Book Free 1 on 1 session with our counsellors

Last date of application: 13/08/2020

Book Free 1 on 1 session with our counsellors

Last date of application: 13/08/2020

Learn from the Eminent Faculty of IIIT Allahabad

Lectures imparted by eminent faculty from IIIT Allahabad and Subject Matter Experts

Hands-on Practical Exposure

Programming languages and tools covered include Python, Scikit Learn, Tensor Flow etc.

Career Coaching & Job Search Support

3.5 Months Long Individual Career Coaching and Job Search Support

Capstone Project

A highly extensive project requiring the participants to apply all of their learning to practical use

6 Days of On-Campus Immersion Modules at IIITA, Prayagraj

Two Campus Immersion modules of 3 days each, at the commencement and culmination of the program

Practical Approach towards Learning

Course completely oriented towards imparting practical and application oriented knowledge through walkthroughs, demonstrations and class exercises to develop hands on skills

Certificate of Completion and Alumni Status of CSTCP-IIITA

On successful completion of the course, earn a Certificate of Completion and gain Alumni Status of CSTCP-IIIT Allahabad, Prayagraj

Details

IT Professionals from Various Domains – Developers, Software Engineers and Database Architects aspiring to gain expertise in the field of Data Science or Machine Learning

Analysts – Business and Data Analysts who are responsible to churn through and infer from large databases and handle big data based projects

Technology Enthusiasts –  Professionals seeking to gain a deeper understanding of ML algorithms

Working Professionals – Executives looking to embark on career in Machine Learning

  • For Indian Students: INR 142,500+ GST
  • For International Students: USD 2850
  • Instalment option available for all applicants
  • Course Commencement: 04 Oct 2020
  • Duration: 12 Months
  • Schedule of Classes: Sundays from 10.00 a.m. to 01.15 p.m. IST

 

Education

  • For Indian Participants – Graduates (10+2+3) or Diploma Holders (only 10+2+3) from a recognized university (UGC/AICTE/DEC/AIU/State Government) in any discipline
  • For International Participants – Graduation or equivalent degree from any recognized University or Institution in their respective country

Work Experience

  • 4 years of work experience

Pre-Requisites

  • Mathematics or Statistics as a subject in Class XII or Graduation
  • Formal education in or knowledge/experience of at least one programming language
  • This course is entirely hands-on and so it is recommended, though not a necessity that students have two devices (laptop/desktop) – one to follow the lecture, and the other for hands-on practice alongside during the class.

The delivery would comprise a judicious mix of live virtual lectures, discussions, case studies and experience sharing through peer discussions. The course design is oriented to facilitate learning through association of the various management concepts and its application in the business world. Across different modules, participants may be encouraged to apply or relate their in-class learning to live situations at work, peer learning therefore would be a key pillar of the process. Take-home projects may be assigned in certain modules.

All enrolled students will also be provided access to our SLIQ Cloud Campus through which they may access other learning aids, reference materials, assessments, case studies, projects and assignments as appropriate. Throughout the duration of the course, students will have the flexibility to reach out to the professors, real time during the class or offline via our SLIQ Cloud Campus to raise questions and clear doubts.

About Course

Organizations today operate in a world surrounded by data and data that is understood and analyzed smartly can play a pivotal role in determining the success of many businesses. Data Science, through its various inherent tools and techniques, is successfully adding value to all the business models by using statistics and deep learning to make better, relevant and timely decisions. Understanding the dynamics of data and knowing how to deal with data has therefore become a critical skill in an organizational context. No wonder that Harvard Business Review has termed Data Scientist as the Sexiest Job of the 21st Century!

The objective of this course is to introduce participants to the intricacies of data science and techniques of machine learning. This course will expose participants to hands on experience of popular and in-demand tools in the BDA and ML area and has been designed with an intention to impart practical problem solving skills to participants which in turn will enhance prospects of career growth in this sunrise domain.

Syllabus

MODULE 1 – FUNDAMENTALS OF PYTHON

Data scientists must know how to code – start by learning the fundamentals of one of the most popular programming languages – Python.

  • Basics of Python
  • Conditional and Loops
  • String and List Objects
  • Functions & OOPs Concepts
  • Exception Handling
  • Database Programming

 

MODULE 2 – DATA WRANGLING

Once you have the core skill of programming covered– dip your feet in the nitty – gritties of working with data by learning how to wrangle and visualize them.

  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & Pandas
  • Relational Databases and Data Manipulation with SQL
  • Scipy Libraries
  • Loading, Cleaning, Transforming, Merging, and Reshaping Data

 

MODULE 3 – STATISTICS AND PROBABILITY

It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.

  • Descriptive Statistics & Data Distributions
  • Probability Concepts and Set Theory
  • Probability Mass Functions
  • Probability Distribution Functions
  • Cumulative Distribution Functions
  • Modeling Distributions
  • Inferential Statistics
  • Estimation
  • Hypothesis Testing
  • Implementation of Statistical Concepts in Python

 

MODULE 4 – MACHINE LEARNING MODELS IN PYTHON

Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.

  • Building Models Using Below Algorithms
  • Linear and Logistics Regression
  • Decision Trees
  • Support Vector Machines (SVMs)
  • Random Forests
  • XGBoost
  • K Nearest Neighbour & Hierarchical Clustering
  • Principal Component Analysis
  • Text Analytics and Time Series Forecasting

 

MODULE 5 – DATA VISUALIZATION USING MATPLOTLIB

Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib.

  • Interactive Visualizations with Matplotlib

 

MODULE 6 – DEEP LEARNING USING TENSORFLOW

Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.

  • Basics of Neural Network
  • Linear Algebra
  • Implementation of Neural Network in Vanilla
  • Basics of TensorFlow
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Models
  • Semi-supervised Learning using GAN
  • Seq-to-seq Model
  • Encoder and Decoder

 

MODULE 7 – HANDLING BIG DATA WITH SPARK

Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.

  • Introduction to Big Data & Spark
  • RDD’s in Spark, Data Frames & Spark SQL
  • Spark Streaming, MLib & GraphX

 

CAPSTONE PROJECT

 

On CAMPUS COMPONENT

This program includes two on-campus components of 3 days each which will take place at IIIT Allahabad campus. On Campus session 1 will be held in early January 2021 and On Campus Session 2 will take place in early June 2021. The dates for On Campus sessions will be communicated in due course. Attendance to On Campus Component is mandatory for all participants of this course.

Assessment

Evaluation methodology is the discretion of the faculty. The methodology includes module based assessments, class contribution, capstone project and any other component as decided by the respective course faculties. Additionally, faculty may choose to conduct a pre-assessment prior to the commencement of certain modules, to ascertain participant readiness to progress further. Basis analysis of the pre-assessment, an individual participant may be required to undertake some refresher/self-study on certain basic areas to help better cope and comprehend the programme contents.

A minimum of 75% attendance to the live classes is a prerequisite for the successful completion of this program. The programme may require participants to work on individual/group assignments and/or projects. The main objective of such assignments/projects will be to help the participants apply their conceptual learning in the programme to actual/real life scenarios. Participants who successfully complete the evaluations and satisfy the requisite attendance criteria, will be awarded a certificate of completion. Participants who are unable to clear the evaluation criteria but have the requisite attendance will be awarded a participation certificate.

About Talentedge

Talentedge is an Ed-Tech firm. We are the first to bring ‘Live & Interactive’ anywhere learning in digital format. Jointly with world’s leading institutes and corporates, we offer courses to working professionals, enabling them to plan their future course of action and fast track their careers. We also partner with top Indian & International institutes including IIMs, XLRI, MICA, Jack Welch Management Institute (JWMI), London School of Business & Finance (LSBF) and also with top corporate names like Society of Human Resource Management (SHRM) and others. Our ability to re-create classroom-type interactions in the virtual world has struck a chord with over 4,50,000 individuals and corporate learners. By bringing eminent subject experts into online education, we initiate industry relevant learning. We are also one of the first Ed-Tech organizations to be credited with an ISO 9001:2008 certification.

About Institute

Established in 1999, as a center of excellence in Information Technology and allied areas. Institute was conferred the Deemed University status by Govt. of India in the year 2000.

In 2014 the IIIT Act was passed, under which IIITA and four other Institutes of Information Technology funded by the Ministry of Human Resource Development were classed as Institutes of National Importance. Recently, IIITA Prayagraj has instituted the Centre for Short Term Certification Programmes (CSTCP) with the primary aim of conducting online courses through suitable online platforms provided by various companies.

IIIT-Allahabad was ranked 119 in BRICS nation by the QS World University Rankings of 2019. Among government engineering colleges in India, IIIT-Allahabad ranked 10th by India Today in 2019 and 18 by Outlook India in 2019. It was ranked 82 among engineering colleges by the National Institutional Ranking Framework (NIRF) in 2019. IIIT-A is very famous for its placements and coding culture.

Faculty

Dr. Manish Kumar

Programme Co-ordinator

Dr. Manish Kumar has completed M.Tech(CS) from BIT Mesra and got Ph.D. degree from Indian institute of information Technology, Allahabad, Prayagraj, India on topic” Data Management in Wireless Sensor Networks. Presently he is working as an Associate Professor in Department of IT, IIIT-Allahabad. He is coordinator of research lab Data Analytics Laboratory (DAL) at IIIT-A. His research interest includes Data aggregation, data processing, inference, Compressive sensing in WSNs & big data analytics.

Prof. Shekhar Verma

Professor (IT)

Prof. Shekhar Verma received the Ph.D. degree in computer networks from the Indian Institute of Technology (BHU), Varanasi, India, in 1993. He is currently working as a Professor (IT) with the Indian Institute of Information Technology, Allahabad, India. His research interest includes machine learning, computer networks, wireless sensor networks, wireless networks, information and networks security, vehicular technology, and cryptography.

Dr. Pritish Kumar Varadwaj

Associate Professor

Dr. Pritish Kumar Varadwaj received his Ph.D. in Information Technology from the Indian Institute of Information Technology-Allahabad, India in the year 2009 and currently working as an Associate Professor at Indian Institute of Information Technology-Allahabad, India. His main research interests are in the field of Machine Learning & Big Data Analytics on Biological Data, Molecular Modelling, Cheminformatics and Stringology.

Dr. Satish Kumar Singh

Associate Professor

Dr. Satish Kumar Singh is with the Indian Institute of Information Technology Allahabad India, which is declared as an Institute of National Importance by Government of India as an Associate Professor at the Department of Information Technology from 2013. Before joining the IIIT Allahabad, he served the Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology Guna, India from 2005 to 2012. He is having about 14 years of academic and research experience in various capacities at JUET Guna and IIIT Allahabad. Presently he is heading the Computer Vision and Biometrics group and in-charge of the Computer Vision and Biometrics Lab (CVBL) at IIIT Allahabad from 2015 onwards. His group is greatly involved in the research and development of the Signal & Image Processing, Vision, and Biometrics algorithms, and system. His areas of interest include Image Processing, Computer Vision, Biometrics, Deep Learning, and Pattern Recognition.

Dr. Venkatesan Subramanian

Associate Professor

Dr. Venkatesan Subramanian is an Associate Professor in the Department of Information Technology, Indian Institute of Information Technology Allahabad. He received Ph.D. in 2010 from the Department of Computer Science and Engineering at Anna University, Chennai, India in the area of Mobile Agent Security. He has published more than 20 papers in reputed International Journals and Conferences. His current research interests includes Blockchain technology, Network and Information Security. His teaching interest includes Compiler Design, Computer Networks, Blockchain and Cryptocurrency and Database Security.

Dr. Neetesh Purohit

Associate Professor

Dr. Neetesh Purohit is an Associate Professor at IIITA Prayagraj. His research and development work in the fields of wireless communications, ICT and DSP has significant involvement of the principles of statistics.

Dr. Krishna Pratap Singh

Associate Professor

Dr. Krishna Pratap Singh is an Associate Professor at Department of Information Technology, Indian Institute of Information Technology Allahabad. Prior to this he worked as an Assistant Professor (2013-2018), and Lecture (2009-2012) at IIIT Allahabad. He did PhD & Master from IIT Roorkee in 2009 & 2004 respectively. He is heading Machine Leaning & Optimization Lab at IIITA. His teaching & research interests are Machine learning, representation learning, Transfer Learning & Optimization.

Get In Touch