From NPTEL offered by IIT Palakkad

This course covers the fundamentals of the compute capabilities and the system software required for implementing artificial intelligence (AI) based solutions with accelerated computing on industrial use cases such as the one in the domains of Smart City. The course discusses end to end deployments of industrial use cases like using large language models with demonstration, and hence helps participants use state-of-the-art AI SDKs effectively to solve complex problems.

From NPTEL offered by IIT Guwahati

What does automatic scheduling or autonomous driving have in common with web search, speech recognition, and machine translation? These are complex real-world problems that span across various practices of engineering! Aim of artificial intelligence (AI) is to tackle these problems with rigorous mathematical tools. The objective of this course is to present an overview of the principles and practices of AI to address such complex real-world problems. The course is designed to develop a basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI. 

From NPTEL offered by IIT Kanpur

The course aims at providing adequate exposure to the basic concepts and policy issues related to the working of the financial markets nationally and globally. The course covers basic economic theories related to banking and finance markets, and then applies those to understand the working of both national and global monetary and other related economic variables. The course follows a macroeconomic perspective and will discuss recent finance market developments and controversies. The course aim to develop the learning skills of the students in a way that they can apply their knowledge not only at the academic level but also at the managerial and leadership level in the field of financial sector.

From Chandigarh University via upGrad platform

The Master's degree program in Data Science offered by Chandigarh University is a two-year online higher education program. It has been uniquely positioned to provide the learner with theoretical knowledge and practical exposure to the fast- transforming field of Data Science.

The courses are designed to deliver in-depth and most-relevant skills in conventional areas of data engineering and analysis such as in businesses, finances, and industrial and scientific research, as well as in relatively newer areas such as in social data analysis, big data, and more.

From Coursera offered by Amazon Web Services

In modern cloud native application development, the goal is to build serverless architectures that are scalable, are highly available, and are fully managed. This means less operational overhead for client and his business, and more focusing on the applications and business specific projects that differentiate client in his marketplace.

The course covers how to build a modern, greenfield serverless backend on AWS. It explores how to build an API driven application using Amazon API Gateway for serverless API hosting, AWS Lambda for serverless computing, Amazon Cognito for serverless authentication, and AWS Step Functions to create a serverless workflow, followed by API optimizations.

From Coursera offered by University of Colorado Boulder

Effective teamwork and group communication are essential for professional and personal success. This course teaches one to: make better decisions, be more creative and innovative, manage conflict and work with difficult group members, negotiate for preferred outcomes, improve group communication in virtual environments, develop a better overall understanding of human interaction, and work more effectively as a team. The goal is to help understand these important dynamics of group communication and learn how to put them into practice to improve overall teamwork.

From Coursera offered by University of Virginia

Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge.

Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. One leaves with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources.

From Coursera offered by LearnQuest

This course introduces the concepts of Artificial Intelligence and Machine learning. It discusses machine learning types and tasks, and algorithms with Python and packages.

It introduces the machine learning tools available in Microsoft Azure. It reviews standardized approaches to data analytics and get specific guidance on Microsoft's Team Data Science Approach. It introduces Microsoft's pre-trained and managed machine learning offered as REST API's in their suite of cognitive services. It implements solutions using the computer vision API and the facial recognition API, and also sentiment analysis by calling the natural language service. 

From Coursera offered by Google Cloud

This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that's not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.

This course teaches how to : (a) Formulate machine learning solutions to real-world problems, (b) Identify whether the data you have is sufficient for ML, (c) Carry a project through various ML phases including training, evaluation, and deployment, (d) Perform AI responsibly and avoid reinforcing existing bias.

From Coursera offered by Amazon Web Services

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it’s estimated that currently there are 300,000 AI engineers worldwide, but millions are needed. This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications – no matter what your skill levels are. Learning the foundations of ML now, will help you keep pace with this growth, expand your skills and even help advance your career.

This course teaches how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

From Coursera offered by LearnQuest

The course discusses the basics of Cloud computing: what it is, what it supports, and how it is delivered. Delve into storage services, Cloud economics, levels of managed infrastructure, and Azure services. Explore different deployment models of Cloud computing, as well as several hosting scenarios. Last but not least, compare some of the cloud platforms and discuss the future of cloud computing.

From NPTEL offered by IIT Kharagpur

The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. This course starts with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then moves to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students acquires the knowledge of applying Deep Learning techniques to solve various real life problems.

From Imarticus, Mumbai

The Data Science curriculum included the following:

Python : Anaconda, Spyder IDE, Data structures, Numpy Arrays, Data Processing, Pandas data analysis.

R : RStudio IDE, Operators and Types, R Functions, Linear Regression, Logistic Regression, SVM, Decision Trees, etc.

SAS : SAS Programming, Reading and manipulating data, Data transformations, Macros, SQL.

Tableau : Visualisation, Dashboard Data presentation.

From Edupristine, Mumbai

The course focuses on Hadoop as a software framework for storing and processing Big Data. It covered the following: 

Basic Java, Basic Unix, Introduction to Hadoop Technology and HDFS.

Pseudo Cluster Environment, Map­Reduce.

HIVE, PIG, SQOOP, ZOOKEEPER, HBASE.

FLUME & IMPALA + Spark Streaming.

R Integration with Hadoop.

MongoDB,

Cloudera CCA-175 Guidance.