DYNAMIC SYSTEM

Introduction to SQL

Database Normalization and Entity Relationship Model(self-paced)

SQL Operators

Working with SQL: Join, Tables, and Variables

Deep Dive into SQL Functions

Working with Window functions

Working with Subqueries

SQL Views, Functions, and Stored Procedures

Deep Dive into User-defined Functions

SQL Optimization and Performance

Importing and Exporting Databases

Advanced Topics

Managing Database Concurrency

Practice Session

Case Study

Writing comparison data between the past year and to present year concerning top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Introduction to R

R Packages

Sorting DataFrame

Matrices and Vectors

Reading Data from External Files

Generating Plots

Analysis of Variance (ANOVA)

Association Rule Mining

Regression in R

Analyzing Relationship with Regression

Advanced Regression

Logistic Regression

Advanced Logistic Regression

Database Connectivity with R

Integrating R with Hadoop

R Case Studies

Descriptive Statistics

The measure of central tendency, the measure of spread, five points summary, etc.

Probability

Probability Distributions, Probability in Data Science

Probability Distributions, Binomial distribution, Poisson distribution, Bayes’ Theorem, central limit theorem Inferential Statistics

Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test,A/B Testing
Hypothesis Testing

Type-I error

Type-II error

Case Study This case study will cover the following concepts:

Building a statistical analysis model that uses quantification, representations, and experimental data

Reviewing, analyzing, and drawing conclusions from the data

Power BI Basics

Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.

SaaS Connectors, Working with Azure SQL database, Python, and R with Power BI

Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.

DAX

Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

Slicers, filters, Drill Down Reports

Power BI Query, Q & A and Data Insights

Row Level Security(RLS), Dynamic Title Reports

Power BI Settings, Administration and Direct Connectivity

Case Study:

This case study will cover the following concepts:

Creating a dashboard to depict actionable insights in sales data

Reading the Data, Referencing in formulas , Name Range, and Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering Working with Charts in Excel, Pivot Table, Dashboards, Data, and File Security VBA Macros, Ranges, and Worksheets in VBA IF conditions, loops, Debugging, etc. Excel For Data Analytics

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc. Data Visualization with Excel


Charts, Pie charts, Scatter, and bubble charts
Bar charts, Column charts, Line charts, Maps
Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
Ensuring Data and File Security

Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.
Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing an action with Sub and ending Sub if condition not met. Statistics with Excel

ONE-TAILED TEST AND TWO-TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL

Introduction to Machine Learning

1.Supervised, Unsupervised learning.
2.Introduction to scikit-learn, Keras, etc. Regression
3.Introduction classification problems, Identification of a regression problem, dependent and independent variables
4.How to train the model in a regression problem?
5.How to evaluate the model for a regression problem?
6.How to optimize the efficiency of the regression model?

Classification

1.Introduction to classification problems, Identification of a classification problem, dependent and independent variables
2.How to train the model in a classification problem?
3.How to evaluate the model for a classification problem?
4.How to optimize the efficiency of the classification model?

Clustering

1.Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables
2.How to train the model in a clustering problem?
3.How to evaluate the model for a clustering problem?
4.How to optimize the efficiency of the clustering model?

Supervised Learning

1.Linear Regression-Creating linear regression models for linear data using statistical tests.
2.Logistic Regression-Creating logistic regression models for classification problems.
3.Support Vector Machine(SVM)
4.Naive Bayes Classifier
5.K-Nearest Neighbour(KNN)
6.Decision Tree
7.Random Forest Classifier

Ensemble Learning

1.Introduction to Baggging and Boosting Algorithms
2.AdaBoost (Adaptive Boosting) Algorithm
3.Gradient Boosting Machines (GBM) Algorithm
4.XGBoost (Extreme Gradient Boosting) Algorithm
5.LightGBM (Light Gradient Boosting Machine) Algorithm
6.Stochastic Gradient Boosting Algorithm
7.LPBoost (Linear Programming Boosting) Algorithm

Unsupervised Learning

1.K-means-The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach
2.Dimensionality reduction-Handling multi dimensional data and standardizing the features for easier computation
3.Linear Discriminant Analysis-LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data
4.Principal Component Analysis-PCA follows the same approach in handling multidimensional data

Performance Metrics

1.Classification reports-To evaluate the model on various metrics like recall, precision, f-support, etc.
2.Confusion matrix-To evaluate the true positive/negative, and false positive/negative outcomes in the model. r2, adjusted r2, mean squared error, etc.
3.Time Series Forecasting-Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting

Time Series Forecasting

1.Resampling, Autocorrelation, Forecasting, Seasonal
2.Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests
3.Autoregressive methods, moving averages, ARIMA, SARIMA

Data Visualization Using Tableau
Introduction to Data Visualization
Introduction to Tableau
Basic Charts and Dashboard
Descriptive Statistics, Dimensions and Measures
Visual Analytics: Storytelling through Data
Dashboard Design & Principles
Advanced Design Components/ Principles: Enhancing the Power of Dashboards
Special Chart Types
Tableau to Analyze Data
Connect Tableau to a variety of dataset
Analyze, Blend, Join and Calculate Data
Tableau to Visualize Data
Visualize Data In the form of Various Charts, Plots and Maps
Data Hierarchies
Work with Data Blending in Tableau
Work with Parameters
Create Calculated Fields
Adding Filters and Quick Filters
Create Interactive Dashboards
Adding Actions to Dashboard
Case Study: Hands-on Using Tableau
Integrate Tableau with Google Sheets

[ARTIFICIAL INTELLIGENCE (AI)]
What is AI?
Types of AI.
Advantages of AI.
Applications of AI in Current Era.
What is Reinforcement Learning?
Types of RL.
Advantages of Reinforcement Learning.
Applications of Reinforcement Learning.
Models used for Reinforcement Learning.
Key features of RL.
Elements of RL.
What is Agent?
What is Environment?
What is reward and Punishment in RL.
Bellman Equation.
Reinforcement Learning Models.
Markov Decision Process.
What is Q-Learning?
Application of RL Models.
[Natural Language Processing (NLP)]
What is NLP?
Advantages of NLP.
Applications of NLP in Practical Fields.
Types of NLP.
Various Tools/Libraries used for NLP.
Installing NLP Packages.
Working with NLTK Package.
Rule based NLP vs. Statistical NLP.
Tokenizing words and sentences with nltk.
Word Level Analysis.
Syntactic Analysis.
Semantic Analysis.
Part of Speech Tagging(PoS)
Stemming, Lemmatization
Chunking, Chinking
Named Entity Recognition (NER).
WordNet
Bag of Words.
Practical Project Implementation of NLP.
[COMPUTER VISION]
What is Computer Vision?
Need of Computer Vision?
Packages used for Computer Vision.
Introduction to Computer Vision
Introduction to OpenCV
Installing OpenCV
Storing Images
Reading Images
Writing Images
Image Conversion
Colored Image to Binary
Grayscale to Binary
Drawing Functions
Drawing a Circle
Drawing a Line
Drawing a Rectangle
Drawing an Ellipse
Drawing Polylines
Adding Text
Blur Techniques
Filtering
Bilateral Filter
Box Filter
SQRBox Filter
Filter2D
Dilation
Erosion
Morphological Operations
Image Pyramids
Thresholding
Simple Threshold
Adaptive Threshold
Adding Borders
Transformation Operations
Laplacian Transformation
Distance Transformation
Camera and Face Detection
Face Detection in a Picture
Face Detection using Live Camera
Geometric Transformation
Affine Translation
Rotation
Scaling
Color Maps
Canny edge Detection
Practical Project Implementation using Open CV.
[DEEP LEARNING]
Introduction to Deep Learning.
Advantages of Deep Learning.
Applications of Deep Learning.
Packages used for Deep Learning.
Models used for Deep Learning.
What is Neural Networks?
Types of Artificial Neural Networks.
Convolutional Neural Networks(CNN)
Application of CNN.
Intuition behind CNN.
Practical Implementation of CNN.
Recurrent Neural Network (RNN).
Application of RNN.
Intuition behind RNN.
Practical Implementation of RNN.
LSTM (Long Short Term Memory).
Application of LSTM.
Multilayer Perceptron.
Application of Multilayer Perceptron.
Generative Adversarial Networks (GANs).
Restricted Boltzman Machine (RBM).
Deep Belief Network.
Auto Encoder.
Application of Deep Learning Models.
[DEEP LEARNING WITH KERAS]
Introduction to Keras.
Installing Keras.
Keras Layers.
Keras Models
Keras Model Compilation
Regression Prediction using MPL.
Time Series Prediction using LSTM and RNN.
Keras Vs. Tensorflow vs.PyTorch Vs.CNTK Vs.Theano
Project Implementations using Keras.
[DEEP LEARNING WITH TENSORFLOW]
Introduction to Tensorflow.
Installing Tensorflow.
CPU vs. GPU vs. TPU.
Tensorflow Basics: Tensors, Shapes, Types, Sessions, Operators.
What is Tensor board?
What is Placeholder?
Data frame and Data range use of pandas.
How to import CSV Datasets in TF?
Linear regression using TF.
Tensorflow Optimizers.
Gradient Decent Optimization.
Convolutional Neural Network using TF.
Recurrent Neural Networks using TF.
CNN vs. RNN
Multilayer Perceptron using TF.
Hidden Layer Perceptron.
Practical Project Implementations of Tensorflow.
What is Google Colab?
How to run Tensor Flow on Google Colab.

What is SAS?

Why SAS is popular in job market?

SAS Modules

How to download and install SAS Software

Base SAS Tutorials

SAS Tutorial for Beginners

How to Import Data

How to Export Data

Data Manipulation and Analysis with SAS

SAS Functions

Advanced SAS : Proc SQL

Advanced SAS : SAS Macros

Practical Problem-Solving SAS Examples

SAS Analytics / Statistics Tutorial

SAS Certification Questions and Answers

SAS Interview Questions and Answers

Analytics Companies using SAS

Data Visualizations with SAS Graphs

Getting Started with SPSS
The SPSS Environment

The Data View Window

Using SPSS Syntax

Data Creation in SPSS

Importing Data into SPSS

Variable Types

Date-Time Variables in SPSS

Defining Variables

Creating a Codebook

Working with DataToggle Dropdown

Computing Variables

Recoding Variables

Recoding String Variables (Automatic Recode)

Weighting Cases

Rank Cases

Sorting Data

Grouping Data

Data Mining - Intro
Data Mining - Tools
Data Mining - Issues
Data Mining - Evaluation
Data Mining - Terminologies
Data Mining - Knowledge Discovery
Data Mining - Systems
Data Mining - Query Language
Classification & Prediction
Data Mining - Decision Tree Induction
Data Mining - Bayesian Classification
Rules Based Classification
Data Mining - Classification Methods
Data Mining - Cluster Analysis
Data Mining - Mining Text Data
Data Mining - Mining WWW
Data Mining - Applications & Trends

Data Mining with RapidMiner
What is RapidMiner ?
Rapidminer as a Data Mining Interpreter
Process setup records
As a matter of fact you have two separate cycles
WindowExamples2OriginalDat
RapidMiner Products
RapidMiner Auto Model
RapidMiner Turbo Prep
RapidMiner Go
RapidMiner Server
RapidMiner Radoop

Web Scraping

What is Web Scraping?
Scrape and Parse Text From Websites
Build Your First Web Scraper
Extract Text From HTML With String Methods
Get to Know Regular Expressions
Extract Text From HTML With Regular Expressions
Python Modules for Web Scraping
Legality of Web Scraping
Data Extraction
Data Processing
Processing Images and Videos
Dealing with Text
Scraping Dynamic Websites
Scraping Form based Websites
Processing CAPTCHA
Use an HTML Parser for Web Scraping in Python
Install Beautiful Soup
Create a BeautifulSoup Object
Use a BeautifulSoup Object
Check Your Understanding
Interact With HTML Forms
Install MechanicalSoup
Create a Browser Object
Submit a Form With MechanicalSoup
Interact With Websites in Real Time

Introduction to Big Data Hadoop

What is Big Data?
What is Hadoop?
Hadoop Installation

Hadoop Modules

HDFS
Yarn
MapReduce
HBase

HBase

What is HBase?
HBase Model
HBase Read
HBase Write
HBase MemStore
HBase Installation
RDBMS vs HBase
HBase Commands
HBase Example

Hive

What is Hive?
Hive Installation
Hive Data Types
Hive Partitioning
Hive Commands
Hive DDL Commands
Hive DML Commands
Hive Sort by Order by
Hive Joins

Pig

What is Pig?
Pig Installation
Pig Run Modes
Pig Latin Concepts
Pig Data Types
Pig Example
Pig UDF

Sqoop

What is Sqoop?
Sqoop Installation
Starting Sqoop
Sqoop Import
Sqoop Where
Sqoop Export

Spark Intro

Spark Installation
Spark Architecture
Spark Components

Spark RDD

What is RDD
RDD Operations
RDD Persistence
RDD Shared Variables

In-built Functions

Map FunctionFiler
FunctionCount
FunctionDistinct
FunctionUnion
FunctionIntersection
FunctionCartesian
FunctionsortByKey
FunctiongroupByKey
FunctionreducedByKey
FunctionCo-Group
FunctionFirst
FunctionTake Function

PySpark Basics

PySpark – Features
PySpark – Advantages
PySpark – Modules & Packages
PySpark – Cluster Managers
PySpark – Install on Windows
PySpark – Install on Mac
PySpark – Web/Application UI
PySpark – SparkSession
PySpark – SparkContext
PySpark – RDD
PySpark – Parallelize
PySpark – repartition() vs coalesce()
PySpark – Broadcast Variables
PySpark – Accumulator

PySpark DataFrame

PySpark – Create an empty DataFrame
PySpark – Convert RDD to DataFrame
PySpark – Convert DataFrame to Pandas
PySpark – show()
PySpark – StructType & StructField
PySpark – Column Class
PySpark – select()
PySpark – collect()
PySpark – withColumn()
PySpark – withColumnRenamed()
PySpark – where() & filter()
PySpark – drop() & dropDuplicates()
PySpark – orderBy() and sort()
PySpark – groupBy()
PySpark – join()
PySpark – union() & unionAll()
PySpark – unionByName()
PySpark – UDF (User Defined Function)
PySpark – transform()
PySpark – apply()
PySpark – map()
PySpark – flatMap()
PySpark – foreach()
PySpark – sample() vs sampleBy()
PySpark – fillna() & fill()
PySpark – pivot() (Row to Column)
PySpark – partitionBy()
PySpark – MapType (Map/Dict)

PySpark SQL Functions

PySpark – Aggregate Functions
PySpark – Window Functions
PySpark – Date and Timestamp Functions
PySpark – JSON Functions

PySpark Datasources

PySpark – Read & Write CSV File
PySpark – Read & Write Parquet File
PySpark – Read & Write JSON file
PySpark – Read Hive Table
PySpark – Save to Hive Table
PySpark – Read JDBC in Parallel
PySpark – Query Database Table
PySpark – Read and Write SQL Server
PySpark – Read and Write MySQL
PySpark – Read JDBC Table

Snowflake

Snowflake - Introduction
Snowflake - Data Architecture
Snowflake - Functional Architecture
Snowflake - How to Access
Snowflake - Editions
Snowflake - Pricing Model
Snowflake - Objects
Snowflake - Table and View Types
Snowflake - Login
Snowflake - Warehouse
Snowflake - Database
Snowflake - Schema
Snowflake - Table & Columns
Snowflake - Load Data From Files
Snowflake - Sample Useful Queries
Snowflake - Monitor Usage and Storage
Snowflake - Cache
Unload Data from Snowflake to Local
Snowflake – Create Database
SnowSQL – CREATE TABLE LIKE
SnowSQL – CREATE TABLE as SELECT
SnowSQL – Load CSV file into Table
SnowSQL – Load Parquet file into table
SnowSQL – Unload Snowflake Table to CSV file
SnowSQL – Unload Snowflake table to Parquet file
SnowSQL – Unload Snowflake table to Amazon S3
Snowflake – Spark Connector
Snowflake – Spark DataFrame write into Table
Snowflake – Spark DataFrame from table

Introduction to Amazon SageMaker

Get started on SageMaker
Customer Churn Prediction with XGBoost

Prepare data

Amazon SageMaker Data Wrangler
Distributed Data Processing using Apache Spark and SageMaker Processing
Get started with SageMaker Processing

Train and tune models

Hyperparameter Tuning with the SageMaker TensorFlow Container

Deploy models

Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
Use SageMaker Batch Transform for PyTorch Batch Inference

Track, monitor, and explain models

Amazon SageMaker Model Monitor

Orchestrate workflows


Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines
SageMaker Pipelines Lambda Step

Popular frameworks

Hugging Face Sentiment Classification
Iris Training and Prediction with Sagemaker Scikit-learn
Train an MNIST model with TensorFlow
Train an MNIST model with PyTorch

BigML

BigML - Using a Decision Tree Model
BigML - Using an Ensemble
BigML - Using a Deepnet Model
BigML - Using a Linear Regression
BigML - Using a Logistic Regression
BigML - Using a Fusion Model
BigML - Using a Time Series
BigML - Using an OptiML
BigML - Using a Cluster
BigML - Using an anomaly detector
BigML - Using a Topic Model
BigML - Using Association Discovery
BigML - Using a PCA
BigML - Creating and executing scripts
BigML - Images Classification
BigML - Images Feature Extraction
BigML - Images Object Detection

MLOps Introduction

What is MLOps?
MLOps Lifecycle
MLOps Capabilities

Getting Started with MLOps

MLOps - ML Development
MLOps - Model Building and Training
MLOps - Training Operationalisation
MLOps - Model Versioning
MLOps - Model Registry
MLOps - Model Governance
MLOps - Model Deployment
MLOps - Prediction Serving
MLOps - Model Monitoring

MLflow

What is MLflow?
Install MLflow, instrument code & view results in minutes
Compare runs, choose a model, and deploy it to a REST API
MLflow Tracking
MLflow LLM Tracking
MLflow Projects
MLflow Models
MLflow Model Registry
MLflow Recipes
MLflow AI Gateway (Experimental)
MLflow Plugins
MLflow Authentication
MLflow vs. Airflow

StreamLit

What is Streamlit?
Why should data scientists use Streamlit?
How to use Streamlit
Install Streamlit
How to run your Streamlit code
Display texts with Streamlit
Display an image, video or audio file with Streamlit
Input widgets
Display progress and status with Streamlit
Sidebar and container
Display graphs with Streamlit
Display maps with Streamlit
Themes
Build a machine learning application
How to deploy a Streamlit app

Generative AI
1. Foundations of Generative AI
a. Machine Learning (ML) Paradigms
b. Neural Networks, Architectures, Activation Functions, Optimization Techniques
c. Representation Learning, Embeddings, Feature Engineering
d. Probabilistic Models, Bayesian Networks, Hidden Markov Models (HMMs)
e. Reasoning and Planning
f. Natural Language Processing, Tokenization, Part-of-Speech (POS) tagging, Named Entity Recognition (NER), Word2Vec
g. Computer Vision, Image classification, Object detection, Image segmentation
h. Foundation Models and Their Roles

2. Language Modeling and Transformers
a. Sequential Data Modeling, Recurrent Neural Networks (RNNs), Encoder-Decoder Models
b. Natural Language Generation and Understanding
c. Multilingual Language Models, Cross-lingual learning
d. Attention Mechanisms, Transformers, Self-attention, Multi-head attention
e. Pre-trained Transformers: BERT, GPT, T5, XLNet, LaMDA, etc.
f. Conditional Language Generation, Text summarization, Question answering
g. Textual Encoding and Decoding: Tokenization, Byte Pair Encoding (BPE)

3. Large Language Models
a. Weight, Bias and Parameters of Language Models
b. Reasoning and Commonsense Knowledge Integration
c. Multimodal Learning and Embeddings
d. Memory and Efficiency Optimization
e. GPT, LLaMA, LaMDA, PaLM, Gemini, Falcon, BLOOM
f. Zero-shot and Few-shot Learning
g. Evaluation Metrics for LLMs

4. Generative AI and LLM Frameworks
a. Tensorflow and PyTorch
b. Hugging Face
c. Lang Chain
d. Llama Index
e. Generative AI providers - OpenAI, Cohere, Anthropic, LLMFlow
f. Generative AI Agents, AutoGPT, AgentGPT, BabyAGI
g. Code Generative Tools - Amazon CodeWhisper, OpenAI Codex
h. Open-source Tools and Resources for Generative AI

5. Image Generative Models
a. Autoencoder and its Variants
b. Generative Adversarial Networks (GANs)
c. Style Transfer and Image Transformation
d. Latent Diffusion Model
e. Stable Diffusion
f. DALL.E
g. Contrastive Language-Image Pre-Training (CLIP)
h. Attention Mechanisms in Image Generation
i. Hierarchical Text-Conditional Image Generation with CLIP Latents

6. Prompt Engineering
a. Prompt Design Strategies
b. Task Formulation in Prompts
c. Prompt Patterns
d. Prompt Tuning Techniques
e. Fine-tuning Prompts for Specific Tasks
f. Domain-specific Prompt Engineering
g. Dynamic and Adaptive Prompting
h. Zero-shot learning, Chain-of-thought, Self-consistency
i. Evaluating Prompt Performance

7. Vector Databases and Search
a. Vector Databases
b. High-dimensional Data Storage
c. Vector Embeddings
d. High-Dimensional Semantic Similarity
e. Personalized Search, Multimodal Search, Knowledge Graph Search
f. Semantic search, Conversational search, Visual search
g. Personalization and Relevance Ranking
h. Evaluation of Search Systems

8. Fine Tuning and Optimizing the LLMs
a. Hyperparameter Tuning and Optimization
b. Data Augmentation for Fine-tuning
c. Prompt Tuning
d. Retrieval-Augmented Generation (RAG)
e. Parameter Efficient Fine Tuning (PEFT) Techniques
f. Reinforcement Learning from Human Feedback (RLHF)
g. Efficient Training Pipeline

9. Deployment and Scaling of Generative Models
a. Model Deployment Strategies
b. Scalability and Resource Management for Generative Models
c. Automated Pipelines for Model Deployment
d. Versioning and Rollback Strategies
e. Model Monitoring and Performance Tracking
f. Interoperability and Compatibility
g. Robustness and Error Handling in Model Deployment
h. Security Measures in Model Deployment
i. AIOps and LLMOps

Graph Theory Fundamentals
Graph Representation and Storage
Graph Databases and Query Languages
Advanced Graph Algorithms
Graph-Based Feature Engineering
Graph Machine Learning
Graph Neural Networks (GNNs)

Introduction to Quantum Computing

Basics of Quantum Mechanics and Quantum Gates
Quantum Algorithms (Shor's Algorithm, Grover's Search)
Quantum Information Theory and Quantum Entanglement
Quantum Circuit Design and Simulation

Quantum Machine Learning (QML)

Variational Quantum Classifiers and Quantum SVM
Quantum Neural Networks (QNNs)
Hybrid Quantum-Classical Algorithms
Use Cases in Optimization, Cryptography, and Drug Discovery

Blockchain Fundamentals for Data Scientists

Blockchain-based Data Storage and Security
Smart Contracts and Data Integrity
Hands-On: Ethereum, Hyperledger, Solidity

Decentralized Machine Learning

Federated Learning on Blockchain
Data Sharing in Decentralized Networks
Privacy and Security in Decentralized Data Models
Real-World Applications: Energy Trading, Supply Chain, AI Governance

Data Science Project Lists

1.Building chatbots
2.Credit card fraud detection
3.Fake news detection
4.Forest fire prediction
5.Classifying breast cancer
6.Driver drowsiness detection
7.Recommender systems
8.Sentiment analysis
9.Exploratory data analysis
10.Gender detection and age detection
11.Recognizing speech emotion
12.Customer segmentation
13.Analyzing the impact of climate change on global food supply
14.Weather Prediction
15.Keyword generation for google ads
16.Traffic Signs Recognition
17.Wine Quality Analysis
18.Stock Market Prediction
19.Video Classification
20.Human Action Recognition
21.Medical Report Generation using CT Scans
21.Email Classification
22.Uber Data Analysis
23.Sound Classification
24.Credit Card Fraud Detection
25.Sign Language Recognition
26.Class of Flower Prediction
27.Colour Detection
28.Loan Prediction
29.Road Traffic Prediction
30.Income Classification
31.Speech Emotion Recognition
32.Celebrity Voice Prediction
33.Store Sales Prediction
34.Detecting Parkinson’s Disease
35.Air Pollution Prediction
36.Age and Gender Detection
37.Optimizing Product Price
38.IMDB Predictions
39.Handwritten Digit Recognition
40.Quora Insincere Questions Classification
41.Driver Drowsiness Detection
42.Web Traffic Time Series Forecasting
43.Survival Prediction on the Titanic
44.Time Series Modelling
45.Image Caption Generator
46.Insurance Purchase Prediction
47.Crime Analysis
48.Customer Segmentation
49.Taxi Trip Time Prediction
50.Job Recommendation System
51.Boston Housing Predictions
52.Sentiment Analysis
53.Interest Level in Rental Properties
54.Keyword generation for Google Ads
55.Employee Computer Access Needs
56.Tweets Classification
57.Movie Recommendation System
58.Product Price Suggestions
59.Brain Tumor Detection with Data Science
60.Forest Fire Prediction
61.Human Action Recognition
62.Generative AI Projects