In this post, we list all freely available Data and Engineering Science programs (online engineering degree).

Free online Programs in Data and Engineering Science degree

16 August 2021

In this post, we list all freely available Data and Engineering Science programs (online engineering degrees). Most of the courses listed here are provided by the leading universities in the fields such as Harvard, and Stanford University.

Give to the immense progress in data science and engineering, in particular, due to the ever growth of the use of data in the state of the art Apps and software, the interest in studying Data Science and Engineering has been boosted.

What are Data Science and Engineering?

Data science is an interdisciplinary discipline centred on obtaining intelligence from data collections, particularly large sets of data. Numerous researchers and experts have disputed that it is not a new discipline or domain, but rather a different title for statistics. However, many scientists who are in favour of this name argue that data science is different from statistics as it concentrates on queries and methods exclusive to digital data.

No matter your position in this argument, I am sure you agree that Data science is becoming among the top attractive research field and business areas, in the current century. Looking at the number of job adverts in the market in both academia and industry, you notice that many jobs require is now good command or knowledge of “Data Science” and “Data management”. That is why it is not surprising to see that many top-tier institutions offer Data Science as a study program at different levels.

Free Online Courses in Data Science (online engineering degree)

Emerges in online learning and studying which was further enhanced because of the COVID-19 pandemic, making online courses in Data science more popular. In response to the needs and interests, now numerous online courses are offered surprisingly FREE on different aspects of Data Science.

Free Online Courses in Data Science offered by Harvard University (online engineering degree)

In this post, we accumulate a list of top online courses in Data Science freely offered by well-known universities. As it stands now, the list is currently limited to courses offered by Stanford and Harvard University (USA) but we will include other courses on the list soon. You can find the list of Harvard courses below and the ones offered by Stanford as follows.

Title: Causal Diagrams: Draw Your Assumptions Before Your Conclusions

  • Availability: Available now
  • Duration: 5 weeks
  • University: Harvard University (online engineering degree)

Summary:

Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.

The first part of this course is comprised of five lessons that introduce the theory of causal diagrams and describe its applications to causal inference. The fifth lesson provides a simple graphical description of the bias of conventional statistical methods for confounding adjustment in the presence of time-varying covariates. The second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.

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Title: Data Science: Inference and Modeling

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

Statistical inference and modelling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting. 

This course will show you how inference and modelling can be applied to develop the statistical approaches that make polls an effective tool and we’ll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast. 

Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modelling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.

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Title: Data Science: Productivity Tools

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.

Part of our Professional Certificate Program in Data Science, this course explains how to use Unix/Linux as a tool for managing files and directories on your computer and how to keep the file system organized. You will be introduced to the version control systems git, a powerful tool for keeping track of changes in your scripts and reports. We also introduce you to GitHub and demonstrate how you can use this service to keep your work in a repository that facilitates collaborations.

Finally, you will learn to write reports in R markdown which permits you to incorporate text and code into a document. We’ll put it all together using the powerful integrated desktop environment RStudio.

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Title: Data Science: Wrangling

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

In this course, part of our Professional Certificate Program in Data Science, we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point. 

Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form is called data wrangling.

This process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.

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Title: Data Science: Linear Regression

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.

In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.

We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.

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Title: Data Science: R Basics

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

We’ll cover R’s functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.

Rather than covering every R skill, you might need, you’ll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.

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Title: Data Science: Visualization

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

As part of our Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.

We’ll also be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important.

The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.

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Title: Data Science: Capstone

  • Availability: Available now
  • Duration: 2 weeks
  • University: Harvard University (online engineering degree)

Summary:

To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.

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Title: Principles, Statistical and Computational Tools for Reproducible Data Science

  • Availability: Available now
  • Duration: 8 weeks
  • University: Harvard University (online engineering degree)

Summary:

Today the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any intensive data research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.

To meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery.

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.

We’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.

Consider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure – and success – stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!

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Title: High-Dimensional Data Analysis

  • Availability: Available now
  • Duration: 4 weeks
  • University: Harvard University (online engineering degree)

Summary:

If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principal component analysis. We will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.

Finally, we give a brief introduction to machine learning and apply it to high-throughput data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates, and cross-validation.

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Free Online Courses in Data Science offered by Staford University (online engineering degree)

In the following list, you can find the available courses offered by Stanford University in different areas of Data Science. It includes courses on Algorithms, Optimisations, Haptic, Internet of Things amongst others. Please note that this list will be updated occasionally, so make sure to subscribe to our mailing list (on the right) or join our “Facebook page” in order to be notified of new content.

Title: Algorithms: Design and Analysis, Part 1

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary:

In this course you will learn several fundamental principles of algorithm design. You’ll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You’ll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we’ll study how allowing the computer to “flip coins” can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?002Online, edX

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Title: Algorithms: Design and Analysis, Part 2

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary: In this course you will learn several fundamental principles of advanced algorithm design. You’ll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You’ll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer.  

Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics.  Learn how shortest-path algorithms from the 1950’s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!

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Title: Compilers

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary:

This self-paced course will discuss the major ideas used today in the implementation of programming language compilers, including lexical analysis, parsing, syntax-directed translation, abstract syntax trees, types and type checking, intermediate languages, dataflow analysis, program optimization, code generation, and runtime systems. As a result, you will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines. Along the way we will also touch on how programming languages are designed, programming language semantics, and why there are so many different kinds of programming languages.

The course lectures will be presented in short videos. To help you master the material, there will be in-lecture questions to answer, quizzes, and two exams: a midterm and a final. There will also be homework in the form of exercises that ask you to show a sequence of logical steps needed to derive a specific result, such as the sequence of steps a type checker would perform to type check a piece of code, or the sequence of steps a parser would perform to parse an input string. This checking technology is the result of ongoing research at Stanford into developing innovative tools for education, and we’re excited to be the first course ever to make it available to students.

An optional course project is to write a complete compiler for COOL, the Classroom Object Oriented Language. COOL has the essential features of a realistic programming language, but is small and simple enough that it can be implemented in a few thousand lines of code. Students who choose to do the project can implement it in either C++ or Java.

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Title: Convex Optimisation

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

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Title: Designing Your Career

  1. Availability: Available now
  2. University: Stanford School of Engineering (online engineering degree)

Summary: This online course uses a design thinking approach to help people of any age and academic background develop a constructive and effective approach to designing their vocation. This course is primarily comprised of 5 career-oriented vocational wayfinding concepts, illustrated through videos and expanded through personal reflections and exercises.

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Title: Introduction to Haptics

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary:

Students in this class will learn how to build, program, and control haptic devices, which are mechatronic devices that allow users to feel virtual or remote environments. In the process, students will gain an appreciation for the capabilities and limitations of human touch, develop an intuitive connection between equations that describe physical interactions and how they feel, and gain practical interdisciplinary engineering skills related to robotics, mechanical engineering, electrical engineering, bioengineering, and computer science.

To participate in lab assignments (which is not strictly required to receive a Statement of Accomplishment), the participant will need to acquire/build the components of a Hapkit, and assemble and program the device. Laboratory assignments using Hapkit will give participants hands-on experience in assembling mechanical systems, making circuits, programming Arduino-based micro-controllers, and testing their haptic creations. After the course, we hope that you will continue to use and modify your Hapkit, and let us know about your haptic creations. Please note that you can still participate in the online course without the Hapkit and receive a Statement of Accomplishment, but you will not be able to do most of the laboratories.

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Title: Introduction to Internet of Things

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary:

The Internet of Things is transforming our physical world into a complex and dynamic system of connected devices on an unprecedented scale.

Advances in technology are making possible a more widespread adoption of IoT, from pill-shaped micro-cameras that can pinpoint thousands of images within the body, to smart sensors that can assess crop conditions on a farm, to the smart home devices that are becoming increasingly popular. But what are the building blocks of IoT? And what are the underlying technologies that drive the IoT revolution?

In this short non-credit course, six Stanford faculty members will deliver an overview of exciting and relevant technical areas essential to professionals in the IoT industry. This introductory course provides a taste of what to expect from courses that are part of the IoT Graduate Certificate program. Academic Director Olav Solgaard will give an introduction to this short course, and then you will be guided through 5 modules:

  • Cool Applications
  • Sensors
  • Embedded Systems
  • Networking
  • Circuits

This short course is designed to give an overview of the Internet of Things graduate certificate. It closely maps to subject focus areas within the certificate, and is intended to assist the student in understanding the focus areas. The faculty in this short course also teach graduate courses within the IoT graduate certificate.

The course is not required for the graduate certificate.

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Title: Nano@Stanford

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary:

In this course, which is constantly being updated, you’ll become familiar with nanotechnology – the branch of technology that deals with dimensions and tolerances of less than 100 nanometers, especially the manipulation of individual atoms and molecules. You’ll learn about the nanotechnology tools, materials and processes used in countless fields, from pharmacy and healthcare to electronics and energy. You’ll gain foundational knowledge of equipment and processes in preparation for in-lab and hands-on training at facilities that include: Stanford’s Nanofabrication Facility, Nano Shared Facilities, Materials Analysis Facility, and Environmental Measurement Facility.

You Will Learn:

  • About the tools and processes used to fabricate and characterize materials at the nanoscale
  • To operate and troubleshoot a microscope, and to use various image capture software
  • To determine if x-ray photoelectron spectroscopy (XPS) or scanning electron microscopy (SEM) is right for your research

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Title: Mining Massive Data Sets

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary: We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general.  The rest of the course is devoted to algorithms for extracting models and information from large datasets.  Participants will learn how Google’s PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.  We’ll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair.  When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we’ll talk about efficient approaches.  Many other large-scale algorithms are covered as well, as outlined in the course syllabus.

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Title: Quantum Mechanics for Scientists and Engineers

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary: This 9 week course aims to teach quantum mechanics to anyone with a reasonable college-level understanding of physical science or engineering. Quantum mechanics was once mostly of interest to physicists, chemists and other basic scientists. Now the concepts and techniques of quantum mechanics are essential in many areas of engineering and science such as materials science, nanotechnology, electronic devices, and photonics. This course is a substantial introduction to quantum mechanics and how to use it. It is specifically designed to be accessible not only to physicists but also to students and technical professionals from a wide range of science and engineering backgrounds.

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Title: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

  • Availability: Available now
  • University: Stanford School of Engineering (online engineering degree)

Summary: The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

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Title: Statistical Learning

  • Availability: Available now
  • University: Stanford School of Humanities and Sciences

Summary:

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

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Title: Quantum Mechanics for Scientists and Engineers 2

  • Availability: Available now
  • University: Stanford School of Engineering

Summary: This course covers key topics in the use of quantum mechanics in many modern applications in science and technology, introduces core advanced concepts such as spin, identical particles, the quantum mechanics of light, the basics of quantum information, and the interpretation of quantum mechanics, and covers the major ways in which quantum mechanics is written and used in modern practice. It follows on directly from the QMSE-01 “Quantum Mechanics for Scientists and Engineers” course and is also accessible to others who have studied some quantum mechanics at the equivalent of a first junior or senior college-level physics quantum mechanics course. All of the material for the QMSE-01 course is also provided as a resource. The course should prepare participants well to understand quantum mechanics as it is used in a wide range of current applications and areas and provide a solid grounding for deeper studies of specific more advanced areas.

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Title: Thermodynamics and Phase Equilibria

  • Availability: Available now
  • University: Stanford School of Engineering

Summary:

This course, based on MATSCI 204 Thermodynamics and Phase Equilibria , introduces learners to thermodynamics and and how it governs phase equilibria. It is intended for a general audience, and is especially useful as a preparatory course for undergarduate and graduate students who are about to take their first class in thermodynamics.

The course reviews concepts including the 1st and 2nd laws of thermodynamics; entropy; equilibrium for isolated systems; materials properties; and continues to review phase equilbria.

What you will learn

  • What thermodynamic functions govern heterogeneous equilibria and how to calculate these functions from measurable materials properties.
  • Unary phase equilibria and first order phase transitions, metastability.
  • Thermodynamics of solutions and its application to binary phase equilibria and binary phase diagrams.
  • Thermodynamics of chemical reactions.

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Title: Reservoir Geomechanics

  • Availability: Available now
  • University: Stanford School of Earth, Energy and Environmental Sciences

Summary:

This interdisciplinary course encompasses the fields of rock mechanics, structural geology, earthquake seismology and petroleum engineering to address a wide range of geomechanical problems that arise during the exploitation of oil and gas reservoirs.

The course considers key practical issues such as prediction of pore pressure, estimation of hydrocarbon column heights and fault seal potential, determination of optimally stable well trajectories, casing set points and mud weights, changes in reservoir performance during depletion, and production-induced faulting and subsidence. The first part of the course establishes the basic principles involved in a way that allows readers from different disciplinary backgrounds to understand the key concepts.

The course is intended for geoscientists and engineers in the petroleum and geothermal industries, and for research scientists interested in stress measurements and their application to problems of faulting and fluid flow in the crust.

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Title: Unconventional Reservoir Geomechanics

  • Availability: Available now
  • University: Stanford School of Earth, Energy and Environmental Sciences

Summary:

In this course we address a range of topics that affect the recovery of hydrocarbons from extremely low-permeability unconventional oil and gas reservoirs. While there are multiple definitions of unconventional reservoirs, we consider in this course oil and gas-bearing formations with permeabilities so low that economically meaningful production can only be realized through horizontal drilling and multi-stage hydraulic fracturing. Despite this extraordinarily low permeability, the scale and impact of the production from unconventional oil and reservoirs over the past decade in the U.S. and Canada has been remarkable. In the first part of the course, we consider topics that become progressively broader in scale, starting with laboratory studies on core samples that investigate the composition, microstructure and pore systems at the nanometer scale (the matter of the rock) and conclude by discussing basin-scale stress fields, fracture and fault systems (which matter as well because they control hydraulic fracture propagation and the effectiveness of reservoir stimulation).

In the second part of the course, we address the process of stimulating production using horizontal drilling and multistage hydraulic fracturing. We briefly review several important engineering aspects of horizontal drilling and multi-stage hydraulic fracturing, the basics of microseismic monitoring, the importance of interactions among the state of stress, pre-existing fractures and faults and hydraulic fracturing which are critical to the production process and a unified overview of flow from nano-scale pores to hydraulic fractures via the fracture network stimulated during hydraulic fracturing. In the final part of the course, we consider the environmental impacts of unconventional oil and gas development, especially induced seismicity.

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In Fastepo, we have a dedicated section on Online Courses wherein we post the latest courses, scholarships, and funding related to Online programs. Some of the top courses currently listed on our page can be found below.

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