Data Science Course in Karur

• Introduction to Data Science
• Applications and tools

Introduction to Python

• Variables
• Operators
• Understanding python blocks

Program Flow Control

• If, else and else if
• For loops
• While loops
• Loop manipulation using pass, continue, break and else

Data Types

• Data types: int, float, complex
• String data type and string operations
• List, Set and Dictionary
• Use of Tuple data type

Functions, Modules and Packages

• Programming using functions
• Programming using modules
• Working with Packages
• Lambda function

String, List and Dictionary Manipulations

• Building blocks of python programs
• Understanding string in build methods
• List manipulation using in-build methods
• Dictionary manipulation
• Programming using string, list, and dictionary

File Operations

• Reading files &Writing files
• Understanding read functions
• Understanding writes functions
• Manipulating file pointer using seek()
• Programming using file operations

Object Oriented Programming (OOPs)

• Class, Object and Instances
• Constructor, class attributes and destructors
• Inheritance, overlapping and overloading operators
• Adding and retrieving dynamic attributes of classes

Exception Handling

• File operation using exception handling
• Handling and helping developer with error code
• Programming using Exception handling

Multithreading

• Understanding threads
• Forking threads
• Synchronizing the threads
• Programming using multithreading

• Introduction to NumPy and Pandas for data manipulation
• Data Handling with NumPy
• Data cleaning and preprocessing with Pandas
• Data Manipulation Using Pandas
• Data Visualization
• Extracting Transform Load
• Introduction to statistical concepts for data analysis

• Introduction to SQL
• Data types and Constraints
• Operators
• Joins & functions
• Clauses
• Views and Stored procedures
• Exception Handling
• Data Wrangling
• Advanced SQL concepts
• User Defined Functions
• SQL Optimization and Performance

• Introduction to Machine Learning
• Supervised learning algorithms
• Unsupervised learning algorithms
• Understanding Regression, Classification and Clustering
• Introduction to Tensor flow
• Data types
• Working with Tensor flow operations
• Feature engineering and selection
• Model evaluation and hyper parameter tuning
• Introduction to MLOps
• Machine Learning Models

• Introduction to deep learning and neural networks
• Working with Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Introduction to Natural Language Processing (NLP)
• NLP applications and sentiment analysis
• Reinforcement learning concepts
• Introduction to time series analysis
• Time series forecasting models

• Introduction to Linux
• Linux Basic commands
• Advanced Linux commands

• Introduction to Linear Algebra and Advanced Statistics
• Descriptive Statistics
• Inferential Statistics
• Understanding Probability

• Evaluating the model on recall, precision, f-support in Classification reports
• Evaluating the true positive/negative and false positive/negative outcomes in the model.
• R2, Adjusted R2, Mean squared error.

• Introduction to Business Case Studies
• Recommendation Engine
• Rating Predictions
• Understanding Census & Housing
• Object Detection
• Stock Market Analysis
• Working Banking Problem
• Implementing AI Chatbot

• Introduction to PowerBI
• Working PowerBI Operations
• Introduction to DAX
• Working with DAX
• Data Visualization with Analytics

• Introduction to Excel
• The building block of spreadsheets
• Editing data in Excel
• Performing operations using formulas
• Formatting & Validating data
• Pivot Charts and Pivot tables
• Creating Dashboard
• Data Analysis With Excel
• Data Visualization
• Power Tools
• Classification Problems
• Information Measure
• Regression Problems

• Working Capstone project kick-off