Get Hired as Data Scientist with the Best Data Science Training Course in Hyderabad

Data Science is essential in today’s age of Big Data, is an optimal mix of Coding, Data Collection, Data Visualisation, Data Management, Statistics, Machine Learning, Operations Research, Critical Thinking, and last but not least, Domain Knowledge. Being an interdisciplinary field, it creates insights and values in ways previously unheard of, for the industry, organizations government and others, thereby providing a competitive advantage.

Course Overview

Data Science has a great influence in Business and Administration right from formulating a strategy to overpowering rival performance. Growth and development in business are possible only through Advanced analytics and superior technology nowadays. It focuses on forecasting future events and behaviours, helping in decision making and predicting risk, which happens through Data extraction and comprehension that revolutionary gains in Organizational performance are successfully implemented.

Course Highlights

Our curriculum for the best Data Science Course in Hyderabad. The Data science participants typically attend courses worth 17 credit points, inclusive of a “Practical project” module and a “Capstone Project”. The courses are designed to offer comprehensive learning and application of Data Science in various domains. A student will master courses like

  • Probability and Statistics using R
  • Python
  • SQL and Introduction to Databases

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  • Data Collection
  • Data Visualisation
  • Text Analytics
  • Big Data Management
  • Statistical Analysis: Estimation and Testing , Regression Modelling, Count Data and Survival Analysis
  • Basics of Simulation
  • Basics of Optimisation
  • Forecasting Analytics
  • Data Mining: Unsupervised Learning & Supervised Learning
  • Deep Learning and IoT Application Modules
  • Marketing Analytics
  • Retail Analytics
  • Pricing Analytics
  • Social Media and Web Analytics
  • Project Modules

  • Practical Projects
  • Capstone
  • Employbility Skills

  • Soft Skill Training
  • Business understanding
  • Business Fundamentals

Pedagogy

The programme adopts adult learning pedagogies, with emphasis on action and application, rather than just theory and concepts. The pedagogy involves integrating faculty lectures with case study discussions, simulations, group activities, and other interactive sessions

Enrol Today! 8 Reasons Why We Are The Leading Data Science Training Institute in Hyderabad

Best Training Course

Best Data Science Course in Hyderabad guaranteeing 100% Placement Assistance

Pedagogy

Comprehensive and Holistic approach in each subject

Support

Regular Doubt-clarification sessions, Assignments, Free E-book and Materials for all Modules

Back-Up

Back-Up classes for the ones you’ve missed out


Best Opportunities

Free Internship for selected candidates

Best Exposure

Real-Time Project Exposure

Certifications

Certification after due-completion of course

Upskill & Upgrade

Employability Skills to get ready for corporate challenges

Learn More about the Data Science course

The most comprehensive and detailed Data Science Training course to facilitate your clear understanding. There will be multiple professional trainers, with the requisite expertise on the subject, who will be conducting the sessions. Each module is designed by the experts which are:

  • Well-elaborated
  • Includes minute details aiding ‘Beginner to Expert’ transformation
  • Career & Personal Development oriented approach

Our friendly teachers who are also Industry Commanders provide extensive support for knowledge transfer through application-based pedagogy & real-time projects.

Data Science Masterclass – Level 1

Introduction to Data Science

Python

  • Python Environment Setup and Essentials
  • Python language Basic Constructs
  • OOP concepts in Python and database connection
  • Operators and Keywords for Sequences
  • Working with missing data
  • Python Important Libraries
  • NumPy
  • Pandas
  • SciPy
  • Matplotlib
  • Math
  • Regex
  • Scraping using BS4

Statistics and Probability

  • Basics of Statistics
  • Linear & logistic Regression
  • Probability
  • How to handle missing data

R Programming

  • Basics of R programming
  • Statistics and Probability using R
  • Web scraping
  • Exploratory data analysis using R
  • Data visualization
  • Descriptive statistics using R

SQL & Data Warehousing

  • Introduction to SQL
  • Creating Tables in SQL Databases
  • AND and OR Operators Usage
  • UPDATE Statement & Conditions
  • Schemas : Usage, Creation
  • JOINS - Table Comparisons Queries
  • Stored procedures
  • Using GROUP BY with WHERE, ON
  • Date & Time Styles, Data Formatting
  • Joining 3 and 4 Tables in T-SQL
  • Analytical Functions
  • Exercise with real time data

Introduction to Big Data

  • What is Big Data?
  • Five Vs of Big Data
  • Big Data Architecture & Technologies
  • Big Data Requirements & Challenge
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem
  • Apache Spark using Python

Python Important Libraries

NumPy

  • Introduction to arrays and matrices
  • indexing of array
  • data types
  • broadcasting of array math
  • standard deviation
  • conditional probability
  • correlation and covariance

Analytics

  • How to build analytical thinking

Visualization & Dashboard

  • Introduction to Data Visualization
  • Theories of Human Perception
  • Basics of Data Visualization
  • Designing your Visualizations
  • Visualization Toolbox
  • Tableau Desktop
  • Data Connections in the Tableau
  • Organizing and Simplifying Data
  • Formatting and Annotations
  • Special Field Types
  • Chart Types
  • Define Advanced Chart Types
  • Calculations
  • Creating and using Parameters
  • Dashboards

Machine Learning & Python

  • Introduction
  • ML Fundamentals
  • Unsupervised Learning
  • Clustering
  • Association Rules
  • Supervised Learning
  • What is supervised Learning
  • Algorithms in Supervised Learning
  • Steps in Supervised learning
  • Regression and Classification
  • Classification
  • Decision Trees and Model Tuning
  • Random Forest and Model Tuning
  • KNN Algorithm
  • Support Vector Machines
  • Basics of Text Mining
  • Basics of Forecasting
  • Basics of optimization

Tensor Flow:

  • Introduction to TensorFlow
  • Tensors
  • Debugging full programs
  • Estimator API
  • Monitoring with TensorBoard

Deep Learning using Tensor Flow and Keras

  • Introduction to deep learning
  • Perceptron
  • Artificial Neural Network and multi layered perceptron
  • Convolution Neural Network (CNN)
  • Recurrent Neural Network (RNN)
  • Tensor Flow codes understanding of different CNN and RNN models
  • Case study: Explanation of Imagenet Model

Case Studies:

  • Case 1 : Unsupervised Model 1 – K mean Clustering
  • Case 2: Unsupervised Model 2 – Parzen Window
  • Case 3: Unsupervised Model 3 -
  • Case 4: Principal Component Analysis
  • Case 5: Recommendation engine
  • Case 6: Supervised Model - Decision Trees
  • Case 7: Supervised Model - KNN
  • Case 8 : Supervised Model - Random Forest
  • Case 9: Support Vector Machines

Data Science Masterclass- Level 2

Data Collection

  • Basis of Data Categorization
  • Types of Data
  • Data Architecture
  • Data Acquisition
  • Data formats
  • Data Quantity
  • Data Quality

Advance Statistics

  • Linear Regression
  • Logistic Regression
  • Count data
  • Survival Analytics

Big Data Management

  • Hive
  • Pig
  • Apache Spark
  • PySpark
  • Spark Core Architecture
  • Spark Internals
  • Spark Streaming
  • Spark GraphX Programming
  • Introducing Mllib

Text Analytics & NLP

  • Text Mining and Analytics
  • Natural Language Processing
  • Sentimental Analysis

Forecasting

  • Introduction to forecasting
  • Components of Time series
  • Smoothing methods
  • Auto regressive model & ETS model