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  • Enrolling in a data science course in Hyderabad is a great way to start a career in tech. Hyderabad is a major IT hub with many opportunities. A good data science training institute in Hyderabad offers courses that cover key topics like machine learning, statistics, and data visualization.
  • The data science training in Hyderabad includes hands-on practice with real data. Experienced professionals teach these courses, sharing useful insights and the latest trends. The city also has a lively tech community and many events for networking with experts and employers.
  • Taking a data science course in Hyderabad gives you the skills and knowledge needed to succeed in this exciting field.

Data Science Course in Hyderabad

About-Us-Course

  • The data science institute in Hyderabad offers a data scientist course in Hyderabad designed to meet several key training objectives:
    • Mastering Data Science Fundamentals: Students will gain a solid understanding of the core concepts of data science, including statistics, machine learning, and data analysis techniques.

      Practical Experience: The course emphasizes hands-on training, allowing students to work with real-world data sets and tools used in the industry.

      Technical Proficiency: Training covers essential data science tools and programming languages such as Python, R, SQL, and big data technologies to ensure students are technically adept.

      Analytical Skills: The course focuses on enhancing analytical and problem-solving skills, enabling students to interpret data effectively and make data-driven decisions.

      Industry-Relevant Projects: Students will engage in projects that reflect current industry practices, providing practical experience and preparing them for real-world data science roles.

      Career Readiness: The institute aims to prepare students for successful careers as data scientists by providing knowledge of the latest industry trends and best practices.

  • After completing the best data science course in Hyderabad, salary expectations vary:
    • Entry-Level: 5 to 8 lakhs per annum for fresh graduates.

      Mid-Level: 8 to 15 lakhs per annum for 2-5 years of experience.

      Senior-Level: 15 to 25 lakhs per annum for over 5 years of experience.

      Specialized Roles: Over 25 lakhs per annum for expertise in niche areas like AI or industry-specific applications.

  • Completing a top-tier course in Hyderabad can significantly boost earning potential and career prospects.

  • Enrolling with the best data science institute in Hyderabad can significantly enhance your career growth. Heres how:
    • Skill Development: Acquire in-depth knowledge of key data science concepts, including machine learning, data analysis, and visualization.

      Practical Training: Gain hands-on experience with real-world data sets and projects, making you job-ready.

      Industry Certification: Earning a certification from a top institute boosts your resume and makes you more attractive to employers.

      Networking: Connect with industry experts, alumni, and peers, expanding your professional network and job opportunities.

      Increased Salary Potential: With advanced skills and certification, you can secure higher-paying roles in the job market.

      Versatile Career Options: Open doors to various industries such as finance, healthcare, retail, and technology, where data science skills are highly sought after.

      Rapid Career Advancement: Move up the career ladder quickly to roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Data Science Manager.

  • A data science course in Hyderabad is popular due to several key factors:
    • Tech Hub: Hyderabad is a major IT hub, creating high demand for data scientists.

      Quality Education: Reputed institutes offer comprehensive programs with experienced faculty.

      Job Opportunities: Numerous tech companies provide ample career prospects for data science professionals.

      Networking: The city hosts tech events and meetups, offering excellent networking opportunities.

      Competitive Salaries: Data science professionals can expect attractive salaries in Hyderabad.

      Innovation: The city's focus on innovation and research fosters a strong learning environment.

      Support: Government and industry initiatives enhance education and job readiness.

  • These factors make data science training in Hyderabad a sought-after choice.

  • After learning Data Science Online Course, you can expect to take on various roles and responsibilities, including:
    • Data Collection and Processing: Gathering data from various sources and preparing it for analysis.

      Data Analysis: Analysing data to uncover patterns, trends, and insights that can inform business decisions.

      Model Development: Creating, testing, and implementing predictive models using machine learning algorithms.

      Data Visualization: Designing and producing clear, compelling visualizations to communicate data findings.

      Statistical Analysis: Applying statistical methods to understand data distributions, relationships, and significances.

      Tool Utilization: Using tools and programming languages such as Python, R, SQL, and Tableau to manipulate and analyse data.

      Collaboration: Working with other teams, such as IT, marketing, and operations, to integrate data-driven insights into various business processes.

      Reporting: Preparing reports and presentations to share findings with stakeholders, providing actionable insights.

      Problem-Solving: Identifying business problems that can be solved with data science, proposing solutions, and implementing them.

      Continuous Improvement: Keeping up with the latest data science trends, techniques, and technologies to continuously improve skills and methods.

  • These roles and responsibilities enable data scientists to contribute significantly to business success by leveraging data to make informed decisions and drive growth.

  • After completing data science training in Hyderabad with placement, you can find job opportunities in several top hiring industries:
    • Information Technology (IT): Major IT companies and tech startups frequently hire data scientists to analyse big data and improve software solutions.

      Finance and Banking: Financial institutions use data science for risk management, fraud detection, and investment strategies.

      Healthcare: Hospitals and healthcare companies rely on data scientists to improve patient care, develop new treatments, and manage healthcare data.

      E-commerce: Online retailers use data science to enhance customer experience, optimize supply chains, and personalize marketing.

      Telecommunications: Telecom companies leverage data science for network optimization, customer analytics, and improving service delivery.

      Retail: Retailers use data science for inventory management, sales forecasting, and understanding consumer behaviour.

      Manufacturing: Manufacturers apply data science for predictive maintenance, quality control, and optimizing production processes.

      Energy: Energy companies use data science to improve efficiency, manage resources, and develop smart grids.

      Logistics and Supply Chain: These industries use data science for route optimization, demand forecasting, and inventory management.

      Media and Entertainment: Companies use data science to analyse viewer preferences, optimize content delivery, and enhance user engagement.

  • Completing data science training in Hyderabad with placement can open doors to these diverse and dynamic industries, offering ample opportunities for career growth.

  • As soon as you complete a Data Science Certification Course, you will receive a training certificate valid worldwide. This certification enhances your credentials and opens up global career opportunities across various industries.

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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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You will learn data analysis, machine learning, and data visualization techniques.

The most reputable institute offers comprehensive courses and experienced instructors.

The course includes training in data processing, statistical analysis, and real-world project experience.

A top institute provides quality education, hands-on practice, and industry connections.

You can pursue roles like Data Scientist, Data Analyst, and Machine Learning Engineer.

Enrolling ensures access to top-tier education, experienced faculty, and better job placement opportunities.

Croma Campus is among the best institutes for Data Science coaching in Hyderabad, offering expert-led training, real-world projects, and placement assistance. Their industry-focused curriculum ensures students gain practical skills and knowledge for a successful Data Science career.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
Are you satisfied with our Training Curriculum?

If yes, Register today and get impeccable Learning Solutions!

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Training Features

instructore

Instructor-led Sessions

The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.

real life

Real-life Case Studies

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.

assigment

Assignment

Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

life time access

Lifetime Access

Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.

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24 x 7 Expert Support

With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.

certification

Certification

Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.

Showcase your Course Completion Certificate to Recruiters

  • checkgreenTraining Certificate is Govern By 12 Global Associations.
  • checkgreenTraining Certificate is Powered by “Wipro DICE ID”
  • checkgreenTraining Certificate is Powered by "Verifiable Skill Credentials"
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Students Placements & Reviews

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Vikash Singh Rana
Vikash Singh Rana
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Shubham Singh
Shubham Singh
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Saurav Kumar
Saurav Kumar
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