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  • Data Science is a well-known name in the IT industry that helps to make discoveries. And Data Scientists are like gems who have just the right skills to protect data from unwanted activities. In brief, we can say that data scientists are like all-time available databases having all essential data that can be used by businesses to make informed decisions. So, there is an unexpected demand for skilled data science experts who could manage their work efficiently and that is why data science has become one of the top career options too.
  • You must be wondering how to start a career in Data Science with no experience. Well, you should always start with expert Data Science Course in Delhi and make yourself eligible for the IT industry. The objective of the best Data Scientist Course in Delhi with Placement is to make you proficient enough in using various computer science techniques, perform data visualizations, data analytics, learn R, python programming, and many other useful concepts.
  • Being a trusted best Data Science Training Institute in Delhi, we help you to learn more technical concepts too like AI, Machine Learning, Deep Learning, Big Data Hadoop, Tableau, etc. We try our best to help students in all possible ways so that they can become technically sound and apply for different roles in Data Science.

Data Science Course in Delhi

About-Us-Course

  • The objective of the Data Scientist Course in Delhi is to deliver detailed training that covers all the concepts from basic to the advanced level.
    • The objective of the Data Scientist Course in Delhi is to deliver detailed training that covers all the concepts from basic to the advanced level.

      You will learn how to manage different types of data in your organization either it is structured or unstructured so that you could manage complex business requirements that will help you in making a sound decision later.

      As the best Data Science Institute in Delhi, we are strongly committed to delivering all essential data science skills right from the beginning to algorithms, data analytics tools, learn complex data models, and also learn to drive meaningful insights from the collected data.

      You will get hands-on expertise in using various computer science techniques, perform data visualizations, data analytics, learn R, python programming, and many other useful concepts.

      You will gain advanced data science skills too that include AI, Machine Learning, Deep Learning, Big Data Hadoop, Tableau, etc.

      This best Data Science Training institute in Delhi will help you to earn all technical skills, analytical skills, and soft skills that will make you confident enough to manage all types of business requirements.

  • Based on one survey report submitted by McKinsey Global Institute, companies require 2lac plus skilled resources by 2020 with excellent logical and analytical capabilities. And this demand will definitely go HIGH in the next few years. Anyone who is already working as a data scientist will experience an unexpected hike in his salary. If you also want to become an in-demand IT resource, then take the Data Science Course in Delhi and give new wings to your career right away.
    • The average salary of a Data science expert is $139K as per Indeed.

      The average salary of a Data science expert is $113K as per Glassdoor.

      The average salary of a Data science expert is $100K as per PayScale.

  • Being an eminent Data Science Institute in Delhi, we make sure that you could maintain a gradual career growth while enrolled with us and even after the completion of training. Also, it would be great if you could clear one certification exam and gain a hike of a minimum of 30 percent as a certified data science expert.

  • This Data Science Course in Delhi with Placement covers all essential concepts from an initial level to the advanced stage and makes you future-ready to protect your career even in tough times. It is all because you will gain all practical skills that are important to start your career and this training will make you eligible for top industries too.
  • We make you sure that best Data Science Training in Delhi will be a game-changing move for your career where you will find yourself highly competent, skilled, and confident to clear the interviews and get hired. Also, you will gain all related skills to apply for the certification exam and gain credentials that can be shown worldwide. In this way, we will help you to get strong roots in the IT industry and the demand for attractive salary lumps as you desire.

  • Data Science is a revolutionary field in the IT industry and an amazing career option too for serious learners. It does not matter either you are a fresher or an experienced learner, best Data Science Course in Delhi with Placement will help you at each step to build a rewarding career that will grow over time. Today, almost every industry generates millions of data Gigs daily and it is vital to analyze these data files thoroughly to drive meaningful insights from it. This is the reason why the demand for Data science professionals is increasing every day that have the right skill set to perform efficiently at the workplace.
  • The implementation of Data Science techniques is not limited to the IT industry but you can work in other industry verticals too like healthcare, education sector, telecom sector, the insurance industry, and more. So, it is one of the most lucrative career options that witness HUGE salary lumps for skilled people. And it would be great to see how data science can change the world around us in the future.
  • So, it is necessary to get an insight into important facts why Data Science is considered the best career option in 2020 and beyond.
    • #1 Skill in the IT industry with an average salary of $106K per year.

      190K jobs are predicted by the next year.

      The Data Science industry is expected to touch $16 Billion by 2025

      Almost every IT industry is looking for skilled Data Science professionals.

  • As the best Data Science Training Institute in Delhi, we assure you to deliver all essential skills and also helps you to start a career in the data science space with a BANG. Today, when so many industries are looking for data science experts, the Data Science Course in Delhi can give new wings to your career to fly higher.

  • You must be wondering what you have to do at the workplace as a Data Science expert. Well, there are multiple roles in the Data Science domain like Business Intelligence Analyst, Data Mining Engineer, Data Architect, Data Scientist, Senior Data Scientist, and more. There are specific job responsibilities for each of the roles. Let us see a few common job roles and responsibilities that you have to obey on the completion of best Data Science Course in Delhi.
    • You have to design patterns and algorithms using R and python programming languages to access and process data from various sources.

      You should have a sound idea of statistical and mathematical concepts to convert the collected data into meaningful information.

      You should be sure of various data science techniques like data cleansing, data filtration, data analysis, data congestion, data mapping, and more.

      You must have hands-on expertise in using various data science tools like Hive, Hadoop, MapReduce, etc.

      You should able to work on complex data models and advanced data science concepts like machine learning, AI, deep learning, and more.

      Make yourself an established leader to implement various data analytics skills.

  • With this discussion, you must have a sound idea of job roles and responsibilities in Data Science. As the eminent Data Science Institute in Delhi, we prepare to handle all these duties more effectively and provide 100 percent placement assistance too.

  • The top hiring industries in Data Science include IBM, Accenture, Amazon, Flipkart, Snapdeal, Microsoft, Google, Infosys, NETFLIX, FEDEX, American Express, and more. And the Industry-Domain you can work in, as a Data Scientist on the successful completion of Data Science Training in Delhi includes, IT Sector, Healthcare and Medical Sector, Banking & Finance, Transportation, Travel Industry, eCommerce, Media & Entertainment, Non-Profit Industries, Insurance Sector, etc.
  • The number of options is really wider for skilled professionals and our Data Science Coaching in Delhi prepares you for all the top industries and various industry verticals too. We ensure that you will feel confident about your skills, on the course completion, and clear all your interviews in the first attempt.
  • Also, get complete placement assistance, resume building guide, and learn all important tips to prepare for an interview. We also do provide a set of top data Science interview questions to evaluate your skills.

  • On the successful completion of the Data Science Certification Course, you will get a training certificate that is valid around the world and clear proof of your skills. If you attempt the global certification exam in Data Science, it will make you a more demanding resource when compared to other people who applied for the job.
  • It does not matter either you start as a fresher or experienced professional who wants to switch to the Data Science domain, it is always recommended moving forward with a thoughtful career plan in your mind.
  • Let us learn to swim in the Data science pool and establish yourself as a competent It resource across the globe by completing a Data Scientist Course in Delhi with the top industry leaders in the education sector.

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CURRICULUM & PROJECTS

Data Science Certification Training

    Understanding Concepts of Excel

    • Creation of Excel Sheet Data
    • Range Name, Format Painter
    • Conditional Formatting, Wrap Text, Merge & Centre
    • Sort, Filter, Advance Filter
    • Different type of Chart Creations
    • Auditing, (Trace Precedents, Trace Dependents)Print Area
    • Data Validations, Consolidate, Subtotal
    • What if Analysis (Data Table, Goal Seek, Scenario)
    • Solver, Freeze Panes
    • Various Simple Functions in Excel(Sum, Average, Max, Min)
    • Real Life Assignment work

    Ms Excel Advance

    • Advance Data Sorting
    • Multi-level sorting
    • Restoring data to original order after performing sorting
    • Sort by icons
    • Sort by colours
    • Lookup Functions
      • Lookup
      • VLookup
      • HLookup
    • Subtotal, Multi-Level Subtotal
    • Grouping Features
      • Column Wise
      • Row Wise
    • Consolidation With Several Worksheets
    • Filter
      • Auto Filter
      • Advance Filter
    • Printing of Raw & Column Heading on Each Page
    • Workbook Protection and Worksheet Protection
    • Specified Range Protection in Worksheet
    • Excel Data Analysis
      • Goal Seek
      • Scenario Manager
    • Data Table
      • Advance use of Data Tables in Excel
      • Reporting and Information Representation
    • Pivot Table
      • Pivot Chat
      • Slicer with Pivot Table & Chart
    • Generating MIS Report In Excel
      • Advance Functions of Excel
      • Math & Trig Functions
    • Text Functions
    • Lookup & Reference Function
    • Logical Functions & Date and Time Functions
    • Database Functions
    • Statistical Functions
    • Financial Functions
    • Functions for Calculation Depreciation
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    SQL Server Fundamentals

    • SQL Server 2019 Installation
    • Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
    • SQL Server Features & Purpose
    • Using Management Studio (SSMS)
    • Conguration Tools & SQLCMD
    • Conventions & Collation

    SQL Server 2019 Database Design

    • SQL Database Architecture
    • Database Creation using GUI
    • Database Creation using T-SQL scripts
    • DB Design using Files and File Groups
    • File locations and Size parameters
    • Database Structure modications

    SQL Tables in MS SQL Server

    • SQL Server Database Tables
    • Table creation using T-SQL Scripts
    • Naming Conventions for Columns
    • Single Row and Multi-Row Inserts
    • Table Aliases
    • Column Aliases & Usage
    • Table creation using Schemas
    • Basic INSERT
    • UPDATE
    • DELETE
    • SELECT queries and Schemas
    • Use of WHERE, IN and BETWEEN
    • Variants of SELECT statement
    • ORDER BY
    • GROUPING
    • HAVING
    • ROWCOUNT and CUBE Functions

    Data Validation and Constraints

    • Table creation using Constraints
    • NULL and IDENTITY properties
    • UNIQUE KEY Constraint and NOT NULL
    • PRIMARY KEY Constraint & Usage
    • CHECK and DEFAULT Constraints
    • Naming Composite Primary Keys
    • Disabling Constraints & Other Options

    Views and Row Data Security

    • Benets of Views in SQL Database
    • Views on Tables and Views
    • SCHEMA BINDING and ENCRYPTION
    • Issues with Views and ALTER TABLE
    • Common System Views and Metadata
    • Common Dynamic Management views
    • Working with JOINS inside views

    Indexes and Query tuning

    • Need for Indexes & Usage
    • Indexing Table & View Columns
    • Index SCAN and SEEK
    • INCLUDED Indexes & Usage
    • Materializing Views (storage level)
    • Composite Indexed Columns & Keys
    • Indexes and Table Constraints
    • Primary Keys & Non-Clustered Indexes

    Stored Procedures and Benets

    • Why to use Stored Procedures
    • Types of Stored Procedures
    • Use of Variables and parameters
    • SCHEMABINDING and ENCRYPTION
    • INPUT and OUTPUT parameters
    • System level Stored Procedures
    • Dynamic SQL and parameterization

    System functions and Usage

    • Scalar Valued Functions
    • Types of Table Valued Functions
    • SCHEMABINDING and ENCRYPTION
    • System Functions and usage
    • Date Functions
    • Time Functions
    • String and Operational Functions
    • ROW_COUNT
    • GROUPING Functions

    Triggers, cursors, memory limitations

    • Why to use Triggers
    • DML Triggers and Performance impact
    • INSERTED and DELETED memory tables
    • Data Audit operations & Sampling
    • Database Triggers and Server Triggers
    • Bulk Operations with Triggers

    Cursors and Memory Limitations

    • Cursor declaration and Life cycle
    • STATIC
    • DYNAMIC
    • SCROLL Cursors
    • FORWARD_ONLY and LOCAL Cursors
    • KEYSET Cursors with Complex SPs

    Transactions Management

    • ACID Properties and Scope
    • EXPLICIT Transaction types
    • IMPLICIT Transactions and options
    • AUTOCOMMIT Transaction and usage
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    Introduction to Power BI

    • Overview of BI concepts
    • Why we need BI
    • Introduction to SSBI
    • SSBI Tools
    • Why Power BI
    • What is Power BI
    • Building Blocks of Power BI
    • Getting started with Power BI Desktop
    • Get Power BI Tools
    • Introduction to Tools and Terminology
    • Dashboard in Minutes
    • Interacting with your Dashboards
    • Sharing Dashboards and Reports

    Power BI Desktop

    • Power BI Desktop
    • Extracting data from various sources
    • Workspaces in Power BI

    Power BI Data Transformation

    • Data Transformation
    • Query Editor
    • Connecting Power BI Desktop to our Data Sources
    • Editing Rows
    • Understanding Append Queries
    • Editing Columns
    • Replacing Values
    • Formatting Data
    • Pivoting and Unpivoting Columns
    • Splitting Columns
    • Creating a New Group for our Queries
    • Introducing the Star Schema
    • Duplicating and Referencing Queries
    • Creating the Dimension Tables
    • Entering Data Manually
    • Merging Queries
    • Finishing the Dimension Table
    • Introducing the another DimensionTable
    • Creating an Index Column
    • Duplicating Columns and Extracting Information
    • Creating Conditional Columns
    • Creating the FACT Table
    • Performing Basic Mathematical Operations
    • Improving Performance and Loading Data into the Data Model

    Modelling with Power BI

    • Introduction to Modelling
    • Modelling Data
    • Manage Data Relationship
    • Optimize Data Models
    • Cardinality and Cross Filtering
    • Default Summarization & Sort by
    • Creating Calculated Columns
    • Creating Measures & Quick Measures

    Data Analysis Expressions (DAX)

    • What is DAX
    • Data Types in DAX
    • Calculation Types
    • Syntax, Functions, Context Options
    • DAX Functions
    • Date and Time
    • Time Intelligence
    • Information
    • Logical
    • Mathematical
    • Statistical
    • Text and Aggregate
    • Measures in DAX
    • Measures and Calculated Columns
    • ROW Context and Filter Context in DAX
    • Operators in DAX - Real-time Usage
    • Quick Measures in DAX - Auto validations
    • In-Memory Processing DAX Performance

    Power BI Desktop Visualisations

    • How to use Visual in Power BI
    • What Are Custom Visuals
    • Creating Visualisations and Colour Formatting
    • Setting Sort Order
    • Scatter & Bubble Charts & Play Axis
    • Tooltips and Slicers, Timeline Slicers & Sync Slicers
    • Cross Filtering and Highlighting
    • Visual, Page and Report Level Filters
    • Drill Down/Up
    • Hierarchies and Reference/Constant Lines
    • Tables, Matrices & Conditional Formatting
    • KPI's, Cards & Gauges
    • Map Visualizations
    • Custom Visuals
    • Managing and Arranging
    • Drill through and Custom Report Themes
    • Grouping and Binning and Selection Pane, Bookmarks & Buttons
    • Data Binding and Power BI Report Server

    Introduction to Power BI Dashboard and Data Insights

    • Why Dashboard and Dashboard vs Reports
    • Creating Dashboards
    • Conguring a Dashboard Dashboard Tiles, Pinning Tiles
    • Power BI Q&A
    • Quick Insights in Power BI

    Direct Connectivity

    • Custom Data Gateways
    • Exploring live connections to data with Power BI
    • Connecting directly to SQL Server
    • Connectivity with CSV & Text Files
    • Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
    • Content packs
    • Update content packs

    Publishing and Sharing

    • Introduction and Sharing Options Overview
    • Publish from Power BI Desktop and Publish to Web
    • Share Dashboard with Power BI Service
    • Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
    • Print or Save as PDF and Row Level Security (Power BI Pro)
    • Export Data from a Visualization
    • Export to PowerPoint and Sharing Options Summary

    Refreshing Datasets

    • Understanding Data Refresh
    • Personal Gateway (Power BI Pro and 64-bit Windows)
    • Replacing a Dataset and Troubleshooting Refreshing
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    Introduction To Python

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.

    Python Keyword and Identiers

    • Python Comments, Multiline Comments.
    • Python Indentation
    • Understating the concepts of Operators
    • Arithmetic
    • Relational
    • Logical
    • Assignment
    • Membership
    • Identity

    Introduction To Variables

    • Variables, expression condition and function
    • Global and Local Variables in Python
    • Packing and Unpacking Arguments
    • Type Casting in Python
    • Byte objects vs. string in Python
    • Variable Scope

    Python Data Type

    • Declaring and using Numeric data types
    • Using string data type and string operations
    • Understanding Non-numeric data types
    • Understanding the concept of Casting and Boolean.
    • Strings
    • List
    • Tuples
    • Dictionary
    • Sets

    Control Structure & Flow

    • Statements - if, else, elif
    • How to use nested IF and Else in Python
    • Loops
    • Loops and Control Statements.
    • Jumping Statements - Break, Continue, pass
    • Looping techniques in Python
    • How to use Range function in Loop
    • Programs for printing Patterns in Python
    • How to use if and else with Loop
    • Use of Switch Function in Loop
    • Elegant way of Python Iteration
    • Generator in Python
    • How to use nested Loop in Python
    • Use If and Else in for and While Loop
    • Examples of Looping with Break and Continue Statement
    • How to use IN or NOT IN keyword in Python Loop.

    List

    • What is List.
    • List Creation
    • List Length
    • List Append
    • List Insert
    • List Remove
    • List Append & Extend using "+" and Keyword
    • List Delete
    • List related Keyword in Python
    • List Reverse
    • List Sorting
    • List having Multiple Reference
    • String Split to create a List
    • List Indexing
    • List Slicing
    • List count and Looping
    • List Comprehension and Nested Comprehension

    Tuple

    • What is Tuple
    • Tuple Creation
    • Accessing Elements in Tuple
    • Changing a Tuple
    • Tuple Deletion
    • Tuple Count
    • Tuple Index
    • Tuple Membership
    • TupleBuilt in Function (Length, Sort)

    Dictionary

    • Dict Creation
    • Dict Access (Accessing Dict Values)
    • Dict Get Method
    • Dict Add or Modify Elements
    • Dict Copy
    • Dict From Keys.
    • Dict Items
    • Dict Keys (Updating, Removing and Iterating)
    • Dict Values
    • Dict Comprehension
    • Default Dictionaries
    • Ordered Dictionaries
    • Looping Dictionaries
    • Dict useful methods (Pop, Pop Item, Str , Update etc.)

    Sets

    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations
    • Frozen Sets

    Strings

    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations

    Python Function, Modules and Packages

    • Python Syntax
    • Function Call
    • Return Statement
    • Arguments in a function - Required, Default, Positional, Variable-length
    • Write an Empty Function in Python -pass statement.
    • Lamda/ Anonymous Function
    • *args and **kwargs
    • Help function in Python
    • Scope and Life Time of Variable in Python Function
    • Nested Loop in Python Function
    • Recursive Function and Its Advantage and Disadvantage
    • Organizing python codes using functions
    • Organizing python projects into modules
    • Importing own module as well as external modules
    • Understanding Packages
    • Random functions in python
    • Programming using functions, modules & external packages
    • Map, Filter and Reduce function with Lambda Function
    • More example of Python Function

    Decorator, Generator and Iterator

    • Creation and working of decorator
    • Idea and practical example of generator, use of generator
    • Concept and working of Iterator

    Python Exception Handling

    • Python Errors and Built-in-Exceptions
    • Exception handing Try, Except and Finally
    • Catching Exceptions in Python
    • Catching Specic Exception in Python
    • Raising Exception
    • Try and Finally

    Python File Handling

    • Opening a File
    • Python File Modes
    • Closing File
    • Writing to a File
    • Reading from a File
    • Renaming and Deleting Files in Python
    • Python Directory and File Management
    • List Directories and Files
    • Making New Directory
    • Changing Directory

    Memory management using python

    • Threading, Multi-threading
    • Memory management concept of python
    • working of Multi tasking system
    • Different os function with thread

    Python Database Interaction

    • SQL Database connection using
    • Creating and searching tables
    • Reading and Storing cong information on database
    • Programming using database connections

    Reading an excel

    • Working With Excel
    • Reading an excel le using Python
    • Writing to an excel sheet using Python
    • Python| Reading an excel le
    • Python | Writing an excel le
    • Adjusting Rows and Column using Python
    • ArithmeticOperation in Excel le.
    • Play with Workbook, Sheets and Cells in Excel using Python
    • Creating and Removing Sheets
    • Formatting the Excel File Data
    • More example of Python Function

    Complete Understanding of OS Module of Python

    • Check Dirs. (exist or not)
    • How to split path and extension
    • How to get user prole detail
    • Get the path of Desktop, Documents, Downloads etc.
    • Handle the File System Organization using OS
    • How to get any les and folder's details using OS
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    Introduction to Machine Learning

    • What is Machine Learning
    • Machine Learning Use-Cases
    • Machine Learning Process Flow
    • Machine Learning Categories

    Time Series Analysis

    • What is Time Series Analysis
    • Importance of TSA
    • Components of TSA
    • White Noise
    • AR model
    • MA model
    • ARMA model
    • ARIMA model
    • Stationarity
    • ACF & PACF

    Statistical Foundations (Self-Paced)

    • What is Exploratory Data Analysis
    • EDA Techniques
    • EDA Classification
    • Univariate Non-graphical EDA
    • Univariate Graphical EDA
    • Multivariate Non-graphical EDA
    • Multivariate Graphical EDA
    • Heat Maps

    Introduction to Text Mining and NLP

    • Overview of Text Mining
    • Need of Text Mining
    • Natural Language Processing (NLP) in Text Mining
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    Introduction to Text Mining and NLP

    • Overview of Text Mining
    • Need of Text Mining
    • Natural Language Processing (NLP) in Text Mining
    • Applications of Text Mining
    • OS Module
    • Reading, Writing to text and word files
    • Setting the NLTK Environment
    • Accessing the NLTK Corpora

    Extracting, Cleaning and Preprocessing Text

    • Tokenization
    • Frequency Distribution
    • Different Types of Tokenizers
    • Bigrams, Trigrams & Ngrams
    • Stemming
    • Lemmatization
    • Stopwords
    • POS Tagging
    • Named Entity Recognition

    Analyzing Sentence Structure

    • Syntax Trees
    • Chunking
    • Chinking
    • Context Free Grammars (CFG)
    • Automating Text Paraphrasing

    Text Classification - I

    • Machine Learning: Brush Up
    • Bag of Words
    • Count Vectorizer
    • Term Frequency (TF)
    • Inverse Document Frequency (IDF)

    Getting Started with TensorFlow 2.0

    • Introduction to TensorFlow 2.x
    • Installing TensorFlow 2.x
    • Defining Sequence model layers
    • Activation Function
    • Layer Types
    • Model Compilation
    • Model Optimizer
    • Model Loss Function
    • Model Training
    • Digit Classification using Simple Neural Network in TensorFlow 2.x
    • Improving the model
    • Adding Hidden Layer
    • Adding Dropout
    • Using Adam Optimizer

    Introduction to Deep Learning

    • What is Deep Learning
    • Curse of Dimensionality
    • Machine Learning vs. Deep Learning
    • Use cases of Deep Learning
    • Human Brain vs. Neural Network
    • What is Perceptron
    • Learning Rate
    • Epoch
    • Batch Size
    • Activation Function
    • Single Layer Perceptron

    Neural Networks

    • What is NN
    • Types of NN
    • Creation of simple neural network using tensorflow

    Convolution Neural Network

    • Image Classification Example
    • What is Convolution
    • Convolutional Layer Network
    • Convolutional Layer
    • Filtering
    • ReLU Layer
    • Pooling
    • Data Flattening
    • Fully Connected Layer
    • Predicting a cat or a dog
    • Saving and Loading a Model
    • Face Detection using OpenCV

    Image Processing and Computer Vision

    • Introduction to Vision
    • Importance of Image Processing
    • Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
    • Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations
    • Noise Reduction – Moving Average & 2D Moving Average
    • Image Filtering – Linear & Gaussian Filtering
    • Disadvantage of Correlation Filter
    • Introduction to Convolution
    • Boundary Effects – Zero, Wrap, Clamp & Mirror
    • Image Sharpening
    • Template Matching
    • Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
    • Effect of Noise
    • Laplacian Filter
    • Smoothing with Gaussian
    • LOG Filter – Blob Detection
    • Noise – Reduction using Salt & Pepper Noise using Gaussian Filter
    • Nonlinear Filters
    • Bilateral Filters
    • Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
    • Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
    • Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
    • Introduction to the dnn module
      • Deep Learning Deployment Toolkit
      • Use of DLDT with OpenCV4.0
    • OpenVINO Toolkit
      • Introduction
      • Model Optimization of pre-trained models
      • Inference Engine and Deployment process
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Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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FAQ's

Croma Campus is an excellent institute to learn Data Scientist Training in Delhi. We provide a comprehensive Data Science Course in Delhi that covers all the important aspects of the subject. Our course syllabus is designed to give students a thorough understanding of the subject and help them become proficient in the field.

The well-equipped laboratories and the actual project would substantially help the Data Scientist Course in Delhi. You'll receive more real-world experience as a consequence, and you'll be better equipped to find a great job.

Croma Campus provides Data Science Course in Delhi with Certification for you, to join in so that you may gain complete skills. The institute's high-quality training will provide you with a diverse set of skills and knowledge.

The duration of a Data Science course in Delhi varies, typically ranging from 3 to 9 months, depending on the program and intensity.

To enroll in the Croma Campus Data Science Training Institute in Delhi, go to helpdesk@cromacampus.com. You may reach us by WhatsApp (+91-9818014543) or phone (+91-9711526942).

Croma Campus is the best institute to learn Data Science in Delhi with Placement.

Croma Campus is the Best Data Science Institute in Delhi with Placement focused training.

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