Data science with Python Training in Tirunelveli

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.