Job Ready Program
Job ready Data Science Master Course
Join this 16 weeks, Job-ready Program to master Data Analytics from scratch with Top Data Analysts from Microsoft, KPMG, Amazon, and Rapido.
11 LPA
Average Salary
34,000
Jobs on LinkedIn Alone
7+
Industry Mentors
3,785 already upskilled!
Who Should Join Data Science Course by WsCube Tech?

Working Professional

Undergrads

Working Professional

Working Professional
Our programs are custom-tailored for Gen-Z career aspirants. The programs aim to equip the learners with industry-ready, hands-on skills and facilitate a confident career kick-off. Focusing on practical learning and real-world application, our programs are designed to transform individuals into skilled professionals ready to tackle industry challenges.
Highlights For Data Science Course
Explore what this online Data Analytic course is powered with.
Guided by the Geeks
Thrive under the mentorship of seasoned industry mentors who impart their vast knowledge and insights. Each session in this online WordPress course is crafted to help you navigate the complexities, boosting your confidence as you master the craft. Incorporate advanced techniques and strategies from industry gurus keen to share their hidden formulas for excellence.
Guided by the Geeks
Thrive under the mentorship of seasoned industry mentors who impart their vast knowledge and insights. Each session in this online WordPress course is crafted to help you navigate the complexities, boosting your confidence as you master the craft. Incorporate advanced techniques and strategies from industry gurus keen to share their hidden formulas for excellence.
Guided by the Geeks
Thrive under the mentorship of seasoned industry mentors who impart their vast knowledge and insights. Each session in this online WordPress course is crafted to help you navigate the complexities, boosting your confidence as you master the craft. Incorporate advanced techniques and strategies from industry gurus keen to share their hidden formulas for excellence.
Guided by the Geeks
Thrive under the mentorship of seasoned industry mentors who impart their vast knowledge and insights. Each session in this online WordPress course is crafted to help you navigate the complexities, boosting your confidence as you master the craft. Incorporate advanced techniques and strategies from industry gurus keen to share their hidden formulas for excellence.
How does this Data Science Course work?
Your Roadmap to Become a pro Data Analyst Developer!
Upskill yourself by gaining insights from leading professionals' vast experience.
Sharpen your skills by learning through course assignments, live projects, and regular assessments and quizzes.
Sharpen your skills by learning through course assignments, live projects, and regular assessments and quizzes.
Sharpen your skills by learning through course assignments, live projects, and regular assessments and quizzes.
WordPress Course Syllabus
Explore the curriculum designed according to the industry standards!
Module 4 - Specialization in Machine Learning OR Deep Learning
Duration: 8/18 Weeks
Within this module, you will work on multiple projects build in partnership with top companies.
You will get your hands dirty by working with messy & unclean datasets from real companies.
You have the flexibility to select either one or both of the offered specializations, based on your interests and career goals.
Topics that will be covered:
Specialization 1: Machine Learning (8 Weeks)
1. Supervised Learning
- MLE, MAP, Confidence Interval
- Classification Metrics
- Imbalanced Data
- Decision Trees
- Bagging
- Naive Bayes
- SVM
2. Unsupervised & Recommender Systems
- Introduction to Clustering, k-Means
- k-Means ++, Hierarchical
- GMM
- PCA, t-SNE
- Recommender Systems
- Time Series Analysis
- USPs of our Delivery
- All topics taught in live classes with limited batch size helping in instant doubt support to accelerate learning.
- Assignment (post-lecture) & their evaluation.
- Hyper-Personalised: Special focus on the individual with a constant touch from student success manager & mentor.
Module 2 - Analytical Proficiency and Business Insights
Duration: 6 Weeks
As a Data Scientist, it is important we know how to break down business situations and design correct metrics.
Moreover, you should also be able to use the powerful language of SQL to extract and analyze data.
Within this module, our aim is for you to become skilled at interpreting data to make informed business decisions and to present your findings with clarity.
Topics that will be covered:
1. SQL
- IntroductioIntroduction to Databases & BigQuery Setupn to Excel and Formulas
- Extracting data using SQL
- Functions, Filtering & Subqueries
- Joins
- GROUP BY & Aggregation
- Window Functions
- Date and Time Functions & CTEs
2.Product Analytics
- Framework to address product sense questions
- Diagnostics
- Metrics, KPI
- Product Design & Development
- Guesstimates
- Product Cases from Netflix, Stripe, Instagram
- USPs of our Delivery
- Hyper-personalization: Depending on student-specific learning pace, multiple revision classes are organized
- Assignments (post-lecture) & their immediate evaluation help to compare your performance against peers
- The focus is not just to remember maths formulas but to help learners visualize the intuition behind concepts, enabling them to identify patterns
- As you work on different business situation & product thinking, you gain a deeper understanding on what insights are important & what insights are not important for a particular scenario.
Module 3 - Foundations of Machine Learning & Deep Learning
Duration: 6 Weeks
Mathematics is the foundation upon which Machine Learning & Deep Learning algorithms are built.
That is why, in this module, you will fall in love with mathematics as you solve engaging problems & build your solid foundations of Machine Learning & Deep Learning.
Topics that will be covered:
Advanced Python & Python Libraries:
1. Python Libraries
- Python Refresher
- Numpy, Pandas
- Matplotlib
- Seaborn
- Data Acquisition
- Web API & Web Scrapping
- Beautifulsoup & Tweepy
2.Advanced Python
- Basics of Time & Space Complexity
- OOPS
- Functional Programming
- Exception Handling & Modules
Maths for Machine Learning:
1. Probability & Applied Statistics
- Probability
- Bayes Theorem
- Distributions
- Descriptive Statistics, outlier treatment
- Confidence Interval
- Central Limit Theorem
- Hypothesis Test, AB Testing
- ANOVA
- Correlation
- EDA, Feature Engineering, Missing value treatment
- Experiment Design
- Regex, NLTK, OpenCV
2.Calculus, Optimization & Linear Algebra
- Classification
- Hyperplane
- Halfspace
- Calculus
- Optimization
- Gradient Descent
- Principal Component Analysis
Introduction to Neural Networks & Machine Learning
1. Fundamentals of ML
- Introduction to Classical Machine Learning
- Linear Regression
- Polynomial, Bias-Variance, Regularisation
- Cross Validation
- Logistic Regression-2
- Perception and Softmax Classification
- Introduction to Clustering, K-Means
- K-Means ++, Hierarchical
- USPs of our Delivery
- Hands-on Learning Experience
- Learn maths from a case-study approach & Fall in love with Mathematics
- Solve multiple real-life case study problems in live classes & understand the tradeoffs of each algorithm
Module 4 - Specialization in Machine Learning OR Deep Learning
Duration: 8/18 Weeks
Within this module, you will work on multiple projects build in partnership with top companies.
You will get your hands dirty by working with messy & unclean datasets from real companies.
You have the flexibility to select either one or both of the offered specializations, based on your interests and career goals.
Topics that will be covered:
Specialization 1: Machine Learning (8 Weeks)
1. Supervised Learning
- MLE, MAP, Confidence Interval
- Classification Metrics
- Imbalanced Data
- Decision Trees
- Bagging
- Naive Bayes
- SVM
2. Unsupervised & Recommender Systems
- Introduction to Clustering, k-Means
- k-Means ++, Hierarchical
- GMM
- Anomaly/ Outlier/ Novelty Detection
- PCA, t-SNE
- Recommender Systems
- Time Series Analysis
Specialization 2: DEEP LEARNING (10 Weeks)
1. Neural Networks
- Perceptrons
- Neural Networks
- Hidden Layers
- Tensorflow
- Keras
- Forward & Backward Propagation
- Multilayer Perceptrons (MLP)
- Callbacks
- Tensorboard
- Optimization
- Hyperparameter tuning
2.Computer Vision
- Convolutional Neural Nets
- Data Augmentation
- Transfer Learning
- CNN
- CNN Hyperparameters Tuning & BackPropagation
- CNN Visualization
- Popular CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
- Object Segmentation, Localisation, & Detection
1.Natural Language Processing
- Text Processing & Representation
- Tokenization, Stemming, Lemmatization
- Vector space modelling, Cosine Similarity, Euclidean Distance
- POS tagging, Dependency Parsing
- Topic Modelling, Language Modelling
- Embeddings
- Recurrent Neural Nets
- Information Extraction
- LSTM
- Named Entity Recognition
2.Generative AI
- Generative Models, GANs
- Attention Models
- Siamese Networks
- Advanced CV
- Attention
- Transformers
- HuggingFace
- BERT
- USPs of our Delivery
- Impactful projects like forecasting the exact number of orders to be placed at a restaurant on New Year’s Eve, or Forecasting the number of oxygen cylinders a hospital will require, and multiple others.
- Hands-on experience with Machine Learning & Deep Learning algorithms
- 1:1 discussion with your mentor regarding project improvements.
Module 5 - ML Pipeline Development & Deployment + DSA
Duration: 8 Weeks ( Optional )
A great Data Scientist or ML Engineer is also capable of developing end-to-end pipelines & building applications powered by machine Learning models.
This is the reason why, Within this module, you will learn how to develop end-to-end ML pipelines. And you will work on the latest cloud platforms to deploy & monitor your models.
Moreover, Data structures & Algorithms are part of interviews at top product companies. That is why, you will also focus on Data Structures & Algorithms to be able to crack these interviews.
Topics that will be covered:
1. Machine Learning Ops
- Streamlit
- Flask
- Containerisation, Docker
- Experiment Tracking
- MLFlow
- CI/CD
- Github Actions
- ML System Design
- AWS Segemaker, AWS Data Wrangler, AWS Pipeline
- Apache Spark
- Spark ML lib
2. Advanced Data Structures & Algorithms
- Arrays
- Linked Lists
- Stacks & Queues
- Trees
- Tries & Heaps
- Searching & Sorting Algorithms
- Recursion
- Hashing & 2 pointers
Module 6 - Get Placed as Data Scientist at Top Companies
Duration: Until you get Placed
Once you have upskilled yourself to become a great data analyst, it is important that we now focus on getting you interview opportunities from diverse companies.
This process is usually in 3 phases:
1. Build a strong profile
2. Applying the right way
3. Acing the interview
We focus on all the above 3 objects in this phase.
Topics that will be covered:
1. Building a strong profile
- Resume Creation
- LinkedIn profile optimization
- Profile creation on other platforms
2. Applying the right way
- Opportunities through Bosscoder Collaboration with 250+ tech companies
- Referral to almost all the top product companies
- Sharing hiring requirement of different companies
3. Acing the interview
- On Demand Mock interviews
- Online Interview Guidelines
- Salary Negotiation
- Outcome
- You getting placed at one of the top product companies like Google, Microsoft, Amazon, Apple & sharing us a personal review Of your journey with us.
- USPs of our Delivery
- Student success manager stay connected with you throughout your placement journey to ensure you achieve best outcome.
- Collaboration with 250+ companies for hiring.
- Collaboration with consultancies who hire for top product companies.
- Referrals from our alumni & mentor community for almost all the companies.
- Resume reviews, profile building increasing your chances Of getting shortlisted.
- On demand mock interviews With mentor before a specific interview.
- 100% support from our team to help you succeeds.