Data Science masters 2.0 by PW Skills: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based

Course overview

The Data Science masters 2.0 certification course is a 8-month course that is taught by PW skills. This certification programme is highly curated keeping the latest industry standards in mind. The course is also uniquely designed to make it easier for both beginners and professional-level students. This is a fully online course that will equip the candidates with certain required skills for building data-driven solutions.

The Data Science masters 2.0 training course instills the students with the skills essential for gaining knowledge that is needed for identifying the novel, standard, and truly differentiated solutions that are essential for decision-making. These include skills that can help in managing, analyzing, querying, visualizing, and extracting important data from huge, and large data sets.

Course and certificate fees

Fees information

₹ 3,500 ₹7,000

The Data Science masters 2.0 certification course is Rs. 7000. Sometimes it's also possible to secure a 50% discount from the website itself.

Data Science masters 2.0 Fee Structure

Particulars	Details
Total Course Fees	Rs. 7,000

certificate availability

Yes

certificate providing authority

PW Skills

Who it is for

Data scientist

If the candidates after the Data Science masters 2.0 course, want to become data scientists then they can enroll themselves.

Eligibility criteria

Academic Qualifications

Anyone interested can participate in the Data Science masters 2.0 online course.

Certification Qualifying Details

Once a candidate has completed all the course, its assignments, videos, and quizzes and scored 60% on the assignments, and 60% on the quizzes will be securing a Data Science masters 2.0 certification by PW Skills.

What you will learn

Natural language processing Knowledge of data visualization

The Data Science masters 2.0 certification syllabus will be offering the candidates with knowledge of Python, Computer vision, Machine learning, Big Data, Statistics, Deep learning, and Natural language processing.

The syllabus

Python Programming

Module 1: Course Induction

Course Overview And Dashboard Description
Introduction Of Data Industry
Lab Introduction
Support System Introduction
Community Introduction

Week 1 - PYTHON - Basic Building

Break And Continue Statement And Range Function
Conditions (If Else, If-Elif-Else), Loops (While, For)
Operators - Arithmetic, Bitwise, Comparison And Assignment Operators, Operator's Precedence And Associativity
Container Objects, Mutability Of Objects
Python Objects, Number & Booleans, Strings.
Introduction Of Python And Comparison With Other Programming Languages

Week 2 - PYTHON - Data Structures

Basic Data Structure In Python
String Object Basics
String Inbuilt Methods
Splitting And Joining Strings
String Format Functions
List Methods
List As Stack And Queues
List Comprehensions
Tuples, Sets & Dictionary Object Methods
Dictionary Comprehensions
Dictionary View Objects

Week 3 - PYTHON - Function

Functions Basics, Parameter Passing, Iterators.
Generator Functions
Lambda Functions
Map, Reduce, Filter Functions.

Week 4 - PYTHON - Oops Concepts

Pillars Of Oops
Inheritance
Polymorphism
Encapsulation
Abstraction
Decorator
Class Methods And Static Methods
Special (Magic/Dunder) Methods
Property Decorators - Getters, Setters, And Deletes

Week 5 - PYTHON - Files & Exception Handling & Memory Management

Working With Files
Reading And Writing Files
Buffered Read And Write
Other File Methods.
Logging, Debugger
Modules And Import Statements
Exceptions Handling With Try-Except
Custom Exception Handling
List Of General Use Exceptions
Best Practice Exception Handling
Multithreading
Multiprocessing

Week 6 - PYTHON - Connecting with Databases & APIs

Connecting Python with Mysql
Connecting Python with MongoDB
What Is Web API?
Difference B/W API And Web API
Rest And Soap Architecture
Restful Services
Flask Introduction
Open Link Flask
App Routing Flask
Url Building Flask
Http Methods Flask
Templates Flask
Flask Project: Hello World
Working with Postman

Week 7 - Python Test

Python Test

ML Toolbox

Module 1: Week 8 - Pandas

Python Pandas - Series
Python Pandas – Data Frame
Python Pandas – Panel
Python Pandas - Basic Functionality
Reading Data From Different File Systems
Python Pandas – Re Indexing Python
Pandas – Iteration
Python Pandas – Sorting.
Indexing & Selecting
Data Statistical Functions
Python Pandas - Window Functions
Python Pandas - Date Functionality
Python Pandas –Time Delta
Python Pandas - Categorical Data
Python Pandas – Visualization
Python Pandas - Tools

Module 2: Week 9 - Numpy & Visualization

Numpy - Nd Array Object.
Numpy - Data Types.
Numpy - Array Attributes.
Numpy - Array Creation Routines.
Numpy - Array From Existing.
Data Array From Numerical Ranges.
Numpy - Indexing & Slicing.
Numpy – Advanced Indexing.
Numpy – Broadcasting.
Numpy - Iterating Over Array.
Numpy - Array Manipulation.
Numpy - Binary Operators.
Numpy - String Functions.
Numpy - Mathematical Functions.
Numpy - Arithmetic Operations.
Numpy - Statistical Functions.
Sort, Search & Counting Functions.
Numpy - Byte Swapping.
Numpy - Copies &Views.
Numpy - Matrix Library.
Numpy - Linear Algebra
Matplotlib
Seaborn
Plotly
Bokeh

Module 3: NUMPY, PANDAS & Visualization Test

NUMPY, PANDAS & Visualization Test

Statistics

Module 1: Week 10 - Statistics - Statistics Basic

Introduction To Basic Statistics Terms
Types Of Statistics
Types Of Data
Levels Of Measurement
Measures Of Central Tendency
Measures Of Dispersion
Random Variables
Set
Skewness
Covariance And Correlation

Week 11 - Statistics - Statistics Advance - 1

Binomial Distribution
Poisson Distribution
Normal Distribution (Gaussian Distribution)
Probability Density Function And Mass Function
Cumulative Density Function
Examples Of Normal Distribution
Bernoulli Distribution
Uniform Distribution
Z Stats
Central Limit Theorem
Estimation
A Hypothesis
Hypothesis Testing’S Mechanism
P-Value
T-Stats
Student T Distribution
T-Stats Vs. Z-Stats: Overview
When To Use A T-Tests Vs. Z-Tests

Module 3 Week 12 - Statistics - Statistics Advance - 2

Type 1 & Type 2 Error
Bayes Statistics (Bayes Theorem)
Confidence Interval(Ci)
Confidence Intervals And The Margin Of Error
Interpreting Confidence Levels And Confidence Intervals
Chi-Square Test
Chi-Square Distribution Using Python
Chi-Square For Goodness Of Fit Test
When To Use Which Statistical Distribution?
Analysis Of Variance (ANOVA)
Assumptions To Use Anova
Anova Three Type
Partitioning Of Variance In The Anova
Calculating Using Python
F-Distribution
F-Test (Variance Ratio Test)
Determining The Values Of F
F Distribution Using Python

Module 4 STATISTICS TEST

STATISTICS TEST

Machine Learning

Module 1 Week 13 - Machine Learning - Introduction To Machine Learning & Feature Engineering

Ai Vs Ml Vs Dl Vs Ds
Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning
Train, Test, Validation Split
Performance
Overfitting, Under Fitting
Bias Vs Variance
Handling Missing Data
Handling Imbalanced Data
Up-Sampling
Down-Sampling
Smote
Data Interpolation
Handling Outliers
Filter Method
Wrapper Method
Embedded Methods
Feature Scaling
Standardization
Mean Normalization
Min-Max Scaling
Unit Vector
Feature Extraction
Pca (Principle Component Analysis)
Data Encoding
Nominal Encoding
One Hot Encoding
One Hot Encoding With Multiple Categories
Mean Encoding
Ordinal Encoding
Label Encoding
Target Guided Ordinal Encoding
Covariance
Correlation Check
Pearson Correlation Coefficient
Spearman’S Rank Correlation
Vif

Module 2 Week 14 - Machine Learning - Exploratory Data Analysis

Feature Engineering And Selection.
Analyzing Bike Sharing Trends.
Analyzing Movie Reviews Sentiment.
Customer Segmentation And Effective Cross Selling.
Analyzing Wine Types And Quality.
Analyzing Music Trends And Recommendations.
Forecasting Stock And Commodity Prices

Week 15 - Machine Learning - Linear Regression & Logistic Regression

Linear Regression
Gradient Descent
Multiple Linear Regression
Polynomial Regression
R Square And Adjusted R Square
Rmse, Mse, Mae Comparison
Regularized Linear Models
Ridge Regression
Lasso Regression
Elastic Net
Complete End-To-End Project With Deployment On Cloud And Ui
Logistics Regression In-Depth Intuition
In-Depth Mathematical Intuition
In-Depth Geometrical Intuition
Hyper Parameter Tuning
Grid Search Cv
Randomize Search Cv
Data Leakage
Confusion Matrix
Precision, Recall, F1 Score, Roc, Auc
Best Metric Selection
Multiclass Classification In Lr
Complete End-To-End Project With Deployment In Multi-Cloud Platform

Module 4 Week 16 - Machine Learning - Feature Selection, Decision Tree & SVMS

Feature Selection
Recursive Feature Elimination
Backward Elimination
Forward Elimination
Decision Tree Classifier
In-Depth Mathematical Intuition
In-Depth Geometrical Intuition
Confusion Matrix
Precision, Recall, F1 Score, Roc, Auc
Best Metric Selection
Decision Tree Repressor
In-Depth Mathematical Intuition
In-Depth Geometrical Intuition
Performance Metrics
Complete End-To-End Project With Deployment In Multi-Cloud Platform
Linear Svm Classification
In-Depth Mathematical Intuition
In-Depth Geometrical Intuition
Soft Margin Classification
Nonlinear Svm Classification
Polynomial Kernel
Gaussian, Rbf Kernel
Data Leakage
Confusion Matrix
Precision, Recall, F1 Score, Roc, Auc
Best Metric Selection
Svm Regression
In-Depth Mathematical Intuition
In-Depth Geometrical Intuition
Complete End-To-End Project With Deployment

Week 17 - Machine Learning - Naïve Bayes & Ensemble Techniques

Bayes Theorem
Multinomial Naïve Bayes
Gaussian Naïve Bayes
Various Type Of Bayes Theorem And Its Intuition
Confusion Matrix
Precision, Recall,F1 Score, Roc, Auc
Best Metric Selection
Complete End-To-End Project With Deployment
Definition Of Ensemble Techniques
Bagging Technique
Bootstrap Aggregation
Random Forest (Bagging Technique)
Random Forest Repressor
Random Forest Classifier
Complete End-To-End Project With Deployment
Boosting Technique
Ada Boost
Gradient Boost
Xgboost

Week 18 - Machine Learning - KNN & Dimensionality Reduction

Knn Classifier
Knn Repressor
Variants Of Knn
Brute Force Knn
K-Dimension Tree
Ball Tree
Complete End-To-End Project With Deployment
The Curse Of Dimensionality
Dimensionality Reduction Technique
Pca (Principle Component Analysis)
Mathematics Behind Pca
Scree Plots
Eigen-Decomposition Approach
Practicals

Week 19 - Machine Learning - Clustering

Clustering And Their Types
K-Means Clustering
K-Means++
Batch K-Means
Hierarchical Clustering
Dbscan
Evaluation Of Clustering
Homogeneity, Completeness, And V-Measure
Silhouette Coefficient
Davies-Bouldin Index
Contingency Matrix
Pair Confusion Matrix
Extrinsic Measure
Intrinsic Measure
Complete End-To-End Project With Deployment
Week 20 - Machine Learning - Anomaly Detection & Time Series
Anomaly Detection Types
Anomaly Detection Applications
Isolation Forest Anomaly Detection Algorithm
Density-Based Anomaly Detection (Local Outlier Factor) Algorithm
Support Vector Machine Anomaly Detection Algorithm
Dbscan Algorithm For Anomaly Detection
Complete End-To-End Project With Deployment
What Is A Time Series?
Old Techniques
Arima
Acf And Pacf
Time-Dependent Seasonal Components.
Autoregressive (Ar)
Moving Average (Ma) And Mixed Arma- Modeler.

Module 9 MACHINE LEARNING TEST

MACHINE LEARNING TEST

Deep Learning

Netron

DEEP LEARNING TEST

DEEP LEARNING TEST

Computer Vision

Module 1 Week 25 - Computer Vision - Convolution Neural Networks & Architectures

Pytorch Installation.
Pytorch Functional Overview.
Pytorch Neural Network Creation.
Cnn Fundamentals
Cnn Explained In Detail - Cnnexplainer, Tensor space
Various Cnn Based Architecture
Training Cnn From Scratch
Building Webapps For Cnn
Deployment In Aws, Azure & Google Cloud
Various Cnn Architecture With Research Paper And Mathematics
Lenet-5 Variants With Research Paper And Practical
Alexnet Variants With Research Paper And Practical
Googlenet Variants With Research Paper And Practical
Transfer Learning
Vggnet Variants With Research Paper And Practical
Resnet Variants With Research Paper And Practical
Inception Net Variants With Research Paper And Practical

Module 2 Week 26 - Computer Vision - Object Detection

FASTER RCNN
YOLO
Introduction To Yolov5
Installation Of Yolov5
Data Annotation & Preparation
Download Data & Configure Path
Download & Configure Pretrained Weight
Start Model Training
Evaluation Curves Yolov5
Inferencing Using Trained Model
Introduction To Yolov6
Installation Of Yolov6
Data Annotation & Preparation
Download Data & Configure Path
Download & Configure Pretrained Weight
Start Model Training
Evaluation Curves Yolov6
Inferencing Using Trained Model
Introduction To Yolov7
Installation Of Yolov7
Data Annotation & Preparation
Download Data & Configure Path
Download & Configure Pretrained Weight
Start Model Training
Evaluation Curves Yolov7
Inferencing Using Trained Model
Introduction To Detecron2
Installation Of Detecron2
Data Annotation & Preparation
Download Data & Configure Path
Download & Configure Pretrained Weight
Start Model Training
Evaluation Curves Detecron2
Inferencing Using Trained Model
Introduction To TFOD2
Installation Of TFOD2
Data Annotation & Preparation
Download Data & Configure Path
Download & Configure Pretrained Weight
Start Model Training
Evaluation Curves TFOD2
Inferencing Using Trained Model

Week 27 - Computer Vision - Image Segmentation

Scene Understanding
More To Detection
Need Accurate Results
Segmentation
Types Of Segmentation
Understanding Masks
Maskrcnn
From Bounding Box To Polygon Masks
Mask Rcnn Architecture
Introduction To Detectron2
Our Custom Dataset
Doing Annotations Or Labeling Data
Registering Dataset For Training
Selection Of Pretrained Model From Model Zoo
Let's Start Training
Stop Training Or Resume Training
Inferencing Using The Custom Trained Model In Colab
Evaluating The Model

Week 28 - Computer Vision - Advanced Computer Vision

What Is Face Recognition?
Face Recognition Pipeline
Data Preprocessing
Face Detection
Face Alignment
Face Identification
Exploring Face net
Exploring Mtcnn
Exploring Arc face
Data Preprocessing
Combining All Pipelines
Building A Desktop App With Tkinter
What Is Object Tracking?
Localization
Motion
Flow Of Optics
Motion Vector
Tracking Features
Exploring Deep Sort
Bytetrack
Data Preprocessing
Using Yolo For Detection
Preparing Deep sort With Yolo
Combining Pipelines For Tracking & Detection
Introduction To Gans
Gan Architecture
Discriminator
Generator
Controllable Generation
Wgans
Dcgans
Stylegans
Gan Practical's Implementation

Module 5 COMPUTER VISION TEST

COMPUTER VISION TEST

NLP

Module 1 Week 31 - NLP - NLP Introduction & Text Processing

Overview Computational Linguistics.
History Of Nlp.
Why Nlp
Use Of Nlp
Web Scrapping.
Text Processing
Understanding Regex
Text Normalization
Word Count.
Frequency Distribution
String Tokenization
Annotator Creation
Sentence Processing
Lemmatization In Text Processing
Word Embedding
Co-Occurrence Vectors
Word2Vec
Doc2Vec

Module 2 Week 32 - NLP - Useful NLP Libraries & Networks

Nltk
Text Blob
Stanford Nlp
Recurrent Neural Networks.
Long Short Term Memory (Lstm)
Bi Lstm
Stacked Lstm
Gru Implementation

Week 33 - NLP - Attention Based Model & Transfer Learning In NLP

Seq 2 Seq.
Encoders And Decoders.
Attention Mechanism.
Attention Neural Networks
Self-Attention
Introduction To Transformers.
Bert Model.
Gpt2 Model.

NLP TEST

NLP TEST

Big Data

Module 1 Week 36 - Big Data - Big Data Introduction, Hadoop & Spark

What Is Big Data?
- Big Data Application
- Big Data Pipeline
- Hadoop Introduction
- Hadoop Architecture
- Hadoop Setup And Installation
- Spark
- Spark Overview.
- Spark Installation.
- Spark Rdd.
- Spark Data Frame.
- Spark Architecture.
- Spark Ml Lib
- Spark Nlp

Module 2 Week 37 - Big Data - Spark & Kafka

Spark Linear Regression
Spark Logistic Regression
Spark Decision Tree
Spark Naive Bayes
Spark Xg Boost.
Spark Time Series
Spark Deployment In Local Server
Spark Job Automation With
Scheduler
Kafka Introduction
Kafka Installation
Spark Streaming
Spark With Kafka

Module 3 BIG DATA TEST

BIG DATA TEST

Data Analytics

Module 1 Week 38 - Data Analytics - Tableau - 1

Talking About Business Intelligence
Tools And Methodologies Used In Bi
Why Visualization Is Getting More Popular
Why Tableau?
Gartner Magic Quadrant Of Market Leaders
Future Business Impact Of Bi
Tableau Products
Tableau Architecture
Bi Project Execution
Tableau Installation In Local System
Introduction To Tableau Prep
Tableau Prep Builder User Interface
Data Preparation Techniques Using Tableau Prep Builder Tool
How To Connect Tableau With Different Data Sources

Module 2 Week 39 - Data Analytics - Tableau - 2

Visual Segments
Visual Analytics In Depth
Filters, Parameters & Sets
Tableau Calculations Using Functions
Tableau Joins
Working With Multiple Data Sources (Data Blending)
Building Predictive Models
Dynamic Dashboards And Stories
Sharing Your Reports
Tableau Server
User Security
Scheduling

Module 3 Week 40 - Data Analytics - PowerBI - 1

Power Bi Introduction And Overview
Key Benefits Of Power Bi
Power Bi Architecture
Power Bi Process
Components Of Power Bi
Power Bi - Building Blocks
Power Bi Vs Other Bi Tools
Power Installation
Overview Of Power Bi Desktop
Data Sources In Power Bi Desktop
Connecting To A Data Source
Query Editor In Power Bi
Views In Power Bi
Field Pane
Visual Pane
Custom Visual Option
Filters
Introduction To Using Excel Data In Power Bi
Exploring Live Connections To Data With Power Bi
Connecting Directly To Sql Azure, Hd Spark, SQL Server Analysis Services/ MySQL
Import Power View And Power Pivot To Power Bi
Power Bi Publisher For Excel
Content Packs
Introducing Power Bi Mobile
Power Query Introduction
Query Editor Interface
Clean And Transform Your Data With Query Editor

Module 4 Week 41 - Data Analytics - PowerBI - 2

Data Type
Column Transformations Vs Adding Columns
Text Transformations
Cleaning Irregularly Formatted Data -Transpose
Date And Time Calculations
Advance Editor: Use Case
Query Level Parameters
Combining Data – Merging And Appending
Data Modelling
Calculated Columns
Measures/New Quick Measures
Calculated Tables
Optimizing Data Models
Row Context Vs Set Context
Cross Filter Direction
Manage Data Relationship
Why Is Dax Important?
Advanced Calculations Using Calculate Functions
Dax Queries

Module 5 POWER BI & TABLEAU TEST

POWER BI & TABLEAU TEST

Job Preparation

Module 1 Week 42 - Job Preparation - Resume & Interview Preparation For Jobs

Resume Templates For Freshers
Resume Templates For 2-4 Years Experience
Resume Templates For 5-8 Years Experience
Resume Templates For 10+ Years Experience
Interview Preparation For Python
Interview Preparation For Statistics
Interview Preparation For Machine Learning

Module 2 Week 43 - Job Preparation - Profile Building & Apply Jobs

GitHub
LinkedIn
Naukri
Monster

Admission details

To join the Data Science masters 2.0 classes, follow these steps:

Step 1: Go through the official link below:

https://pwskills.com/course/data-science-masters-2-hindi

Step 2: Clicking on the ‘Enrol Now’ button, the candidates will be able to log in or sign up with their email ids.

Step 3: This way the candidates will get onboarded and start their learning process.

How it helps

The Data Science masters 2.0 certification benefits the candidates by teaching them ways in which they can play with large data sets. They can thereby learn to manage, then analyse, visualise, and then ultimately extract meaning from these data sets.

Instructors

Mr Krish Naik
Instructor
Freelancer

Mr Sudhanshu Kumar
CEO
PW Skills

Dr Ayan Debnath
Senior Data Scientist
Freelancer

Ph.D

Mr Subhodeep Dey
Senior Faculty
International School...

Mr Rishab Malhotra
Senior Data Scientist
Reliance Jio Infocom...

FAQs

Is it possible to enroll if the candidates have no knowledge of programming?

Yes, even beginners with no knowledge of programming can easily enroll.

How long is the Data Science masters 2.0 online course duration?

The length of the course is only 8-9 months.

What kind of tools are taught during the programme on data science?

Some of the tools taught are namely Matplotlib, Anaconda, Python, Jupyter, Flask, Scipy, Numpy, Pandas, Seaborn, MongoDB, Scikit-Learn, Git, TensorFlow, and more.

Will the candidates get a chance to talk to the Expert Advice Counsellor of the course?

Once contacted, the candidates will be connected within 24 hours.

From which companies do the mentors belong?

The Data Science masters 2.0 mentors belong to companies like Jio, Wipro, HCL, Honeywell, and more.

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Data Science masters 2.0

Online

8 Months

₹ 3500 7000