Upcoming Paid Online Courses (9)

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Starts : 2017-03-30 in 5 days
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edX Free English Chemistry EdX Engineering KyotoUx Physics

The motion of falling leaves or small particles diffusing in a fluid is highly stochastic in nature. Therefore, such motions must be modeled as stochastic processes, for which exact predictions are no longer possible. This is in stark contrast to the deterministic motion of planets and stars, which can be perfectly predicted using celestial mechanics.

This course is an introduction to stochastic processes through numerical simulations, with a focus on the proper data analysis needed to interpret the results. We will use the Jupyter (iPython) notebook as our programming environment. It is freely available for Windows, Mac, and Linux through the Anaconda Python Distribution.

The students will first learn the basic theories of stochastic processes. Then, they will use these theories to develop their own python codes to perform numerical simulations of small particles diffusing in a fluid. Finally, they will analyze the simulation data according to the theories presented at the beginning of course.

At the end of the course, we will analyze the dynamical data of more complicated systems, such as financial markets or meteorological data, using the basic theory of stochastic processes.

Starts : 2017-05-22 in 58 days
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edX Free English Business & Management ColumbiaX EdX

Data is the lifeblood of an organization. Competency in programming is an essential skill for successfully extracting information and knowledge from data.

The goal of this course is to introduce learners to the basics of programming in Python and to give a working knowledge of how to use programs to deal with data.

In this course, we will first cover the basics of programming and then focus on using Python on the entire data management process from data acquisition to analysis of data big data and small data.

This is an intensive hands-on course that will equip and reward learners with proficiency in data management skills.

Starts : 2017-05-30 in 66 days
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edX Free English Computer Science EdX Engineering MITx Science

This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3.5. Even if you took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning. 

Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not "computation appreciation" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.

Starts : 2017-06-01 in 68 days
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edX Free English Computer Science Data Analysis & Statistics EdX UC San DiegoX

In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.).  But if you had access to a large dataset, would you be able to find the answers you seek?

This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

  • python
  • jupyter notebooks
  • pandas
  • nump
  • matplotlib
  • git
  • And many other tools  

You will learn these tools all within the context of solving compelling data science problems.

After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports. 

By learning these skills, you’ll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.

Starts : 2017-06-01 in 68 days
No votes
edX Free English Chemistry Computer Science EdX HarvardX Physics

Welcome to The Quantum World!

This course is an introduction to quantum chemistry: the application of quantum theory to atoms, molecules, and materials. You’ll learn about wavefunctions, probability, special notations, and approximations that make quantum mechanics easier to apply. You’ll also learn how to use Python to program quantum-mechanical models of atoms and molecules.

HarvardX has partnered with DataCamp to create assignments in Python that allow students to program directly in a browser-based interface. You will not need to download any special software, but an up-to-date browser is recommended.

This course has serious prerequisites. You will need to be comfortable with college-level chemistry and calculus. Some prior programming experience is also encouraged.

The Quantum World is ideal for:

  • Chemistry majors who want extra material alongside an on-campus course
  • Chemistry majors at an institution that does not offer quantum chemistry
  • Physics or CompSci majors who want to branch out to chemistry
  • Graduate students refreshing on quantum mechanics before their qualifying exams
  • Professional chemists who want to brush up on their skills

Starts : 2017-08-21 in 149 days
No votes
edX Free English Computer Science Data Analysis & Statistics EdX GTx

Please note that the verified certificate option is not currently open for this course. Please enroll in the audit track and you will be emailed when the verified certificate option is open for enrollment.

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies  numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

Starts : 2017-09-28 in 187 days
No votes
edX Free English Data Analysis & Statistics EdX Math UC San DiegoX

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL. 

Starts : 2018-01-03 in 284 days
No votes
edX Free English Computer Science Data Analysis & Statistics EdX UC San DiegoX

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

Starts : 2018-01-04 in 285 days
No votes
edX Free English Business & Management Data Analysis & Statistics EdX GTx

Please note that the verified certificate option is not currently open for this course. Please enroll in the audit track and you will be emailed when the verified certificate option is open for enrollment.

Businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses.

This course, part of the Analytics: Essential Tools and Methods MicroMasters program, prepares you to understand data and business analytics and become a leader in these areas in business organizations.

It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing you to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches you how to avoid the common pitfalls, emphasizing theimportance of applying proper business analytics techniques.

In addition to cases, this course features hands-on experiences with data collection, analysis, and visualization using Python programs and analytics software such as SAS.

This course includes a significant analytics project.