Why Data Science, AI-ML & Deep Learning ?

Technology is continuously improving with time to get our lives better. Deep learning, natural language processing, and computer vision are examples of technologies that have emerged as a result of the rise of Data Science as a field of research and practical applications in the industry. In general, it has aided the development of Machine Learning (ML) as a means of achieving Artificial Intelligence (AI) & Deep Learning (DL).

JG University enables professionals and enterprises to succeed in the fast-changing digital economy. The University’s Blended Learning curriculum combines self-paced Classroom Interactions, Hands-on projects, Capstone projects and Industry Internship with 24/7 global teaching assistance.

Below are some of the researches shared by global market research survey organisations regarding the trends for Data Science, AI-ML & Deep Learning skills in the industries

Opportunities

The US Bureau of Labour Statistics (BLS) predicted approximately 11.5 million employment worldwide by 2026

According to AIM research, 47.1 percent growth of jobs in India from June 2020 to June 2021

As per Glassdoor Economic Research survey, Data Scientists and AI-ML professionals are amongst the Top 10 best jobs globally for 2021 with a high level of job satisfaction and salary.

4 Lakh jobs are vacant in Data Science, AI-ML and Big Data roles according to NASSCOM.

The world will notice a deficit of 2,30,000 Data Science professionals by 2021.

As per Harvard Business Review, ‘Data Scientist is the most glamorous job of 21st century.

Industry Remuneration

Salary In India

The average salary varies from ₹ 7,00,000 to ₹ 24,00,000 per annum.

Salary In US

The average salary varies from 66,000 USD to 200,000 USD per annum.

Who Can Apply?

Programme Overview

Objectives

  • Familiarise participants with the basics, problem-solving and learning methods using Data Science, AI-ML & Deep Learning technologies along with their impact on the growth of the industries
  • Detailed understanding in these technologies with maximum Hands-on experience and the capstone projects related to industries
  • Leverage the skills and potential to be world-class industry-ready professionals
  • Impart a complete understanding of Data Science, AI-ML & Deep Learning technology concepts to build a successful career in these technologies
AI

Outcomes

Learn to use the fundamental principles of mathematics, statistical analysis, computing science using data sets, AI-ML, and mobile technologies to design, analyse and develop a solution for specific industry problems

Learn to use research-based knowledge and research methods including design of experiments, analysis, interpretation of data, and synthesis of the information to provide valid conclusions

Ability to develop an algorithm to solve complex problems in industries and conduct investigations of complex computing problems

Integrate data from diverse sources and transform the data to generate meaningful outcomes to get business insights

Equip participants with the ever-changing market trends

Course Commencement

Delivery Mode

Offline

duration

Programme Duration

12 Months

Scholarship Fee

Area of Study

Data Science, AI-ML & Deep Learning

Job Opportunities

  • Data Scientist
  • Senior Behavioural Science Manager
  • AI & Data Science Specialists
  • Programme Information Adviser
  • Senior Business Analyst – Product Team
  • Senior Analytical Manager
  • Senior Data Engineer
  • Data Architects
  • Head of Data-as-a-Service
  • Business Intelligence Developer
  • Data Science Analyst
  • Business Intelligence Analyst
  • Business Intelligence Manager
  • Research Associate in Statistical Machine Learning

Key Industry Verticals Data science, AI-ML & Deep Learning is prominent

Government

Government and public sector services

Manufacturing and Production

Manufacturing and Production Industry

Information Technology

Information Technology, ITeS

agriculture

Agriculture

Sports

Sports

Banking and Finance

Banking and Finance

Hospitality

Hospitality

Educational

Educational sectors

Logistics

Transportation, Logistics & Supply Chain

Entertainment

Entertainment

Programme Outline

Module-1 - Introduction to Data Science, AI-ML & Deep Learning

  • Introduction to Data Science, Artificial Intelligence, Machine Learning and Deep Learning fundamentals and understanding of their benefits to the industries. Discussion on case studies for deeper understanding

Module-2 - R & Python programming Basics

  • Developing the skills of R & Python programming, along with Hands-on experience. This knowledge will be useful for developing logic in Data science, AI-ML and Deep learning applications

Module-3 - Statistics for Data Science

  • Various statistical models such as T-Test, Anova, Bayes theorem, Standard Distribution, Chi-Square Test to analyse the data which are useful for getting the business insights by using industry data to overcome the problems
  • Apply the acquired learning with Hands-on session

Module-4 Advance Statistics for Data Science

  • Understand the data better to prescribe and predict the future by applying advance statistical methods such as descriptive statistics, Correlation analysis, Z Test, IQR Kurtosis and Skewness which are useful for making business-critical decisions
  • Apply the acquired learning with Hands-on session

Module-5 (A) - Working with NumPy

  • Working with NumPy libraries to perform a wide variety of mathematical and scientific calculations. This creates a foundation for building Machine Learning algorithms
  • Apply the acquired learning with Hands-on session

Module-5 (B) - Working with Pandas

  • Understand the working of Pandas, an open-source Python package, which is used for data science/data analysis and machine learning projects
  • Apply the acquired learning with Hands-on session

Module-6 - Data visualization using Power BI / Tableau

  • Knowledge of data visualisation techniques and the use of Power BI / Tableau. Participants can develop an analytical dashboard for management in an organisation
  • Apply the acquired learning with Hands-on session

Module-7 - Linear Regression

  • Understanding of technologies such as Predictive Equation, Gradient Descent Algorithm, OLS Approach, R2, MAPE, and RMSE to predict the value of variables
  • This knowledge is useful for the prediction of relationship between variables and forecasting in Machine Learning
  • Apply the acquired learning with Hands-on session

Module-8 - Logistic Regression

  • Understanding of mathematical modelling, Sigmoid function, Confusion Matrix Analysis, SKLearn, F1 Score, etc. to understand the relationship between the dependent and independent variables by estimating probabilities using a logistic regression equation
  • This type of analysis can help you predict the likelihood of an event happening or a choice being made
  • Apply the acquired learning with Hands-on session

Module-9 - KNN & Decision Tree

  • Understanding of KNN techniques such as distance matrix, regression and classification, over-fitting and under-fitting, etc. for Supervised Learning
  • Knowledge of mathematics forming Decision Tree, Entropy & Gini Entropy Approach, Variance, visualization using graph-viz with Hands-on
  • Both methods are used to develop Machine Learning models

Module-10 - SVM & Ensemble Learning

  • Understanding of concepts and working principles, mathematical modelling, Slack Variable, Kernel method and Non-linear Hyperplanes to solve both classification and regression problems using SVM
  • Understanding of concepts, bagging and boosting, Random Forest, Gradient Boosting Trees, XGBoost, AdaBoost, etc. to solve both classification and regression problems using Ensemble Learning
  • Apply the acquired learning with Hands-on session

Module-11 - Unsupervised learning

  • Understanding of K Means Clustering, Hierarchical Clustering, Customer Segmentation, Dimensionality Reduction, Data Compression, Multicollinearity, Factor analysis to develop an unsupervised learning model
  • The main goal of unsupervised learning is to discover hidden and interesting patterns in unlabeled data
  • Apply the acquired learning with Hands-on session

Module-12 - Time-series prediction

  • Understanding of Simple and Weighted Moving Average method, Single/Double/Triple Exponential Smoothing method, ARIMA models, etc. for Time series prediction
  • It involves building models through historical analysis and using them to make observations and derive future strategic decision-making
  • Apply the acquired learning with Hands-on session

Module-13- Principle component analysis and Anomaly detection

  • Understanding of Curse of Dimensionality, Multicollinearity, Factor analysis.
  • Anomaly detection using Moving Average Filtering Mean, Standard Deviation, Statistical approach, Isolation Forest, One Class SVM, etc
  • Principal component analysis (PCA) simplifies the complexity of high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features
  • Apply the acquired learning with Hands-on session

Capstone Project-1 based on the above Learning

Module-14 - Image processing with OpenCV

  • Understanding the concept of Image processing /acquisition/ manipulation/ scaling, Video processing, Edge detection, Corner detection, Face detection, Object detection using OpenCV
  • Apply the acquired learning with Hands-on session

Module-15 - Natural language processing

  • Understanding of NLTK and Textblob, Tokenization, Stemming and Lemmatization, TF-IDF, Count Vector, Sentiment Analysis using Google, Bing, IBM Speech to Text API
  • Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks
  • Apply the acquired learning with Hands-on session

Module-16 - Recommendation System

  • Understanding of popularity, content, collaborative based filtering to suggest the relevant items to users
  • Apply the acquired learning with Hands-on session

Module-17 - Working with TensorFlow and Theano

  • Understanding of Tensor Board, Linear & Logistic regression and data manipulation using Tensor flow
  • Learning to work with Theano and building Linear & Logistic regression with Theano
  • An open-source software library to carry out numerical computation using data flow graphs, the base language for TensorFlow is C++ or Python, whereas Theano is a completely Python based library that allows users to define, optimize and evaluate mathematical expressions evolving multi-dimensional arrays efficiently
  • Apply the acquired learning with Hands-on session

Module-18 - Deep learning introduction and Convolutional Neural Network

  • CNN is a Neural Network that has one or more convolutional layers and is used mainly for image processing, classification, segmentation and also for other autocorrelated data
  • Learning of CNN Architecture, Convolution process, Maths behind CNNs, MaxPooling, Efficient Convolution Algorithms, Neuroscientific basis for Convolutional Networks, implementing CNN using Keras and Digit classification
  • Apply the acquired learning with Hands-on session

Module-19 - Recurrent Neural Networks

  • Recurrent neural networks (RNN) are a class of Neural Networks that are helpful in modelling sequence data
  • Understanding the basic concept of RNN, Vanishing and Exploding gradient problems, LSTM Networks, LSTM for NLP, Word Embedding, Text Classification, Stochastic Encoders and Decoders, etc
  • RNNs exhibit similar behaviour to how human brains function and produce predictive results in sequential data that other algorithms can't
  • Apply the acquired learning with Hands-on session

Module-20- Artificial neural network

  • Understanding of Single Layer Perceptron Model, Multilayer and Feed Forward Neural Network, Cost Function Formation, Backpropagation Algorithm
  • Artificial Neural Networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data
  • Apply the acquired learning with Hands-on session

Module-21 - Neural network revisiting

  • Understanding of activation functions for Neural Networks, Optimization techniques- SGD, ADAM, LBFGS, Momentum in Neural Networks, Softmax classifier and ReLU classifier, Deep Neural Networks
  • Neural networks reflect the behaviour of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of Artificial Intelligence, Machine Learning and Deep Learning
  • Apply the acquired learning with Hands-on session

Capstone Project-2 based on the above Learning

Faculty

sathish-narayanan

Mr. Sathish Narayanan Programme Director

Mr. Sathish Narayanan is a visionary leader with 23+ years of notable contribution in the entire gamut of delivering executive-level consulting to service & manufacturing organizations. Mr. Narayanan’s areas of expertise are Supply chain COE, Manufacturing Excellence – ZERO Loss/ ZERO Waste/ ZERO Inefficiencies, Organizational Excellence – People Capability/Talent Development/ Coaching, Data Analysis – Minitab/ Value stream mapping, DNA – Distributor Network Analysis/ Network Design and more. Mr. Narayanan has also been rewarded with the prestigious Shell “Role Model Award”, “LSS Leadership Award”, and Wow “Leadership Award” with Intuit Inc.

Why JG University?

  • Faculties with Industry Expertise and Academic Experience
  • Hand-on Industry Use-Cases using advance technologies in Digital Lab
  • Capstone Projects – Working with Industries
  • Full-Time access to “Cyber Bay” Cyber Range
  • State-of-the-art Industrial IoT Lab with Drone technology experience
  • World Class Tech-Enabled Digital Library
  • Cross Cultural & Cross Sectoral Internship
  • Collaboration with Foreign Universities
  • Roof Top Cafeteria

Admission Process

Our Admission process enables us to meticulously give importance to every individual applying. The admission of the applicant will majorly be based on our admission process scores.

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