List of Online Courses and Certifications

In Non-Parametric Statistical Inference, as opposed to Parametric Statistical Inference, we do not know the underlying distribution. Parametric Inference also uses samples from the population to estimate the distribution parameters. However, the assumptions of Parametric Inference, such as normality, independence, and homoscedasticity may not always be accurate. 

In this case, non-parametric information is used. The Course in Non-Parametric Statistical Inference by Swayam will help the candidates to fill in the gaps of parametric statistics as non-parametric methods can be used for different inferencing purposes. The simple non-parametric techniques can help you to establish a hypothesis.  

The Non-Parametric Statistical Inference online programme has several practical applications in real life. This is an ongoing, undergraduate-level course that spans four weeks. The course will be taught by a faculty member from the esteemed Mathematics Department of IIT, Delhi. Moreover, this course is entirely free of cost, and the paid certificate is optional. 

The course AI Applications in Marketing and Finance will help you get a precise understanding of applying artificial intelligence in business in general and in finance and marketing in particular. AI Applications in Marketing and Finance Certification Course, the online course offered by Coursera, is a flexibly structured programme that enables you to fix the deadline as per your convenience and schedule and will facilitate getting acquainted with artificial intelligence for finance. 

AI Applications in Marketing and Finance Certification Syllabus will dive into fraud, credit risks, safeguarding consumer data with the application of artificial intelligence and machine learning  and the implication of AI and ML in finance and marketing. Administered by the University of Pennsylvania, AI Applications in Marketing and Finance Certification by Coursera can be covered by the learners within approximately 6 hours. 

The Data Informed Decision Making Certification Course is a 4-week program. The Institute of Product Leadership is in charge of awarding the certification for this course and teaching the syllabus.

Data Informed Decision Making certification training enables learners to establish their credentials as professionals in the field of data science with decision-making skills in addition to receiving early appreciation from their peers.

Data Informed Decision Making Certification by the Institute of Product Leadership, Bangalore helps boost career profiles and improve professional equity.

The Python Project for Data Engineering certification course, developed by IBM Skills Network and taught by Ramesh Sannareddy, a subject matter expert in data engineering, and Joseph Santarcangelo, a Ph.D., data scientist at IBM, is made available by Coursera, which is intended for participants who want to evaluate their expertise by using web scraping and extracting data with APIs, all with the help of numerous hands-on experiments.

Python Project for Data Engineering online classes provides 9 hours of prerecorded learning materials with an emphasis on assisting participants in obtaining the knowledge necessary to apply fundamental Python abilities by utilizing various data collection and data processing strategies. The Python Project for Data Engineering online course covers subjects such as Watson Studio, Jupyter notebooks, and API functions as well as demonstrates how to extract data from various file formats, change it into particular datatypes, and then load it into a particular source for analysis.

Business and analytics go hand in hand in the technologically advanced era. It is crucial to know about making data-driven decisions with the help of business acumen and raw data. Incidentally, the Introduction to R Programming and Data Analytics programme is designed to help the candidates with the same. It is an online, short-term training that houses nearly 15 hours of interactive lectures. Candidates have to attend weekend sessions. 

The IIT Kharagpur Introduction to R Programming and Data Analytics course is offered by Vinod Gupta School of Management, IIT Kharagpur, India. The instructors of the course will comprise faculty members of IIT Kharagpur, along with industry leaders who will deliver the lectures. The curriculum involves live lecturing sessions, case study discussions, hands-on practices, and live problems for better and immersive learning outcomes.

Moreover, the Introduction to R Programming and Data Analytics online course has a follow-up course: Data Analytics with R Programming. It will be conducted post the conclusion of this one. The programme is aimed at developing the candidates into introductory data scientists and adept data analysts who have a thorough understanding of the applications of analytics in managerial positions.

We are witnessing and adapting to the fourth industrial revolution, Industry 4.0, powered by Big Data and AI. At the global level, data integration has become highly essential for organisations and businesses to achieve global competitiveness. With the prevalence of Big Data all around, it becomes highly relevant to explore the underlying information and reap optimum rewards. In this scenario, data science and analytics enable decision-makers and business leaders to understand, manage and utilise data effectively.

R is a free, open-source programming language that is widely used by data scientists and analysts for developing software and applications in a data science setting. R offers tools and techniques for data analysis, statistical computations and data visualisation. R is available to use on various operating systems and is widely recognised for its extensibility, which allows developers to develop and distribute their own software as add-on packages. These features make R programming an ideal choice for data science experts and data analysts to utilise R in a variety of fields and areas.

The R Programming Environment online certificate course offered by Johns Hopkins University via the Coursera platform aims at providing useful knowledge of R Programming Language to data scientists, data analysts and data science experts to efficiently work in data science settings. This 100% online programme is of intermediate level and the first course in the Mastering Software Development in R specialisation.

By taking up the Specialized Models: Time Series and Survival Analysis online course the students will be working on the ways to identify the modelling challenges that are common within the domain of time series data. The course will further be helping to understand the process of how trends are gauged and accessed in a time series of data. In the Specialized Models: Time Series and Survival Analysis certification syllabus the students will be covering chapters on- Identifying Nonstationarity, Common Transformations, Smoothing Moving Averages, Time Series Smoothing, Deep Learning Notebook, Survival Analysis and Censoring and others. The online course on Specialized Models: Time Series and Survival Analysis by Coursera will be elaborating on the domains of “time-series data” and will be working on its applications.

Performing data science with Azure Databricks requires about 3 weeks to complete. Microsoft is the associate of Coursera in designing and delivering the online programme. This certification is a sub-programme in the five-course umbrella programme of the Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate. As experienced and expert professionals prepare the programme at Microsoft, the learners can have intensive training on data science processes with Azure Databricks, Databricks Azure, Azure Spark and Apache Spark. 

Perform data science with Azure Databricks Certification Course, offered by Coursera, an intermediate-level programme. The learners of this programme should have experience with using  Python for data exploration and machine learning models with open-source frameworks, such as TensorFlow, Scikit-Learn and  PyTorch. Perform data science with Azure Databricks Certification by Coursera can be taken and covered at the learners’ pace. 

The Introduction to Machine Learning certification course is offered by Duke University. It will help you know your strengths and apply your skills in the most efficient manner to get work done. It will give you additional knowledge that will make you more confident in your work and perform better at your workplace or start your own business. 

The course will give you a brief overview of machine learning models like natural language processing, logistic regression, convolution neural networks, and multilayer perceptrons, while also explaining how these models can be put to use for solving technical problems in various industries like medical, image recognition, and text prediction. 

It is a combination of practical as well as theoretical knowledge about Machine learning, that will provide you with a framework for understanding how you can implement data science models and machine learning algorithms. The  Introduction to Machine Learning Training Course will allow you to work on a real-world hands-on project that is required in the fast-developing technological world. 

The Machine Learning Rapid Prototyping with IBM Watson Studio certification course will be allowing the students to work in the emerging technologies of AI. The syllabus is included in the online course Machine Learning Rapid Prototyping with IBM Watson Studio by Coursera. The online programme is  100% online programme and the course is having a duration of eight hours. The Machine Learning Rapid Prototyping with IBM Watson Studio certification syllabus includes the chapters of- Classification Prep Demo, Regression Prep Demo, Multi-armed Bandit Approach, Demo Classification: Making Changes to the Models, Automated Data Preparation, DAUB Algorithm, The model selection problem and others.

The course 3D Data Visualization for Science Communication gives an introduction to 3D data visualisation and emphasises on cinematic design and science communication for attracting a wide range of audience. The candidates will build visualisation literacy, they will be able to write and create their own visualisations. The course is provided by The University of Illinois at Urbana-Champaign which is a top university when it comes to teaching, public engagement and research.

The course will cover a wide range of topics under 3D Data Visualization for Science Communication which includes an introduction to scientific visualisation, critiquing visualisation, computer graphics, visualising pipeline, 3D geometry, computational resources, programming, data representation, virtual coordinates, transformations and attributes, data artefacts, pre visualisation, camera design, image artefacts, to name a few.

This course will be purely taught online using lecture videos, quizzes, discussion forum and reference reading materials. The candidates will be expected to clear all the quizzes and tests included in the course to be eligible for being certified in the course if on the event of purchasing the certification.

Introduction to Data Science in Python is an online certification programme provided by the University of Michigan and available on Coursera which is one of the five specializations of Data Science. Thus, this course must be pursued prior to the other course in the specialisation i.e Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, and  Applied Social Network Analysis in Python. Learners will be able to study data science in Python in greater depth with the tutelage of experienced instructors. 

Introduction to Data Science in Python Certification, accessible on the platform of Coursera, will expose the candidates to basic techniques of Python programming such as lambdas, reading the CSV files, and the NumPy library and python programming environment. By enrolling in the Introduction to Data Science in Python Online Course, the learner will develop the capacity to deal with tabular data and manipulate it along with running the fundamental inferential statistical analyses. 

Introduction to Data Science in Python Certification by Coursera will shed light on data manipulation, and the process of using the group by and pivot tables in an effective fashion. Introduction to Data Science in Python Certification Syllabus will help the students explore the cleaning techniques using the popular python pandas data science library and usage of the Series and DataFrame for data analysis and many more. 

If you have already learnt about the basics of Algorithms, you are now ready to take the next logical step into more complex algorithms and problem-solving. The idea behind this Advanced Algorithm and Complexity course is to teach you how to build upon the algorithm basics and utilize new ideas. In this course, we will be starting with network flows that are employed in the typical applications like detecting disjoint paths, optimal matching, and flight schedules along with a few other surprising applications like the segmentation of images in computer vision.

In the next segment, we teach linear programming with its application in allotting and optimizing budgets, Portfolio optimization, finding a diet that is cheap and satisfies all the requirements. Further, we discuss the inherent hard problems that have no perfectly good solutions known (and neither are likely to be found) and how you can solve them in practice. The course finishes with a slight introduction to the streaming of algorithms that are used, most heavily in Big Data Processing. These algorithms are mostly designed to process huge data sets without being capable of storing even a single dataset.

Microsoft Azure Databricks for Data Engineering is a Microsoft-offered course for intermediate-level learners, including data engineers and developers. The programme will enable you to learn and make aware of Microsoft Azure Databricks. Microsoft Azure Databricks for Data Engineering Certification Course is the eighth course in the 10-course Microsoft Azure Data Engineering Associate (DP-203) Professional Certificate offered by Microsoft. 

Offered by Coursera, Microsoft Azure Databricks for Data Engineering Training is entirely provided in online mode. The course will provide you with a deep insight into Microsoft Azure Databricks and related technologies and concepts such as Microsoft Azure Databricks documentation and characteristics of Azure Databricks. You can complete the Microsoft Azure Databricks for Data Engineering Certification by Coursera within the minimum duration of 21 hours. The eligible students are expected to have an understanding of data processing languages like SQL, Python, or Scala and knowledge of data architecture patterns and parallel processing. 

Part six in the series of programs on the same domain the online course on AI Workflow: AI in Production by Coursera can be persuaded by students who have an earlier and strong understanding of - NumPy, IBM Watson studio, Python, Pandas, Linear Algebra, and Descriptive and inferential statistical concepts. 

The online course focuses on the models of productions that are aimed at streaming media companies. The course will be introducing the students to the core concepts of IBM Watson Learning. The online program over the period of 17 hours covers the topics of - data analysis, visual recognition, machine learning, and hypothesis testing,  bias detection, NLP and others. The AI Workflow: AI in Production certification syllabus will be covered by professionals.

Many firms use Artificial Intelligence to optimize procedures and boost productivity. Digital Skills: Artificial Intelligence course explains AI in action, its benefits and drawbacks, and studies the ethics of responsible AI, and Digital Skills: Artificial Intelligence certification is completely developed by Accenture's professionals which FutureLearn offers. Digital Skills: Artificial Intelligence syllabus includes three weeks of duration, two hours of weekly study, extensive learning resources, and unrestricted access to the course for people who wish to build and analyze how AI breakthroughs may affect their work and increase their understanding of artificial intelligence.

In Building Deep Learning Models with TensorFlow course, learners will obtain a hands-on experience with the TensorFlow library as a strong mechanism for the application of Deep Learning to real-world issues. 

The course efficiently combines Machine Learning and Data Science to impart an all-rounder learning on building Deep Learning models. As a part of the IBM AI Engineering Professional Certificate, students will grasp all fundamentals of Machine learning and Deep Learning, including the use of Python. 

Hands-on projects will provide the course takers with essential Data Science skills and unsupervised learning. Candidates will also build, deploy and train their Deep Learning models and adopt various features of the TensorFlow library in them. Since the majority of data in the world is unstructured and unlabelled, shallow neural networks find it difficult to capture structures in data comprising sound, images and texts. 

However, deep networks are capable of performing this function and hence this course has been created to provide an Intermediate Level of knowledge of how to go about it. IBM is a major player in the industry by providing a broad portfolio to its trainees for software development, predictive analytics, and systems management. Its expertise in technology and R&D is incorporated into the course curriculum.

The Advanced R Programming certification course is the second one in the ‘Mastering Software Development in R Specialization’ series offered by Coursera. The course curriculum explores the advanced topics such as functional programming in R, object-oriented programming, robust error handling, profiling and benchmarking, debugging, as well as the proper design of functions.

The Coursera Advanced R Programming online course, offered in collaboration with Johns Hopkins University, aims to prepare students for unique data challenges and to identify everyday data analysis tasks. They will also learn how to define new data types in R, create functionalities specific to these for enabling cleaner execution of tasks related to data science, and stronger reusability within a team. All this is taught through pre-recorded videos, readings, graded quizzes, and assignments, for a valuable learning experience.

Furthermore, the Coursera Advanced R Programming online course is designed, keeping in mind the convenience of students. Hence, the modules are self-paced and entirely online to help students learn from the comfort of their own space. Upon satisfactory completion, participants get a \shareable certificate.

Databases contain much of the world's data. Structured Query Language or SQL is a robust language used to extract data and communicate with databases. If you are looking to build a career as a data scientist or in a related field, the Databases and SQL for Data Science with Python course is ideal for you. This training will help you understand the SQL language’s foundational and relational database concepts. You will also learn about conducting SQL access in a data science environment. 

The Databases and SQL for Data Science with Python certification syllabus concentrates on practical and hands-on learning. You will have the opportunity to work with real-world data sets, real data science tools, and real databases. There will also be periodic practice and graded quizzes for motivation and evaluation. 

Databases and SQL for Data Science with Python certification does not need any prior knowledge of Python, databases or SQL to join. You will have flexibility with the course material and audit it for free. Upon completion, you can also receive the shareable certificate if you choose to pay for it.

The term relational database refers to a particular kind of database that stores and makes available connected data items. On the relational model, an easy-to-understand method of representing data in tables, relational databases are built. Introduction to Relational Databases (RDBMS) certification course is taught by Sandip Saha Joy - Data Scientist, Rav Ahuja - Global Program Director, Lin Joyner - Senior Instructional Designer & Content Developer, and is developed by IBM Skills Network and Rose Malcolm - Senior Instructional Designer & Content Developer, which is delivered by Coursera.

Introduction to Relational Databases (RDBMS) online course focus on assisting individuals in recognising the many database types that are suitable for the particular sorts of data they are working with and the processing requirements as well as providing individuals with a good understanding of how information is stored, analyzed, and retrieved. Introduction to Relational Databases (RDBMS) online classes incorporate 2 weeks of hands-on practical learning materials to help individuals in learning the methodologies for building relational data models and the approaches to use them with their data.

The term ETL describes a set of procedures used to extract data from one system, change it, and load it into a target network. A more general name for any group of operations that transfer data from one system to another, possibly transforming it along the way, is a data pipeline. ETL and Data Pipelines with Shell, Airflow, and Kafka certification course is developed by IBM Skills Network and taught by Yan Luo - Ph.D., Data Scientist & Developer, Jeff Grossman - Certified Instructor, Sabrina Spillner - Senior Instructional Designer & Content Developer, and Ramesh Sannareddy - Data Engineering Expert, which is made available by Coursera. 

ETL and Data Pipelines with Shell, Airflow, and Kafka online training is designed to help the students gain an understanding of the two different approaches to converting raw data into analytics-ready data including the ETL process and ELT process. ETL and Data Pipelines with Shell, Airflow, and Kafka online course explains the differences between ELT and ETL processing, identifies instances for each, and goes over the procedures and tools needed for extracting data, combining it logically or physically, and importing it into data sources.

“Everyday Excel, Part 2” is the second part of the famous “Everyday Excel” training programme. Intensifying on the theories studied in the first course the learners will carry on expanding their knowledge in the uses of Excel. Although this training program is intended for intermediate users, advanced users too will benefit from it, in learning new tools and skills in Excel. After finishing this course, the candidates will possess enough skills to take up part 3 of the series Everyday Excel, which is project-based.

This specialisation series, which is divided into three parts aims to teach the candidates basic as well as advanced tools in Excel.  This curriculum is intended to be intriguing and enjoyable. It aims to encompass a wide range of learners so that a variety of experiences and abilities come together to teach something new to everyone.

A distinctive characteristic of this course is the compulsion to complete weekly assignments in the application itself, i.e. in Excel, which will push students into hands-on learning.

The Data Manipulation at Scale: Systems and Algorithms online course by Coursera is the first step in the Data Science at Scale Specialization series. The University of Washington offers the certification course in collaboration with Coursera.

The Data Manipulation at Scale: Systems and Algorithms online certification course aims to teach the landscape of relevant systems to the students and how these systems are evaluated along with the principles on which they rely. Students will also learn the context of data science as well as the history, skills and challenges that come while working with data. Moreover, students will get hands-on experience in learning how to structure a data science project. 

Additionally, the Data Manipulation at Scale: Systems and Algorithms class programme is a self-paced learning course so that students never have to worry about missing a class. At the same time, the course is 100% online so that they can learn from the comfort of their home. The students will be awarded a shareable certificate after completing the course, which can be shared on professional platforms. 

The University of Alberta and Alberta Machine Intelligence Institute offer the Sample-based Learning Methods training programme in conjunction with Coursera. An intermediate-level course, the programme is spread across four weeks, with intensive modules on reinforcement learning. The course will also help you understand how learning from experience can help you attain optimal behaviour. 

The Sample-based Learning Methods certification course will help you understand concepts like Temporal Difference learning, Monte Carlo strategy, exploration, dynamic programming, and more. Moreover, the certification course will provide you with a chance to apply TD algorithms, Expected Sarsa, Q-learning, and more.

Instructors will conclude the Sample-based Learning Methods online course by looking at how algorithms can combine model-based planning and temporal difference updates to fast-track learning. Additionally, the certification course offers the flexibility to learn at your own pace in your own time and set your own deadlines. 

You will also receive a combined shareable certificate from the University of Alberta and Coursera, upon completion of the Sample-based Learning Methods course.

The course Fundamentals of Reinforcement Learning certification course is offered by Coursera by collaboration with the University of Alberta to make the aspirants skilful with machine learning and applications of Artificial Intelligence. The course is free to enrol and can access all the course materials free of cost. Therefore aspirants can use the opportunity for growing their career with a course completion certificate and increase the chances of hiring.

With the digital connection over the world, Business Intelligence over decision making is made essential in organizations. Decision making increases the prosperity of the business. Therefore the use of decision making with Artificial Intelligence and machine learning became most popular when the programme comes into existence. Organizations are looking forward to candidates who are skilled in this type of Artificial Intelligence, Robotics, Machine learning and, the Internet of things (IoT) as well

Coursera is the digital platform to learn the concepts that are offered for free. Candidates can learn throughout their lifetime and can avail of certificates after completion of the Fundamentals of Reinforcement Learning Training course. 

Machine Learning With Big Data is an online certification programme administered by the University of California San Diego to help the students explore machine learning with big data concepts. Machine Learning With Big Data  Certification Syllabus, developed by Mai Nguyen, the Lead for Data Analytics, and Ilkay Altintas, the Chief Data Science Officer, will give the students a clear understanding of strategies of machine learning for the analysis and exploration of data. 

In the Machine Learning With Big Data Certification Course, administered by  Coursera, the candidates will learn to build machine learning models with numerous algorithms and tools as well various concepts of big data. Machine Learning With Big Data Certification by Coursera is the fourth in six programmes of the Big Data Specialization. 

The course of inferring causal effects from the observational data offered by the platform of Coursera is free of cost course. The enrolled candidate can get a certificate for this course after paying a fixed amount according to the guidelines of the Coursera platform. The duration for this course is 5 weeks. This course is taught under the guidance of experts from the University of Pennsylvania. The E-certificate is available after the completion of this course.

The enrolled candidates will be provided with all the quizzes, assignments, video lectures, web links for books and references. As this course is free of cost and online, it is easily accessible for all the sections of society. Financial aid is also provided. The course of inferring causal effects from the observational data is highly recommended for all the students, teachers and researchers who are interested in this field. Financial aid is also available for all learners who cannot afford the fee. Total 33 percent of people started a new career after completing this course and 29 percent of them got career benefits after pursuing this course.

Taking the Introduction to Graph Theory certification course will provide you with useful insights into how engineers design integrated circuits and how GPS systems find shortest routes. You will also learn why a political map can only be coloured using a few colours and how biologists assemble genomes. The course curriculum includes the Ramsey Theory, which proves that complete disorder is impossible in an extensive system.

Additionally, the curriculum for the Introduction to Graph Theory certification course will cover a wide variety of graphs, including Airlines Graphs, Directed Graphs, Bipartite Graphs, and more. You will become adept in applying various algorithms to solve real-world problems via practice exercises and hands-on projects. 

Moreover, National Research University Higher School of Economics and the University of California and San Diego are offering the Introduction to Graph Theory programme jointly. As a result, candidates will be able to find better career opportunities upon course completion. 

The Introduction to Graph Theory online course is highly suitable for professionals working in the IT sector and IT aspirants.

Here most standard combinatorial settings are addressed in the Combinatorics and Probability course that can help answer questions of this nature. In particular, this course focuses on improving the ability to differentiate these settings from algorithmic problems in real life. This will assist candidates to apply new knowledge in practice. Besides that recursive counting technique that is necessary for algorithmic implementations is also addressed here.

The Combinatorics and Probability course is part of Introduction to Discrete Mathematics for Computer Science Specialisation. There are total of five courses in it and Combinatorics and Probability is the second one. The language of computer science is known as Discrete Mathematics. In many areas including data science, software engineering and machine learning, one needs to work fluently. Via a fun approach, candidates are introduced to this language. Firstly they will solve several interactive puzzles that are specially designed especially for this online specialisation. Then actual solving methods and significant ideas will be introduced to them. It is sure that candidates will gain a deeper understanding in this way and will appreciate the beauty of the underlying concepts better. Programming examples, projects and problems are integrated into the specialisation in order to get the knowledge closer to IT applications.

Introduction to Social Determinants of Health Certification Course being offered by the University of Minnesota which is amongst the largest public research universities in the United States, through Coursera under the Social Determinants of Health: Data to Action Specialization is taught by instructors Daniel J. Pesut and Karen A. Monsen, professors at The University of Minnesota.

The course Introduction to Social Determinants of Health, helps in defining the social determinants of health, understanding how our definition of social determinants has changed over time and evaluating the effect of health inequity on society at large. 

Introduction to Social Determinants of Health Certification by Coursera evaluates a large pool of ideas to address health inequities in the society. The course teaches us to review different ways of using community knowledge to augment data and influence health-related policies. The Introduction to Social Determinants of Health Certification Benefits the learner by enabling him to assess and evaluate data and foster partnerships which can be leveraged to create a transformative change in health-related policies.

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