Course Content
Data Science Course Syllabus
0/2
What is Data Analytics, DS, AI/ML
02:01:30
Interview Preparation Roadmap on Data Analytics, Data Science, AI & ML
01:26:51
Induction Classes
0/6
What is Data, Importance of Data
19:22
What are Mathematical Equations? Why ME?
11:31
Fourier Transformation & Fourier Series
10:17
What is Probability and Why Probability
15:57
What is Geometry, What is Linear Algebra, Eigen Values and Eigen Vectors
19:04
What is Calculus
07:58
Mastering Python[Zero to Hero]
0/30
Introduction to Python
01:02:54
Why Python, Value, Variable, Function, Library[Roadmap on Python]
01:04:10
IDE in Python, Different Data Types
01:01:53
List, Tuple, Set & Dictionary Overview
57:16
Different List Methods
55:48
Different Tuple Methods
01:04:52
Set & Frozenset
01:00:50
Dictionary & String Manipulations
50:56
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
01:03:15
Types of Operators, Conditional Statements
23:42
While Loop, List Comprehension, Break, Continue, Arguments
01:01:18
Functions, Escape Sequences, Lambda Functions
52:23
Hackathon-1
54:32
Introduction to OOPS
16:16
Instance Variable, Class Variable, Class Method
56:53
Association vs Composition & Aggregation
50:02
Oops Concept
36:29
Encapsulation, Inheritance
01:07:07
Polymorphism, Method OverLoading, Method Overriding
53:59
Introduction to Pandas
01:05:22
Data Analysis using Pandas
01:34:29
Introduction to Numpy
57:26
Different Numpy Commands
01:03:00
Introduction to Data Visualisation
51:41
Data Visualisation using Matplotlib
01:00:20
Data Visualization using Seaborn
59:34
Data Visualization using Plotly
41:44
Why Data Cleaning?
47:05
Data Cleaning with Sklearn & Pandas
49:57
Regular Expression Basics
54:01
Advanced Data Structures and Algorithms
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
0/40
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
Mastering Excel
0/22
Introduction to Excel
Basics of Excel Commands
Conditional Formatting in Excel
Different Calculations in Excel
Different Charts in Excel
Pivot Tables in Excel
Slicers in Excel
Filters in Excel
VlookUp, HLookup
Index & Match
Conditional Statements in Excel
SumIF & Count IF
Data Analysis Tool Kit
Forecasting in Excel
Correlation in Excel
Regression in Excel
What-If Analysis
Goal Seek
Data Tables
Scenario Manager
End-to-End Dashboard using Excel
Power Query in Excel
Statistics with Real-Time Project Demonstration on EDA
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
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
Real-Time Project Demonstration on Data Analysis Using Excel
0/1
Real-Time Project Demonstration on Data Analysis using Excel
03:31:41
Real-Time Project Demonstration on Data Analysis using Power BI & MySQL
0/4
End-to-End Data Analysis using Power BI
01:43:44
Data Analysis using MySQL
01:45:33
Data Analysis using MySQL & DAX Calculations Power BI
01:18:27
How to Prepare for Data Analytics Interviews
57:30
Real-Time Project Demonstration on Data Analysis using Tableau
0/1
End-to-End Data Analysis using Tableau
03:12:00
Mastering MySQL
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
31:31
MySQL Data Handling Part-1
41:21
MySQL Data Handling Part-2
41:22
Advanced MySQL
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
Subquery in MySQL
Views in MySQL
Stored Functions
Stored Procedures
Triggers in MySQL
Create Events in MySQL
Different Functions in MySQL
Mastering Tableau[Zero to Hero]
0/11
Introduction to Tableau
01:14:02
Basics of Tableau
32:02
Different Types of Charts
30:01
Table Calculations & Parameters
21:51
Waterfall, Pareto Chart, Filters in Tableau
25:12
LOD Expression & Pareto Chart
16:08
MySQL Connection with Tableau
12:10
End-to-End Project using Tableau
19:25
Connect Tableau Desktop with Server
11:35
Tableau Server, Scheduling & Alerts
04:54
Tableau Interview Preparation
07:34
Mastering Power BI[Zero to Hero]
0/27
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
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
0/28
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
Supervised Learning[70% Interview Questions]
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
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
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
0/15
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
MongoDB Mastering
0/10
Introduction to Mongo DB
Installation of MongoDB
Basics Operations of MongoDB
Create Operations in MongoDB
Update Operations in MongoDB
Read Operations in MongoDB
Delete Operations in MongoDB
Query & Projection Operations
Python with MongoDB
Spark with MongoDB
Real-Time Projects to Work as Part of Job Simulation
0/11
Data Analytics Real-Time Project-1
Data Analytics Real-Time Project-2
Data Analytics Real-Time Project-3
Data Analytics Real-Time Project-4
Data Science Real-Time Project-1
Data Science Real-Time Project-2
Data Science Real-Time Project-3
Data Science Real-Time Project-4
Real-Time NLP Project-1
Real-Time NLP Project-2
Real-Time NLP Project-3
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
Advanced Time Series with Real-Time Projects
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
Azure Machine Learning Engineering
0/10
Introduction to Azure Environment
Training & Tuning ML Models using Azure
Working with Data in Azure
Training ML Models in Azure ML
Tuning ML Models in Azure
Azure Automated ML
Deploying & Explaining ML Models
Deploying ML Model for Real-Time Inferencing
Deploying ML Models for Batch Scoring
Responsible AI
AWS Sagemaker Studio
0/2
Setting up AWS Sagemaker & Deploying Simple ML Model
15:13
AIOps with AWS Sagemaker Studio
01:02:44
Hadoop
0/4
Introduction to Hadoop
Understanding HDFS
Using HDFS Command Line
Using HDFS Web Interface
Hadoop MapReduce
0/5
Introduction to MapReduce
MapReduce Platform
Parallel MapReduce
MapReduce Examples
MapReduce YARN Examples
HIVE Database
0/3
Introduction to HIVE
Working on HIVE “SQL” commands
Various Examples on Hive
Deep Learning, Tensorflow & Keras
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
Computer Vision Mastering using Tensorflow
0/9
Introduction to Object Detection vs Object Classification
Introduction to CNN
Convolution Matrix
Max Pooling Layer
Point-Wise & Element-Wise Convolution
Batch Normalisation
Strides & Padding
Convolution Filters
End-to-End Computer Vision
02:44:06
Transfer Learning with OpenCV & YOLO
0/5
Introduction to Transfer Learning
Headless & With Head Transfer Learning
Object Detection using YOLO
Object Detection using Open CV
Transfer Learning with Tensorflow Hub
R Programming
0/9
Installation of R Studio
19:42
Basics of R Studio
39:40
Data Visualisation with R Studio
33:25
Data Modelling with R Studio
13:53
Data Joining with R Studio
09:45
Data Visualisation with GGPlot2
11:03
Data Preparation with R Studio
09:39
Data Cleaning with Regexp
34:30
Data Visualisation with Plotly
44:40
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
0/11
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
Course Files
0/1
Course Files
Handwritten Notes
0/2
Python Handwritten Notes
SQL Handwritten Notes
Full Stack Data Science Career Transition Program with DSA
About Lesson

Previous
Next
0% Complete