Online courses directory (209)

Sort by: Name, Rating, Price
Start time: Any, Upcoming, Recent started, New, Always Open
Price: Any, Free, Paid
Starts : 2017-09-26
No votes
edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX MITx Social Sciences

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal

A randomized evaluation, also known as a randomized controlled trial (RCT), field experiment or field trial, is a type of impact evaluation that uses random assignment to allocate resources, run programs, or apply policies as part of the study design.

This course will provide step-by-step training on how to design and conduct an RCT. You will learn about why and when to conduct RCTs and the key components of a well-designed RCT.

In addition, this course will provide insights on how to implement your RCT in the field, including questionnaire design, piloting, quality control, data collection and management. The course will also go over common practices to ensure research transparency.

No previous economics or statistics background is required. However, economic and statistics concepts and vocabulary will be used and some familiarity is advised.

19 votes
ALISON Free Mathematics Course Type: diploma Free to Access Mime Type - Scorm 1.2

ALISON.com's free online Diploma in Mathematics course gives you comprehensive knowledge and understanding of key subjects in mathematics. This course covers calculus, geometry, algebra, trigonometry, functions, vectors, data distributions, probability and probability and statistics. Math qualifications are in great demand from employers and this math course will greatly enhance your career prospects.<br />

No votes
ALISON Free Course Type: diploma Free to Access Mime Type - Scorm 1.2

The free online course Diploma in Modern Project Management Theory and Practice is designed for anyone who wants to learn about contemporary project management. With organisations looking to deliver projects faster and cheaper project management has evolved continuously since the 1980’s to help address these needs. Using the Project Management Institute's Book of Knowledge, along with other core theory, this course describes in detail the modern theory and best practices of project management. The course begins by introducing you to essential topics such as profiling projects, the phases and organizational structures within a project, and the tasks performed in a project. You will then learn how the role of project managers needs to shift emphasis; project managers need to move from managing the tasks to taking a leadership role. As projects become more complex project managers need to get a good understanding of what the clients' expectations are. They also need to know how decisions are reached, how information is communicated, and how and why changes are made to the project plans. The essential element of project management is about getting projects completed on time and within budget. In this course you will learn how a project, with its associated goals and processes, must be definitively established by a project manager. You will also learn how a project schedule covering both the team members and equipment needs to be drawn up, reviewed and approved. The course then describes why rigorous implementation of quality control is a very important aspect of project management, and why the project manager and the project team must be risk aware during each phase of the project. You will also learn about how the project manager has to oversee the procurement process by ensuring that the relevant contracts with suppliers are in place and that each supplier meets all the necessary requirements. The final part of the course explains how a project manager oversees the successful closure of a project, from gathering all the key statistics and summarising them, to closing out the supplier contracts, and archiving all the key documents. This free Diploma course will be of great interest to all professionals working in the area of project management or those who have become involved in a work project or learners who just want to be able to manage personal projects more effectively.<br />

13 votes
ALISON Free Mathematics Course Type: diploma Free to Access Mime Type - Scorm 1.2

Statistics and statistical methods play a major role in the work environment in areas such as business, science, finance, economics, engineering to mention just a few. It is very important that people are comfortable with reading statistics and using statistical methods. This free online Diploma in Statistics will give you the knowledge and understanding of basic statistical methods such as sampling and collecting data, probability, distributions, regression analysis. By completing this course you will gain the knowledge and understanding to confidently read statistics and apply statistical methods within your daily working environment.

Starts : 2016-10-17
No votes
Iversity Free English Interdisciplinary

Over the past decade, young people around the globe has faced very different but great challenges: unemployment, skills gap, vocational trainings, outdated educational formats, school-to-work transition etc. Consequently, "empowerment" has become the buzzword in business, evaluation and youth development. Because of its wide use, the word "empowerment" has many different meanings to people. “Discover Yourself: Build a Career and Make an Impact” is an interactive MOOC that will empower youth through a holistic approach on four different skills sets and dimensions:

Chapter 1: Personal Development: What do you love doing?

Chapter 2: Professional Development: What do you do well?

Chapter 3: Community Development: What is the community around you?

Chapter 4: Sustainable Development: What the world needs?

Empowerment is defined as a way to provide tips and opportunities for youth to develop the competencies they need to become successful contributing members of their communities.

The MOOC is part of a youth-strategic partnership project titled “Cease Cowering: Youth Empower Action Here!” (CC: YEAH) supported by the programme Erasmus +. The project is based on partnership between the Association for Education Mladiinfo International from Macedonia, Mladiinfo Slovensko (Slovakia), The Global Experience (Germany) and Iversity (Germany). As a part of the project, the course content was developed, the video shooting was organized in Bratislava, Slovakia and a special brochure containing the materials from the course was produced. The expected project results can be summed up as following:
- Development of an innovative approach in the process of self-learning based on Open Education Resources;
- Enlarged awareness on required skills and competences needed for youth career development;
- Increased soft, entrepreneurial and digital skills of youth through non-formal online education opportunities;
- Created a strategic approach and mutual action on youth development among involved partner organizations.

Who is this course for?

Discover Yourself: Build a Career and Make an Impact will be an inclusive, free and open course for all vision-driven, knowledge-hungry and goal-oriented young individuals. We aim to attract young people aged 18 to 30, from all around the globe who are eager to work on themselves and to grow both personally and professionally. The idea is to provide those interested youth with concrete skills and tools to boost their potentials as well as to bring relevant information to them that will encourage them to undertake concrete activities in the communities where they live. In the same time, we are also targeting youngsters who have fewer opportunities and face economic, social, educational or geographical difficulties and through the MOOC we want to bring them closer to the world of opportunities.

What will I learn?

The whole course content will be organized around 4 chapters devoted on: personal, professional, community and sustainable development.

At the end of the course, you will be able to:

Personal development chapter:

- Discover what your interests, values and personal characteristics are;

- Identify exactly what you want and what you do not want to do.

Professional development chapter:

- Understand the drivers behind your career preferences and choices;

- Learn how to recognize your talents and interests and how to match them with your concrete professional career;

- Identify the skills which you can offer as well as learn about your current skills gaps.

Community development chapter:

- Demonstrate knowledge and ability to create a plan to help you improve your soft skills;

- Recognize opportunities for your career enhancement both within and beyond your current status;

- Summarize your achievements and skills in appropriate formats for future employers or academic institutions.

Sustainable development chapter:

- Extend and make most effective use of your professional network both on- and off-line

- Learn what the world career trends are;

- Discover how your career choice can influence the world challenges.

What do I need to know?

This is an introductory course. Previous knowledge is not required. The course is designed for people interested in building up a career and make an impact.

Course Structure

Chapter 1: Personal Development

Our journey to self-discovery will start from the personal development. In the first chapter, we will give you the overview of your personality and how it could influence your career choice. In the three chapter units, you will identify what the personal values you appreciate the most are and how these values are influencing your choices. Also, you will be guided to discover your personality type and will have a chance to test yourself and find out which personality type you belong to and how you can connect it to your potential career.

Chapter 2: Professional Development

Once you have discovered who actually you really are and what you love doing, it is time to see what kind of career will fit your personality and interests. Your professional self is under huge pressure lately, right? So many trends, reports and statistics show new, undiscovered ways of working and imagining your job of the future. But nothing will ever make sense unless you get to know your professional self and make choices based on your skills, needs and capacities. In this chapter you will be able to walk the thin line between self-discovery and self-creation, while articulating your experience and skills into a successful career plan.

Chapter 3: Community Development

In the Community Development chapter, we will help you discover what and how the empowered YOU can do for your community, for your peers, for your neightbours. In this chapter, we will show you the ways how you can make an impact: as an individual, as part of non formal group, and as a member of an organization. At the end of the chapter, you will be able to identify a project idea, draft a basic proposal and find a suitable programme where you can apply for a project grant for your idea. But first and foremost, you will be inspired to take the community in your hands and move the things forward!

Chapter 4: Sustainable Development

In the last chapter we will take a step back. What is the big picture? What are the challenges in the world today? We will speak about Sustainable Development Goals and discover ways how you can expand your circle of influence. We will finish the course with three elements of sustainable development - the economic, social and environmental. We want to motivate you to take personal social responsibility for your surrounding and for the whole world.

At the end of the course, the students will be able to draft a personal development plan that will present their pathway to their personal, professional, community and sustainable development.

Starts : 2016-07-11
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX UC BerkeleyX

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization. Learning algorithms enable a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications like self-driving cars and personalized medicine. In the age of ‘big data’, with datasets rapidly growing in size and complexity and cloud computing becoming more pervasive, machine learning techniques are fast becoming a core component of large-scale data processing pipelines.

This statistics and data analysis course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. You will gain hands-on experience applying these principles using Spark, a cluster computing system well-suited for large-scale machine learning tasks, and its packages spark.ml and spark.mllib. You will implement distributed algorithms for fundamental statistical models (linear regression, logistic regression, principal component analysis) while tackling key problems from domains such as online advertising and cognitive neuroscience.

4 votes
Saylor.org Free Closed [?] Business Economics

In this course, you will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge.  You will learn about how statistics and probability work together.  The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data.  Statistics formalizes the process of making decisions, and this course is designed to help you use statistical literacy to make better decisions.  Note that this course has applications for the natural sciences, economics, computer science, finance, psychology, sociology, criminology, and many other fields. We read data in articles and reports every day.  After finishing this course, you should be comfortable evaluating an author's use of data.  You will be able to extract information from articles and display that information effectively.  You will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistic…

4 votes
Saylor.org Free Closed [?] Business Economics

Math for Economists will help you assemble a toolkit of skills and techniques to solve fundamental problems in both macroeconomics and microeconomics.  The material covers both precalculus and calculus concepts and should help you identify the best approach to solving problems.  For example, an economist may be called upon to determine the right mix allocation of capital to a production process.  The tools in this course will help you evaluate the options and select from the best alternatives.  Advanced courses in economics typically utilize mathematical techniques beyond basic calculus; so, gaining practice in fundamental skills can serve as a good basis for further study.  Of note, this course applies precalculus and calculus; this is different from “applied math,” which economists typically use to refer to probability and statistics. This course begins with a survey of basic optimization tools and then applies them to solve problems over several periods in time.  These optimization tools descri…

Starts : 2015-08-24
No votes
JANUX Free Closed [?] Department of Economics Janux University of Oklahoma

Elementary Business Statistics is the first level statistics class required for all Business and Economics majors and can be used to satisfy the statistics requirement in other departments if you are enrolled as a for-credit OU-student. It is designed as an introductory course to statistics theory and methodology. The foundation of statistical inference is first developed through coverage of descriptive statistics and then close coverage of probability theory and probability density functions: specifically the binomial, Poisson, geometric, and normal distributions. The leap from probability theory to actual inferential statistics by way of the Central Limit Theory is accomplished with the use of a gaming model in which students actively participate to solve a constrained goals a constrained goal that requires an understanding of the link between the Central Limit Theorem and the sample size. Confidence internals and Hypothesis testing follow with careful development of both the theory and philosophy of the hypothesis test. Specific hypotheses are covered including test for means, proportions, differences in mean and differences in proportions, and for variances. Finally correlations and regression are covered in detail. The foundation OLS model is developed and students will be able to propose and test various hypotheses concerning a causal relationship assumed to be linear. For credit Oklahoma University students will have the opportunity to interact with face2face teaching assistants once per week in regularly scheduled lab classes.

1 votes
OLI. Carnegie Mellon University Free General & Interdisciplinary Studies Carnegie Mellon University Open Learning Initiative

In this course you will learn how to conduct research using empirical methods, which rely on observation and experimentation. This course is appropriate for those interested in using empirical research methods in their field, particularly students in the social and behavioral sciences. Topics include the formulation of the question to be investigated and the of resulting hypotheses, the collection of data and the analysis of the data collected, and the interpretation and study of analysis results. We assume that learners entering Empirical Research Methods (ERM) have taken at least a semester or year-long course in statistics and, through this or some other experience, have been exposed to the following concepts: Random Variables Population and Samples Data Tables (rows=sample units and columns=variables) Summary Statistics: Mean, Median, Variance, Covariance, Correlation Graphs: Boxplots, Barcharts, Histograms, Scatterplots Inference: standard errors, confidence intervals, hypothesis tests, etc. Models: Bivariate Regression, perhaps ANOVA If learners have not had such exposure, they can follow the appropriate links into the OLI introductory statistics course to review the required concepts.

Starts : 2014-09-01
No votes
MIT OpenCourseWare (OCW) Free Graduate MIT Nuclear Science and Engineering OpenCourseWare

This half-semester course introduces computational methods for solving physical problems, especially in nuclear applications. The course covers ordinary and partial differential equations for particle orbit, and fluid, field, and particle conservation problems; their representation and solution by finite difference numerical approximations; iterative matrix inversion methods; stability, convergence, accuracy and statistics; and particle representations of Boltzmann's equation and methods of solution such as Monte-Carlo and particle-in-cell techniques.

Starts : 2015-09-28
No votes
Canvas.net Free Closed [?]

Every person working or aspiring to work in the business world needs quantitative skills, which include sufficient comfort with equations and statistics to make informed decisions. This course will provide you with these essential competencies.

Starts : 2017-07-22
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX Math Microsoft

This course is part of the Microsoft Professional Program Certificate in Data Science.

If you’re considering a career as a data analyst, you need to know about histograms, Pareto charts, Boxplots, Bayes’ theorem, and much more. In this applied statistics course, the second in our Microsoft Excel Data Analyst XSeries, use the powerful tools built into Excel, and explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives. Learn about descriptive statistics, basic probability, random variables, sampling and confidence intervals, and hypothesis testing. And see how to apply these concepts and principles using the environment, functions, and visualizations of Excel.

As a data science pro, the ability to analyze data helps you to make better decisions, and a solid foundation in statistics and basic probability helps you to better understand your data. Using real-world concepts applicable to many industries, including medical, business, sports, insurance, and much more, learn from leading experts why Excel is one of the top tools for data analysis and how its built-in features make Excel a great way to learn essential skills.

Before taking this course, you should be familiar with organizing and summarizing data using Excel analytic tools, such as tables, pivot tables, and pivot charts. You should also be comfortable (or willing to try) creating complex formulas and visualizations. Want to start with the basics? Check out DAT205x: Introduction to Data Analysis using Excel. As you learn these concepts and get more experience with this powerful tool that can be extremely helpful in your journey as a data analyst or data scientist, you may want to also take the third course in our series, DAT206x Analyzing and Visualizing Data with Excel. This course includes excerpts from Microsoft Excel 2016: Data Analysis and Business Modeling from Microsoft Press and authored by course instructor Wayne Winston.

This course is also part of the Microsoft Excel for the Data Analyst XSeries.

Starts : 2015-07-07
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX KIx Math

Do you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics!

Skilled professionals who can process and analyze data are in great demand today. In this course you will explore concepts in statistics to make sense out of data. You will learn the practical skills necessary to find, import, analyze and visualize data. We will take a look under the hood of statistics and equip you with broad tools for understanding statistical inference and statistical methods. You will also perform some really complicated calculations and visualizations, following in the footsteps of Karolinska Institute’s researchers.

Statistical programming is an essential skill in our golden age of data abundance. Health science has become a field of big data, just like so many other fields of study. New techniques make it possible and affordable to generate massive data sets in biology. Researchers and clinicians can measure the activity for each of 30000 genes of a patient. They can read the complete genome sequence of a patient. Thanks to another trend of the decade, open access publishing, the results of such large scale health science are very often published for you to read free of charge. You can even access the raw data from open databases such as the gene expression database of the NCBI, National Center for Biotechnology Information.

We will dive into this data together. Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course. 

No votes
ALISON Free Course Type: diploma Free to Access Mime Type - Scorm 1.2

This free diploma course provides students with the mathematical knowledge and skills needed to study Business or Commerce at third-level. The course consists of maths tutorial videos in which qualified maths teachers resolve problems in real-time. Furthermore, a comprehensive assessment tests students on all aspects of mathematics which are related to Business &amp; Commerce. This course will appeal to third-level students who are lacking confidence in their mathematical knowledge and skills. This course will also appeal to students who are re-entering formal education after a significant absence. This diploma course will help to help to build students’ confidence in their mathematical ability and ensure that they are prepared for third-level study. The topics covered in this course are, Introduction to Mathematics, Algebra, Equations and Functions, Calculus, Probability and Statistics, Calculus, Matrices, Statistics and Introduction to Business Mathematics.<br />

No votes
ALISON Free Course Type: diploma Free to Access Mime Type - Scorm 1.2

This free diploma course provides students with the mathematical knowledge and skills needed to study a Science, Technology or Engineering discipline at third-level. The course consists of maths tutorial videos in which qualified maths teachers resolve problems in real-time. Furthermore, a comprehensive assessment tests students on all aspects of mathematics which are related to science, technology and engineering. This course will appeal to third-level students who are lacking confidence in their mathematical knowledge and skills. This course will also appeal to students who are re-entering formal education after a significant absence. This diploma course will help to build students’ confidence in their mathematical ability and ensure that they are prepared for third-level study. The topics covered in this course are, Introduction to Mathematics, Algebra, Equations and Functions, Calculus, Probability and Statistics, Calculus, Matrices, Trigonometry and Complex Numbers.<br />

Starts : 2017-06-01
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Math UTAustinX

In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.

This course will consist of:

  • Instructional videos for statistical concepts broken down into manageable topics
  • Guided questions to help your understanding of the topic
  • Weekly tutorial videos for using R Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets
  • Weekly wrap-up questions challenging both topic and application knowledge

We will cover basic Descriptive Statistics – learning about visualizing and summarizing data, followed by a “Modeling” investigation where we’ll learn about linear, exponential, and logistic functions. We will learn how to interpret and use those functions with basic Pre-Calculus. These two “units” will set the learner up nicely for the second part of the course: Inferential Statistics with a multiple regression cap.

Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Join us in learning how to look at the world around us. What are the questions? How can we answer them? And what do those answers tell us about the world we live in?

Starts : 2017-06-01
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Math UTAustinX

In the second part of a two part statistics course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data. Then, we’ll apply the correct statistical tool to help answer our question of interest – using R and hands-on Labs. Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion.

We will cover basic Inferential Statistics – integrating ideas of Part 1. If you have a basic knowledge of Descriptive Statistics, this course is for you. We will learn how to sample data, examine both quantitative and categorical data with statistical techniques such as t-tests, chi-square, ANOVA, and Regression.

Both parts of the course are intended to cover the same material as a typical introductory undergraduate statistics course, with an added twist of modeling. This course is also intentionally devised to be sequential, with each new piece building on the previous topics. Once completed, students should feel comfortable using basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R).

This course will consist of:

  • Instructional videos for statistical concepts broken down into manageable topics
  • Guided questions to help your understanding of the topic
  • Weekly tutorial videos for using R
  • Scaffolded learning with Pre-Labs (using R), followed by Labs where we will answer specific questions using real-world datasets
  • Weekly wrap-up questions challenging both topic and application knowledge

With these new skills, learners will leave the course with the ability to use basic statistical techniques to answer their own questions about their own data, using a widely available statistical software package (R). Learners from all walks of life can use this course to better understand their data, to make valuable informed decisions.

Join us in learning how to look at the world around us. What are the questions? How can we answer them? And what do those answers tell us about the world we live in?

Starts : 2007-09-01
14 votes
MIT OpenCourseWare (OCW) Free Life Sciences Brain and Cognitive Sciences MIT OpenCourseWare Undergraduate

We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory.

The goals of this course are to help students become savvy and critical readers of the current neuroimaging literature, to understand the strengths and weaknesses of the technique, and to design their own cutting-edge, theoretically motivated studies. Students will read, present to the class, and critique recently published neuroimaging articles, as well as write detailed proposals for experiments of their own. Lectures will cover the theoretical background on some of the major areas in high-level vision, as well as an overview of what fMRI has taught us and can in future teach us about each of these topics. Lectures and discussions will also cover fMRI methods and experimental design. A prior course in statistics and at least one course in perception or cognition are required.

Starts : 2015-06-08
No votes
JANUX Free Closed [?] Janux School of Industrial and Systems Engineering

This course provides fundamental concepts in probability and statistical inference, with application to engineering contexts. Probability topics include counting methods, discrete and continuous random variables, and their associated distributions. Statistical inference topics include sampling distributions, point estimation, confidence intervals and hypothesis testing for single- and two-sample experiments, nonparametric statistics, and goodness-of-fit testing. Excel will be used to demonstrate how to solve some class examples, and you'll be expected to use Excel to solve some homework problems. The statistical software package R will be introduced to address very basic statistics problems. Course prerequisites include calculus (differentiation and integration).

Trusted paper writing service WriteMyPaper.Today will write the papers of any difficulty.