Brochure
Enquire- Course Overview And Dashboard Description
- Introduction Of Data Industry
- Lab Introduction
- Support System Introduction
- Community Introduction
Online
8 Months
₹ 3,500 7,000
Quick facts
| particular | details | |
|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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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.
Also Read
The highlights
- 8 months of Course duration
- 200+ hours classes
- English language course
- Online learning mode
Program offerings
- Resume building
- 50 + real-time projects
- Resources
- Pwlab access
- Quiz in every module
- Doubt clearing sessions
- Practice exercises
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
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.
Who it is for
If the candidates after the Data Science masters 2.0 course, want to become data scientists then they can enroll themselves.
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.
The syllabus
Python Programming
Module 1: Course Induction
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
Module 1: Neural Network Overview And Its Use Case.
- Neural Network Overview And Its Use Case.
- Detail Mathematical Explanation
- Various Neural Network Architect Overview.
- Activation Function -All Name
- Multilayer Network.
- Loss Functions. - All 10
- The Learning Mechanism.
- Optimizers. - All 10
- Forward And Backward Propagation.
- Weight Initialization Technique
- Vanishing Gradient Problem
- Exploding Gradient Problem
- Visualization Of Neural Network
- Colab Pro Setup
- TensorFlow Installation 2.0 .
- TensorFlow 2.0 Neural Network Creation.
- Mini Project In TensorFlow.
- Tensor space
- Tensor board Integration
- TensorFlow Playground
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
- Naukri
- Monster
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.
