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Job Ready Program

Data Analytics Mentorship Program

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

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3,785 already upskilled!

Our learners work at top companies

Who Should Join Data analytic 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 Analytic 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 Analytic Course work?

Your Roadmap to Become a pro Data Analyst  Developer!

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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!

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Week 1 : Overview of Excel

  • Introduction to Data Analytics
  • Basic Features in Excel
  • Formatting in Excel
  • Dealing with Raw Data
  • Functions in Excel

Week 2 : Deep Dive with Excel - II

  • Data Connectors in Excel
  • Cleaning in Power Query Editor
  • Adding Conditional Columns using Power Query Editor
  • Data Modelling and its Importance
  • Cardinality and Filter Direction in Power Pivot

Week 3 : Master Advanced Excel - III

  • Pivot Tables in Excel
  • Charts in Excel
  • Slicers in Excel
  • Measures in Excel
  • Creating a Dashboard in Excel
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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.
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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
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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.
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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
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