College Student

Data Science Career Transition Program

(For Students)

Become a Data Scientist who can work from Business understanding, Machine Learning Solution Architecture building to Model Release and Maintenance. Gain real time expertise on every topic of Data Science.

Co-Developed by Senior Data Scientist's 

5000+

4Lac - 48Lac

Online

4 Months

Learners

Avg. Salary/annum

Mode of Learning

Learning Duration

Industry Specific Projects

15+ Real Life Projects while learning

Live projects going to be provided based on your current experience & industry. 

Get eligibility to collect internship certificate for 4Months

2 Hackathon's on ML

Connect with Trainer's 24x7 help

Build one POC(Proof of Concept) from specific industry with complete solution with deployment

We believe in connecting our trainers with students via whatsapp, linkedin and instagram.

3+ Live Projects Demonstration

Starting from Business Case, Project Charter, Agile, Machine Learning Architecture to final Project release.

No Cost EMI

No need to worry about payment, we got you No cost EMI option. Start now!

MOVE 

AS A 

DATA 

SCIENTIST

Tools Covered

Data Scientist must be flexible with tools and coding. 

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Lead Instructor

Mr. Kanth

Data Scientist | Consultant | Podcaster | Youtuber | Mentor 

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Mr. Kanth is a Data Scientist and Six Sigma Certified. He is a Data Science Consultant for various Top-MNC's like Nokia, EY, Cognizant, BMW etc. He delivered end-to-end AI Solutions using Machine Learning and Deep Learning on Embedded Devices. Built various AI Solutions which impacted financial gains and human effort reduction. 

 

Mr. Kanth is an Orator & Mentor delivered AI, Machine Learning, Deep Learning, IOT, Industry 4.0 & Digital Twin Customised Training Programs across Dubai, Malaysia, Singapore, South Africa, Sudan etc. He delivered more than 100+ trainings on both sides like client side and vendor side. 

Q & A Session from Recently Placed Participants:

We're trying all the possible ways to make your career transition more simplified with Q & A Session from recently placed participants, to build great exposure towards real-time interviews and how every learner need to prepare for their program as well as for their interviews. We host Q & A Sessions from different learners who got placed recently. 

Q & A Sessions helping every learner to crack their Data Science Interviews or AI Interviews with a more simplified approach. 

Program Syllabus

Best in industry career transition based syllabus built by Senior Data Scientist's which helps learners to build knowledge from zero level to real time working confidence on Data Science Projects.

Program Syllabus

Introduction to Life cycle of a Data Scientist


  1. Life - cycle of a Data Scientist
  2. Why Statistics? Why SQL? Why Machine Learning? Why Deployment?
  3. Bird eye view of Machine Learning Architecture




Advanced Statistics & Data Understanding


  1. Introduction to Statistics

  2. What is Statistical Analysis

  3. Purpose of Statistical Analysis

  4. What is the Definition of Data & Different sources of Data

  5. Descriptive Analytics vs Predictive Analytics vs Prescriptive Analytics

  6. Measures of Central Tendency, Sample and Population

  7. Measures of Dispersion

  8. Different Measurement Scales

  9. Sources of Data

  10. Random Variable and Types of Random Variable

  11. Probability Distribution

  12. Conditional Probability

  13. Normal Distribution

  14. Skewness and Kurtosis

  15. Central Limit Theorem

  16. Hypothesis Testing

  17. Types of errors in Hypothesis

  18. Confidence Interval

  19. Estimates

  20. Z-Scores

  21. Statistical Testings




MySQL Tool


  1. Database Basics
  2. Designing your Database
  3. Data Types
  4. Creating Databases and Tables
  5. Querying Table Data
  6. Modifying Table Data
  7. Functions
  8. Joining Tables
my sql data science




Python Programming


  1. Installation of Anaconda and Setting up Google Colabs
  2. Basics of Python Programming
  3. Introduction to Data Structures
  4. Values, Variables, Functions and Librarys
  5. Various libraries in python and purpose of each library
  6. Deep Dive into List, Tuple, Set & Dictionary
  7. Selection/Conditional Branching Statements
  8. User Defined Functions
  9. Lamda Functions
  10. Iterators
  11. Loop Structures/Iterative Statements




Machine Learning Deep Dive & Algorithms


Life cycle of Machine Learning Project

  1. Introduction to Pandas
  2. Deep Dive into Pandas Commands on Data Understanding
  3. Project Demonstration: Business Case Study and Data Understanding Case Study with Visualization(Matplotlib & Seaborn)
  4. Assignment-1: Data Understanding Project Allocation
  5. Assignment Discussion and Documentation Guidance
  6. Project Demonstration: Data Cleaning Case Study
  7. Assignment-2: Data Cleaning Project Allocation
  8. Assignment Discussion and Data Cleaning Report
  9. Machine Learning Introduction
  10. Data Mining, Supervised, Unsupervised, Semi -Supervised and Reinforcement Learning
  1. Data Analyst vs Data Science vs Machine Learning vs Deep Learning

  2. Supervised Learning Flowchart

  3. Feature Scaling, Feature Selection, DR

  4. Sklearn - Dummies/ One hot encoding, Standardization or z-values, binarization, normalization

  5. Regression vs Classification

  6. Linear Regression, Logistic Regression SLR, MLR

  7. Correlation, Assumptions of Correlation

  8. Linear Regression - Based on two points, OLS

  9. Gradient Descent

  10. Assumptions of Linear Regression

  11. Coding on Linear Regression & Learning Assignment

  12. Deep Math on Logistic Regression & Coding

  13. Hypothesis, Confusion Matrix, Classification Report, ROC and AUC

  14. Anova Test, 2 Sample t test, Proporation Test, Chi-Square Test

  15. Coding using Scipy

  16. Project on Logistic and Learning Assignment

  17. Project clarification & Guidance

  18. Decision Tree Math.

  19. Parametric and Non-Parametric

  20. Bagging and Boosting Hyperparameters

  21. POC Allocation based on 5 Algorithms

  22. Interview Questions based on 5 Algorithms & Projects

  23. Mock Interview - 1

  24. Final Project Allocation




Machine Learning Deployment & Architecture


1. What are the skillset required from companies for machine learning

2. Skills evaluations for individual participants

3. Life cycle of a machine learning developer

3.1 Problem Understanding

3.2 Data collection

3.3 Data Wrangling

3.4 Choosing right algorithm

3.5 Building Model

3.6 Model Evaluation

3.7 Model Performance Improvement

3.8 Model Finalisation

3.9 Model Deployment

3.10. Model Documentation

4. Best Practices on Machine Learning

4.1 Scrum Methodology Implementation on Machine Learning

4.2 Agile CRISP Implementation on Machine Learning

5. Building Machine Learning Solution Architecture for Banking Domain Project

5.1 Data Flow Design

5.2 Data Capturing

5.3 Running Machine Learning Engine in AWS using Docker

5.4 Implementations of Jenkins for Machine Learning Deployment

6. Live Project on Machine Learning Practical

6.1 Design Business Case for Machine Learning

6.2 Designing Machine Learning Architecture

6.3 Work allocation

6.4 Data Validation

6.5 Data Understanding Phase

6.6 Domain Understanding Phase

6.7 Feature Extraction Phase

6.8 Environment Study and Algorithm Finalisation

6.9 Model Building

6.10 Model Evaluation & Deployment

6.11 Client Feedback or Stakeholder Feedback on Deployed ML Engine

6.12. Performance Improvement

6.13. Change in Tools for better deployment and better features on improvement

6.14. Machine Learning Model Release

7. How to learn different Machine Learning Tools Faster

7.1 List of tools

7.2 Google Collabs, Google ML Kit

7.3 Apache Mahout, Apache Spark

7.4 ML Deployment Eco-System Docker, Jenkins, Django, Flask

7.5. Documentation and Github Release





ANY PROFESSIONAL FROM ANY INDUSTRY WITH ANY EXPERIENCE CAN ENROLL FOR THIS PROGRAM.

YOU RECEIVE INTERNSHIP CERTIFICATE BY COMPLETING EVERY PROJECT PROVIDED.

SYLLABUS & TRAINING FORMAT DESIGNED FOR WORKING PROFESSIONALS AS WELL AS FRESHERS.

WE ASSUME OUR LEARNERS ARE FROM BANKING, BFSI, MANUFACTURING, HEALTHCARE, OIL & GAS, RETAIL ETC.

"I'm a Project Manger with 15+ years of experience in BFSI. I'm impressed with projects they provided and articulated my CV and detailing of every topic as a project manager to master like pitching, CBA, ML Design etc are greatly nailed by Mr.Kanth"

Avg. 10+ Career Transitions Every

Month

Every month we are making our students and participants from different backgrounds and different learning formats we're making them to get placed. I fix every block and hurdle to make your career transition. 

FRESHERS

Majority of freshers are confused with what to do? How to attend interviews and how to crack them? We support them end-to-end until they get placed.

EXPERIENCED

Making a career transformation with experience is difficult. But we make it possible based on your current experience as reference.

NON-PROGRAMMERS

Non-technical or non-programmers feels pretty confusing to move into data science career. But we take care of your non-programmer journey to Data Scientist.

Best in industry projects to work & to place in resume

Dedicated team to Build your resume

Mock interviews before attending live interviews

Feedback on interviews & support until getting placed as Data Scientist

Data Science Career Transition Program

Program Main Topics:

Data Science with Python

Advanced Machine Learning & NLP

Live Project Simulation Environment

Interview Preparation Program

Mock & Resume Building

Forecasting 

MYSQL 

90% OFF FOR STUDENTS

Our Alumni Working At

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