THE FOUNDER'S ELITE
Introducing the
MOST AFFORDABLE PATH
to become a Data Scientist
at per month.
A StepbyStep 6Month Online Program for aspiring individuals who want to become a FULLSTACK Data Scientist without quitting their job.
LEARN
Learn EndtoEnd Data Science Pipeline from scratch.
BUILD
Build a rad portfolio of RealWorld FullStack 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.
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
FirstCome, FirstServed
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 indemand tech skill of 2020.
Other programs jump straight into the technical knowhow 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 realworld 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 endtoend 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 KNearest Neighbors
 Overview of KNearest Neighbors
 Bias vs Variance  Classification Perspective
 Complete Implementation of KNearest 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 ReSampling 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 realworld problems by working on TWO fullstack Data Science projects in an endtoend 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 indemand tech skill of 2020.
Other programs jump straight into the technical knowhow 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 realworld 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 endtoend 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 KNearest Neighbors
 Overview of KNearest Neighbors
 Bias vs Variance  Classification Perspective
 Complete Implementation of KNearest 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 ReSampling 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 realworld problems by working on TWO fullstack Data Science projects in an endtoend 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.
Here's What You'll Get

1
PREPARATORY CONTENT

2
WEEKLY LIVE Training Sessions

3
WEEKLY 2Hour Long LIVE Q&A Session

4
LIFETIME Access to HDQuality recording of every WEEKLY LIVE Training Session

5
WEEKLY Assignments & Quizzes

6
TOPICWISE Videos Access

7
PRIVATE FACEBOOK GROUP for Direct Access to THE DATUMGUY's TEAM & Peers learning along with you.

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 hyperfocused 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 hyperincreases 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 wordofmouth, 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
to become a MARKETREADY FULLSTACK Data Scientist.
Admission Details
The program is structured around THE DatumGuy's 3Step Mission to
 To convert aspiring Data Scientists into credible, hardcore & MarketReady FULLSTACK Data Scientists.
 Give them handson experience with REALWORLD FULLSTACK 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 EndtoEnd fashion & help them achieve their goals.
Is there an Application Deadline?
Is there an Application Deadline?
We follow the FIRSTCOME, FIRSTSERVED 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!
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.
Registration Closes in:
Registration Closes in:
days
hours
minutes
seconds
Switch Your Career To
Data Science.
Kickstart your journey of becoming a FULLSTACK Data Scientist by filling out this application form.
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