THE FOUNDER'S ELITE

Introducing the
MOST AFFORDABLE PATH
to become a Data Scientist
at per month.

A Step-by-Step 6-Month Online Program for aspiring individuals who want to become a FULL-STACK Data Scientist without quitting their job.

LEARN

Learn End-to-End Data Science Pipeline from scratch.



BUILD

Build a rad portfolio of Real-World Full-Stack Data Science Projects that operate at an Industrial Scale.

GET PLACED

Get direct access to the Personal Mentorship & Exclusive Tools that have empowered 9700+ Professionals to kickstart their career as a Data Scientist.

live_tv

Delivery Format

Delivery Format

Online

Available Seats

Available Seats

450

Program Duration

Program Duration

6 Months

Batch Start Date

Batch Start Date

August 08, 2020

Admission Process

Admission Process

First-Come, First-Served

Remaining Seats

Remaining Seats

231

THE DATUMGUY's Approach

Here's the Detailed SYLLABUS of the Program.

THE DATUMGUY's Approach

Here's the Detailed SYLLABUS of the Program.

Month 1

Rishabh Malhotra REVEALS WHY Machine Learning is used AND is the hottest & the most in-demand tech skill of 2020.

Other programs jump straight into the technical know-how without explaining why do we need Machine Learning at all.

This is the month in which you will go through the complete breakdown of the IDEA of Machine Learning & its functions. You will attain the ability to identify a real-world problem that can be best solved using Machine Learning.

  • Machine Learning - Netflix Case Study
  • Where to use Machine Learning
  • Why do we use Machine Learning - Technical Perspective
  • What is Machine learning
  • How accurate is Machine Learning
  • Different types of ML methods
  • Introduction to Simple Linear Regression
  • Explaining Simple Linear Regression
    • Representation of Simple Linear Regression
    • Problem Statement for Simple Linear Regression
    • Coefficients & relation with the straight line
    • How to estimate coefficients
  • Implementing Simple Linear Regression Model in Python from scratch
  • Introduction to Multiple Linear Regression
    • Overview of Multiple Linear Regression
    • Detailed Explanation of the working of Gradient Descent for Multiple Linear Regression
    • Complete Implementation of Multiple Linear Regression using Gradient Descent in Python from scratch

Month 2

You will invest your time in THE MOST EXHAUSTIVE understanding of creating predictive models using regression techniques.

This month will be concluded by you building a modular project focused on putting all the theoretical concepts learned throughout the month into practice.

  • Introduction to Polynomial Regression
    • Overview of Polynomial Regression
    • Representation of Polynomial Regression
    • Implementation of Polynomial Regression
  • Introduction to Lasso & Ridge Regression
    • Overview of Lasso & Ridge Regression
    • Regularization and Shrinkage Methods
    • Representation of Lasso & Ridge Regression
    • Bias & Variance Tradeoff for Lasso & Ridge Regression
    • Implementation of Lasso & Ridge Regression
  • Complete development and exploration of the project including all the major data cleaning, preprocessing & feature engineering techniques.
  • Model Development
  • Model Refinement and Retuning
  • Model Evaluation and Interpretation

Month 3

Get an end-to-end AND detailed understanding of classification models by learning how to create, interpret, and evaluate classification models using several classification techniques.

  • Introduction to Logistic Regression
    • Overview of Logistic Regression
    • Representation of Logistic Regression
    • Gradient Descent for Logistic Regression
    • Complete Implementation of Logistic Regression using Gradient Descent in Python from scratch
  • Introduction to K-Nearest Neighbors
    • Overview of K-Nearest Neighbors
    • Bias vs Variance - Classification Perspective
    • Complete Implementation of K-Nearest Neighbors in Python from scratch
  • Introduction to Decision Trees
    • Overview of Decision Trees
    • Detailed working of Decision Trees
    • How to tune Decision Trees effectively using different hyperparameters
    • Complete Implementation of Decision Trees in Python from scratch
  • Introduction to Naive Bayes
    • Introduction to Conditional Probability
    • Bayes Theorem
    • How to use of Bayes Theorem for Classification
    • Naive Bayes Theorem
    • Complete Implementation of Naive Bayes in Python from scratch
  • Introduction to SVM
    • Introduction to HyperPlane
    • Introduction to Maximum Margin Classifier
      • Overview of Maximum Margin Classifier
      • Representation of Maximum Margin Classifier
      • Issues with Maximum Margin Classifier
    • Introduction to Support Vector Classifier
      • Overview of Support Vector Classifier
      • Representation of Support Vector Classifier
    • Support Vector Machine & Kernels

Month 4

Understand Re-Sampling techniques completely AND conclude the concept of classification models by building a modular project focused on testing your deep understanding of the topic.

  • Overview of Cross Validation
  • Different Types of Cross Validation Techniques
  • Bias Variance Tradeoff for Cross Validation Techniques
  • Complete development and exploration of the project including all the major data cleaning, preprocessing & feature engineering techniques.
  • Model Development
  • Model Refinement and Retuning
  • Model Evaluation and Interpretation

Month 5

This month, you will dive deep into the concept of Ensembling AND start playing with the idea of Unsupervised Learning in Machine Learning.

  • Overview of Clustering
  • Practical Applications of Clustering in real case scenarios
  • Introduction to K Means Partition Clustering
    • Overview of K Means Partition Clustering
    • Representation of K Means Partition Clustering
    • Practical Implementation approach for K Means Clustering
    • How to understand the quality of clusters
  • Introduction to Agglomerative Hierarchical Clustering
    • Overview of Hierarchical Clustering
    • Divisive Hierarchical Clustering vs Agglomerative Hierarchical Clustering
    • Working of Agglomerative Hierarchical Clustering & Linkages
    • How to interpret Agglomerative Hierarchical Clusters
  • Overview of Dimensionality Reduction
  • Practical Applications of Dimensionality Reduction in real case scenarios
  • Introduction to Principal Component Analysis
    • Overview of Principal Component Analysis
    • Representation of Principal Component Analysis
    • Introduction to Eigenvectors & Eigenvalues.
    • Step by Step Process for Principal Component Analysis
    • Explained Variance
  • Overview of Ensembling
  • How Models are Combined
  • Introduction to Stacking
  • Introduction to Bagging
    • How Bagging Works
    • Random Forest Demystified
  • Introduction to Boosting
    • How Boosting Works
    • AdaBoost Demystified

Month 6

This is it. This is THE GRAND FINALE!

You will learn how to solve real-world problems by working on TWO full-stack Data Science projects in an end-to-end fashion.

This final month, you will learn how Data Science projects are ideated, worked on, and deployed at an industrial scale.

THE DATUMGUY's Approach
to OCR

THE DATUMGUY's Approach

Here's the Detailed SYLLABUS of the Program.
  • Month 1
  • Month 2
  • Month 3
  • Month 4
  • Month 5
  • Month 6

Month 1

Rishabh Malhotra REVEALS WHY Machine Learning is used AND is the hottest & the most in-demand tech skill of 2020.

Other programs jump straight into the technical know-how without explaining why do we need Machine Learning at all.

This is the month in which you will go through the complete breakdown of the IDEA of Machine Learning & its functions. You will attain the ability to identify a real-world problem that can be best solved using Machine Learning.

  • Machine Learning - Netflix Case Study
  • Where to use Machine Learning
  • Why do we use Machine Learning - Technical Perspective
  • What is Machine learning
  • How accurate is Machine Learning
  • Different types of ML methods
  • Introduction to Simple Linear Regression
  • Explaining Simple Linear Regression
    • Representation of Simple Linear Regression
    • Problem Statement for Simple Linear Regression
    • Coefficients & relation with the straight line
    • How to estimate coefficients
  • Implementing Simple Linear Regression Model in Python from scratch
  • Introduction to Multiple Linear Regression
    • Overview of Multiple Linear Regression
    • Detailed Explanation of the working of Gradient Descent for Multiple Linear Regression
    • Complete Implementation of Multiple Linear Regression using Gradient Descent in Python from scratch

Month 2

You will invest your time in THE MOST EXHAUSTIVE understanding of creating predictive models using regression techniques.

This month will be concluded by you building a modular project focused on putting all the theoretical concepts learned throughout the month into practice.

  • Introduction to Polynomial Regression
    • Overview of Polynomial Regression
    • Representation of Polynomial Regression
    • Implementation of Polynomial Regression
  • Introduction to Lasso & Ridge Regression
    • Overview of Lasso & Ridge Regression
    • Regularization and Shrinkage Methods
    • Representation of Lasso & Ridge Regression
    • Bias & Variance Tradeoff for Lasso & Ridge Regression
    • Implementation of Lasso & Ridge Regression
  • Complete development and exploration of the project including all the major data cleaning, preprocessing & feature engineering techniques.
  • Model Development
  • Model Refinement and Retuning
  • Model Evaluation and Interpretation

Month 3

Get an end-to-end AND detailed understanding of classification models by learning how to create, interpret, and evaluate classification models using several classification techniques.

  • Introduction to Logistic Regression
    • Overview of Logistic Regression
    • Representation of Logistic Regression
    • Gradient Descent for Logistic Regression
    • Complete Implementation of Logistic Regression using Gradient Descent in Python from scratch
  • Introduction to K-Nearest Neighbors
    • Overview of K-Nearest Neighbors
    • Bias vs Variance - Classification Perspective
    • Complete Implementation of K-Nearest Neighbors in Python from scratch
  • Introduction to Decision Trees
    • Overview of Decision Trees
    • Detailed working of Decision Trees
    • How to tune Decision Trees effectively using different hyperparameters
    • Complete Implementation of Decision Trees in Python from scratch
  • Introduction to Naive Bayes
    • Introduction to Conditional Probability
    • Bayes Theorem
    • How to use of Bayes Theorem for Classification
    • Naive Bayes Theorem
    • Complete Implementation of Naive Bayes in Python from scratch
  • Introduction to SVM
    • Introduction to HyperPlane
    • Introduction to Maximum Margin Classifier
      • Overview of Maximum Margin Classifier
      • Representation of Maximum Margin Classifier
      • Issues with Maximum Margin Classifier
    • Introduction to Support Vector Classifier
      • Overview of Support Vector Classifier
      • Representation of Support Vector Classifier
    • Support Vector Machine & Kernels

Month 4

Understand Re-Sampling techniques completely AND conclude the concept of classification models by building a modular project focused on testing your deep understanding of the topic.

  • Overview of Cross Validation
  • Different Types of Cross Validation Techniques
  • Bias Variance Tradeoff for Cross Validation Techniques
  • Complete development and exploration of the project including all the major data cleaning, preprocessing & feature engineering techniques.
  • Model Development
  • Model Refinement and Retuning
  • Model Evaluation and Interpretation

Month 5

This month, you will dive deep into the concept of Ensembling AND start playing with the idea of Unsupervised Learning in Machine Learning.

  • Overview of Clustering
  • Practical Applications of Clustering in real case scenarios
  • Introduction to K Means Partition Clustering
    • Overview of K Means Partition Clustering
    • Representation of K Means Partition Clustering
    • Practical Implementation approach for K Means Clustering
    • How to understand the quality of clusters
  • Introduction to Agglomerative Hierarchical Clustering
    • Overview of Hierarchical Clustering
    • Divisive Hierarchical Clustering vs Agglomerative Hierarchical Clustering
    • Working of Agglomerative Hierarchical Clustering & Linkages
    • How to interpret Agglomerative Hierarchical Clusters
  • Overview of Dimensionality Reduction
  • Practical Applications of Dimensionality Reduction in real case scenarios
  • Introduction to Principal Component Analysis
    • Overview of Principal Component Analysis
    • Representation of Principal Component Analysis
    • Introduction to Eigenvectors & Eigenvalues.
    • Step by Step Process for Principal Component Analysis
    • Explained Variance
  • Overview of Ensembling
  • How Models are Combined
  • Introduction to Stacking
  • Introduction to Bagging
    • How Bagging Works
    • Random Forest Demystified
  • Introduction to Boosting
    • How Boosting Works
    • AdaBoost Demystified

Month 6

This is it. This is THE GRAND FINALE!

You will learn how to solve real-world problems by working on TWO full-stack Data Science projects in an end-to-end fashion.

This final month, you will learn how Data Science projects are ideated, worked on, and deployed at an industrial scale.

Tools & Libraries

to be used throughout the program.
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Here's What You'll Get

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  1. 1

    PREPARATORY CONTENT

  2. 2

    WEEKLY LIVE Training Sessions

  3. 3

    WEEKLY 2-Hour Long LIVE Q&A Session

  4. 4

    LIFETIME Access to HD-Quality recording of every WEEKLY LIVE Training Session

  5. 5

    WEEKLY Assignments & Quizzes

  6. 6

    TOPIC-WISE Videos Access

  7. 7

    PRIVATE FACEBOOK GROUP for Direct Access to THE DATUMGUY's TEAM & Peers learning along with you.

  8. 8

    PRIVATE ATTENTION & SUPPORT

Here's What Our Students Have To Say About
THE DatumGuy

and we provide PLACEMENT ASSISTANCE too...(which comes bundled with the program)

12 PLACEMENT MENTORSHIP Sessions

A CV built for you, from scratch

Build one Resume/CV and send it to all...that strategy works no more in 2020. Your resume MUST be hyper-focused and built around a specific skill.

Our team will craft you a Resume built around your goals and objectives, from scratch. Whether you wish to get a job as a Data Scientist or further your specialisation via a Masters or PhD, we've got you covered.

LinkedIn Profile OVERHAUL

Employers, University Admission Council...everyone is on LinkedIn.

A bad LinkedIn profile kills your dreams. A good LinkedIn profile hyper-increases your chances of getting your dream job or admission into a prestigious university.

Our team will completely overhaul your LinkedIn profile to best represent you in the Data Science market.

A CONTENT STRATEGY customised to your goals

If not via word-of-mouth, then you definitely found us via our online content.

Putting out content works for influencers, bloggers, actors, THE DATUM GUY. Sharing your thoughts on the internet builds enormous opportunities that one can't imagine.

Not everyone is a writer, and we get it. But you don't need to be a word wizard to share your Data Science learning journey. We will give you a content strategy customised to your goals AND a content skeleton to build your writing pieces around.

Access to the PREMIUM NEWSLETTER dedicated to Data Science Jobs

Get exclusive access to the newsletter, where we publish the most lucrative Data Science jobs curated by our team. You'll get PRIVATE INSIGHTS from Rishabh and Industry Experts on how to best prepare for the job interviews and navigate through the process of every job mailed to you.

BONUS REFERRAL SYSTEM - Unlocked IFF You Meet Our Standards

People trust people. That's why we make our most important decisions based on personal bits of advice. That's why universities seek Letters of Recommendation. That's why freelancers get their best work via referrals. And so on.

If we observe that you're progressing with the course at a great pace, completing weekly assignments on time, helping your peers in the FB group, involved in asking questions...basically, giving your 100% to the journey of becoming a Data Scientist, we'll capitalise on our network of companies. We'll drop a call and discuss with you about the companies you'd like to work for. Then, Rishabh will refer you to those companies.

Imagine the possibilities here...!

Since THE DatumGuy's Inception 2 Years ago, we have helped over 9700+ professionals from companies like

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to become a MARKET-READY FULL-STACK Data Scientist.

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Admission Details

The program is structured around THE DatumGuy's 3-Step Mission to-

  • To convert aspiring Data Scientists into credible, hardcore & Market-Ready FULL-STACK Data Scientists.
  • Give them hands-on experience with REAL-WORLD FULL-STACK Data Science projects that mimic the experience of working as a Data Scientist at a company.
  • Provide them PLACEMENT ASSISTANCE, so they could access THE DatumGuy's proprietary tools that help them land a lucrative job in the domain.

Why are there ONLY LIMITED Seats available?

Why are there ONLY LIMITED Seats available?

We accept a limited number of students in the program, so Rishabh could personally mentor them in an End-to-End fashion & help them achieve their goals.

Is there an Application Deadline?

Is there an Application Deadline?

We follow the FIRST-COME, FIRST-SERVED Admission process.

So, we stop accepting any applications as soon as all seats are booked.

NOTE: As of now, 231 seats are remaining out of the total number of seats, which were 450.

To make sure that you're a part of this group of ambitious individuals, Enroll Today!

Watch Demo, Of Course!

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Watch this video, as Rishabh teaches GRADIENT DESCENT to one of the 'THE FOUNDER'S ELITE' batches in the past.
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Earn this Certificate

by successfully completing the program &

  • Attach it your CV to boost its credibility.
  • Showcase it on LinkedIn to amplify your chances of being spotted by the desired employer.
ENROLL NOW

Registration Closes in:

Registration Closes in:

7
days
23
hours
29
minutes
47
seconds

Switch Your Career To
Data Science.

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