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Data Science Training in Kovilpatti

Data Science Training in Kovilpatti

In the digital age, data is the new currency, and mastering the art of Data Science is your key to unlocking its hidden treasures. Get ready to embark on a creative and captivating Data Science training in Getin Technologies Kovilpatti, where we’ll dive into the realm of data with an imaginative twist.

Imagine you’re embarking on a thrilling adventure, equipped with nothing but your curiosity. Data Science, like a treasure map, guides you through the vast landscape of information. We will start by understanding what data is and why it’s so valuable.

What Is Data Science?

Data science is a multidisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines expertise from various domains such as statistics, computer science, domain knowledge, and data engineering to analyze, interpret, and understand complex data. 

Data science is widely used in various fields, including business, healthcare, finance, marketing, social sciences, and more, to uncover hidden patterns, make predictions, optimize processes, and inform decision-making. Data Science plays a crucial role in the era of big data, where organizations generate and collect vast amounts of information, and it helps turn this data into valuable knowledge that can drive innovation and competitiveness.

At Getin Technologies in Kovilpatti, we believe in the fusion of data science and creativity, where data becomes your medium and you, the maestro. Together, we’ll unravel the mysteries of data and empower you to tackle real-world challenges with innovation and imagination.

Data Science Training in Kovilpatti Features

Data Science Placement

Our Data Science program is designed with a strong emphasis on employability. We provide comprehensive placement assistance to our students, connecting them with leading companies in the industry. Our dedicated placement team conducts mock interviews, resume-building workshops, and industry networking sessions, ensuring that you are well-prepared to secure your job.

Industry Expert Trainers

Learn from the best in the field! Our trainers are seasoned industry professionals with extensive experience in data science and analytics. They bring a wealth of knowledge and real-world insights to the classroom, helping you grasp complex concepts and techniques. With their guidance, you will not only learn theoretical foundations but also gain practical skills.

Real-world Project

Experience hands-on learning with our real-world projects that mimic industry challenges. You’ll work on data sets and case studies sourced from actual companies, allowing you to apply your skills in a practical context. This project-based approach enhances your problem-solving abilities and prepares you for the challenges you’ll face in the workplace.

End-to-End Proficiency

Our curriculum covers the entire data science lifecycle, from data collection and cleaning to analysis and visualization. You will gain end-to-end proficiency in tools and techniques such as Python, R, SQL, machine learning algorithms, and data visualization frameworks. This comprehensive knowledge equips you with the skills.

Industry Based Syllabus

We keep our syllabus aligned with current industry standards and trends. Our curriculum is regularly updated based on feedback from industry experts and market needs, ensuring that you learn relevant skills and technologies. This industry-based syllabus prepares you to meet the demands of potential employers effectively.

Flexibility

We understand that everyone has different learning needs and schedules. Our courses offer flexibility in terms of timing and delivery modes, including online and offline options. You can choose the format that best fits your lifestyle, allowing you to learn at your own pace while balancing work or personal commitments.

You Will Learn

SQL

Python

Machine Learning

Deep Learning

Tableau

Big Data

Unlock exclusive savings on our courses with personalized coupon codes –  Contact us now to elevate your learning experience at a discounted Price! (Only Online Class)

Call Now: +91 89258 31826

Data Science Training - Module 1

Data Science Training - Module 2

Data Science Training - Module 3

Data Science Training - Module 4

Data Science Course Syllabus

Introduction

  • The Relational Model

Understanding Basic SQL Syntax:

  • Basic SQL Commands – SELECT
  • Basic SQL Commands – INSERT
  • Basic SQL Commands – UPDATE
  • Basic SQL Commands – DELETE

Querying Data with the SELECT Statement:

  • The SELECT List
  • SELECT List Wildcard (*)
  • The FROM Clause
  • How to Constrain the Result Set
  • DISTINCT and NOT DISTINCT

Filtering Results with the Where Clause:

  • WHERE Clause
  • Boolean Operators
  • The AND Keyword
  • The OR Keyword
  • Other Boolean Operators BETWEEN, LIKE, IN, IS, IS NOT

Shaping Results with ORDER BY and GROUP BY:

  • ORDER BY
  • Set Functions
  • Set Function And Qualifiers
  • GROUP BY
  • HAVING clause

Matching Different Data Tables with JOINS:

  • CROSS JOIN
  • INNER JOIN
  • OUTER JOINs
  • LEFT OUTER JOIN
  • RIGHT OUTER JOIN
  • FULL OUTER JOIN
  • SELF JOIN

Creating Database Table stamp:

  • CREATE DATABASE
  • CREATE TABLE
  • NULL Values
  • PRIMARY KEY
  • CONSTRAINT
  • ALTER TABLE
  • DROP TABLE

Introduction to Python

  • What is Python and the history of Python?
  • Unique features of Python
  • Install Python and Environment Setup
  • First Python Program
  • Python Identifiers, Keywords, and Indentation
  • Comments and document interlude in Python
  • Command-line arguments
  • Getting User Input
  • Python Data Types
  • What are the variables?

Control Statements

  • If
  • If-elif-else
  • while loop
  • for loop
  • Break
  • Continue
  • Assert
  • Pass
  • return

List, Ranges & Tuples in Python

  • Introduction
  • Lists in Python
  • Generators and Yield
  • Generators Comprehensions and Lambda Expressions
  • Next() and Range()
  • Understanding and using Range

Python Dictionaries and Sets

  • Introduction to the section
  • Python Dictionaries
  • More on Dictionaries
  • Sets

Python built-in function

  • Python Modules & Packages
  • Python User defined functions
  • Defining and calling Function
  • The anonymous Function

Python Object Oriented

  • Overview of OOP
  • Creating Classes and Objects
  • Constructor
  • The self variable
  • Types Of Variables
  • Namespaces
  • Inheritance
  • Types of Methods
  • Instance Methods Static Methods Class Methods
  • Accessing attributes
  • Built-In Class Attributes
  • Destroying Objects
  • Abstract classes and Interfaces
  • Abstract Methods and Abstract class
  • Interface in Python
  • Abstract classes and Interfaces

Introduction to Machine Learning:

  • What is Machine Learning?
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement
  • Learning)
  • Applications of Machine Learning
  • Python and Libraries for Machine Learning (NumPy, Pandas, Scikit-Learn)

Data Preprocessing

  • Data Cleaning and Exploration
  • Feature Engineering
  • Data Scaling and Normalization
  • Handling Missing Data

Machine Learning Techniques

  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design

Supervised Learning

  • Regression
  • Classification

Supervised Learning – Regression

  • Linear Regression & Logistic: A Model-Based Approach
  • Regression fundamentals : Data and Models
  • Feature selection in Model building
  • Evaluating over fitting via training/test split
  • Training/ Test curves
  • Adding other features
  • Regression ML block diagram

Supervised Learning – Classification

  • Classification fundamentals : Data and Models
  • Understanding Decision Trees and Naive Bayes
  • Feature selection in Model building
  • Linear classifiers
  • Decision boundaries
  • Training and evaluating a classifier
  • False positives, false negatives, and confusion matrices
  • Classification ML block diagram

Unsupervised Learning

  • Clustering
  • Recommendation
  • Deep Learning

Unsupervised Learning – Clustering

  • Clustering System Overview
  • Clustering fundamentals : Data and Models
  • Feature selection in Model building
  • Prioritizing important words with tf-idf
  • Clustering and similarity ML block diagram

Unsupervised Learning – Deep Learning

  • Deep Learning: Searching for Images
  • Learning very non-linear features with neural networks
  • Application of deep learning to computer vision
  • Deep learning performance
  • Demo of deep learning model on ImageNet data
  • Deep learning ML block diagram

Natural Language Processing (NLP)

  • Text Preprocessing
  • Bag of Words and TF-IDF
  • Sentiment Analysis
  • Text Classification
  • Word Embeddings (Word2Vec, GloVe)

Neural Networks and Deep Learning

  • Introduction to Neural Networks
  • Feedforward Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Transfer Learning and Pretrained Models

Reinforcement Learning

  • Introduction to Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Q-Learning
  • Deep Q-Networks (DQN)
  • Policy Gradient Methods

Model Deployment and Production

  • Model Serialization
  • REST APIs for Model Deployment
  • Cloud Services for Model Deployment

Introduction to Deep Learning

  • Overview of Deep Learning
  • History and Evolution of Neural Networks
  • Key Deep Learning Concepts
  • Python and Deep Learning Libraries (TensorFlow, Keras, PyTorch)

Fundamentals of Neural Networks

  • Perceptrons and Sigmoid Neurons
  • Activation Functions
  • Feedforward Neural Networks (FNN)
  • Backpropagation Algorithm

Advanced Neural Network Architectures

  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)

Training Deep Neural Networks

  • Loss Functions and Optimization
  • Vanishing and Exploding Gradients
  • Regularization Techniques
  • Weight Initialization
  • Batch Normalization

Deep Learning for Computer Vision

  • Image Classification
  • Object Detection
  • Image Segmentation
  • Style Transfer
  • Transfer Learning with Pretrained Models

Deep Learning for Natural Language Processing (NLP)

  • Word Embeddings (Word2Vec, GloVe)
  • Recurrent Neural Networks for NLP
  • Sequence-to-Sequence Models
  • Attention Mechanisms
  • Transformer Models (e.g., BERT)

Generative Models

  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)
  • Applications in Image and Text Generation

Reinforcement Learning and Deep Reinforcement Learning

  • Introduction to Reinforcement Learning
  • Q-Learning
  • Deep Q-Networks (DQN)
  • Policy Gradient Methods
  • Applications in Game Playing and Robotics

Unsupervised Learning with Deep Learning

  • Autoencoders
  • Self-Organizing Maps (SOM)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Clustering with Deep Learning

Advanced Topics in Deep Learning

  • Attention Mechanisms and Transformer Architectures
  • Transfer Learning Strategies
  • Model Interpretability and Explainability
  • Ethics and Bias in Deep Learning

Introduction

  • Start Page
  • Show Me
  • Connecting to Excel Files
  • Connecting to Text Files
  • Connect to Microsoft SQL Server
  • Connecting to Microsoft Analysis Services
  • Creating and Removing Hierarchies
  • Bins
  • Joining Tables
  • Data Blending

Creating Your First visualization

  • Getting started with Tableau Software
  • Using Data file formats
  • Connecting your Data to Tableau
  • Creating basic charts (line, bar charts, Treemaps)
  • 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

Introduction to BIG DATA and HADOOP

  • Types of Digital Data
  • Introduction to Big Data
  • Big Data Analytics
  • History of Hadoop
  • Apache Hadoop
  • Analysing
  • Data with Unix tools
  • Analysing Data with Hadoop
  • Hadoop Streaming
  • Hadoop Echo System

HDFS(Hadoop Distributed File System)

  • The Design of HDFS
  • HDFS Concepts
  • Command Line Interface
  • Hadoop file system interfaces
  • Data flow
  • Data Ingest with Flume and Scoop and Hadoop archives
  • Hadoop I/O: Compression, Serialization, Avro and File-Based
  • Data structures.

Map Reduce

  • Anatomy of a Map Reduce Job Run
  • Failures
  • Job Scheduling
  • Shuffle and Sort
  • Task Execution
  • Map Reduce Types and Formats
  • Map Reduce Features.

Hadoop Eco System

  • Pig
    • Introduction to PIG Execution
    • Modes of Pig
    • Comparison of Pig with Databases
    • Grunt
    • Pig Latin
    • User Defined Functions
    • Data Processing operators.
  • Hive
    • Hive Shell
    • Hive Services
    • Hive Metastore
    • Comparison with Traditional Databases
    • HiveQL
    • Tables
    • Querying
    • Data and User Defined Functions.
  • Hbase
    • HBasics Concepts
    • Clients
    • Example
    • Hbase Versus RDBMS.

Who can join this course

Freshers (2023 - 2025) Passouts

Eligible: BE, ME, BTech, MTech BSC, BCom, BA, BCA, MBA, MSC, MCA, BBA, MCom

Not Eligible: Diploma

Year Gap (2010 - 2022 Passout)

Eligible: BE, ME, BTech, MTech BSC, BCom, BA, BCA, MBA, MSC, MCA, BBA, MCom

Not Eligible: Diploma

Experienced

Share your resume to Our WhatsApp +91 8925831826. Our Placement Team will Validate your Profile and get back to you shortly.

Our Realtime Projects in Data Science Training in Kovilpatti

Predictive Customer Churn Analysis

  • Build a model to predict customer churn for a subscription-based business.
  • Analyze historical data to identify factors that contribute to customer attrition and create a predictive model to reduce churn.

Sentiment Analysis for Social Media

  • Develop a sentiment analysis tool that processes social media posts and comments.
  • Determine whether user sentiments are positive, negative, or neutral, and visualize trends over time.

Recommendation System for E-commerce

  • Create a personalized recommendation system for an e-commerce platform.
  • Use collaborative filtering or content-based methods to suggest products to users based on their browsing and purchase history.

If you want to Customize the Course Syllabus for Data Science, Call us to  +91 89258 31826

If you want to Group Discount for Data Science course, Call us to  +91 89258 31826

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School Student Offer

offer

30% Offer for School Students from Total Course Fees.

  1. Bring Valid School ID Card while Admission. 2. 6th – 12th Std can enroll this course. 3. Terms and conditions apply.

College Student Offer

Offer20% Offer for College Students from Total Course Fees.

  1. Bring Valid College ID Card while Admission. 2. All Stream (Arts & Engineering) students can use this offer. 3. Terms and conditions apply.

Disabled Student Offer

offer

50% Offer for Disabled Students from Total Course Fees.

  1. Bring Govt Approved Disabled Person ID Card while come to admission. 2. Terms and Conditions Apply.

Why Learn Data Science?

Learning data science offers numerous compelling reasons:

  • In-Demand Skills: Data scientists are in high demand across various industries, making it a lucrative and stable career choice.
  • Data-Driven Decision-Making: Data science empowers you to extract meaningful insights from data, aiding businesses in making informed decisions and solving complex problems.
  • Versatile Applications: Data science is versatile, and applicable in fields such as healthcare, finance, marketing, and more, allowing you to work on projects that align with your interests.
  • Innovation: Data science enables innovation through predictive analytics, machine learning, and AI, driving technological advancements and shaping the future.
  • High Earning Potential: Data scientists often command competitive salaries due to their specialized skill set and the value they bring to organizations.
  • Career Opportunities: Data science offers a range of career paths, including data analyst, machine learning engineer, data engineer, and more, ensuring diverse and exciting job prospects.

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