Data Science Interview Preparation Program

Data Science Internship Projects To Work & To Place in Resume

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

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

ML Deployment & MLOps

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

Advanced Data Structures and Algorithms[Self-Study]

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]

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
2-Sample T Test
ANOVA Test
p-Value Calculations
Chi-Square Test
Project Demonstration using all the Statistical Techniques

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

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

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]

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

31:31
MySQL Data Handling Part-1

41:21
MySQL Data Handling Part-2

41:22

Advanced MySQL[Self-Study]

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
Subquery in MySQL
Views in MySQL
Stored Functions
Stored Procedures
Triggers in MySQL
Create Events in MySQL
Different Functions in MySQL

Mastering Power BI[Self-Study]

Introduction to Power Bi

04:33
Creating a Bar Plot

09:58
Creating Pie Chart

03:32
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

02:26
Creating KPI Card

03:13
Creating Gauge Card

04:21
Creating Slicers

03:22
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

03:06
Creating DAX Columns

03:06
More DAX Expressions

02:52
Publishing Power BI Visualizations

06:39

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

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]

Feature Scaling Techniques
Normalisation
Standardisation
Min Max Scaler
Robust Scaler
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]

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
Math Behind KNN

32:21
Math Behind SVM

47:20
Plotting SVM
Introduction to PCA
Math behind PCA
Coding PCA
AutoML Using Pycaret

Mastering NLP, NLU, NLG[Self-Study]

PySpark: BigData Processing[Self-Study]

Introduction to Spark
Various divisions in Spark
Python with Spark
Pyspark for MLLib
Spark DataFrames
Spark Operations
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]

Interview Questions: Frequently Asked Statistics

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

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

Live Doubt Clarification Classes

Data Science Interactive Q & A with Data Scientists: Weekly 2-3 Live Sessions to Resolve Your Doubts and Enhance Learning

Data Science Interview Preparation Program

What You Will Learn In This Program: Just Start This Program with 4999/-

Data Science Interview Preparation Syllabus

Data Science Internship Projects To Work & To Place in Resume

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

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

Time Series

Introduction to Time Series Modelling

Time Series Modelling Part-2

Characteristics of Time Series Modelling

Time Series Modelling Techniques

Simple Time Series Project End-to-End

End-to-End Project on Time Series Modelling

ML Deployment & MLOps

End-to-End Deployment Using MLOps & Streamlit

Setup Virtual Environment

Docker Installation

How to Activate Virtual Environment

Docker Desktop

Regression Model using Pycaret

Regression Model using Pycaret Part-2

Interpretability of Model using SHAP

What is SHAP

Application Development using Gradio

Creating API using FASTAPI

Creating Docker Image

Model Versioning using Pycaret & MLFlow

End-to-End ML Proof of Concept

Generative AI End-to-End Projects

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

Advanced Data Structures and Algorithms[Self-Study]

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]

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