What is Data Analytics, DS, AI/ML

Course Content

Data Science Internship Projects To Work & To Place in Resume

0/9

End-to-End Data Modelling using Snow Flake

Data Ingestion, Data Transformation, Data Storage and Data Analysis – DataOps Pipeline Demonstration

Real-Time Data Integration and Analytics Platform

End-to-End Data Science Project with Amazon S3, Snow Flake, Spark & Snow Park

End-to-End Data Science Project Using Spark & Microsoft Fabric

AGILE CRISP: End-to-End Data Science Project Pipeline

Agile DataOps for Real-Time Credit Risk Scoring and Analysis

MLOps Pipeline: End-to-End Data Science Lifecycle using MLOps

Streaming Analytics: End-to-End Data Science Project

Resume, Mock Interviews, Documents, Certificates & Job Portal Optimization

0/5

Data Science Resume Optimization to Increase your Interview Calls

LinkedIn & Job Portal Optimization To Increase Interview Calls

Mock Interview: How To Introduce about your self?

Mock Interview: How to Explain your Project End-to-End?

How to explain Notice period, Documentation, Certificates, e.t.c

Advanced Time Series with Real-Time Projects using PySpark & Microsoft Fabric

0/7

Time Series

12:50

Introduction to Time Series Modelling

20:43

Time Series Modelling Part-2

50:18

Characteristics of Time Series Modelling

47:56

Time Series Modelling Techniques

47:05

Simple Time Series Project End-to-End

01:13:04

End-to-End Project on Time Series Modelling

01:06:10

ML Deployment & MLOps

0/14

End-to-End Deployment Using MLOps & Streamlit

03:11:38

Setup Virtual Environment

04:49

Docker Installation

03:47

How to Activate Virtual Environment

03:03

Docker Desktop

02:51

Regression Model using Pycaret

13:26

Regression Model using Pycaret Part-2

02:10

Interpretability of Model using SHAP

04:46

What is SHAP

01:16

Application Development using Gradio

03:31

Creating API using FASTAPI

03:54

Creating Docker Image

05:29

Model Versioning using Pycaret & MLFlow

06:51

End-to-End ML Proof of Concept

00:00

Generative AI End-to-End Projects

0/1

Document Summariser Project using LangChains, Open AI, Streamline & AWS Deployment

Advanced Data Structures and Algorithms[Self-Study]

0/23

Introduction to DSA

Non-Primitive Data Structures

Non-Linear Data Structures

What is an Algorithm

Theory & Code Implementation of Linked List

Stacks & Queues Assignment

Coding Stack Data Structure

Coding Queue Data Structure

Tree Data Structures

Types of Tree Data Structure

Tree Traversal

BFS Traversal

Bubble Sort Theory

Bubble Sort Code Implementation

Selection Sort Theory

Selection Sort Code Implementation

Insertion Sort Theory

Insertion Sort Code Implementation

Merge Sort Theory

Merge Sort Code Implementation

Quick, Merge Sort Performance

Quick Sort Theory

Linear Search & Bisection Search

Applied Statistics[Self-Study]

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What is Data? Various Types of Data

Levels of Measurement

Categorical Variables & Visualisation Techniques

Continuous Variables and Visualisations

Measures of Central Tendency

Measuring Skewness

Measures of Dispersion

Covariance & Correlation Coefficient

Box & Whisker Plot

Outlier Identification

Treating Outliers

Sampling Techniques

Statistical Analysis Consideration

Types of Analytics

Sample vs Population

Introduction to Inferential Statistics

Law of Large Numbers

Central Limit Theorem

Probability Distributions

Conditional Probability

Continuous Probability Distribution & Discrete Probability Distribution

PMF, CDF, PDF, PPF in Probability

Binomial Distribution

Poisson Distribution

Normal Distribution

Exponential Distribution

Laplace Distribution

Log-Normal Distribution

Standard Error

Confidence Interval Calculations

Z-Table for Confidence Interval

T-Table for Confidence Interval

Hypothesis Testing

Null & Alternate Hypothesis

Type-1 Error & Type-2 Error

Project Demonstration using all the Statistical Techniques

Statistics with Real-Time Project Demonstration on EDA[Self-Study]

0/4

Basics of Statistics

01:41:48

Central Limit Theorem, Normal Distribution, Skewness

01:29:44

Real-Time Project Demonstration on EDA with Real-World Project

01:01:55

End-to-End Statistics using Excel

03:31:41

Real-Time Project Demonstration on Probability Distributions, Hypothesis Testing[Self-Study]

0/8

Probability Distribution, Random Variable, Binomial, Poisson, Exponential

13:07

Normal Distribution

23:00

Real-Time Project Demonstration on Probability

01:34:20

Hypothesis Testing Part-1

13:07

Hypothesis Testing Part-2

05:58

How to Calculate P-Value? Coding Hypothesis Testing

01:15:23

How to solve PDF, CDF, PMF?

10:03

Chebyshevs, Log, Power Law, Q-Q, CLT

22:14

Mastering MySQL[Self-Study]

0/9

Introduction to MySQL

23:29

Creating Database in MySql

19:23

Deep Dive into MySQL

15:42

SELECT, UPDATE, DELETE Operations

29:03

Clauses in MySql Part-1

18:39

Clauses in MySql Part-2

23:31

Clauses in MySQL Part-3

31:34

MySQL Data Handling Part-1

41:21

MySQL Data Handling Part-2

41:22

Advanced MySQL[Self-Study]

0/15

Data Integrity & Referential Integrity

Data Normalisation

First & Second Normal Form

Functional Dependency, Transitive Dependency & 3rd Normal Form

Boyce-Codd Normal Form

Denormalization

Temporary Table, Common Table Expression, Recursive CTE

When to use Temporary Table, CTE & Recursive CTE

Create Events in MySQL

Different Functions in MySQL

Mastering Power BI[Self-Study]

0/27

Introduction to Power Bi

Creating Ribbon Chart

02:37

Creating Scatter Plot

04:10

Creating WaterFall Charts

04:41

Creating Funnel Chart

01:59

Creating Line Plot & Area Plot

05:12

Creating Matrix & Conditional Formatting

06:09

Creating Decomposition Tree

Creating Animated Bar Plot

02:37

Creating Sunburst Chart

02:34

Different Filers in Power BI

04:07

Include & Exclude Operations in Power BI

02:37

Introduction to Power Query

06:11

Groupby & Replace in Power Query

01:52

Merge and Append Operations

02:36

Prefix, Suffix, Length

02:43

Pivot in Power Query

03:51

Introduction to DAX Expressions

03:05

Creating DAX Measures

Publishing Power BI Visualizations

06:39

Unsupervised Learning with Real-Time Projects[Self-Study]

0/9

Supervised vs Unsupervised Learning

21:51

Fundamentals of Machine Learning Part-1

32:19

Fundamentals of Machine Learning Part-2

48:41

Insights from Train & Test Accuracy

21:23

Associate Rules Part-1

23:02

Associate Rules Part-2

02:27

Problem Identification & Approach Designing

01:02:13

Associate Rules Scripting

57:15

Why to do Dimensionality Reduction? Project Implementation using PCA & T-SNE

01:03:36

Feature Engineering Techniques[Self-Study]

0/28

Feature Scaling Techniques

Label Encoding Technique

One-Hot Encoding Technique

Dummies Technique

Ordinal Encoding Technique

Imputation Technique

Feature Selection Techniques

Filter Method: Feature Importance

Filter Method: Fishers Score Technique

Filter Method: Chi-Square Technique

Filter Method: Variance Threshold

Filter Method: Correlation Coefficient

Filter Method: Mutual Information

Wrapper Method: Forward Selection Technique

Wrapper Method: Backward Selection Technique

Wrapper Method: Recursive Feature Elimination Technique

Wrapper Method: Exhaustive Feature Selection Technique

Wrapper Method: Sequential Feature Selection Technique

Embedded Methods: Regularisation

Embedded Method: Elastic Net Regression

Embedded Method: Decision Tree

Outlier Treatment

Over Sampling Technique

Under Sampling Techniques

ML Interview Preparation[Self-Study]

0/34

How to Prepare Machine Learning Math for Interviews

06:52

Why to use Linear Regression?

06:13

What is Linear Regression

04:42

Case Study on Linear Regression

06:21

Math Behind Linear Regression Part-1

07:03

Math Behind Linear Regression Part-2

06:25

Math behind OLS Technique

04:32

Assumptions of Linear Regression

23:08

Evaluation Metrics for Regression Model

15:50

Accuracy Improving Techniques

14:29

Coding Linear Regression Model

Regularisation Techniques

08:56

Why Logistic Regression

04:27

Math Behind Logistic Regression

17:44

Evaluation Metrics Behind Classification Algorithms

17:10

ROC & AUC Curve

10:20

Coding Logistic Regression Model

Introduction to Decision Tree

06:12

Intuition Behind Decision Tree

07:32

Math Behind Decision Tree

16:03

Math Behind Decision Tree using GINI

04:30

Drawbacks of Decision Tree

05:07

Random Forest and Gradient Boosting

13:08

Coding Decision Tree, Random Forest, GB

SMOTE Technique to Handle Imbalanced Dataset

06:35

Filter Method, Wrapper Method, Embedded Method – Feature Selection Techniques

08:47

Coding Feature Selection Techniques

Mastering NLP, NLU, NLG[Self-Study]

0/6

Introduction to NLP, NLP Applications

09:12

NLP Challenges

11:59

NLP Pre-Processing Techniques

14:18

NLP Feature Extraction Techniques

10:19

NLP Practical Example

15:48

NLU, Word2Vec, RNN, NER

02:39:52

PySpark: BigData Processing[Self-Study]

0/15

Introduction to Spark

Various divisions in Spark

Grouping, Aggregating and Joining with Spark

Introduction to Machine Learning with PySpark

Machine Learning Pipeline with Spark

Frequent Pattern Mining and Statistics

Feature Transformation with Spark

Data Cleaning & Pre-Processing with Spark

NLP with Pyspark

Spark Structured Streaming

Streaming ML Solutions

Deep Learning, Tensorflow & Keras[Self-Study]

0/7

Introduction to Neural Networks

14:02

What are Optimisation Techniques

14:32

Math Behind Feed Forward Neural Networks

10:13

Back Propagation Techniques

12:12

Why Activation Functions

11:11

Regression using Tensorflow

18:36

Feed Forward Classification using Keras

11:41

Interview Questions: Frequently Asked Statistics

0/10

Introduction to Statistics

Properties of Data

Sample vs Population

Types of Sampling Techniques

Exploratory Data Analysis

Analysing Data with Applied EDA

Measures of Dispersion

Skewness & Normality

Correlation & Scatterplot

Barplot & Lineplot

Mindmaps for Interview Revision

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Data Science Interview Revision Mindmap

Statistics Mindmap

Python Learning Mindmap

SQL Interview Mindmap

Machine Learning Accuracy Improving Techniques Mindmap

Machine Learning Math Mindmap

Real-Time Project Procedure Mindmap

ML Framework Mindmap

Power BI Interview Mindmap

NLP Interview Mindmap

Deep Learning Interview Mindmap

Interview Preparation

0/5

How to build Linkedin Profile

01:10:27

Resume Preparation for Data Science

52:37

Do’s and Don’t while Marketing our Resume

12:54

Do’s and Don’ts in LinkedIn/Job Portals

09:47

3 Elements to Crack Any Interview

11:57

Data Science Interview Preparation Program

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