Fellowship
7 Months | Full Time | On Campus | Bengaluru | Chennai | Coimbatore
1 month Online Pre work + 3 months ML Boot Camp + 3 months Paid Internship
Program fees sponsored by Industry Partners.
The Fellowship program with the focus on AI/ML is a full time, in-house, 1+3+3 months program that turns any candidate with thorough knowledge in mathematics and statistics; with or without experience into a Machine Learning Engineer/Data Scientist. The program fee of Rs. 4.65 lacs inclusive of taxes is fully sponsored by Industry Partners for fellows. Those who don’t get selected as fellows can join the program as a scholar. The scholarship will be determined based on the selection score.
Fellows are immersed with hands-on experience in a wide range of ML concepts, techniques and their application in different domains.. At the completion of the program GradValley Fellows will be equipped to understand problem statement, analyse, develop and deliver solutions on their own, leveraging the hands-on learning and developed potential of AI/ML skills to address real-life situations and social issues.
After a highly-stringent vetting process, prospective applicants are matched to industry use cases and paired with in-house and external mentors from Industry. One of the Fellowship’s goal is to bridge the opportunity gap by giving access to AI education to “potential” candidates in tier 2 areas or from communities for whom the affordability matters. On the other hand, to meet the supply and demand gap of highly trained and skilled Data Science Professionals or ML Engineers that the global market is facing right now in this data economy.
We believe that the program will provide an opportunity, environment and encourage more people to take up Data Science as their career path, specifically, AI/ML. It is positive that fellows/scholars who successfully complete the program are equipped to lead innovation in AI/ML.
“2019 Passout Applicants” – Please apply for June 2019 Cohort. Application for June 2019 cohort opens in February 2019.
The GradValley Fellowship Edge
Program Designed for Purpose
An intensive, 1-month Online prework + 3 months ML Boot camp + 3 months paid internship to hone your data science skills and capabilities.
Mentoring
Get mentored by subject-specific faculty, domain experts and by industry veterans.
Be In-Demand, Not On-Demand
Always be in-demand on your new career in the Big Data field with introductions to companies across India.
Project Portfolio On Industry Problems
Build your portfolio through projects and internships on real-world problems.
Community and Network
Be part of the expert Data Science community and network that is created as part of the program.
Fellow’s Day
Display your project portfolio and professional strengths to hiring managers and business leaders.
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Apply
Apply for the GradValley Data Science Fellowship Program. We accept 15 aspirants per cohort as fellows/scholars for those who are looking for an entry, change their career, or boost their salaries to work with startups and corporates in Big Data and Data Science.
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Enroll
There will be screening and selection rounds for GradValley Fellows. The first round will be a Data Science Entrance Test (DSET) and the second round will be a case study challenge that may include coding and a personal interview with the internal team and an external selection panel. We encourage prospective fellows to be relaxed, genuine and be their authentic self during the entire selection process.
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Boot Camp
We immerse Fellows in concepts, tools, and technicals for eight weeks. Beyond that, there will be sessions on project lifecycle management and career workshops. We have daily scrums, and are very diligent about it. We have internal slack channels, shared GitHub repos, and JIRA Software. There is a weekly retrospective and iteration planning and we spend 50% on data wrangling, 40% on modelling, and the remaining time on communicating results to business clients.
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Match
While the boot camp is underway, fellows will interface with the startups and the corporates via our networks. There will be a portal that handles all of the online match-makings such as creating fellow profiles, viewing company profiles, and marking those companies that are of interest. We review all of the matches and schedule an online interaction or in-person interview with the matched companies. Fellows will do the live project from week 9 -12 with the matched companies and may continue beyond 12 weeks depending on the project.
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Internship
In continuation of the live project, the fellows continue to work as a paid intern for three months (mandatory) on actual machine learning projects that may be used in production environments. They work under the supervision of the in-house and external mentors who are actively involved in the delivery of projects at their businesses. External mentors may or may not be included in the projects that the fellows are working on. Fellows get an opportunity to interact directly with the project owners and get immediate feedback on their results. Besides, each fellow will lead, participate and conduct three data science workshops and three mini code days or hackathons.
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Fellow's Day
This four-week project and the internship determines the direct hiring opportunity for the project owners. At the end of the six months, on "Fellow’s Day,” Fellows will showcase their project results to the project owners or hiring companies. The startups and corporates willing to hire or partner with fellows to work on their projects may sponsor the cost of the program. There are no direct or hidden costs for the candidates selected as fellows.
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Requirements
Please apply even if you do not fit all of the required elements but find what you will need to become a Fellow!
Applicant must hold a Master’s degree / PhD; or any engineering / Professional degree. Math as primary or ancillary subjects or part of their curriculum and fundamental statistical knowledge in UG or PG. Basic Programming/Coding skills necessary in languages like C, C++, Python, R, Java, SQL etc.,
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Selection Schedule
The GradValley Fellowship program aims for careful selection of learners who have the apt skill-set that can be enhanced. The tentative schedule is listed below. The actual time and date will be communicated during the selection process.
Online Data Science Entrance Test (DSET) - 15th, 16th, 22nd & 23rd of December 2018
| MODULES | TOPICS | LEARNING OUTCOMES | PROJECT | |||
|---|---|---|---|---|---|---|
| WEEK 1 Introduction to ML |
Introduction to Machine Learning concepts Approach to solve a machine learning problem Scikit Learn Simple Linear Regression GitHub ( Creating repo, Uploading codes to repo, Cloning, Pull requests ) Mendley (maintaining library of your research papers)s |
On Week One, the fundamentals like types of data, machine learning foundations, basic applications of Machine Learning is taught. The learners are trained to explore the GitHub and use it regularly for the upcoming weeks. The learners are taught to be proactive in their learning and to explore more on the Data science concepts on their own |
Implementing matrix multiplications from scratch using numpy only. Visualising Linear regression in a real time manner. |
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| Career Session on Industry Trends: Data Science | ||||||
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WEEK 2 Logistic Regression, Activation Functions |
Advanced Regression concepts Solving Classification Problem using Logistic Regression Activation functions such as tanh, Relu, Leaky Relu |
At the end of week two, the learners will be able to understand the advanced regression concepts and apply the same on different use cases. Different activation functions to be introduced in this week. Implementation of different functions with the technical insights behind each is to be understood. |
Implementing Logistic regression classifier to classify between two images without using any sklearn library from scratch. Predicting movies for customers using KNN algorithm. Other related use case to implement SVM and Random Forests. |
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WEEK 3 Supervised Learning |
Conditional probability and Bayes theorem Naïve Bayes Algorithm KNN Decision Trees & Ensemble Learning SVM |
It is the week of supervised learning where both the classification and regression use cases in the banking domains are solved here with the application of algorithms like Naïve Bayes Classifier, K-Nearest Neighbours, Random Forest, Adaboost, Xgboost, and Support Vector Machines |
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WEEK 4 Time Series Modeling |
Trend analysis Moving average method (MA) Least square method AutoRegression (AR) Autoregressive Moving Average (ARMA) Autoregressive integrated Moving Average (ARIMA) Autocorrelation Function (ACF) Partial Autocorrelation Function (PACF) |
To predict a variable associated with time elements such as sales, demand, revenue, profit, etc, time series concepts are taught to the learners during this week using the Python SciPy ecosystem. Also the learners will be working on the use case of predicting the monthly sales of French Champagne using time series models. |
Related use case for the above problems Timesheet analytics for employees in an organisation. Classifying timesheets for all employees. Predicting tasks for an employee for the day. Analyse time spent on a categorised task on daily, weekly and monthly basis. |
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| Career Session on Resume preraration | ||||||
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WEEK 5 Natural Language Processing |
Natural Language Processing - Regex, Spacy, Textblob and NLTK. Web scraping with BeautifulSoup and Selenium Word representations - BOW, TF, TF-IDF, ngrams, embedding matrices ( Word2vec, Glove etc ) |
Natural Language Processing will be the theme for week six. The learners will be taught to gather the unstructured data in the form of text using BeautifulSoup and Selenium. Also the learners will be involved in solving a use case of analysing the text using WordNet as well as the sentiments behind the texts with help of NLP libraries like NLTK and Regex. |
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WEEK 6 Unsupervised Learning |
K-means Clustering Anamoly Detection Hierarchical clustering DBSCAN Dimensionality Reduction |
This is an unsupervised week after getting a hang of the supervised learning algorithms earlier. During this week, the learners are made to explore various use cases with the application of the below algorithms like K-means clustering, hierarchical clustering, DBSCAN, etc. Also, one of the machine learning core concepts, dimensionality reduction is covered with the relevant use case. |
Related use case based on the above algorithms. Implementing a simple logistic regression model using any framework and tuning it using the deep learning framework tools. Implementing Architectures on standard datasets. CIFAR, MNIST, CONLL etc |
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WEEK 7 Introduction to Neural Networks |
Introduction to Neural Networks ( Single neuron, Activation Functions, Loss Functions, Multiple layer perceptron, Representations in terms of numpy ) Deep Learning: necessity, evolution, Frameworks Hands-on in any one framework ( Tensorflow/ Pytorch) |
During this week, the learners will be introduced to the deep learning concepts using TensorFlow. The learners will be involved in the use cases of classifying images using CNN and text prediction using RNN. Also, the learners will be using Tensorboard to visualize the graphs and plotting the quantitative metrics. |
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WEEK 8 Deep Learning Architectures |
Convolution Neural Network CNN for texts and images Recurrent Neural Network Hyperparameters ( Learning Rate, Regularisation, Data Augmentation, Batch Normalisation, Dropouts, Activation functions, Loss functions, Momentum, weight Decay, Vanishing gradients etc) GRU, LSTMs, BiLSTMs Alexnet, Resnet, VGG etc |
Deep Learning Architectures will be the subject for this week. Fellows will dive deep into how these architectures are designed, how they function, tuning the architectures etc. |
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WEEK 9-12 RStacking Neural Networks |
Stacked Neural Networks | Siamese neural networks, Auto-Encoders | Generative adversarial networks (GANs) | Reinforcement Learning |
On this final phase, the learners will be working on a real time project. The real time project the learners indulge in may be based on their own interest or it can be project assigned to them by the hiring partners according to the business requirement. Also the learners will have to attend few instructional sessions on Spark during the week of nine. |
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| Career Session on Mock Interviews | ||||||
| 3 Months Internship Begins | ||||||
MODULES
WEEK 1Introduction to MLb
TOPICS
Introduction to Machine Learning concepts
Scikit Learn
Simple Linear Regression
GitHub
LEARNING OUTCOMES
On Week One, the fundamentals like types of data, machine learning foundations, basic applications of Machine Learning is taught. The learners are trained to explore the GitHub and use it regularly for the upcoming weeks. The learners are taught to be proactive in their learning and to explore more on the Data science concepts on their own
Career Session on Industry Trends: Data Science
MODULES
WEEK 2Advanced Regression and Business Intelligence
TOPICS
QlikSense
Tableau
Advanced Regression concepts
Solving Classfication Problem using Logistic Regression
LEARNING OUTCOMES
At the end of week two, the learners will be able to understand the advanced regression concepts and apply the same on different use cases. An insurance use case which is a classification problem is solved in this week by applying Logistic Regression. The learners will also have sessions on Business Intelligence and Visualization tools like QlikSense and Tableau and will be able to understand, analyze and visualize the data better easily.
PROJECT
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Career Session on Industry Trends: Data Science
MODULES
WEEK 3Supervised Learning
TOPICS
Conditional probability and Bayes theorem
Naïve Bayes Algorithm
KNN
Decision Trees & Ensemble Learning
SVM
LEARNING OUTCOMES
It is the week of supervised learning where both the classification and regression use cases in the banking domains are solved here with the application of algorithms like Naïve Bayes Classifier, K-Nearest Neighbours, Random Forest, Adaboost, Xgboost, and Support Vector Machines
PROJECT (WEEK 2 & WEEK 3)
House Price Prediction
Most of the people dream about buying a house. And everyone aspirations differ from person to person and the houses too differ from each other according to their attributes. Identifying what is real cost of the house according to the market trends is difficult for both the buyer as well as the seller.
For the first three weeks, the learners will be working on this real time use case in the Real estate domain. Initially the learners will be collecting the data, clean and prep the data, handle the missing values, predict the prices of the house based on their properties by applying regression algorithms. Also the learners will be predicting whether the property is hot in the house market or not using classifiction algorithms
MODULES
WEEK 4Time Series Modeling
TOPICS
Trend analysis
Moving average method (MA)
Least square method
AutoRegression (AR)
Autoregressive Moving Average (ARMA)
Autoregressive integrated Moving Average (ARIMA)
Autocorrelation Function (ACF)
Partial Autocorrelation Function (PACF)
LEARNING OUTCOMES
To predict a variable associated with time elements such as sales, demand, revenue, profit, etc, time series concepts are taught to the learners during this week using the Python SciPy ecosystem. Also the learners will be working on the use case of predicting the monthly sales of French Champagne using time series models.
Career Session on Resume preraration
MODULES
WEEK 5Natural Language Processing
TOPICS
Natural Language Processing - Regex and NLTK
Web scraping with BeautifulSoup and Selenium
LEARNING OUTCOMES
Natural Language Processing will be the theme for week six. The learners will be taught to gather the unstructured data in the form of text using BeautifulSoup and Selenium. Also the learners will be involved in solving a use case of analysing the text using WordNet as well as the sentiments behind the texts with help of NLP libraries like NLTK and Regex.
PROJECT (WEEK 4 & WEEK 5)
Sentiment Analysis
When the data is structured in the form of databases, it is easy for anyone to analyse and predict the results. The going gets tough when we try to analyse the text data and we have to do it as one cannot read all the text in the whole world. Almost all the business domains have this problem but this issue affects the retail industry more.
During this phase, the learners will be working on the e-commerce use case where the learners will be scraping the data, clean and process the text, apply the relevant algorithm and build a model to identify the sentiments behind the reviews using NLP and unsupervised learning techniques.
MODULES
WEEK 6Unsupervised Learning
TOPICS
K-means Clustering
Anamoly Detection
Hierarchical clustering
DBSCAN
Dimensionality Reduction
LEARNING OUTCOMES
This is an unsupervised week after getting a hang of the supervised learning algorithms earlier. During this week, the learners are made to explore various use cases with the application of the below algorithms like K-means clustering, hierarchical clustering, DBSCAN, etc. Also the one of the machine learning core concept dimensionality reduction is covered with the relevant use case.
MODULES
WEEK 7Introduction to Deep Learning
TOPICS
Introduction to Neural Networks
Convolution Neural Network
Recurrent Neural Network
LEARNING OUTCOMES
During this week, the learners will be introduced to the deep learning concepts using TensorFlow. The learners will be involved in the use cases of classifying images using CNN and text prediction using RNN. Also the learners will be using Tensorboard to visualize the graphs and plotting the quantitative metrics.
MODULES
WEEK 8Big Data Framework with Hadoop
TOPICS
Introduction to Big data frameworks and Hadoop
Big data tools - Hive, Pig, HBase, ZooKeeper, Scoop, Flume, Kafka, and Oozie.
Use case analysis using Map Reduce concepts and Hadoop
LEARNING OUTCOMES
Now the learners will be able exposed to Big Data Framework and Ecosystem on Hadoop and MapReduce concepts. In this week the learners will understand the challenges in handling big data and will be able to work with the big data tools like Hive, Pig, HBase, ZooKeeper, Scoop, Flume, Kafka, and Oozie.
PROJECT (WEEK 6, WEEK 7 & WEEK 8)
During this phase, the learners will be working on this CT Scan images where they will be processing the image files, developing the deep learning algorithms to find the critical issues in the images and validate the same using CNN algorithm.
This real time project will make the learners understand and develop the automated screening and diagnosis systems in the field of Health Care which is the need of the hour
MODULES
WEEK 9-12Real Time Project
LEARNING OUTCOMES
On this final phase, the learners will be working on a real time project. The real time project the learners indulge in may be based on their own interest or it can be project assigned to them by the hiring partners according to the business requirement. Also the learners will have to attend few instructional sessions on Spark during the week of nine.
PROJECT (WEEK 6, WEEK 7 & WEEK 8)
During this phase, the learners will be working on this CT Scan images where they will be processing the image files, developing the deep learning algorithms to find the critical issues in the images and validate the same using CNN algorithm.
This real time project will make the learners understand and develop the automated screening and diagnosis systems in the field of Health Care which is the need of the hour
Career Session on Mock Interviews
GradValley is a Data Science research institution that focuses and converges on its core principles; Innovate and empower through education and entrepreneurship. With the scientific principles and framework of Big Data, GradValley will drive excellence in training the students and members of the workforce in data science through its innovative pedagogy and intensive curriculum that are designed and delivered in collaboration with world-class faculty and industry.
A GradValley Fellow is sponsored by a specific employer organization seeking to expand leadership in their field. It is awarded to prospective candidates, on the basis of their academic or research achievements.
The GradValley Fellowship program has been designed to support:
Rigorous graduate study in a specific field
Research to advance work on a problem statement
Training to support the Fellow’s growth
Provide opportunities to be a skilled data scientist who could understand a problem, assess and able to provide solution.
In the current scenario we are witnessing the struggle of talented people to find jobs upon the completion of their Graduation / Masters / PhDs. They are deliberately waiting for the opportunity to excel and specialize themselves in a specific domain.
From Fellows perspective: GV Fellowship hand pick candidates and provides them with the holistic knowledge, skills, opportunity and mentorship necessary to create a body of work, that is business-relevant; making them "in demand" for businesses looking for experts in the field.
From business perspective: GV Fellowship adds value primarily by saving time & training cost, mitigating hiring risk and creates a no-lose proposition for our partner businesses. We believe this generates real economic value and a win-win situation that allows everyone(Fellow, Partnering business & GradValley) to succeed.
Applicant must hold a Master’s degree / PhD; or any engineering / Professional degree.
Math as primary or ancillary subjects or part of their curriculum and fundemental statistical knowledge in UG or PG.
Basic Programming/Coding skills necessary in languages like C, C++, Python, R, Java, SQL etc.,
February 2019
1 Month of online pre work, 3 Months ML Boot camp followed by 3 months mandatory paid internship.
They will be working for live projects, with the partnering companies. In addition to that, its mandatory for fellows to lead and conduct Data Science workshops, hackathons or developer code days as part of the professional development during the internship period.
Depending upon the partnering company & the projects and the Fellow's proficiency, the stipend could be upto Rs. 30000/month.
Our program aims to build developing key skills in the areas like AI/ML.
On completion of 3 months ML Boot camp + 3 months of paid internship, the Fellows will be placed by GradValley Career Services.
The program will be considered complete upon the successful completion of the following:
3 Months ML Boot camp and
3 Months mandatory paid internship.
- The course fee will be completely sponsored by the industry partners.
- Intensive and exhaustive Learning in Data Science primarily focusing AI & ML
- Minimum 3 real time projects
- Lead and conduct 3 Data Science workshops (On various topics)
- Lead and conduct 3 coding hackathons
- 3 months of paid internship upto Rs.30,000/-
- Potential hiring by industry partners
Fellows may choose their domain during their final project and depending on the industry partners. We expect industry partners from healthcare, technology (IoT), e-commerce (retail), manufacturing and finance domains. We have mentors from industry to guide you in these domains.
The selected Fellows will have to provide their 100% commitment to the program by signing a mutual pact with GradValley, must attend all the program activities throughout the duration of the program. Failing to do so will result in elimination of the Fellow from the Fellowship Program, would also undoubtedly invite financial penalty. Along with that, Fellows would lose their chance to become a Fellow in future, rather they could only able to join as Scholar by paying the full program fee.
This Fellowship is Full Time. It requires a full-time on campus presence & commitment, Monday through Saturday.
Yes. You will be eligible for the program, provided satisfying the following conditions:
Having aptitude for the subjects like Maths, probability, statistics Exposure/experience in programming languages, scripting, or statistical packages.
We don't however have any strong preferences about academic discipline as far as they can think data. Fellows are even accepted from backgrounds as diverse as Anthropology, Political Science, and Sociology, as well as Mathematics, Physics, Chemistry, and many others.
If you have the urge for the above, you may be the perfect candidate for our Fellowship program.
GradValley will issue a Certificate of Completion (Fellow in AI/ML).
Absolutely! Candidates with industry experience are welcome to apply, as long as they have the requisite qualification for our Fellowship with a strong background in probability, statistics, and experience with programming.
Once Selected, the link for the Data Science Entrance Test (DSET) preparation materials will be shared via email.
Note: That materials are just references, Data Science Entrance Test (DSET) may includes additional topics.
We accept only a very small number of applicants as Fellows (upto 15). Another option to join the program would be as Scholars who would enjoy all the benefits of the Fellows except that Scholar have to pay the program fee. Terms and Conditions apply which would be informed during the course of the program.
All applications will be screened. Only shortlisted candidates will be invited for a Data Science Entrance Test (DSET) designed to check aptitude, quantitative, reasoning abilities, statistical & basic programming skills.
Applicants who qualify the written test will be invited for a case study challenge and personal interview by the Fellowship granting committee.
Based on the recommendation from the Fellowship Granting committee, the selected Fellows will be notified for Admission Process.
Selection of the candidate for the Fellowships is at the discretion of the Fellowship committee.
This Fellowship Program is an intensive, on campus, full time program. This program has been designed primarily to skill you with advanced AI & ML Concepts in a more application oriented way with the related use cases which would give more understanding on the fundamental concepts. As the program would be intensive, in order to help the prospective candidates, we will provide resources for references which will be assessed before commencement of the Fellowship Program. During the course of the program the fellows will get lot of opportunities to work on real time product viable projects which would make them job ready while doing the paid internship. Also during the Paid Internship period you will get chances to lead and conduct hackathons and data science workshops.
Yes it is important to have basic knowledge in coding at least C, C++,Python.
Statistics is the first step for Data Science and its applications. Hence it is mandatory that Statistical knowledge is required.
Python, Machine Learning Tools and Libraries, Big Data framework Tools, Data Visualization Tools
The prework assessment is mandatory.
This prework will prepare you very well for the upcoming Fellowship if you are selected either as a fellow/scholar.
Basic Mathematics
Fundamentals of Statistics
Basic Python etc.,
You will need your computer, your brain, and urge to learn. Your computer needs to have at least 4GB RAM, 2GHz, and a 100 GB HD. If you are a Windows user and your computer is fairly powerful, you could run a Linux Virtual Machine inside your normal Windows which is already installed. This requires some configuration.
Project are assigned based on mutual preferences expressed by both the Fellows and the companies participating in the Fellowship, however not a mandate. A networking event is organised at the beginning of each Fellowship with the aim of finding a good match for each Fellow.
Selected candidates will be notified for the Data Science Entrance Test (DSET) within a week.
We are not directly involved in finding accommodation for our Fellows. We are more than happy to recommend websites to help you in your search.
Yes, must apply only through Gradvalley Career services.
The titles vary from business, industry and roles. Generally we expect the roles to be Junior Data Scientist or Machine Learning Engineer.
GradValley Fellows are expected to be interviewed by many companies or recruiters towards the end of the Fellowship, that is at the end of six months. We expect all candidates to be placed within 6 months by the program completion depending on their performance. We will make sure that our Fellows are one of the best candidates in the market.
The program is sponsored by industry partners and the employers sponsoring the candidate will have the upper hand. During the program we have industry specific domain mentors to identify the domain interest of the fellows and GradValley will guide to match the interest of the fellows and the available opportunities. Further, if the fellow is not showing interest to join the company they have been recruited or sponsored, fellow would have to pay the program fee and GradValley will help (not assured) to find the next job.
It would be depending on the partnering / hiring companies. GradValley does not create any commitment bond on behalf of the prospective employers.
Depends on the hiring employer and your performance. GradValley will protect fellows interest and try identify the pay scale that they deserve in a fair market.
Yes.
Yes. Information will be shared during the program.
The program has been designed in such a manner that the Fellow will get placement as there are continuous career guidance sessions incorporated throughout the program. GradValley also would arrange Fellow Day / Campus Interviews to showcase the talent of Fellows to the prospective employers. Not to mention, one should understand that the placement also depends on the fellows continuous performance during the program and in the interviews. Minimum 5 interviews will be arranged.
Fellowship is 100% free of charge to fellows. This is possible because of the generous support of our partnering and hiring organizations.
You will know depending on your performance during the program.
Doing the 3 month internship after the first 3 months of boot camp is the mandatory step towards completing the Fellowship program.
Partnering companies including but not limited to recruiting firms and consulting firms, academia, product companies and research organizations.
Salary in Bangalore city ranges from 4 lacs to 20 lacs/annum, and median ML Engineer salary for Bangalore City is seven lacs/annum. You can expect the median wage of 7 lacs if you are at the entry level and successfully graduate from the program. However, your performance and past work experience determine the pay package. Graduating from the program requires either two publications or a minimum viable product or PoC for the business partners or a contribution to open source.
For the first cohort, the live use cases are provided by
1) Ideas2it (Data Science, Blockchain and IoT services company on predictive analytics in procurement.
2) Sense7ai, a Silicon Valley-based startup working on accelerating Drug Development processes. We are lined up with use cases from more Drug Development, HR, Food Service Industries and Hospitals.
For the second cohort, we expect a batch size of 15 -20, both fellows and scholars. The number of fellowships is determined by the cutoff score for the specific cohort. The rest will be offered as Scholars.
NO. This program is for ANYONE who is GOOD at MATH and can PROGRAM or CODE.
NOT FOR THIS COHORT. This is an intensive 7-months FULL TIME program and DOES NOT ALLOW any BREAK.
NO. All part-time programs and MOOCs can only teach or train you on fundamentals. This program is DESIGNED for anyone who is looking forward to DEEPDIVE into MACHINE LEARNING/Data Science. This program CANNOT be COMPARED to any PART TIME or even academic MASTERS programs.
In general, we DO NOT RECOMMEND. However, the DECISION may depend on multiple FACTORS. It may worth if you fall into any of these scenarios.
1) If you are a developer or in any other domain for many years and now looking for a CHANGE from the monotonous job.
2) You can AFFORD to QUIT a job and have no major personal and financial commitments 3) You are HUNGRY to upskill and ready to make a career shift from what you are doing right now.
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Apply
Apply for the GradValley Data Science Fellowship Program. We accept 15 aspirants per cohort as fellows/scholars for those who are looking for an entry, change their career, or boost their salaries to work with startups and corporates in Big Data and Data Science.
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Enroll
There will be screening and selection rounds for GradValley Fellows. The first round will be a Data Science Entrance Test (DSET) and the second round will be a case study challenge that may include coding and a personal interview with the internal team and an external selection panel. We encourage prospective fellows to be relaxed, genuine and be their authentic self during the entire selection process.
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Boot Camp
We immerse Fellows in concepts, tools, and technicals for eight weeks. Beyond that, there will be sessions on project lifecycle management and career workshops. We have daily scrums, and are very diligent about it. We have internal slack channels, shared GitHub repos, and JIRA Software. There is a weekly retrospective and iteration planning and we spend 50% on data wrangling, 40% on modelling, and the remaining time on communicating results to business clients.
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Match
While the boot camp is underway, fellows will interface with the startups and the corporates via our networks. There will be a portal that handles all of the online match-makings such as creating fellow profiles, viewing company profiles, and marking those companies that are of interest. We review all of the matches and schedule an online interaction or in-person interview with the matched companies. Fellows will do the live project from week 9 -12 with the matched companies and may continue beyond 12 weeks depending on the project.
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Internship
In continuation of the live project, the fellows continue to work as a paid intern for three months (mandatory) on actual machine learning projects that may be used in production environments. They work under the supervision of the in-house and external mentors who are actively involved in the delivery of projects at their businesses. External mentors may or may not be included in the projects that the fellows are working on. Fellows get an opportunity to interact directly with the project owners and get immediate feedback on their results. Besides, each fellow will lead, participate and conduct three data science workshops and three mini code days or hackathons.
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Fellow's Day
This four-week project and the internship determines the direct hiring opportunity for the project owners. At the end of the six months, on "Fellow’s Day,” Fellows will showcase their project results to the project owners or hiring companies. The startups and corporates willing to hire or partner with fellows to work on their projects may sponsor the cost of the program. There are no direct or hidden costs for the candidates selected as fellows.
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Requirements
Please apply even if you do not fit all of the required elements but find what you will need to become a Fellow!
Applicant must hold a Master’s degree / PhD; or any engineering / Professional degree. Math as primary or ancillary subjects or part of their curriculum and fundamental statistical knowledge in UG or PG. Basic Programming/Coding skills necessary in languages like C, C++, Python, R, Java, SQL etc.,
-
Selection Schedule
The GradValley Fellowship program aims for careful selection of learners who have the apt skill-set that can be enhanced. The tentative schedule is listed below. The actual time and date will be communicated during the selection process.
Online Data Science Entrance Test (DSET) - 15th, 16th, 22nd & 23rd of December 2019
| WEEK 1 Introduction to ML |
| TOPICS |
Introduction to Machine Learning concepts Scikit Learn Simple Linear Regression GitHub |
|---|
| LEARNING OUTCOMES |
On Week One, the fundamentals like types of data, machine learning foundations, basic applications of Machine Learning is taught. The learners are trained to explore the GitHub and use it regularly for the upcoming weeks. The learners are taught to be proactive in their learning and to explore more on the Data science concepts on their own |
| Career Session on Industry Trends: Data Science |
| WEEK 2 Advanced Regression and Business Intelligence |
| TOPICS |
QlikSense, Tableau, Advanced Regression concepts, Solving Classfication Problem using Logistic Regression |
|---|
| LEARNING OUTCOMES |
At the end of week two, the learners will be able to understand the advanced regression concepts and apply the same on different use cases. An insurance use case which is a classification problem is solved in this week by applying Logistic Regression. The learners will also have sessions on Business Intelligence and Visualization tools like QlikSense and Tableau and will be able to understand, analyze and visualize the data better easily. |
| WEEK 3 Supervised Learning |
| TOPICS |
Conditional probability and Bayes theorem Naïve Bayes Algorithm KNN Decision Trees & Ensemble Learning SVM |
| LEARNING OUTCOMES |
It is the week of supervised learning where both the classification and regression use cases in the banking domains are solved here with the application of algorithms like Naïve Bayes Classifier, K-Nearest Neighbours, Random Forest, Adaboost, Xgboost, and Support Vector Machines. |
| PROJECT (WEEK 2 & WEEK 3) |
House Price Prediction Most of the people dream about buying a house. And everyone aspirations differ from person to person and the houses too differ from each other according to their attributes. Identifying what is real cost of the house according to the market trends is difficult for both the buyer as well as the seller. For the first three weeks, the learners will be working on this real time use case in the Real estate domain. Initially the learners will be collecting the data, clean and prep the data, handle the missing values, predict the prices of the house based on their properties by applying regression algorithms. Also the learners will be predicting whether the property is hot in the house market or not using classifiction algorithms |
| WEEK 4 Time Series Modeling |
| TOPICS |
Trend analysis, Moving average method (MA) Least square method AutoRegression (AR) Autoregressive Moving Average (ARMA) Autoregressive integrated Moving Average (ARIMA) Autocorrelation Function (ACF) Partial Autocorrelation Function (PACF) |
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| LEARNING OUTCOMES |
To predict a variable associated with time elements such as sales, demand, revenue, profit, etc, time series concepts are taught to the learners during this week using the Python SciPy ecosystem. Also the learners will be working on the use case of predicting the monthly sales of French Champagne using time series models. |
| Career Session on Resume preraration |
| WEEK 5 Natural Language Processing |
| TOPICS |
Natural Language Processing - Regex and NLTK Web scraping with BeautifulSoup and Selenium |
| LEARNING OUTCOMES |
Natural Language Processing will be the theme for week six. The learners will be taught to gather the unstructured data in the form of text using BeautifulSoup and Selenium. Also the learners will be involved in solving a use case of analysing the text using WordNet as well as the sentiments behind the texts with help of NLP libraries like NLTK and Regex. |
| PROJECT (WEEK 4 & WEEK 5) |
Sentiment Analysis When the data is structured in the form of databases, it is easy for anyone to analyse and predict the results. The going gets tough when we try to analyse the text data and we have to do it as one cannot read all the text in the whole world. Almost all the business domains have this problem but this issue affects the retail industry more. During this phase, the learners will be working on the e-commerce use case where the learners will be scraping the data, clean and process the text, apply the relevant algorithm and build a model to identify the sentiments behind the reviews using NLP and unsupervised learning techniques. |
| WEEK 6 Unsupervised Learning |
| TOPICS |
K-means Clustering Anamoly Detection Hierarchical clustering DBSCAN Dimensionality Reduction |
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| LEARNING OUTCOMES |
This is an unsupervised week after getting a hang of the supervised learning algorithms earlier. During this week, the learners are made to explore various use cases with the application of the below algorithms like K-means clustering, hierarchical clustering, DBSCAN, etc. Also the one of the machine learning core concept dimensionality reduction is covered with the relevant use case. |
| WEEK 7 Introduction to Deep Learning |
| TOPICS |
Introduction to Neural Networks Convolution Neural Network Recurrent Neural Network |
| LEARNING OUTCOMES |
During this week, the learners will be introduced to the deep learning concepts using TensorFlow. The learners will be involved in the use cases of classifying images using CNN and text prediction using RNN. Also the learners will be using Tensorboard to visualize the graphs and plotting the quantitative metrics. |
| WEEK 8 Big Data Framework with Hadoop |
| TOPICS |
Introduction to Big data frameworks and Hadoop Big data tools - Hive, Pig, HBase, ZooKeeper, Scoop, Flume, Kafka, and Oozie. Use case analysis using Map Reduce concepts and Hadoop |
| LEARNING OUTCOMES |
Now the learners will be able exposed to Big Data Framework and Ecosystem on Hadoop and MapReduce concepts. In this week the learners will understand the challenges in handling big data and will be able to work with the big data tools like Hive, Pig, HBase, ZooKeeper, Scoop, Flume, Kafka, and Oozie. |
| PROJECT (WEEK 6, WEEK 7 & WEEK 8) |
Critical Findings From Head CT Scan Data During this phase, the learners will be working on this CT Scan images where they will be processing the image files, developing the deep learning algorithms to find the critical issues in the images and validate the same using CNN algorithm. This real time project will make the learners understand and develop the automated screening and diagnosis systems in the field of Health Care which is the need of the hour |
| WEEK 9-12 Real Time Project |
On this final phase, the learners will be working on a real time project. The real time project the learners indulge in may be based on their own interest or it can be project assigned to them by the hiring partners according to the business requirement. Also the learners will have to attend few instructional sessions on Spark during the week of nine. |
| Career Session on Mock Interviews |
GradValley is a Data Science research institution that focuses and converges on its core principles; Innovate and empower through education and entrepreneurship. With the scientific principles and framework of Big Data, GradValley will drive excellence in training the students and members of the workforce in data science through its innovative pedagogy and intensive curriculum that are designed and delivered in collaboration with world-class faculty and industry.
A GradValley Fellow is sponsored by a specific employer organization seeking to expand leadership in their field. It is awarded to prospective candidates, on the basis of their academic or research achievements.
The GradValley Fellowship program has been designed to support:
Rigorous graduate study in a specific field
Research to advance work on a problem statement
Training to support the Fellow’s growth
Provide opportunities to be a skilled data scientist who could understand a problem, assess and able to provide solution.
In the current scenario we are witnessing the struggle of talented people to find jobs upon the completion of their Graduation / Masters / PhDs. They are deliberately waiting for the opportunity to excel and specialize themselves in a specific domain.
From Fellows perspective: GV Fellowship hand pick candidates and provides them with the holistic knowledge, skills, opportunity and mentorship necessary to create a body of work, that is business-relevant; making them "in demand" for businesses looking for experts in the field.
From business perspective: GV Fellowship adds value primarily by saving time & training cost, mitigating hiring risk and creates a no-lose proposition for our partner businesses. We believe this generates real economic value and a win-win situation that allows everyone(Fellow, Partnering business & GradValley) to succeed.
Applicant must hold a Master’s degree / PhD; or any engineering / Professional degree.
Math as primary or ancillary subjects or part of their curriculum and fundemental statistical knowledge in UG or PG.
Basic Programming/Coding skills necessary in languages like C, C++, Python, R, Java, SQL etc.,
February 2019
1 Month of online pre work, 3 Months ML Boot camp followed by 3 months mandatory paid internship.
They will be working for live projects, with the partnering companies. In addition to that, its mandatory for fellows to lead and conduct Data Science workshops, hackathons or developer code days as part of the professional development during the internship period.
Depending upon the partnering company & the projects and the Fellow's proficiency, the stipend could be upto Rs. 30000/month.
Our program aims to build developing key skills in the areas like AI/ML.
On completion of 3 months ML Boot camp + 3 months of paid internship, the Fellows will be placed by GradValley Career Services.
The program will be considered complete upon the successful completion of the following:
3 Months ML Boot camp and
3 Months mandatory paid internship.
- The course fee will be completely sponsored by the industry partners.
- Intensive and exhaustive Learning in Data Science primarily focusing AI & ML
- Minimum 3 real time projects
- Lead and conduct 3 Data Science workshops (On various topics)
- Lead and conduct 3 coding hackathons
- 3 months of paid internship upto Rs.30,000/-
- Potential hiring by industry partners
Fellows may choose their domain during their final project and depending on the industry partners. We expect industry partners from healthcare, technology (IoT), e-commerce (retail), manufacturing and finance domains. We have mentors from industry to guide you in these domains.
The selected Fellows will have to provide their 100% commitment to the program by signing a mutual pact with GradValley, must attend all the program activities throughout the duration of the program. Failing to do so will result in elimination of the Fellow from the Fellowship Program, would also undoubtedly invite financial penalty. Along with that, Fellows would lose their chance to become a Fellow in future, rather they could only able to join as Scholar by paying the full program fee.
This Fellowship is Full Time. It requires a full-time on campus presence & commitment, Monday through Saturday.
Yes. You will be eligible for the program, provided satisfying the following conditions:
Having aptitude for the subjects like Maths, probability, statistics Exposure/experience in programming languages, scripting, or statistical packages.
We don't however have any strong preferences about academic discipline as far as they can think data. Fellows are even accepted from backgrounds as diverse as Anthropology, Political Science, and Sociology, as well as Mathematics, Physics, Chemistry, and many others.
If you have the urge for the above, you may be the perfect candidate for our Fellowship program.
GradValley will issue a Certificate of Completion (Fellow in AI/ML).
Absolutely! Candidates with industry experience are welcome to apply, as long as they have the requisite qualification for our Fellowship with a strong background in probability, statistics, and experience with programming.
Once Selected, the link for the Data Science Entrance Test (DSET) preparation materials will be shared via email.
Note: That materials are just references, Data Science Entrance Test (DSET) may includes additional topics.
We accept only a very small number of applicants as Fellows (upto 15). Another option to join the program would be as Scholars who would enjoy all the benefits of the Fellows except that Scholar have to pay the program fee. Terms and Conditions apply which would be informed during the course of the program.
All applications will be screened. Only shortlisted candidates will be invited for a Data Science Entrance Test (DSET) designed to check aptitude, quantitative, reasoning abilities, statistical & basic programming skills.
Applicants who qualify the written test will be invited for a case study challenge and personal interview by the Fellowship granting committee.
Based on the recommendation from the Fellowship Granting committee, the selected Fellows will be notified for Admission Process.
Selection of the candidate for the Fellowships is at the discretion of the Fellowship committee.
This Fellowship Program is an intensive, on campus, full time program. This program has been designed primarily to skill you with advanced AI & ML Concepts in a more application oriented way with the related use cases which would give more understanding on the fundamental concepts. As the program would be intensive, in order to help the prospective candidates, we will provide resources for references which will be assessed before commencement of the Fellowship Program. During the course of the program the fellows will get lot of opportunities to work on real time product viable projects which would make them job ready while doing the paid internship. Also during the Paid Internship period you will get chances to lead and conduct hackathons and data science workshops.
Yes it is important to have basic knowledge in coding at least C, C++,Python.
Statistics is the first step for Data Science and its applications. Hence it is mandatory that Statistical knowledge is required.
Python, Machine Learning Tools and Libraries, Big Data framework Tools, Data Visualization Tools
The prework assessment is mandatory.
This prework will prepare you very well for the upcoming Fellowship if you are selected either as a fellow/scholar.
Basic Mathematics
Fundamentals of Statistics
Basic Python etc.,
You will need your computer, your brain, and urge to learn. Your computer needs to have at least 4GB RAM, 2GHz, and a 100 GB HD. If you are a Windows user and your computer is fairly powerful, you could run a Linux Virtual Machine inside your normal Windows which is already installed. This requires some configuration.
Project are assigned based on mutual preferences expressed by both the Fellows and the companies participating in the Fellowship, however not a mandate. A networking event is organised at the beginning of each Fellowship with the aim of finding a good match for each Fellow.
Selected candidates will be notified for the Data Science Entrance Test (DSET) within a week.
We are not directly involved in finding accommodation for our Fellows. We are more than happy to recommend websites to help you in your search.
Yes, must apply only through Gradvalley Career services.
The titles vary from business, industry and roles. Generally we expect the roles to be Junior Data Scientist or Machine Learning Engineer.
GradValley Fellows are expected to be interviewed by many companies or recruiters towards the end of the Fellowship, that is at the end of six months. We expect all candidates to be placed within 6 months by the program completion depending on their performance. We will make sure that our Fellows are one of the best candidates in the market.
The program is sponsored by industry partners and the employers sponsoring the candidate will have the upper hand. During the program we have industry specific domain mentors to identify the domain interest of the fellows and GradValley will guide to match the interest of the fellows and the available opportunities. Further, if the fellow is not showing interest to join the company they have been recruited or sponsored, fellow would have to pay the program fee and GradValley will help (not assured) to find the next job.
It would be depending on the partnering / hiring companies. GradValley does not create any commitment bond on behalf of the prospective employers.
Depends on the hiring employer and your performance. GradValley will protect fellows interest and try identify the pay scale that they deserve in a fair market.
Yes.
Yes. Information will be shared during the program.
The program has been designed in such a manner that the Fellow will get placement as there are continuous career guidance sessions incorporated throughout the program. GradValley also would arrange Fellow Day / Campus Interviews to showcase the talent of Fellows to the prospective employers. Not to mention, one should understand that the placement also depends on the fellows continuous performance during the program and in the interviews. Minimum 5 interviews will be arranged.
Fellowship is 100% free of charge to fellows. This is possible because of the generous support of our partnering and hiring organizations.
You will know depending on your performance during the program.
Doing the 3 month internship after the first 3 months of boot camp is the mandatory step towards completing the Fellowship program.
Partnering companies including but not limited to recruiting firms and consulting firms, academia, product companies and research organizations.
Salary in Bangalore city ranges from 4 lacs to 20 lacs/annum, and median ML Engineer salary for Bangalore City is seven lacs/annum. You can expect the median wage of 7 lacs if you are at the entry level and successfully graduate from the program. However, your performance and past work experience determine the pay package. Graduating from the program requires either two publications or a minimum viable product or PoC for the business partners or a contribution to open source.
For the first cohort, the live use cases are provided by
1) Ideas2it (Data Science, Blockchain and IoT services company on predictive analytics in procurement.
2) Sense7ai, a Silicon Valley-based startup working on accelerating Drug Development processes. We are lined up with use cases from more Drug Development, HR, Food Service Industries and Hospitals.
For the second cohort, we expect a batch size of 15 -20, both fellows and scholars. The number of fellowships is determined by the cutoff score for the specific cohort. The rest will be offered as Scholars.
NO. This program is for ANYONE who is GOOD at MATH and can PROGRAM or CODE.
NOT FOR THIS COHORT. This is an intensive 7-months FULL TIME program and DOES NOT ALLOW any BREAK.
NO. All part-time programs and MOOCs can only teach or train you on fundamentals. This program is DESIGNED for anyone who is looking forward to DEEPDIVE into MACHINE LEARNING/Data Science. This program CANNOT be COMPARED to any PART TIME or even academic MASTERS programs.
In general, we DO NOT RECOMMEND. However, the DECISION may depend on multiple FACTORS. It may worth if you fall into any of these scenarios.
1) If you are a developer or in any other domain for many years and now looking for a CHANGE from the monotonous job.
2) You can AFFORD to QUIT a job and have no major personal and financial commitments 3) You are HUNGRY to upskill and ready to make a career shift from what you are doing right now.
Applications for August 2018 cohort is closed.
Please stay tuned for the next cohort announcement.
