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4.7
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Google Rating
4.7
Based on 286 reviews
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Data Science Course with Placement Guarantee

*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully after course completion.

  • 320+ hours Live Interactive Sessions with Eminent Data Scientists

  • Gain 1.5 years of Research Project Experience Certificate

  • 100% Job Guarantee Program in collaboration with Top PAN India Consulting Firms.

  • Opportunity to conduct research work with GEEKLURN AI Singapore

  • Receive Research Project Scholarship up to Rs. 2 Lakhs.

  • Start paying your EMI only after placements

Geeklurn is now a Member of NASSCOM

GeekLurn is now a Member of NASSCOM & IBM Partner

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*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully after course completion.

  • 320+ hours Live Interactive Sessions with Eminent Data Scientists

  • Gain 1.5 years of Research Project Experience Certificate

  • 100% Job Guarantee Program in collaboration with Top PAN India Consulting Firms.

  • Opportunity to conduct research work with GEEKLURN AI Singapore

  • Receive Research Project Scholarship up to Rs. 2 Lakhs.

  • Start paying your EMI only after placements

Geeklurn is now a Member of NASSCOM

GeekLurn is now a Member of NASSCOM & IBM Partner

5000+

Alumni Students

Online

Format

24 Months

Program Duration

110+

Hiring Partners

EMI Options

Rs 6441* per month

GeekLurn is now a Member of NASSCOM & IBM Partner

In our journey towards excellence, we are proud to become a member of NASSCOM. Thankful to all our learners, team and cheerleaders who keep on inspiring us to do better each day.

Program Overview

Key Highlights

*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully after course completion.

  • 24-Months Course Duration

    - 6 months of Live Interactive Classes offered by Principal or Senior Data Scientists and Industry Experts
    - 18 months of Sponsored Project Work at Authorized Research Centres funded by IISC, ISB, and IIM

  • Receive 320+ hours Live Training Sessions

  • Gain 1.5 years of real-time Sponsored Project Experience Certificate from Recognized Research Centres

  • Start paying your EMI only after placements at the end of the course

  • Enjoy scholarships from Day One up to Rs 2 Lakhs based on the type of Research Projects

  • Get the opportunity to conduct research work with Singapore based GEEKLURN AI

  • Work on 50+ Sponsored & Funded Research Projects

  • Get Certified from Top Companies (IBM, Accenture, Microsoft, Oracle, etc...)

  • 100% Job Guarantee Program In collaboration with PAN India Top HR Consulting Firms

Data Science Job Guarantee Overview

100% Job Guarantee

Secure exciting Data Science jobs in top companies through our network of over 500 hiring partners and HR companies. Get 100% refund if you’re not able to land a job within 180 days of graduation.

4 million+ Data Jobs

Data Science professionals earn an average salary of ₹21 lakhs, with professionals getting a minimum 40% salary hike compared to the previous jobs.

1-on-1 Career Mentorship

Enhance your resume, prepare for technical interviews, and gain career growth hacks with valuable insights from industry experts and principal data scientists.

Easy Financing Options

GeekLurn offers an easy financing option at 0% interest rate with no hidden costs. Become a scholarship recipient, and GeekLurn will pay your EMIs till you get a job placement.

Placement Stats

40% Average Salary Hike

16 LPA Average Salary

8000 + Jobs Sourced

500+ Hiring Partners

Our Authorised Hiring Partners

Our Alumni

Meet Our GeekLurn-ers who are now Certified Data Science Professionals and were placed in Top MNCs by our HR Team!.

Mahadev Kadam

Data Scientist at Hexaware Technologies

Offer 29 LPA

Product Development Architect - Senior Specialist In FIS

Data Scientist

Teena Prithviraj Jain

Data Scientist at Hexaware Technologies

Adinath Solutions Logo Software Developer at Adinath Solutions

Data Scientist

Bhargav Nishanth

Data Scientist at Hexaware Technologies

Offer 5.5 LPA

Technical Support Specialist at Tech Mahindra

Data Scientist

Shashi Kiran

Data Scientist at
Tata Consultancy Services

Offer 14 LPA

Data Analyst at HCL

Machine Learning Engineer

Vamshi Krishna

Data Scientist
at Accenture

Software Test Engineer

Data Scientist

Shammer Khan

Professional 1 Product Delivery at DXC Technology

Offer 9 LPA

Data Science Trainee

Professional 1 Product Delivery

Chethan

Data Scientist at smart Data Enterprises Inc.

Offer 8 LPA

Design Engineer at Vengala Engineering Design Services (VEDS)

Data Scientist

Aathir Laeeque

Data Scientist at
Brightmoney

Offer 8 LPA

Catalog Specialist at Amazon

Data Analyst

Jay Prakash

Assistant Manager at Flipkart

Offer 30% Hike

Machine Learning Trainer at Vaidehi Software Technologies

Assistant Manager

T G Preetham

Data Engineer at GeekLurn AI software solutions Pvt Ltd

Offer 25% Hike

Data Engineer at Vaidehi Software Technologies

Data Engineer

Data Analyst

Student Reviews

Jay Prakash Sethy
Jay Prakash Sethy
Senior Executive Data Analyst @ Flipkart
Read More
I attended the 24-month Data Science Architect program at GEEKLURN and it was a fantastic experience. The curriculum is structured to help students master the essential vocabulary in Python (and R). All-in-all I felt the program delivered exactly the skills that I was looking to build. I'd recommend it highly to anybody who wants an effective foundation in Data Science and wants to Upscale.

Shameer Khan
Shameer Khan
Product Delivery Manager @ DXC Technology Massachusetts Institute of Technology
Read More
Great place to learn Machine Learning/Data Science and related tools and technologies! Provides excellent placement support as well. I can say from my personal experience that it is the place to go for learning Data Science.

Sagar Talele
Sagar Talele
Project Lead at LTI @ Larsen & Toubro Infotech Pvt. Ltd, Pune
Read More
Great Course!. Very well structured modules and neat concepts. The trainers were amazing. I would highly recommend to anyone looking out to kickstart their career and upgrade in the field of Data Science, Geeklurn is the place, guys!.

Sashi Kiran C
Sashi Kiran C
Senior Data Analyst @ HCL Technologies , Bangalore
Read More
The session was good, I had a good learning experience . I had taken few courses before but I found Geeklurn’s curriculum upto the mark and better according to job requirements standards. The mentor well experienced and held expertise in their following field. I would certainly recommend this course to every Data enthusiast

Deepak edukula
Deepak edukula
SAP BASIS & Hana Senior Analyst at HCL in India
Read More
It is an amazing place to learn Data science from scratch. All trainers are working as data scientist professional in top companies. Hence trainers teach topic which are irrelevant to industry standard. Since trainers are using very simple language, it is easy for us to understand and it makes more interesting towards our learning. Assessment and mock interview will be conducted after completion of each module. Good to join. Hope I made good decision.

Krushna Bhalke
Krushna Bhalke
Junior Engineer at Pinnacle Piling (India) Pvt Ltd
Read More
Teaching is good, Support and assistance is great. They give you special time for your doubts.

Chaitra Raju
Chaitra Raju
Data Science Intern at GEEKLURN ASIA
Read More
I’m glad to be a part of Geeklurn. Thanks to my trainer and entire team of Geeklurn for helping me achieve my dream job.

Naresh Katturi
Naresh Katturi
Datascience(Research Associate) at GeekLurn
Read More
It's been great pleasure to mention my words about geeklurn. For the last 6 months I have been getting trained from geeklurn by the trainer Adhvaith sir. He teaches every topic from basic level so that any person can understand the concept easily. And the support from geeklurn in terms of LMS and mentoring session for doubts clarification also very helpfull. I hope this will continue till I get a job. Thank you Geeklurn Team.

Ayush Sharma
Ayush Sharma
EV Technical Expert at Morris Garages India
Read More
It's been great pleasure to write about geeklurn. For the last 6-7 months I have been getting trained from geeklurn by the trainer Adhvaith sir. He teaches every topic from scratch so that any person can understand the concept easily and the support from geeklurn in terms of LMS and mentoring session for doubts clarification also is very helpfull. I hope this will continue till I get a job. Thank you Geeklurn Team and Special thanks to Mr. Advaith.

Gurukiran PS
Gurukiran PS
WFM Real Time Analyst at Groupon
Read More
Best institute to learn Data Science. 1. Well designed course 2. Good Advisors (Mr. Murthy Adivi sir) 3. Daily assignments 4. Ensuring best placements. 5. We can start this course with completely no prior knowledge. 6. Best institute to become Data scientist.

Harshini T
Harshini T
Associate Consultant at Capgemini
Read More
Very Good course to start learning data science that has a curated content, well structured from the basics. Very Helpful instructor's and Geeklurn team.

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Alumni Success Meet at GeekLurn HQ

Core Stages of Our DSAP Program

Stage 1

A conducive learning environment with 6 months of live interactive real-time training sessions with 1:1 mentorship offered by top industry experts who are working as Principal & Senior Data Scientists

Stage 2

After hands-on applied training, start working as a Data Science Intern on real-time industry assignments at an authorized GeekLurn incubation zone, which is recognised as a trusted research centre and is funded by IISC, IIM and ISB.

Stage 3

Get an opportunity to work on real- time industry-led projects officially funded by IISC, ISB and IIM. By displaying efficiency, calibre and good performance, you can be eligible for a scholarship of up to Rs. 2 lakh

Stage 4

The successful completion of the project will fetch you an Experience Certificate of 1+years issued by the respective research centre and will to help you add credible projects to your resume, which will prove to be  advantageous during placements.

Stage 5

Along with the Experience Certificate, you can fine-tune your resume by getting certified as a Data Science Specialist accredited by top brands in the industry like Microsoft, IBM, HarvardX, DASCA, and SAS.

Stage 6

With 100% Job Guarantee & career support  from industry experts in resume building and mock interviews practices, you will be eligible to get hired by more than 110+ Data Science Companies across India and earn up to Rs. 25 lacs/annum

Stage 7

From the 7th months after the completion of the live training session, you can start attending interviews while simultaneously working on research projects.

Data Science Architect Program Overview

GEEKLURN’s Data Science Architect Program is designed by industry experts and practitioners. It will provide you with tech-enabled job-relevant skills through the design, development and deployment of Big Data to convert it into real-time applications. The course is intended for both freshers and working professionals and will aim to positively and significantly impact their careers. Work on real-time case studies, & gain project mentorships & certifications through our 100% Job Guarantee program that, will make you job-ready and more relevant in the industry for various data science job roles.

With exposure to the key concepts and tools, towards the end of this program, you will have expertise in Testing, Analysis Modules, Hadoop Development, Administration, Statistical Computing, working with Real Analytics, analysing machine-generated data, and developing NoSQL Applications along with the mastering of Deep Learning and Artificial intelligence.

  • Students from NON-IT background like B.Com & MBA who wants to build a career in Business Analytics / Data Analytics / Data Science Project Management.
  • Any Engineering / BCA / MCA graduate who wants to build a career in Data Science.
  • IT and Computer Science Graduates who want to build a career in Data Science.
  • IT Professionals who wish to make a career shift to Data Sciences.
  • Data Science Professionals who wish to Upskill and become Data Architects.

There are no prerequisites as such to get on-board this program.

  • On a daily basis, over 5 billion consumers interact with data and that
    number will increase to 6 billion by 2025, representing
    three-quarters of the world’s population.
  • The Global Big Data Market is expected to reach $122 billion in revenue by
    2025 – Frost & Sullivan
  • Research shows 94% of data science graduates
    have gotten jobs in this field since 2011.
To reap out the benefits of this growing career opportunity,
embrace Data and secure your career as a Data Scientist by
getting certified with our 100% Job Guarantee Data Science Architect Program  Today!.

After completion of this course, the Learner will be eligible for any of the following Job Roles:

Advisor of Data Science

Dr. Subramanyam Chandrasekhar

Advisor - Data Science Department

Prof. S. Chandrasekhar is a senior Professor and Director of Business Analytics at IFIM Business School, Bengaluru, since November 2013.
He was Chair Professor (July 1998–Feb 2013) and officiating Director at FORE School of Management, New Delhi (April 2009–Jan 2010). He joined FORE School in 1988 as a Senior Professor. He also headed the Software Development wing at the FORE School of Management, which is currently developing prototype of products in Business Intelligence, Risk Management, and Customer Relations Management. He has a total of about 34 years of experience in R&D, Academics, and Industry proficiency in the area of Quantitative Techniques & IT. He has also taught Post Graduate level courses in the area of IT, QT, and Advanced Market Research.
Prior to this, he worked at the Indian Institute of Management, Lucknow for about ten years (1988–1998) as a professor in Computers & Information Systems. He was also a Member Secretary of lIM-L Governing Board for about three years.
He holds a Bachelor’s degree in Electrical Engineering, a Master’s degree in Computer Science from IIT, Kanpur. He received his doctorate (PhD)) in Quantitative & Information Systems from the University of Georgia, USA. He has worked in India, USA, and Canada in reputed organizations like TIFR, ISRO, NRSA, FORD Aerospace Corporation, and the National Research Council before joining IIM Lucknow.
Professor Chandrasekhar has conducted and provided many Management Development Programs in Predictive Analytics, Text Mining, and Machine Learning to senior executives in the tech Industry.

Head of Data Science

Laksh S.

Principal Data Scientist

An Analytical Thought Leader and AIML Practitioner, Mr. Santanam has a proven track record of enterprise-first behaviours and skills pertaining to Productivity, Quality, and Integrity.

Leadership experience:
With over 10+ Years of leadership learning, he has worked as a Profit Centre Head, managing various units, and a Gut Leader with Project Management and Product Development skills. He has immense experience working with an onsite-offsite business model. He has also had significant exposure to TOGAF, LEAF, Zachman frameworks, daily workflow design, and Interaction Model, and has been instrumental in helping key clients on their AIML Projects.

He also has hands-on experience in implementing MicroStrategy, Power Bl, Tableau, QlikView, Spotfire, Cognos, and Yellowfin.

Analytical Excellence:
Mr. Santanam has had hands-on experience working with Anaconda for Analytics, and has been the practice head for Process Automation Solutions using Al and ML tools and techniques, both on-cloud such as Amazon, Azure, GCP, IBM, and oracle cloud, and also on-premise Hortonworks, Cloudera suites. He has worked closely with Text, Vision, and speech and Developed AIDDA, A framework for controls enhancing assurance. He also has significant knowledge of Pattern recognition, Anomalies Detection, and behaviour analysis and is therefore the go to trainer for freshers who wish to build their Data science foundation and data visualization. He is a specialist in Risk, Risk driven problem-solving, and knowledge discovery around “Risk”, be it legal, operational, financial, industrial, and market.

Process Excellence:
Mr. Santanam is a Certified Six Sigma Black Belt, proficient in Lean and Six Sigma, ESSA, DFSS, DOE, DMIAC Concepts and Principles, and has considerable clout in elimination of waste, standardisation, simplification and automation. He has worked closely in Minitab16, PHStat, and SigmaXL.

Customer Excellence:
As a champion of Customer First Principles and Qualities, he has managed cross-domain stakeholders, handled overseas and Indian customers, travelled and transitioned 11 FTE of work from EU, APAC, and EMEA regions. He has also been the recipient of several Customer Champion and Customer Delight Awards throughout his career.

Tech Talks by Eminent Industry Experts

Mr. Havish Madhvapaty

Founder – Havish M Consulting

Mr. Priyank Ahuja

Product Manager – Accenture

Mr. Dhaval Thanki

Vice President, APAC, MEA  – LogiNext

Mr. Anand Gurumurthy

CEO and Founder – Cosmitude Softwares Pvt Ltd

Mr Sujit Sukumaran

Founder & CEO – Optimus Management Consultants, Dubai, United Arab Emirates

Dr.Rajashekhar Karjagi

Manager – Analytics Solutions & POC – Accenture

Mr. Mukesh Jain

Global CTIO “Data”, Global Head of Innovation & VP Insights & Data – Capgemini

Mr. Joshua Devadas

Data Science Manager – Accenture

Mr. Nitin Bhosekar

Head Advanced Analytics, Artificial Intelligence, Automation, Big Data, DW Practice & Executive Vice President – Aress Software

Mr. Utsab Chakraborty

Senior Analytics Manager (Asst. Director) – Flipkart

Major Dr. Alexander M John

Vice President, Operations – Foundation AI

Mr. Manoj Kumar Rajendran

Principal Data Scientist – MiQ Digital India

Mr. Vinayakaram Gururajan

Data Scientist – Tata Consultancy Services

Mr Shriram Vasudevan

Project Manager – L&T Technology Service Ltd

Mr Balaji Subudhi

Senior Data Scientist – Mojro

Mr. Anish Raj

Human Resources Director

Mrs. Keru Chen

Analytics Manager – Product Grab, Singapore

Mrs. Mithila Harish

Data Scientist Micron Technology, Singapore

Mr. Ravi Parthasarathy

CEO/Director People Plus Consultants, Singapore

Advisors

Learning Objectives: Business Intelligence vs Data Analysis vs Data Scientist

Data Scientist Roles

Different Disciplines of Data Science:

  • Machine Learning
  • Natural Language Processing
  • Deep Learning

Applications of Machine Learning

Why Machine Learning is the Future

  • Creating the “Hello World” code
  • Demonstrating Conditional Statements
  • Variables
  • Demonstrating Loops

What are Prerequisites for Data Science?

  • Statistics
  • Python Essentials for Data Science

  • Descriptive Statistics and Inferential Statistics
  • Sample and Population
  • Variables and Data types
  • Percentiles   
  • Measures of Central Tendency
  • Measures of Spread
  • Skewness, Kurtosis
  • Degrees of freedom
  • Variance, Covariance, Correlation
  • Descriptive statistics and Inferential Statistics in Python
  • Test of Hypothesis
  • Confidence Interval
  • Sampling Distribution
  • Standard Probability Distribution Functions
  • Bernoulli, Binomial-Distributions
  • Normal Distributions

  • Data Transformations
  • Outlier Detection and Management
  • Charts and Graphs
  • One Dimensional Chart
  • Box plots
  • Bar graph
  • Histogram
  • Scatter plots
  • Multi-Dimensional Charts
  •  

Learning Objectives: You will get a brief idea of what Python is and touch on the basics.

Topics:

  • Overview of Python
  • Different Applications where Python is used
  • Values, Types, Variables
  • Conditional Statements
  • Command Line Arguments
  • The Companies using Python
  • Discuss Python Scripts on UNIX/Windows
  • Operands and Expressions
  • Loops
  • Writing to the screen

Hands-On/Demo: 

  • Creating the “Hello World” code
  • Demonstrating Conditional Statements
  • Variables
  • Demonstrating Loops

Skills:

  • Fundamentals of Python Programming

Learning Objectives: In this module, you will learn how to create generic Python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

Topics:

  • Functions
  • Global Variables
  • Lambda Functions
  • Standard Libraries
  • The Import Statements
  • Package Installation Ways
  • Handling Multiple Exceptions
  • Function Parameters
  • Variable Scope and Returning Values
  • Object-Oriented Concepts
  • Modules Used in Python
  • Module Search Path
  • Errors and Exception Handling

Hands-On/Demo:

  • Functions - Syntax, Arguments, Keyword Arguments, Return Values
  • Sorting - Sequences, Dictionaries, Limitations of Sorting
  • Packages and Module - Modules, Import Options, Sys Path
  • Lambda - Features, Syntax, Options, Compared with the Functions
  • Errors and Exceptions - Types of Issues, Remediation

Skills:

  • Error and Exception Management in Python
  • Working with Functions in Python

Learning Objectives: Through this module, you will understand in-detail about Data Manipulation

Topics:

  • Basic Functionalities of a data object
  • Concatenation of Data objects
  • Exploring a Dataset
  • Merging of Data Objects
  • Types of Joins on data Objects
  • Analysing a Dataset

Hands-On/Demo:

  • Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
  • Aggregation
  • Merging
  • GroupBy operations
  • Concatenation
  • Joining

Skills:

  • Python in Data Manipulation

Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.

Topics:

  • Python Revision (Numpy, Pandas, scikit learn, matplotlib)
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Gradient Descent
  • What is Machine Learning?
  • Machine Learning Process Flow
  • Linear Regression

Hands-On/Demo:

  • Linear Regression – Boston Dataset

Skills:

  • Machine Learning Concepts
  • Linear Regression Implementation
  • Machine Learning Types

Learning Objectives: In this module, you will explore Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier, etc.

Topics:

  • What are Classification and its use cases?
  • Algorithm for Decision Tree Induction
  • Confusion Matrix
  • What is Decision Tree?
  • Creating a Perfect Decision Tree
  • What is a Random Forest?

Hands-On/Demo:

  • Implementation of Logistic Regression
  • Random Forest
  • Decision Tree

Skills:

  • Supervised Learning Concepts
  • Evaluating Model Output
  • Implementing different types of Supervised Learning Algorithms

Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing the LDA model.

Topics:

  • Introduction to Dimensionality
  • PCA
  • Scaling Dimensional Model
  • Why Dimensionality Reduction
  • Factor Analysis
  • LDA

Hands-On/Demo: 

  • PCA
  • Scaling

Skills: 

  • Implementing Dimensionality Reduction Technique

Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, 

Topics:

  • What is Naïve Bayes?
  • Implementing Naïve Bayes Classifier
  • Illustrate how Support Vector Machine works?
  • Grid Search vs Random Search
  • How Naïve Bayes works?
  • What is Support Vector Machine?
  • Hyperparameter Optimization
  • Implementation of Support Vector Machine for Classification

Hands-On/Demo:

  • Implementation of Naïve Bayes, SVM

Skills:

  • Supervised Learning Concepts
  • Evaluating Model Output
  • Implementing different types of Supervised Learning Algorithms

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyse the data.

Topics:

  • What is Clustering & its Use Cases?
  • How does the K-means algorithm work?
  • What is C-means Clustering?
  • How does Hierarchical Clustering works?
  • What is K-means Clustering?
  • How to do Optimal clustering
  • What is Hierarchical Clustering?

Hands-On/Demo:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering

Skills:

  • Unsupervised Learning
  • Implementation of Clustering – Various Types

 

Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with the Apriori Algorithm.

Topics:

  • What are Association Rules?
  • Calculating Association Rule Parameters
  • How do Recommendation Engines work?
  • Content-Based Filtering
  • Association Rule Parameters
  • Recommendation Engines
  • Collaborative Filtering

Hands-On/Demo:

  • Apriori Algorithm
  • Market Basket Analysis

Skills:

  • Data Mining using Python
  • Recommender Systems using Python

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyse the data.

Topics:

  • What is Reinforcement Learning?
  • Elements of Reinforcement Learning
  • Epsilon Greedy Algorithm
  • Q values and V values
  • α values
  • Why Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Markov Decision Process (MDP)
  • Q – Learning

Hands-On/Demo:

  • Calculating Reward
  • Calculating Optimal quantities
  • Setting up an Optimal Action
  • Discounted Reward
  • Implementing Q - Learning

Skills:

  • Implement Reinforcement Learning using Python
  • Developing Q - Learning model in Python

Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for Time Series Modeling such that you analyse a real time-dependent data for forecasting.

Topics:

  • What is Time Series Analysis?
  • Components of TSA
  • AR Model
  • ARMA Model
  • Stationarity
  • Importance of TSA
  • White Noise
  • MA Model
  • ARIMA Model
  • ACF & PACF

Hands-on/demo:

  • Checking Stationarity
  • Implementing the Dickey-Fuller Test
  • Generating the ARIMA plot
  • Converting a non-stationary data to stationary
  • Plot ACF and PACF
  • TSA Forecasting

Skills:

  • TSA in Python

Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.

Topics:

  • What is the Model Selection?
  • Cross-Validation
  • How Boosting Algorithms work?
  • Adaptive Boosting
  • The need for Model Selection
  • What is Boosting?
  • Types of Boosting Algorithms

Hands-On/Demo:

  • Cross-Validation
  • AdaBoost

Skills:

  • Model Selection
  • Boosting algorithm using Python

Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

Topics:

  • Python files I/O Functions
  • Strings and Related Operations
  • Lists and Related Operations
  • Sets and Related Operations
  • Numbers
  • Tuples and related operations
  • Dictionaries and related operations

Hands-On/Demo:

  • Tuple - properties, Related Operations, compared with a list
  • Dictionary - properties, related operations
  • List - properties, related operations
  • Set - properties, Related Operations

Skills:

  • File Operations using Python
  • Working with data types of Python

Learning Objectives: This module helps you get familiar with the basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualisation.

Topics:

  • NumPy - Arrays
  • Indexing Slicing and iterating
  • Pandas - Data Structures & Index Operations
  • Matplotlib Library
  • Markers, Colours, Fonts and Styling
  • Contour Plots
  • Operations on Arrays
  • Reading and Writing Arrays on Files
  • Reading and Writing Data from Excel/CSV formats into Pandas
  • Grids, Axes, Plots
  • Types of Plots - Bar Graphs, Pie Charts, Histograms

Hands-On/Demo:

  • NumPy Library- Creating NumPy array, operations performed on NumPy array
  • Matplotlib - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
  • Pandas Library- Creating series and data frames, Importing and exporting data

Skills:

  • Probability Distributions in Python
  • Python for Data Visualisation

This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are a few case studies, which are part of this course:

  • Case Study 1: Maple Leaves Ltd is a start-up company that makes herbs from different types of plants and leaves. Currently, the system they use to classify the trees that they import in a batch is quite manual. A labourer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process.
  • Case Study 2: BookRent is the largest online and offline book rental chain in India. The company charges a fixed fee per month plus rental per book. So, the company makes more money when the user rents more books. You are an ML expert and must model a recommendation engine so that the user gets a recommendation of books based on the behaviour of similar users. This will ensure that users are renting books based on their individual tastes. The company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User-Based vs Item Based. You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference.
  • Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with a random forest classifier. Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy.
  • Case Study 4: Principal component analysis using scikit learn. Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy. Using scikit learn to perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label.
  • Case Study 5: Handling GIS data and working with maps. Creating, cleaning, collating and visualizing maps of India at different levels – state, district, taluka, and villages. Using Geo Pandas, Mapviz, and Leaflet in Python to perform spatial analytics and visualizing statistics with geographical context. Using public data of government expenditure, identify the areas and districts with the highest expenditure per capita in different states and all over India.

Project #1: Industry: Social Media

  • Problem Statement: You, as ML expert, have to do analysis and modeling to predict the number of shares of an article given the input parameters.
  • Actions to be performed: Load the corresponding dataset. Perform data wrangling, visualisation of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimise your model to the fullest.

Project #2: Industry: FMCG

  • Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.
  • Actions to be performed: You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across the years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components that explain the max variance.

WHAT YOU WILL LEARN

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimisation in word2vec
  • Understand and implement GloVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis

WHAT YOU WILL LEARN

  • Build natural language processing systems using TensorFlow
  • Process text, including tokenisation and representing sentences as vectors 
  • Apply RNNs, GRUs, and LSTMs in TensorFlow 
  • Train LSTMs on existing text to create original poetry and more

  • Getting Started 

  • Knowing each other 

  • Welcome to the Course 

  • About the Course

  • Introduction to Natural Language Processing 

  • Exercise: Introduction to Natural Language Processing 

  • Podcast with NLP Researcher Sebastian Ruder

  • Installation steps for Linux

  • Installation steps for Mac 

  • Installation steps for Windows

  • Packages Installation 

  • Introduction to Python 

  • Variables and Operators 

  • Exercise: Variables and Operators 

  • Python Lists 

  • Exercise: Python Lists

  • Dictionaries 

  • Exercise: Dictionaries 
  • Conditional Statements 
  • Exercise: Conditional Statements 
  • Loops 
  • Exercise: Loops 
  • Functions 
  • Python Functions Practice 
  • Exercise: Functions 
  • Packages 
  • Exercise: Packages 
  • Files 
  • Exercise: Files

  • Welcome to Module 
  • Understanding Regular Expression 
  • Implementing Regular Expression in Python 
  • Exercise: Implementing Regular Expression in Python 
  • Regular Expressions in Action

  •  Welcome to Module 
  • Tokenization and Text Normalisation 
  • Exercise: Tokenisation and Text Normalisation 
  • Exploring Text Data 
  • Part of Speech Tagging and Grammar Parsing 
  • Exercise: Part of Speech Tagging and Grammar Parsing 
  • Implementing Text Pre-processing Using NLTK
  • Exercise: Implementing Text Pre-processing Using NLTK 
  • Natural Language Processing Techniques using spaCy

  • Welcome to Module 
  • Understanding Named Entity Recognition 
  • Exercise: Understanding Named Entity Recognition 
  • Implementing Named Entity Recognition
  • Exercise: Implementing Named Entity Recognition 
  • Named Entity Recognition and POS tagging using spaCy
  • POS and NER in Action: Text Data Augmentation 
  • Assignment: Share your learning and build your profile

  • Introduction to Text Feature Engineering 

  • Count Vector, TFIDF Representations of Text 

  • Exercise: Introduction to Text Feature Engineering 

  • Understanding Vector Representation of Text 

  • Exercise: Understanding Vector Representation of Text 

  • Understanding Word Embeddings 

  • Word Embeddings in Action - Word2Vec

  • Word Embeddings in Action - GloVe

  • Introduction to Text Cleaning Techniques Part 1 

  • Exercise: Introduction to Text Cleaning Techniques Part 1 

  • Introduction to Text Cleaning Techniques Part 2 

  • Exercise: Introduction to Text Cleaning Techniques Part 2 

  • Text Cleaning Implementation 

  • Exercise: Text Cleaning Implementation 

  • NLP Techniques using spaCy

  • Project I - Social Media Information Extraction

  • Introduction to Topic Modelling 
  • Exercise: Introduction to Topic Modelling
  • Understanding LDA 
  • Exercise: Understanding LDA 
  • Implementation of Topic Modelling 
  • Exercise: Implementation of Topic Modelling 
  • LSA for Topic Modelling

  • Understanding the Problem Statement 
  • Importing Dataset 
  • Text Cleaning and Pre-processing 
  • Categorising Articles using Topic Modelling

  • Types of Machine Learning Algorithms 
  • Logistic Regression 
  • Decision Tree 
  • Naive Bayes 
  • SVM (Support Vector Machine) 
  • Random Forest

  • Overview of Text Classification 
  • Exercise: Overview of Text Classification 
  • Assignment: Share your learning and build your profile

  • Getting started with Neural Network 
  • Exercise: Getting started with Neural Network 
  • Understanding Forward Propagation 
  • Exercise: Forward Propagation 
  • Math Behind forwarding Propagation 
  • Exercise: Math Behind forwarding Propagation 
  • Error and Reason for Error
  • Exercise: Error and Reason for Error 
  • Gradient Descent Intuition 
  • Understanding Math Behind Gradient Descent
  • Exercise: Gradient Descent 
  • Optimiser 
  • Exercise: Optimiser 
  • Back Propagation 
  • Exercise: Back Propagation 
  • Why Keras? 
  • Exercise: Why Keras? 
  • Building a Neural Network for Text Classification 
  • Why CNN? 
  • Exercise: Why CNN? 
  • Understanding the working of CNN Filters
  • Exercise: Understanding the working of CNN Filters 
  • Introduction to Padding 
  • Exercise: Introduction to Padding 
  • Padding Strategies 
  • Exercise: Padding Strategies 
  • Padding Strategies in Keras 
  • Exercise: Padding Strategies in Keras 
  • Introduction to Pooling 
  • Exercise: Introduction to Pooling 
  • CNN architecture and it's working 
  • Exercise: CNN architecture and it's working

  • Deep Learning for NLP Part 1 
  • Exercise: Deep Learning for NLP Part 1 
  • Deep Learning for NLP Part 2 
  • Exercise: Deep Learning for NLP Part 2 
  • Text Generation Using LSTM 
  • Exercise : Text Generation Using LSTM

  • Dataset download 
  • Text Cleaning
  • Feature Engineering 
  • Advanced Feature Engineering 
  • Combining Features
  • ML Classifier 
  • Spam Classification using Deep Learning

  • Project III

  • Overview of Auto-Tagging System 
  • Introduction to Dataset and Performance Metrics
  • Auto-Tagging Implementation Using Machine Learning Part-1 
  • Auto-Tagging Implementation Using Machine Learning Part-2 
  • Auto-Tagging Implementation Using Deep Learning

  • Why RNN
  • Introduction to RNN: Shortcomings of an MLP 
  • Introduction to RNN: RNN Architecture 
  • Training an RNN: Forward propagation 
  • Training an RNN: Backpropagation through time 
  • Need for LSTM/GRU 
  • Long Short Term Memory (LSTM) 
  • Gated Recurrent Unit (GRU) 
  • Project: Categorisation of websites using LSTM and GRU I 
  • Dataset and Notebook 
  • Project: Categorisation of websites using LSTM and GRU II

  • Overview: Language Modelling 
  • What is a Language Model in NLP? 
  • N-gram Language Model 
  • Implementing an N-gram Language Model - I 
  • Implementing an N-gram Language Model - II 
  • Neural Language Model
  • Implementing a Neural Language Model

  • Intuition Behind Sequence-to-Sequence Modelling 
  • Need for Sequence-to-Sequence Modelling 
  • Understanding the Architecture of Sequence-to-Sequence 
  • Understanding the Functioning of Encoder and Decoder 
  • Case Study: Building a Spanish to English Machine Translation Model 
  • Pre-processing of Text Data 
  • Converting Text to Integer Sequences 
  • Model Building and Inference

  • Introduction 
  • Pre-processing and Feature Creation 
  • Model Building and Summary Genera

  • Introduction 
  • About this module 
  • Overview of Conversational Agents 
  • Project - Foodbot 
  • Overview of Rasa Framework 
  • System Setup 
  • Rasa NLU: Understanding user intent from a message
  • Rasa NLU: Extracting intents from a user's message 
  • Rasa Core: Making your chatbot conversational 
  • Working with Zomato API 
  • Create a Workspace in Slack 
  • Deploying to Slack 
  • Assignment: Share your learning and build your profile

  • Getting started with Bonus Section 
  • Text Classification & Word Representations using FastText (An NLP library by Facebook)
  • Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library 
  • Introduction to Stanford NLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code) 
  • A Step-by-Step NLP Guide to Learn Elmo for Extracting Features from Text
  • Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python 
  • 8 Excellent Pretrained Models to get you started with Natural Language Processing (NLP) 
  • Geo-coding using NLP by Shantanu Bhattacharyya and Farhat Habib 
  • Demystifying the What, the Why and How of Chatbot by Sonny Laskar 
  • Sentiment Analysis using NLP and Deep Learning by Jeeban Swain 
  • Identifying Location using Clustering and Language Model - By Divya Choudhary 
  • Building Intelligent Chatbots from Scratch

This is another interesting machine learning project idea for data scientists/machine learning engineers working or planning to work with the finance domain. Stock prices predictor is a system that learns about the performance of a company and predicts future stock prices. The challenges associated with working with stock price data are that it is very granular, and moreover there are different types of data like volatility indices, prices, global macroeconomic indicators, fundamental indicators, and more. One good thing about working with stock market data is that the financial markets have shorter feedback cycles making it easier for data experts to validate their predictions on new data. To begin working with stock market data, you can pick up a simple machine learning problem like predicting 6-month price movements based on fundamental indicators from an organisations’ quarterly report. You can download Stock Market datasets from Quandl.com or Quantopian.com.

The smartphone dataset consists of fitness activity recordings of 30 people captured through smartphone-enabled with inertial sensors. The goal of this machine learning project is to build a classification model that can precisely identify human fitness activities. Working on this machine learning project will help you understand how to solve multi-classification problems. One can become a master of machine learning only with lots of practice and experimentation. Having theoretical surely helps but it’s the application that helps progress the most. No amount of theoretical knowledge can replace hands-on practice. There are many other machine learning projects for beginners like the ones mentioned above that you can work with. However, it will help if you familiarise yourself with the above-listed projects first. If you are a beginner and new to machine learning then working on machine learning projects designed by industry experts at DeZyre will make some of the best investments of your time. These machine learning projects have been designed for beginners to help them enhance their applied machine learning skills quickly whilst giving them a chance to explore interesting business use cases across various domains – Retail, Finance, Insurance, Manufacturing, and more. So, if you want to enjoy learning machine learning, stay motivated, and make quick progress then DeZyre’s machine learning interesting projects are for you. Plus, add these machine learning projects to your portfolio and land a top gig with a higher salary and rewarding perks.

From Netflix to Hulu, the need to build an efficient movie recommender system has gained importance over time with increasing demand from modern consumers for customised content. One of the most popular datasets available on the web for beginners to learn how to build recommender systems is the Movielens Dataset which contains approximately 1,000,209 movie ratings of 3,900 movies made by 6,040 Movielens users. You can get started working with this dataset by building a world-cloud visualisation of movie titles to build a movie recommender system.

This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are a few case studies, which are part of this course:

Case Study 1: Maple Leaves Ltd is a start-up company that makes herbs from different types of plants and leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process.

Build a system that can have a conversation with you. The user types messages and your system replies based on the user's text. Many approaches here ... you could use a large twitter corpus and do language similarity.

  • Flask basics
  • Deployment of the model on Heroku
  • AWS basics 
    •  S3 
    •  EC2 
    •  AWS Lambda 
  • Deployment of the model on EC2
  • Deployment on AWS Lambda (Optional)
  • Google Cloud Platform Basics
  • Deployment of the Model on GCP
  • Microsoft Azure basics
  • Deployment of the Model on Azure
  •  Pyspark Basics
  • DeVops Concepts

  • Data Visualization with Tableau & Power BI
  • SQL
  • MSOFFICE
  •  Introduction to Operational , HR, Finance, Marketing Analytics

  •  Leading Data Science Teams & Processes
  • Exploring Methodologies
  •  How to manage data science projects and lead a data science team
  •  Agile Data Science
  • Scrum Data Science
  •  Emerging Approaches – Microsoft TDSP
  • Data Science Methodology understanding
  • Business & Data understanding
  • Modelling & Evaluation
  • Plan Deployment
  • Data Science Project Report

  • Introduction to Bigdata & HDFS along with Linux concepts , its importance in data science
  • Core Components of Hadoop
  • HDFS Architecture
  • HDFS Commands

  • Apache Scoop Fundamentals & basics
  • Apache Spark fundamentals & advanced concepts and its importance in data science
  • Introduction to Kafka
  • Bigdata on Cloud and its importance in Data Science

Addon Syllabus

Hello, world!

Program Highlights

  • 24 Months Program Duration

  • 6 Months of live Interactive Classes by Principal or Senior Data Scientists across Industry

  • 18 Months of Sponsored Project Work at Authorized Research Centre funded by IISC, ISB, IIM

  • Gain 1+ years of Real Time Sponsored Project Experience Certificate from Recognized Research Centres

  • 100% Job Guarantee Program In collaboration with Top HR Consulting Firms across PAN INDIA

Our Data Science & AI Community

Real-Time Projects

Some of the industry verticals that we work closely with sponsor Funded Research Projects in collaboration with our principal data scientists, who are currently working with our authorized research centres.

Sentiment Analysis on Twitter data regarding 2020 Indian Elections

Analytics Industry

Study Finds that an AI Doctor Can Now Diagnose Diseases Better Than Your Doctor!

Health care/ Pharma Industry

Applying Artificial Neural Network to Predict Semiconductor Machine Outliers

Chip Design / Semiconductor Industry

Fault Tolerance & Redundant System with Seamless Integration to Development on AWS

Cloud Computing Industry

Robo-Advisors Common Place in the Financial Domain

Financial Services & Software

Fight Food Scarcity and Empower Small Farmers

Agriculture Sector

Simulating COVID-19 Infection in a Strategy Game

Gaming Industry

Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Switch Gear & Electrical Engineering

Predict the Student Achievement Rating of Public Schools?

Education Industry

Project Certificate

Data Science Architect Project Certification from Top MNCs

Complete the designated project to acquire this prestigious certification from reputed research centres.

Course Features

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Guidance from Corporate Specialists

Resolve all theory and project related queries from our with our mentors, who are eminent industry experts.

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Corporate Boost Camps

Participate in workshops and live webinars to understand technical terms and trends and make your learning reasonable.

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Peer Networking

Build a cohesive learning network with workmates, mentors, and experts to share ideas, address intra-queries, and resolve projects, and practical learning ambiguities.

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Placement Guarantee

Our one-of-a-kind learning program promises 100% Job Guarantee and prepares you to be industry ready and equipped with all the requisites for a successful career in Data Science Industry.

Tools Covered

Program Fees

₹ 2,78,000

Inclusive of 18% GST.

*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully  After course completion.

  • 24-Months Program Duration – 6 Months of live interactive classes – 18 Months of Sponsored Project Work
  • 320+ Hours Live Training Sessions
  • Gain 1.5 years of Real-Time Experience Certificate
  • Scholarships from day one up to Rs 2 lakhs
  • 50+ Sponsored Funded Research Projects
  • Get Certified from Top Companies
  • 100% Job Assistance Program

Apply Now

EMI Option Available

Tenure Interest % (Fixed p.a.)
12 Months
9% - 13%
18 Months
24 Months
36 Months
48 Months

We proudly partner with the safe, secure and best financial services in the industry.

Our Hiring Partners

Opening the doors to the best in the industry

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Frequently Asked Questions

Yes, We have weekend batches available for working professionals.

Our DSAP program encapsulate both theory and practical sessions where you can work on live on live industry projects. 

Yes, you can. In our online instructor-led training sessions, industry experts will train you with a group of  other  learners for 3420 + hours through conferencing  software like Zoom . Online Classes will be conducted weekday,  from Monday to Friday, and on weekends for working professionals.

Yes, you can pursue data science with fundamental coding ideas of C and C++.

Our 24/7 LMS support program is there to help you overcome any lags that you may experience and compensate for the missed lessons.

Yes, You can enrol with us without any programming or coding background. Our course is designed to a guide you through the the nuances of Data Science, beginning with basics of coding.

The courses come with a span of 3420 hours timeline. Course completion will   depend on your schedule and the type of batch you are enrolled into.

Yes, absolutely! We make sure your experience of learning with us is worth it
by assuring you proper carrier assistance. and guaranteed placements. We will guide you with:
1. Career expo
2. Resume building
3. Pre-placement training
4. Mock interviews
5. Shortlisting CVs  in collaboration with job portals

At GeekLurn, our Data Science Architect course comes with a placement guarantee. In fact you can start paying your EMIs after placement. We make this possible on account of the fact that the course has unique modules crafted in line with the latest industry trends. Being a member of NASSCOM & and an IBM partner, our corporate connect ensures our students are placed in top consulting firms among others.

With a robust course that has its modules crafted as per latest industry trends, a wide range of opportunities open up for freshers. Research data reveals that India recruits a high number of employees in the field of data science, second only to the United States. The demand for data experts span a wide range of industries including IT, Finance, Retail, E-commerce, Consulting & more. At GeekLurn we have a strong industry connect and we offer 100 percent job placements in collaboration with the top consulting firms to our fresh graduates.

Data Science opens up a wide range of opportunities across multiple industries all of which need to mine data into actionable insights for business growth. Whether it is Information Technology, Banking & Finance, Retail or Consulting, the demand for data science professionals is on the rise. After a robust data science course that is crafted in line with the latest industry trends you can look at building a career as a Business Analyst, Data Architect, Machine Learning Engineer and more.

  • Business and financial analytics
  • Bioinformatics
  • Health & pharma
  • Robotics & AI
  • Chip design
  • Cloud computing
  • Marketing analysis
  • Agriculture
  • Gaming
  • Education

At GeekLurn, we offer extremely robust courses that are in line with the latest industry trends and that keep you ahead of the curve. The courses come with the opportunity to conduct on ground research and also offer the opportunity to interact with eminent data scientists. With our strong industry connect we are able to offer 100 percent placement in collaboration with the top consulting firms across India.

The Data Science Architect Program at GeekLurn comes with robust modules that are in line with the latest industry trends and give you a definite edge. In addition the course offers you the opportunity to conduct on ground research and also interact with eminent data scientists. With GeekLurn’s strong industry connect, its membership with NASSCOM & IBM partnership, we offer 100 percent placement to our graduates.

Students are also provided career guidance and mentoring by industry professionals to face job interviews that GeekLurn provides through 400+ hiring partners and top HR companies in the industry.

For Queries, Feedback or Assistance

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Program Fees

₹ 2,78,000

Inclusive of 18% GST.

*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully  After course completion.

  • 24-Months Program Duration
    – 6 Months of live interactive classes
    – 18 Months of Sponsored Project Work
  • 320+ Hours Live Training Sessions
  • Gain 1.5 years of real time Experience Certificate
  • Scholarships from day one up to Rs 2 lakhs
  • 50+ Sponsored Funded Research Projects
  • Get Certified from Top Companies
  • 100% Job Assistance Program

Apply Now

Program Fees

₹ 2,78,000

Inclusive of 18% GST.

*100% MONEY BACK GUARANTEED

We assure you 100% money return in case of being not placed successfully  After course completion.

  • 24-Months Program Duration – 6 Months of live interactive classes – 18 Months of Sponsored Project Work
  • 320+ Hours Live Training Sessions
  • Gain 1.5 years of real-time Experience Certificate
  • Scholarships from day one up to Rs 2 lakhs
  • 50+ Sponsored Funded Research Projects
  • Get Certified from Top Companies
  • 100% Job Assistance Program

Apply Now