Data Science Career Transition Program
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
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Lead Instructor
Mr. Kanth
Data Scientist | Consultant | Podcaster | Youtuber | Mentor
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.
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
- Life - cycle of a Data Scientist
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Why Statistics? Why SQL? Why Machine Learning? Why Deployment? -
Bird eye view of Machine Learning Architecture
Advanced Statistics & Data Understanding
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Introduction to Statistics
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What is Statistical Analysis
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Purpose of Statistical Analysis
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What is the Definition of Data & Different sources of Data
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Descriptive Analytics vs Predictive Analytics vs Prescriptive Analytics
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Measures of Central Tendency, Sample and Population
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Measures of Dispersion
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Different Measurement Scales
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Sources of Data
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Random Variable and Types of Random Variable
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Probability Distribution
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Conditional Probability
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Normal Distribution
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Skewness and Kurtosis
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Central Limit Theorem
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Hypothesis Testing
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Types of errors in Hypothesis
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Confidence Interval
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Estimates
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Z-Scores
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Statistical Testings
MySQL Tool
- Database Basics
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Designing your Database -
Data Types -
Creating Databases and Tables -
Querying Table Data -
Modifying Table Data -
Functions -
Joining Tables
Python Programming
- Installation of Anaconda and Setting up Google Colabs
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Basics of Python Programming -
Introduction to Data Structures -
Values, Variables, Functions and Librarys -
Various libraries in python and purpose of each library -
Deep Dive into List, Tuple, Set & Dictionary -
Selection/Conditional Branching Statements -
User Defined Functions -
Lamda Functions -
Iterators -
Loop Structures/Iterative Statements
Machine Learning Deep Dive & Algorithms
Life cycle of Machine Learning Project
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Introduction to Pandas -
Deep Dive into Pandas Commands on Data Understanding -
Project Demonstration: Business Case Study and Data Understanding Case Study with Visualization(Matplotlib & Seaborn) -
Assignment-1: Data Understanding Project Allocation -
Assignment Discussion and Documentation Guidance -
Project Demonstration: Data Cleaning Case Study -
Assignment-2: Data Cleaning Project Allocation -
Assignment Discussion and Data Cleaning Report -
Machine Learning Introduction -
Data Mining, Supervised, Unsupervised, Semi -Supervised and Reinforcement Learning
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Data Analyst vs Data Science vs Machine Learning vs Deep Learning
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Supervised Learning Flowchart
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Feature Scaling, Feature Selection, DR
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Sklearn - Dummies/ One hot encoding, Standardization or z-values, binarization, normalization
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Regression vs Classification
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Linear Regression, Logistic Regression SLR, MLR
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Correlation, Assumptions of Correlation
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Linear Regression - Based on two points, OLS
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Gradient Descent
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Assumptions of Linear Regression
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Coding on Linear Regression & Learning Assignment
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Deep Math on Logistic Regression & Coding
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Hypothesis, Confusion Matrix, Classification Report, ROC and AUC
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Anova Test, 2 Sample t test, Proporation Test, Chi-Square Test
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Coding using Scipy
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Project on Logistic and Learning Assignment
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Project clarification & Guidance
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Decision Tree Math.
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Parametric and Non-Parametric
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Bagging and Boosting Hyperparameters
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POC Allocation based on 5 Algorithms
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Interview Questions based on 5 Algorithms & Projects
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Mock Interview - 1
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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
"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.
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