Course Details
Full Stack Data Science
Data Science
Created On
July 07, 2023Description
Full Stack Data Science refers to the comprehensive skill set needed to work with data across the entire data science pipeline. It includes expertise in data acquisition, cleaning, analysis, modeling, and visualisation, along with programming, statistics, and machine learning. Full stack data scientists can handle end-to-end projects, from data collection to deploying models, and have strong communication and problem-solving abilities.
Overview
This comprehensive self-paced data science program offered by VinEdu provides a holistic learning experience for individuals aspiring to enter the data science, data analytics, and big data industries. With access to recorded mentor-led sessions and a year-long full-time internship opportunity, you will acquire the essential skills and knowledge needed to thrive in this field. The curriculum covers a wide range of topics, including machine learning operations, cloud infrastructure, and real-world project development. As part of the program, you will also collaborate with the VinEdu product development team, gaining practical experience and contributing to cutting-edge projects. This immersive program equips you with the expertise to succeed in the dynamic world of data science and positions you for a rewarding career in the industry
Features
- Comprehensive Data Science Curriculum: Extensive coverage of all aspects of data science
- Instructor Lead live Class
- One-Year Internship: Opportunity for a year-long internship to gain real-world experience
- Live teaching by experienced instructors for interactive sessions
- Hands-On Industry Projects: Engage in over 56 industry real-time projects for practical experience.
- 450+ Hours of Live Classes: Extensive live classes for in-depth learning and clarification of concepts.
- Lifetime Dashboard Access: Access to course materials and resources even after course completion
- Module Assignments: Assignments provided for every module to reinforce learning and assess progress
- Internal Hiring
What you'll learn
- Programming Language: Python
- Statistics: Stats
- Algorithmic Modeling: Machine learning
- Neural Networks: Deep learning
- Image Analysis: Computer vision
- Text Analysis: Natural language processing
- Data Insights: Data analytics
- Large-scale Data Processing: Big data
- System Design: Architecture
- Data Storage: Databases
Prerequisites
Curriculum
- 94 modules
Core Concepts of Python
Working with Text and Lists
Advanced Data Structures in Python
Conditional Statements and Loops
Iteration and Looping Techniques
Applying Loops in Python Programs
Introduction to Functions in Python
Advanced Function Concepts
Iteration and File Handling
Introduction to Exception Handling
Advanced Exception Handling
Working with Modules and Packages
Object-Oriented Programming (OOP) Basics
Understanding Polymorphism in OOP
Introduction to SQL (Structured Query Language)
Advanced SQL Concepts and Techniques
Object-Oriented Programming (OOP) Principles and Discussion
Introduction to MongoDB NoSQL Database
Integration of Python with MongoDB (Part 1)
Integration of Python with MongoDB (Part 2)
Exploring SQL Lite Database and Python Data Manipulation Techniques (map, reduce, filter, zip)
Introduction to the Pandas Library
Hands-on Practice with Pandas
Exploratory Data Analysis with Pandas -Basic
Exploratory Data Analysis with Pandas -Basic +
Integration and Interplay between Pandas and Numpy
Essential Methods and Operations in Numpy
Building GUI Applications with Tkinter
Fundamentals of Data Visualization
Data Visualization in Python
Testing APIs for Functionality and Performance
Building a Complete Web Application with Flask
Developing a Scrapper to Extract Reviews from Websites
Creating an Image Scrapper and Deploying it on Heroku, AWS, and Azure
EDA and Feature Engineering
Techniques and Methods for Exploratory Data Analysis and Feature Engineering
Linear Regression
Advanced Regression Techniques
Naive Bayes Algorithm and Applied Regression Methods
Practical Applications of Logistic Regression, SSVM, and SVR
Decision Tree Classification Algorithm
Random Forest and Support Vector Machines (SVM)
Adaboost Algorithm
Gradient Boosting Algorithm
Introduction to Clustering Algorithms
Live Coding Demonstration of Linear Regression (Part 1)
Live Coding Demonstration of Linear Regression (Part 2)
Project: Admission Prediction using Lasso, Ridge, and Elastic Net
Deploying Machine Learning Projects in Heroku, Azure, and AWS
Logistic Regression Modeling
Implementation of Logistic Regression
Decision Tree Algorithm
Advanced Decision Tree Techniques, Ensemble Learning, Random Forest, and Boosting
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
Practical Implementation of Decision Trees
Live Coding Demonstration of Decision Trees and Grid Search
Grid Search, Bagging Classifier, and Random Forest
K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Support Vector Regressor (SVR), and Stacking
Clustering and Principal Component Analysis (PCA)
Practical Applications of PCA, DBSCAN, and Naive Bayes
XG Boost Algorithm, Natural Language Toolkit (NLTK), and TF-IDF
A Machine Learning Project
Explanation of the ML Project Pipeline from Start to Finish
Detailed Explanation of an ML Project with Integration of GitHub and Docker
Live Coding Demonstration of Machine Learning Pipelines (Part 1)
Live Coding Demonstration of Machine Learning Pipelines (Part 2)
Recording of a Live Class Session on Machine Learning
Revision Session for Machine Learning Concepts
Process of Training, Evaluating, and Deploying a Model
Process of Training, Evaluating, and Deploying a Model
Revision Session for Machine Learning Concepts
Understanding and Applying Principal Component Analysis (PCA)
Implementation of Principal Component Analysis (PCA) in Machine Learning
Applying Natural Language Processing (NLP) Techniques in Machine Learning
Classification of Spam Messages using NLP Techniques
Fundamentals of Time Series Data and Analysis
Implementing Time Series Analysis Techniques
Introduction to Statistics
Overview of Descriptive and Inferential Statistics
Understanding the Difference between Population and Sample
Measures of Central Tendency: Mean, Median, and Mode
Measures of Variability: Variance and Standard Deviation
Explanation of Bessel's Correction for Sample Variance
Further Understanding of Standard Deviation
Introduction to Variables in Statistics
Understanding Random Variables
Calculating Percentiles and Quartiles
Summary Statistics in Data Distribution: 5 Number Summary
Visualizing Data Distribution with Histograms
Exploring Gaussian (Normal) Distribution
Understanding the Standard Normal Distribution
Utilizing Z-scores in Statistical Analysis
Introduction to Probability Theory
Applying the Addition Rule in Probability
Using the Multiplication Rule in Probability
Combinations and Permutations
Understanding Log-Normal Distribution
Exploring the Central Limit Theorem
Relationship between Skewness and Measures of Central Tendency
Calculation and Interpretation of Covariance
Understanding Pearson and Spearman Rank Correlation
Definition and Interpretation of P-Value
Understanding Confidence Intervals
Steps for Hypothesis Testing and Deriving Conclusions
Further Exploration of Hypothesis Testing
Continued Examination of Hypothesis Testing
Summarizing Key Concepts in Statistics
Explanation of the Detailed Project Report
Implementation of Wafer Fault Detection
Deploying the ML Project in Heroku using Docker and CircleCI
Introduction
The problem statement and Data Description
The Application Flow
Ingestion and Validation Part 1
Validation Part 2
DB Operations
Data Preprocessing
Clustering
Model Selection and Tuning
Prediction
Deployment
Introduction
The Problem Statement and Data Description
The Application Flow
Code Intro and Logging
Validation and Transformation
DB Operations
Data Preprocessing
Clustering
Model Selection and Tuning
Prediction
Deployment
Introduction
The Problem Statement and Data Description
The Application Flow
Code intro and Logging
Validation and Transformation
DB Operations
Data Preprocessing
Deployment
Arima, Sarima, Auto Arima
RNN LSTM for Time Series Forecasting: Predicting NIFTY Stock Price
Time Series Analysis with RNN LSTM: Forecasting NIFTY Stock Price
The Significance of Deep Learning
Why Studying Deep Learning?
Artificial Neural Networks (ANN) vs. Bayesian Neural Networks (BNN)
The Birth of Artificial Neurons
Introduction to Perceptron
Exploring Perceptron Architecture
Implementing Perceptron in Python - Part 1
Implementing Perceptron in Python - Part 2
Implementing Perceptron in Python - Part 3
Implementing Perceptron in Python - Part 4
Implementing Perceptron in Python - Part 5
Implementing Perceptron in Python - Part 6
Implementing Perceptron in Python - Part 7
Python Scripting and Modularity for Perceptron
Basics of Python Logging and Docstrings
Python Packaging, Github Actions, and PyPI Integration
Introduction to Multilayer Perceptron
Understanding Forward Propagation in Neural Networks
Role and Significance of Activation Functions in Neural Networks
Implementing ANN with tf.keras - Part 1
Implementing ANN with tf.keras - Part 2
Implementing ANN with tf.keras - Part 3
Implementing ANN with tf.keras - Part 4
Enhancing ANN with Callbacks: Tensorboard, Early Stopping, Model Checkpointing
Vector
Differentiation
Partial differentiation
Maxima and minima concept
Gradient descent basics
In-depth understanding of Gradient descent with mathematical proof
Chain rule
Back propagation
General problems in training Neural Networks
Vanishing and Exploding gradients
Activation Function Basics
Weight initialization
Activation Functions - 1
Activation functions - 2
Activation functions - 3
Transfer learning
Batch normalization -1
Batch normalization -2
Batch normalization -3
Introduction to fast optimizers
Momentum optimization
NAG
Elongated bowl problem | AdaGrad
RMSProp
Adam
Loss functions
Regularization
Dropout
Course Introduction
Course Overview and Objectives
Installation Guide: Anaconda, Pycharm, and Postman
Managing Conda Environments
Introduction to Pycharm IDE
Configuring Pycharm with Conda
Configuring Pycharm with venv
Configuring Pycharm with Pipenv
Why CNN? Understanding the Power of Convolutional Neural Networks
CNN Basics: Kernels, Channels, Feature Maps, Stride, Padding
Receptive Fields and Image Output Dimensionality: Exploring MNIST Dataset with CNN
Intuitive Understanding of CNN: MNIST, Tensorspace.js, and CIFAR 10 Dataset Exploration
Improving CNN Performance: Dropout and Custom Image Classification (Dog Cat Dataset)
Deployment Options: Heroku, AWS, Azure
Deployment Options: GCP, AWS EBS
LeNet-5
LeNet-5 Practical
AlexNet
AlexNet Practical
VGGNet
VGG16 Practical
Inception
Inception Practical
ResNet
Resnet Practical
Parameter Tuning
Keras Tuner
Building a simple model
Tuning with Keras Tuner
Introduction to Data Augmentation
Advantages of Data Augmentation
Exploring Research Papers: RICAP, Random Erasing, Cutout
Understanding Augmentor Library
Exploring Roboflow Platform
Introduction to Object Detection
Object Detection Competitions and Challenges
Understanding Bounding Boxes in Object Detection
Regression Techniques for Bounding Box Estimation
Intersection over Union (IoU) Metric in Object Detection
Precision and Recall Evaluation in Object Detection
Exploring Average Precision in Object Detection
Object Detection Family
RCNN
RCNN Network Architecture
Cons of RCNN
FAST RCNN
FAST RCNN Network Architecture
Cons of FAST RCNN
FASTER RCNN
FASTER RCNN Network Architecture
YOLO
YOLO Architecture
YOLO Limitations
SSD
SSD Network
Introduction to TensorFlow Object Detection (TFOD1.x)
Using Google Colab with Google Drive
Library Installation in Colab Environment
Setting up TFOD1.x in Colab
Exploring the Model Zoo
Inferencing in Colab
Inferencing on Local Machine
Important Configuration Files
Webcam Testing for Object Detection
Custom Model Training in TensorFlow 1.x
Preparing Our Custom Dataset
Annotation and Labeling of Data
Selecting a Pretrained Model from Model Zoo
Setting up Files for Training
Training Process in Colab Environment
Exporting Frozen Inference Graph
Inference with Trained Model in Colab
Training on Local Machine
Inferencing with our trained model in Local
Understanding the Code
Workflow of the Web App
Code Analysis
Making Predictions with Postman
Troubleshooting and Debugging
Introduction to TensorFlow Object Detection (TFOD2.x)
Utilizing the Default Colab Notebook
Setting up Google Colab and Google Drive
Exploring TFOD2.x Model Garden
Inference using a Pretrained Model
Local Inferencing with a Pretrained Model
Custom Model Training in TensorFlow Object Detection (TFOD2.x)
Preparing Our Custom Dataset in TensorFlow 2
Setting Up Files for Training
Initiating the Training Process
Resuming or Stopping Training
Evaluating the Trained Model
Converting CKPT to Saved Model
Inferencing with the Custom Trained Model in Colab
Inferencing with the Custom Trained Model on Local PC
Pycharm Project Creation and Environment Setup for TensorFlow 2
Workflow of the Chess Piece Detector Web App
Understanding the Code
Testing the Application with Postman
Debugging the Chess Piece Detector Application
Introduction to Detectron2
Setting up Detectron2 in Colab
Exploring Detectron2 Model Zoo
Performing Object Detection with Pretrained Models in Detectron2
Detectron2 Custom Training
Dataset Exploration for Training
Registering the Dataset for Training
Initiating the Training Process
Inferencing with the Custom Trained Model in Colab
Model Evaluation and Performance Analysis
Pycharm Project Creation and Environment Setup for Detectron2
Workflow of the Custom Detector Web App
Understanding the Code
Testing the Application with Postman
Debugging the Custom Detector Application
Introduction to YoloV5
YoloV5 Colab Setup
Inferencing using Pre Trained Model
Custom Training with YOLOv5
Dataset Exploration for Training
Annotation and Labeling of Data
Setting up Google Colab and Google Drive
Initiating the Training Process
Inferencing with the Custom Trained Model in Colab.
Pycharm Project Creation and Environment Setup for YOLOv5
Workflow of the Warehouse Apparel Detector Web App
Understanding the Code
Testing the Application with Postman
Debugging the Warehouse Apparel Detector Application
Segmentation Introduction
Bounding Box to Polygon Masks Conversion
Understanding Image Segmentation
Types of Image Segmentation
Exploring MASK RCNN
MASK RCNN Architecture
Segmentation using TFOD1.x
Setting up MASK RCNN on Local Machine
Exploring the Dataset for MASK RCNN
Annotation of Data for MASK RCNN
Selection of Model for Training
File Setup for MASK RCNN Training
Training the MASK RCNN Model
Exporting Frozen Inference Graph
Making Predictions with the Trained MASK RCNN Model
Introduction to Detectron2
Detectron2 Colab Notebook Setup
Exploring the Model Zoo in Detectron2
Setting up Detectron2 in Colab
Custom Training with Detectron2
Exploring the Dataset for MASKRCNN
Annotation of Data for MASKRCNN
Data Preparation for Training
Setup for Training with Detectron2
Initiating the Training Process
Inferencing with the Custom Trained Model in Colab
Model Evaluation and Performance Analysis
Introduction to Face Recognition Project
Gathering Project Requirements
Selection of Tech Stack
Installation of Project Dependencies
Project Demonstration
Workflow of the Project
Core Components of the Application
Data Collection Module
Face Embeddings Generation
Training the Face Recognition Module
Prediction Pipeline
Entry Point of the Application
Application Workflow
Debugging the Face Recognition Application
Object tracking project
Project Installation Tracking
Project Demo
Code Understanding
Introduction to GANS
GAN Architecture
GAN PRACTICALS Implementation
Introduction to Traffic Vehicle Detection Project
Gathering Project Requirements
Selection of Framework for Vehicle Detection
Detailed Workflow of the Project
Scraping and Collecting Traffic Vehicle Data
Preparing the Data for Training
Augmenting the Data for Better Training Performance
Performing Data Annotations for Vehicle Detection
Training the Vehicle Detection Model
Creating a Pycharm Project and Setting up the Environment for Traffic Vehicle Detection
Workflow of the Traffic Vehicle Detection Web Application
Understanding the Code for Vehicle Detection
Making Predictions with Postman
Debugging the Traffic Vehicle Detection Application
Introduction to Helmet Detection Project
Gathering Project Requirements
Selection of Technology Stack for Helmet Detection
Detailed Workflow of the Project
Data Collection for Helmet Detection
Preparing the Data for Training
Applying Data Augmentation Techniques
Performing Data Annotations for Helmet Detection
Training the Helmet Detection Model
Creating a Pycharm Project and Setting up the Environment for Helmet Detection
Workflow of the Helmet Detection Web Application
Understanding the Code for Helmet Detection
Making Predictions with Postman
Debugging the Helmet Detection Application
Introduction to Fashion Apparel Detection Project
Gathering Project Requirements
Selection of Technology Stack for Fashion Apparel Detection
Detailed Workflow of the Project
Data Collection for Fashion Apparel Detection
Preparing the Data for Training
Applying Data Augmentation Techniques
Performing Data Annotations for Fashion Apparel Detection
Training the Fashion Apparel Detection Model
Creating a Pycharm Project and Setting up the Environment for Fashion Apparel Detection
Project Demonstration
Workflow of the Fashion Apparel Detection Web Application
Understanding the Code for Fashion Apparel Detection
Making Predictions with Postman
Debugging the Fashion Apparel Detection Application
Introduction to the Image to Text OCR Project
OCR Project Installation
Project Demonstration
Application Workflow for Image to Text Conversion
Understanding the Code for OCR
Debugging the OCR Application
Exploring Different OCR Solutions Available
Introduction to Shredder Systems
Gathering Project Requirements
Selection of Technology Stack for Shredder Systems
Data Collection for Shredder System
Applying Data Augmentation Techniques
Preparing the Data for Training
Performing Data Annotation for Shredder System
Selection of Pretrained Model from Model Zoo
Training the Shredder System Model
Creating a Pycharm Project and Setting up the Environment for Shredder System
Workflow of the Shredder System Application
Project Demonstration
Understanding the Code for Shredder System
Debugging the Shredder System Application
Overall Project Workflow
Introduction to Automatic Number Plate Recognition (ANPR) Project
Gathering Project Requirements
Selection of Technology Stack for ANPR
Data Collection for ANPR
Applying Data Augmentation Techniques
Preparing the Data for Training
Performing Data Annotation for Number Plates
Selection of Pretrained Model from Model Zoo
Training the ANPR Model
Creating a Pycharm Project and Setting up the Environment for ANPR
Workflow of the ANPR Application
Setting Up Google OCR API Key for Text Extraction
Project Demonstration
Understanding the Code for ANPR
Debugging the ANPR Application
NLP Overview
NLP very basic
TF-IDF
Introduction to Word Embeddings
Word Embeddings in NLP - Part 1
Word Embeddings in NLP - Part 2
- RNN basic
RNN Implementation
LSTM Introduction
GRU
Encoder-Decoder Architecture and Attention Mechanism
Understanding the "Attention Is All You Need" Paper
GPT and BERT Models
State-of-the-Art (SOTA) Models with Paper Discussions
Discussion on Albert and DistillBert Projects
Megatron Project
Brand Measures Project
Introduction
Project Setup Text to Speech
Project Demo
Code Explanation
Project Workflow
Prediction with Postman
Debugging Application
Introduction to Speech to Text Project
Project Setup for Speech to Text
Project Demonstration
Explanation of the Code for Speech to Text
Workflow of the Project
Making Predictions with Postman
Debugging the Application
Introduction to Spell Corrector Project
Project Setup for Spell Corrector
Project Demonstration
Explanation of the Code for Spell Corrector
Workflow of the Project
Making Predictions with Postman
Debugging the Application
NER using BERT (Bidirectional Encoder Representations from Transformers)
NLP Project: Machine Translation
NLP Project: Keyword Spotting
Keyword Extraction
Extractive Text Summarization
Rephrase Project
Introduction to Big Data and Data Engineering
Big Data Engineering
Introduction to Distributed Systems
Hadoop and MapReduce
Hands-on with Hadoop MapReduce
Apache hive
Apache hive Hands On
Hands-on with HBase
Big Data Sqoop
Big Data Sqoop Hands On
Working with RDDs in Spark
Hands-on Experience with RDDs in Spark
Core Concepts and Architecture of Spark
Actions and Transformations in Spark
Caching and Performance Optimization in Spark
Working with Shared Variables, Coalesce, and Repartition in Spark
Working with Dataframes in Spark
Hands-on Experience with Dataframes in Spark
Using Databricks for Spark Development
Catalyst and Tungsten: Advanced Optimization Techniques in Spark
Big Data Engineering using PySpark- MLLib
Spark hands On - Spark ML Lib
PySpark Streaming
Hands-on Experience with Spark Streaming
Interactive Experience with Kafka in Big Data
Introduction to Workflow Management Platform
Interactive Experience with Apache Airflow in Big Data
IoT Sensor Data Pipeline with Kafka-Spark Streaming
Product Recommendation Engine with Kafka-Spark Streaming
Analytics for Short Video App
Column Chart in Power BI
Stacked Column Chart in Power BI
Pie Chart in Power BI
Donut Chart in Power BI
Funnel Chart in Power BI
Ribbon Chart in Power BI
Include and Exclude in Power BI
Exporting Data from Visual in Power BI
Visualizing Data with Filled Maps in Power BI
Combining Pie Charts with Maps in Power BI
Customizing Map Formatting in Power BI
Modifying Background in Map Visuals
Creating Map of India in Power BI
Designing Map of Australia in Power BI
Creating and Customizing Tables in Power BI
Formatting Options for Tables
Advanced Conditional Formatting in Tables
Aggregation Techniques in Tables
Exploring Matrix Visualizations in Power BI
Conditional Formatting in Matrix Views
Utilizing Hierarchies in Matrix Visualizations
Adding Subtotals and Totals in Matrix Views
Number Formatting in Tables and Matrix
Line Chart in Power BI
Exploring Drill Down Functionality in Line Chart
Area Chart in Power BI
Comparing Line and Column Charts in Power BI
Scatter Plot Visualization in Power BI
Analyzing Data with Waterfall Chart in Power BI
Utilizing TreeMap in Power BI
Visualizing Data with Gauge Chart in Power BI
Overview
Number Card Visualization
Text Card Visualization
Customizing Formatting of Text Card
Date Card Visualization
Utilizing Relative Filtering with Date Card
Multi-Row Card Visualization
Applying Filters on Visuals
Filtering Data on Current Page
Filtering Data on All Pages
Exploring Drillthrough Functionality in Power BI
Text Slicers in Power BI
Customizing Formatting of Text Slicers
Date Slicers in Power BI
Customizing Formatting of Date Slicers
Number Slicers in Power BI
Installation and Setup of Tableau
Comparing Tableau and Excel
Column Chart in Tableau
Horizontal Bar Chart in Tableau
Stacked Column Chart in Tableau
Stacked Bar Chart in Tableau
Using "Keep Only" and "Exclude" Filters
Using "Keep Only" and "Exclude" Filters - Normal Mode
Publishing to Tableau Public
Pie Chart
Multiple Pie Chart
TreeMap_Editing
Packed Bubble Chart
Word Cloud OR Word Map
Formatting payal
Data Types in Tableau
Filled Map
Symbol Maps
India Map
Histogram
SQL Fundamentals
SQL Constraints
SQL Data Definition Language (DDL)
SQL Data Query Language (DQL)
SQL Data Manipulation Language (DML)
SQL Joins
SQL Import and Export
SQL Aggregate Functions
SQL Order by, Having, and Limit Clause
SQL String Functions
SQL Date and Time Functions
SQL Regular Expressions
SQL Nested Queries
SQL Views
SQL Stored Procedures
SQL Window Functions
Python-SQL Connectivity
Built-in Functions
Date and Time Functions
Text Functions
Mathematical Functions
Lookup Functions
Logical and Error Functions
Statistical Functions
Working with Images in Excel
Excel Formatting Techniques
Custom Formatting in Excel
Conditional Formatting in Excel
Creating Charts in Excel
Data Analysis using Excel
Working with Pivot Tables
Creating Dashboards in Excel
Other Excel Features and Tools
What-If Analysis Tools - Scenario Manager, Goal Seek
Instructors
Experienced Technostragist with three decades in IT, excelling in Sales, Product Management, and Marketing across IT hardware, networking, and software. Proven strategic planner, startup pioneer, and mentor for large teams. Marketing authority in areas such as Business Incubation, Branding, and Sales. Vast industry expertise spans ITES, IT Hardware, IT Training (Software), Distribution, and Retail. Recognized for interpersonal leadership, intuitive decision-making, and a collaborative approach. Complemented by four years as a Data Scientist, enhancing analytical and problem-solving skills with a deep passion for coding and a knack for simplifying complex concepts,
₹20000.00
- 94 Modules
- 450 Hours
- Data Science
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