Transformation,Benefits of Data Transformation
  • Shape or Transform Data using Power Query

Power Query

  • Overview of Power Query/Query Editor, Query Editor User Interface The
  • Ribbon(Home,Transform, Add Column,View Tabs)
  • The Queries Pane,The Data View/Results Pane,The Query Settings Pane,Formula Bar
  • Saving the Work
  • Datatypes,Changing the Data type of a Column Filter in Power Query
  • Auto Filter/Basic Filtering
  • Filter a Column using Text Filters
  • Filter a Column using Number Filters
  • Filter a Column using Date Filters
  • Filter Multiple Columns
  • Remove Columns/Remove Other Columns
  • Name/Rename a Column
  • Reorder Columns or Sort Columns
  • Add Column/Custom Column Split
  • Columns
  • Merge Columns
  • PIVOT,UNPIVOT Columns
  • Transpose Columns
  • Header Row or Use First Row as Headers
  • Keep Top Rows,Keep Bottom Rows Keep
  • Range of Rows
  • Keep Duplicates,Keep Errors
  • Remove Top Rows, Remove Bottom Rows,Remove Alternative Rows
  • Remove Duplicates,Remove Blank Rows,Remove Errors
  • Group Rows/Group By

Visualizations

    • Visualizing Data,Why Visualizations
    • Visualization types,Create and Format Bar and Column Charts
    • Create and Format Stacked Bar Chart Stacked Column Chart Create
    • and Format Clustered Bar Chart,Clustered Column Chart
    • Create and Format 100% Stacked Bar Chart,100% Stacked Column Chart Createand
    • Format Pie and Donut Charts
    • Create and Format Scatter Charts
    • Create and Format Table Visual,Matrix Visualization
    • Line and Area Charts
    • Create and Format Line Chart,Area Chart,Stacked Area Chart
    • Combo Charts
    • Create and Format Line and Stacked Column Chart,Line and Clustered Column Chart
    • Create and Format Ribbon Chart,Waterfall Chart,Funnel Chart

Power Bi Service

  • Power Bi Service Introduction,PowerBI Cloud Architecture
  • Creating Power Bi Service Account, SIGN IN to Power Bi Service Account
  • Publishing Reports to the Power Bi service,Import/Getting the Report to PBI Service My
  • Workspace/App Workspaces Tabs
  • DATASETS,WORKBOOKS,REPORTS,DASHBOARDS
  • Working with Datasets,Creating Reports in Cloud using Published Datasets

Creating Dashboards

  • P in Visuals and Pin LIVE Report Pages to Dashboard
  • Advantages of Dashboards
  • Interacting with Dashboards
  • Formatting Dashboard
  • Sharing Dashboard

====================================================

Module 3

Mathematics

  • Calculus
  • Vector Algebra
  • Matrices Algebra
  • Introduction to Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Probability Theory
  • Random Variables
  • Discrete & Continuous Distribution
  • Joint Distribution
  • Central Limit Theorem
  • Hypothesis Testing
  • Statistics Interview Questions

====================================================

Module 4

Machine Learning

  • Introduction to ML
  • Supervised and Unsupervised Machine Learning
  • Linear Regression & Linear Regression Implementation
  • Bias Variance Tradeoff
  • Cross Validation and Hyper Parameter Tuning
  • Logistic Regression
  • Decision Trees and Ensembles of Decision Trees
  • K-Nearest Neighbours
  • Naive Bayes Classifier
  • Support Vector Machine
  • Principal Component Analysis
  • K-Means Clustering
  • Introduction to Natural Language Processing
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Finding Movie Similarities
  • Improving the Results of Movie Similarities
  • Making Movie Recommendations to People
  • Improve the recommender’s results
  • Machine Learning in Healthcare
  • Machine Learning in Fraud Risk Analytics
  • Machine Learning in Healthcare
  • Machine Learning Interview Questions

====================================================

Module 5

Artificial Intelligence (AI)

  • Decision Trees and Ensembles of Decision Trees
  • K-Nearest Neighbours
  • K-Means Clustering
  • Introduction to Natural Language Processing
  • Artificial Intelligence in Healthcare
  • Artificial Intelligence in Fraud Risk Analytics
  • Artificial Intelligence in Healthcare
  • Artificial Intelligence Interview Questions
  • Project: Project will be given as per latest topic.

====================================================

 

Project: Project will be given as per latest topic.

Wish you all the best for your career!!!

+ Certification

You will get certification after successful course completion.

+ Are you eligible?

Students who are looking for job after completion of the course

  1. Should be Graduate(From IT /Computer Science) ,ME, BE, M Tech. with Min 50% Passing Criteria
  2. Qualifications Include: BE/ME/MTech(ALL BRANCHES), BCA/MCA,BCS/MCS,BSC(IT/CS) ,PG(IT/CS)
  3. No coding background necessary.
  4. Knowledge of basic coding is preferred.
  5. Anyone who wants boost knowledge and switch the job.
  6. Anyone who is having Laptop/ Computer.
  7. Anyone who wants to switch jobs to Data Science.
  8. Basic knowledge of English is mandatory.
+ Benefits

  1. Live Interactive Sessions will take you to the industry based learning and activities fulfill the need of Industry.
  2. Hands-on project knowledge
  3. Notes provided by expert trainer
  4. There are career opportunities available like Data Engineer, Data Scientist, Tableau Developer and Project Engineer etc.
  5. Focus on basic concepts of Python makes you professional Data Scientist
  6. A beginner also go for this training and explore may opportunities
+ People says about this
Nitya,

Good Experience about learning Data Science.  There were very less Students in my batch which helped me to understand the course easily which I have taken. The batch timings were flexible. The professor and staff are very helpful and co-operative. Would recommend to join this institute if you want to gain knowledge in Data Science.

Megha,

the training was great. Teaching faculty is very nice to give practical with each and every concept. I was not having a single knowledge about coding but that after joining Puneri Pattern, I have great knowledge about Data Science and now able to writing codes by own way. I refer to all if you want to practically grow your knowledge then 100% join to Puneri Pattern. Thank you giving me training.  Also service very nice provided and friendly nature of all faculty.

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