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Top 8 Online Data Science Courses — Guide & Reviews. Data Science vs. Data Analytics vs. Machine Learning. Top Platforms & Resources to Learn Data Science & Machine.

Unadjusted and covariate-adjusted regression results are presented in Table 3. Our analysis of cost savings associated with the introduction of blended or online instruction has two steps. First, we calculate and compare costs of instructor compensation for in-person, blended, and fully online modalities for EM and CMT courses at all MHES-funded universities that do not have a prestigious designation of National Research University, Federal University, Special Status, or Project university and do not receive additional state funding for this status and that require these courses.

Second, we calculate how many students these universities can enroll in EM and CMT courses if they adopt the blended or online model with the amount of funding that is currently available to them. Our calculations are based on four assumptions. First, our analysis focuses on resource-constrained state universities that do not have the prestigious designation of National Research University, Federal University, Special Status, or Project university.

We analyze university-level data from of these universities that receive funding through the Ministry of Higher Education and Science. Other 74 universities receive funding through the Ministry of Agriculture or the Ministry of Transport of Russian Federation.

For these 74 universities, we had only instructor compensation data but not the data on other costs per student. Cost savings on instructor compensation in blended and online instruction are several percentage points higher if these universities were included in the sample. The data include average instructor compensation and all other costs per student per credit for in-person instruction at each university.

All universities that participated in the RCT belong to this group. Second, we calculate cost savings in public spending on higher education in STEM fields. Currently, each university receives an annual subsidy per student that covers both instructor compensation and all other costs e.

The amount of subsidy per university varies and depends on geographical location and specialization. On average, instructor compensation accounts for In a scaled-up version of this program, part of state subsidy for each of non-elite university would likely be spent on production, delivery, and proctoring of an online version of EM and CMT at one of the top universities.

Resource-constrained universities can then reduce instructor compensation costs using blended or online instruction and enroll more students with the same state subsidy.

Our model assumes that all universities will introduce either online or blended instruction. Third, our calculations are made for the same teaching model that was used in the experiment. For in-person instruction, we assume that all students attend a common lecture no more than students and are assigned to discussion sections of no more than 30 students. The CMT course lasts 16 weeks with two academic hours of lectures per week and two academic hours of discussion sections per week.

We calculate the cost of the in-person modality Eq. Compensation for a lecture is twice as higher compared to the discussion section. LG ai is the number of lecture groups at the university i for the course a , LH a is the number of lecture academic hours for the course a , SG ai is the number of discussion section groups at the university i for the course a , SH a is the number of discussion section academic hours for the course a , and N ai is the number of students at university i for the course a.

For blended instruction, students watch lectures online and then attend the same discussion sections as the in-person group. We calculate the cost of the blended modality Eq. Our estimates of the cost of developing and supporting online courses are based on the actual costs of the Ural Federal University to develop and support EM and CMT courses for OpenEdu.

For online instruction, students watch lectures, communicate with instructors, and complete assignments online. We calculate the cost of the online modality Eq. The cost of proctoring per student was also provided by the Ural Federal University. Fourth, when we calculate increases in enrollment in the blended or online modalities, we assume that all the costs besides instructor compensation will remain the same.

This assumption leads to a rather conservative estimation in the increase in the number of students, especially in the online modality. In reality, other costs will also go down because blended or online instruction will require less spending on, for example, building maintenance and utilities and hiring instructors.

However, these changes are much harder to estimate, and thus, we assume here that they will stay the same. We estimate the number of students in the courses with the blended and online modalities using Eqs.

Summary statistics and the results of cost savings analysis are shown in Table 4. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Science Advances 08 Apr Abstract Meeting global demand for growing the science, technology, engineering, and mathematics STEM workforce requires solutions for the shortage of qualified instructors.

Download high-res image Open in new tab Download Powerpoint. Data collection Data were collected during an RCT at three universities.

There were several conditions for participation: The courses were selected on the basis of the following criteria: Table 1 Characteristics of instructors. View popup View inline.

Table 2 Student level summary statistics in each condition. GPA, grade point average. Covariate measures Before the start of the course, we collected data on student characteristics that we used as covariates. Outcome measures We consider three outcomes in this study to measure final learning outcomes, academic achievement during the course, and satisfaction in the course.

Analytical approach Missing values were imputed 50 times using predictive mean matching. Table 3 Unadjusted and covariate-adjusted regression estimates for three outcome measures: Cost-savings analysis Our analysis of cost savings associated with the introduction of blended or online instruction has two steps. Table 4 Cost-savings summary statistics.

Woessmann, The Knowledge Capital of Nations: Triumph of the BRICs? Stanford University Press, Quantitative and qualitative indicators. Data Brief American Institutes for Research, Yuchtman , Can online learning bend the higher education cost curve?

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Nygren , Interactive learning online at public universities: Evidence from a six-campus randomized trial. Harmon , A randomized assessment of online learning. Taylor , Virtual classrooms: How online college courses affect student success.

VanLehn , The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Handbook of Research on Learning and Instruction Routledge, National Platform of Open Education ; https: Semyonov , Institutional diversity in Russian higher education: Engineering Mechanics Online Course ; https: Construction Materials Technology Online Course ; https: Altman , Statistics notes: BMJ , Motivations and self-regulated learning in MOOCs.

Schneider , Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale. Hanson , Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior.

Xu , How do online course design features influence student performance? Deci , Intrinsic and extrinsic motivations: Classic definitions and new directions.

Vallieres , On the assessment of intrinsic, extrinsic, and amotivation in education: Evidence on the concurrent and construct validity of the academic motivation scale. Osin , Academic motivation scales questionnaire. Psihologicheskii Zhurnal 35 , 96 — An essential motive to learn.

Student Experience in the Research University Consortium ; https: Terentev , The mismatch between student educational expectations and realities: Prevalence, causes, and consequences. Small-sample degrees of freedom with multiple imputation. Biometrika 86 , — Zellner , An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. We thank the Ural Federal University, V. Ignatchenko, and all participated instructors and coordinators for support in data collection.

We also thank three anonymous reviewers and the editors for suggestions that increased the clarity of the manuscript. The authors declare that they have no competing interests.

Data and materials availability: Whereas, the folks with job titles such as Computer Scientist, Data Miner or Programmer used only two platforms. If you refer to the figure 2 above , you can see that all platforms got pretty good reviews. Most of the platforms received the rating of either very useful or somewhat useful. Below is the snapshot. Data Science and Machine Learning are extremely complex, evolving and vast fields. You cannot master everything in one go.

You need to start with the basics. More importantly, you must get your hands dirty by implanting your learning on real-world projects. You have to refer to multiple platforms and resources. Data science, in particular, is a very broad field and covers a variety of domains from business to bioinformatics.

There is no fixed path to becoming a data scientist. You will come across a lot of advertisements for online courses and graduate MS programs. The main job of a data scientist is coming up with a new meaningful way to interpret the data. There is no clear winner between online course MOOC and a full-time program e. MS in Data Science.

It really depends on your background, existing skillset and career stage. Even if you get admitted to an MS in Data Science program at a top university , you will need to take a few online courses as well. Similarly, online courses are good to get started.

But, getting a few online certifications will not be enough to become a data scientist. You need to focus on the skills and techniques.

You can have skills without degrees, and degrees without skills. No matter what, if you are lacking in the understanding and skills, no one can help you.

Additionally, you need to have solid domain knowledge. Due to its advanced nature, you should have experience with single and multivariate calculus, as well as Python programming. With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. This is one of the only data science courses around that actually touches on every part of the data science process.

Also available using R. A very reasonably priced course for the value. The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects.

A huge benefit to this course over other Udemy courses are the assignments. This course focuses more on the applied side, and one thing missing is a section on statistics. If you plan on taking this course it would be a good idea to pair it with a separate statistics and probability course as well. An honorary mention goes out to another Udemy course: Deep Learning Specialization — Coursera Created by Andrew Ng, maker of the famous Stanford Machine Learning course , this is one of the highest rated data science courses on the internet.

This course series is for those interested in understanding and working with neural networks in Python. I found the lecturers to be really passionate about what the teach, making it a pleasant experience taking the courses. Mathematics for Machine Learning — Coursera This is one of the most highly rated courses dedicated to the specific mathematics used in ML.

This is more of an advanced course that teaches you the intuition behind why you should pick certain ML algorithms, and even goes over many of the algorithms that have been winning competitions lately. From Concept to Data Analysis — Coursera Bayesian, as opposed to Frequentist, statistics is an important subject to learn for data science.

Many of us learned Frequentist statistics in college without even knowing it, and this course does a great job comparing and contrasting the two to make it easier to understand the Bayesian approach to data analysis. The instructor makes this course really fun and engaging by giving you mock consulting projects to work on, then going through a complete walkthrough of the solution. When joining any of these courses you should make the same commitment to learning as you would towards a college course.

One goal for learning data science online is to maximize mental discomfort. Vik Paruchuri from Dataquest produced this helpful video on how to approach learning data science effectively:. All in all, the project should be the main focus, and courses and books should supplement that. When I first started learning data science and machine learning, I began as a lot do by trying to predict stocks.

I found courses, books, and papers that taught the things I wanted to know, and then I applied them to my project as I was learning. I learned so much in a such short period of time that it seems like an improbable feat if laid out as a curriculum. It turned out to be extremely powerful working on something I was passionate about. It was easy to work hard and learn nonstop because predicting the market was something I really wanted to accomplish.

For hard skills, you not only need to be proficient with the mathematics of data science, but you also need the skills and intuition to understand data. After going through the list you might have noticed that each course is dedicated to one language: So which one should you learn? Python is an incredibly versatile language, and it has a huge amount of support in data science, machine learning, and statistics.

27 Best Online Learning Platforms - Learnworlds. Feb 24,  · The 9 Best Free Online Data Science Courses In Adobe Stock. This means that practically anyone can upgrade their employability and career prospects by learning the . May 16,  · It is the joy of data scientists to use a data science and machine-learning platform that enables them to work both online and offline. With the introduction of cloud .

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Learning Data Science (4 Untold Truths)

Meeting global demand for growing the science, technology, engineering, and mathematics STEM workforce requires solutions for the shortage of qualified instructors. We propose and evaluate a model for scaling up affordable access to effective STEM education through national online education platforms. We find that online and blended instruction produce similar student learning outcomes as traditional in-person instruction at substantially lower costs.

Adopting this model at scale reduces faculty compensation costs that can fund increases in STEM enrollment. A shortage of professionals in science, technology, engineering, and mathematics STEM fields is slowing down growth and innovation in the global knowledge economy 1 — 2. Developed and developing countries alike have introduced multibillion-dollar programs to increase the supply of STEM graduates 3 — 5. However, institutions of higher education face a need to curb the rising costs associated with attracting qualified instructors and serving more graduates as STEM degree programs cost more to run than most other majors 6.

We present an affordable approach to addressing this global challenge and demonstrate its efficacy in a randomized field experiment. Experts in education and economics have touted blended or fully online content delivery as a vehicle for expanding access to higher education 7. Experimental or quasi-experimental evidence indicates that online and blended approaches to content delivery can produce similar or somewhat lower academic achievement compared with in-person programs 8 — These findings are based on comparisons between the modality of course delivery within the same university or the same course taught by one instructor.

Yet, scaling up affordable access to STEM education requires a concerted effort across multiple universities and instructors at the national level. In addition to reducing costs, the integration of online courses into the curriculum can potentially enable resource-constrained universities to enrich student learning by leveraging the expertise of the instructors from top departments or universities. These universities established OpenEdu to address growing concerns about the quality of higher education 13 and to improve the cost-effectiveness of the massified higher education system during an economic recession.

Students at the purchasing institution can take the course for credit with either fully online or blended instruction—an online course augmented with in-person discussion sections.

We conducted a multisite randomized controlled trial RCT to test the effectiveness of the OpenEdu model, including testing the effect of instruction modality fully online instruction, blended instruction, and in-person instruction on student learning and calculating cost savings that the model could bring to the higher education system.

We randomly assigned students to one of three conditions: The course content learning outcomes, course topics, required literature, and assignments was identical for all students.

FSES sets the requirements for both student learning outcomes and the topics that should be covered within academic programs with limited discretion for the instructor and most online courses on OpenEdu comply with the FSES. To further minimize variation in course materials between courses, we worked with course instructors before the experiment to ensure that all students received the same assignments and required reading. We also surveyed course instructors and collected information about their sociodemographic characteristics, research, and teaching experience.

Before the start of the course, students under all three conditions attended an in-person meeting at their home university with the instructor and the research team, where they were informed about the experiment and given the opportunity to opt out without consequences 5 of students opted out.

During the meeting, students took a knowledge pretest assessing their mastery of the course content. They also completed a survey questionnaire asking about their sociodemographic characteristics and prior learning experiences. At the end of the course, all students took a standardized final exam assessing their mastery of the course content and completed a questionnaire about their course experience and satisfaction; both were administered by course instructors and the research team on-site at each university.

In total, second-year college students from three universities were randomly assigned to one of three experimental conditions: The distribution across conditions was uneven by design. We verified using attendance sheets that students remained in their assigned instruction modality. We consider three student outcomes: Students received the same exam and weekly assessment materials under all conditions, except that online students were permitted three attempts instead of a single attempt on the weekly assessments but not on the final exam.

We analyze the data with and without covariate adjustment using ordinary least squares OLS regression with fixed effects for university, pooling multiple imputations to address missing values see Materials and Methods for model details.

This effect is likely an artifact of the more lenient assessment submission policy for online students, who were permitted three attempts on the weekly assignments. Results with covariate adjustment, as reported and illustrated in Fig.

National online education platforms have the potential to address the shortage of resources and qualified STEM instructors by bringing cost savings for the economically distressed higher education institutions.

Given looming shortages of faculty and growing demand from students, national online platforms can mitigate budget constraints as the supply of qualified instructors puts upward pressure on faculty compensation. To demonstrate the cost efficacy, we estimate whether the introduction of blended or fully online instruction of EM and CMT scaled to all state-funded resource-constrained higher education institutions in Russia would allow these institutions to enroll more STEM students at the same or lower cost for the national system.

In , these universities admitted 29, freshmen students who were required to take EM and 72, freshmen students who were required to take CMT. Tuition for these students is paid by the Ministry: Each university receives an annual subsidy per student that covers both instructor compensation and all other costs e.

However, blended and online instruction will reduce instructor compensation expenses and enable resource-constrained universities to enroll more students with the same state subsidy. On the basis of university-level data on instructor salaries and the number of enrolled students per year at these universities, we calculate instructor compensation per student for each course when offered in-person, blended, or fully online.

Our estimates are based on the same teaching model that was used by the universities in the experiment: All students attend a common lecture no more than students and are then assigned to discussion sections in groups of no more than 30 students. Our estimates for blended and online modalities account for the cost of production media production and faculty and staff compensation , support, and proctoring of online courses.

Compared to the instructor compensation cost of in-person instruction, blended instruction lowers the per-student cost by These cost savings can fund increases in STEM enrollment with the same state funding. Conservatively assuming that all other costs per student besides instructor compensation at each university remain constant, resource-constrained universities could teach 3. If universities relied on online instruction, then they could teach This study demonstrates the potential of national online education platforms for scaling up affordable access to STEM education.

In the model we propose, national online education platforms license online courses created by top universities to resource-constraint universities with a shortage of qualified instructors.

The platform not only provides information technology infrastructure for hosting online courses but also supports universities with the design, production, and delivery of online courses.

For each course, the learning outcomes, topics, required reading, and assignments are standardized with iterative quality assurance from domain experts and psychometric evaluators. Resource-constrained universities can offer the licensed courses for credit either fully online or in a blended format. Blended instruction is facilitated by providing instructors at resource-constrained universities with a set of teaching materials lecture slides, assigned reading, and problem sets. There are challenges and limitations associated with using a national online education platform to expand STEM education.

First, there are substantial start-up costs for the platform itself, for faculty professional development programs to improve online teaching and for the introduction of new models of instruction at universities. These costs can be covered by the state or by a consortium of universities that will become course providers on the platform.

Second, these platforms will be more efficient in settings where academic programs are synchronized both in terms of student learning outcomes and academic calendars. State or private accreditation boards and professional associations could play a more active role in harmonizing curricula across universities.

Cross-country variation in the impact of online education platforms on student learning and the cost of instruction warrants further investigation. Third, more research is needed on how combining different instruction modalities in degree programs affect learning and career outcomes for students with diverse backgrounds. Policy makers and course designers need to experiment with new approaches that can improve student outcomes relative to traditional forms of instruction.

To achieve tight control between the three instruction modalities, the online learning approach tested in this study did not include novel learning activities available in modern online learning platforms. Automated formative assessment, just-in-time learning, and interactive learning materials with immediate feedback can provide learning benefits similar to one-on-one tutoring at a lower cost 14 — Our estimates of the online learning benefits may therefore be conservative, and models that make better use of the online affordances might show benefits for learning outcomes and efficiency that offset the higher up-front development cost 8.

A centralized national model for STEM education can also provide an unprecedented opportunity for systematic research into improving learning resources and pedagogy with national student samples, fine-grained longitudinal outcome measures, and experimental control over the learning environment. At a time when both developed and developing countries experience a shortage of resources and qualified instructors to expand STEM education, national or accredited online education platforms can provide a feasible alternative to traditional models of instruction.

Our study shows that universities can use these platforms to increase enrollments without spending more resources on instructor compensation and without losses in student learning outcomes. A cost-effective expansion of STEM programs will allow countries facing instructor shortages and rising costs to be more competitive in the global knowledge economy.

OpenEdu was developed using an open source platform Open edX. OpenEdu is administered as a nonprofit organization and, as of April , offered more than online courses from the best Russian universities. FSES are detailed requirements to student learning outcomes, structure of academic programs, and required resources staffing, financial resources, and physical infrastructure within a particular academic field. All state-accredited higher education institutions must design their academic programs and courses as compliant to the FSES, or otherwise, their accreditation will be terminated.

FSES are developed by interuniversity curricular boards in each academic field. Universities can include online courses from other universities at OpenEdu in their accredited academic programs because course content is aligned with FSES. OpenEdu relies on the evaluations from content-matter experts to assess the alignment of a particular course with FSES before launch. The merits of such a coordinated system are not part of the discussion in this paper. Data were collected during an RCT at three universities.

Data collection was organized in six stages, as detailed below. At the first stage, we selected universities for the study based on the list of 12 higher education institutions provided by OpenEdu. These higher education institutions either already collaborated with OpenEdu integrating online courses in their academic programs or had inquired with OpenEdu about the possibility of doing so.

Location in the European part of Russia was important so that the research team is able to travel no more than 10 hours from Moscow to conduct organizational meetings and proctor examinations. We then sent invitations to the five selected institutions to participate in the study. There were several conditions for participation:.

The university has STEM academic programs and is willing to replace one of the in-person courses with an online course from OpenEdu. The university has a cohort of second-year students majoring in one of the STEM subjects, and this cohort is larger than 75 students. We have chosen second-year students because we wanted to include in our study a foundational STEM course that is required for the whole cohort of students. These courses are most common during the first and the second year of study.

We chose the second-year students because they have already adapted to the university environment. We needed a cohort of 75 students to have the size of control and experimental groups no less than 25 students. Cohorts of students and smaller per major are typical for Russian higher education because majors especially in engineering tend to reflect narrow specializations In Russia, similar to most universities outside of the United States, students choose their major upon admission to the university and the cohort admitted to the same major study together.

Lectures are organized for the entire cohort, and discussion groups and labs convene in groups of 20 to 30 students assigned by the administration. The university can provide a field coordinator who cannot be the course instructor to work with the research team on the implementation of the experiment. Three universities agreed to participate in the study under these conditions.

U1 is a polytechnic university with an enrollment of 10, students and located in a city with a population of more than , people in the European part of Russia.

In the national ranking by selectivity of admission, U1 was ranked in the bottom half of higher education institutions. A cohort of second-year students majoring in Mechanical Engineering at U1 was selected to participate in the experiment. U2 is also a polytechnic university with an enrollment of 15, students located in a city of more than 1 million people in the European part of Russia.