About this course

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn basic algorithmic techniques and ideas for computational problems, which arise in practical applications such as sorting and searching, divide and conquer, greedy algorithms and dynamic programming.

This course will cover theories, including:

• how to sort data and how it helps for searching;
• how to break a large problem into pieces and solve them recursively;
• when it makes sense to proceed greedily;
• how dynamic programming is used in genomic studies.

You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

What you’ll learn

• Essential algorithmic techniques – greedy algorithms, divide and conquer, binary search, sorting, dynamic programming
• Best practices of implementing algorithms efficiently
• Ways of testing and debugging programs

Prerequisites

• Basic knowledge of at least one programming language: loops, arrays, stacks, recursion.
• Basic knowledge of mathematics: proof by induction, proof by contradiction.

Who can take this course?

All

About MIT horizon

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About This Course:

In this course, part of our Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.

Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.

What You’ll Learn:

• Important concepts in probability theory including random variables and independence
• How to perform a Monte Carlo simulation
• The meaning of expected values and standard errors and how to compute them in R

Frequently Asked Questions:

About This Course:

Statistical inference and modeling 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 modeling 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 modeling. 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.

What You’ll Learn:

• The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
• How to use models to aggregate data from different sources
• The very basics of Bayesian statistics and predictive modeling

Frequently Asked Questions:

Honor code statement
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

Research statement
By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX’s instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University’s stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.

Please read the edX Privacy Policy for more information regarding the processing, transmission, and use of data collected through the edX platform.

Nondiscrimination/anti-harassment statement
Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

About This Course:

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.

What You’ll Learn:

• How to apply the knowledge base and skills learned throughout the series to a real-world problem
• How to independently work on a data analysis project

Frequently Asked Questions:

Honor code statement
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

Research statement
By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX’s instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University’s stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.

Please read the edX Privacy Policy for more information regarding the processing, transmission, and use of data collected through the edX platform.

Nondiscrimination/anti-harassment statement
Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

About This Course:

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 ofourProfessional 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.

What You’ll Learn:

• How linear regression was originally developed by Galton
• What is confounding and how to detect it
• How to examine the relationships between variables by implementing linear regression in R

Frequently Asked Questions:

Honor code statement
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

Research statement
By registering as an online learner in our open online courses, you are also participating in research intended to enhance HarvardX’s instructional offerings as well as the quality of learning and related sciences worldwide. In the interest of research, you may be exposed to some variations in the course materials. HarvardX does not use learner data for any purpose beyond the University’s stated missions of education and research. For purposes of research, we may share information we collect from online learning activities, including Personally Identifiable Information, with researchers beyond Harvard. However, your Personally Identifiable Information will only be shared as permitted by applicable law, will be limited to what is necessary to perform the research, and will be subject to an agreement to protect the data. We may also share with the public or third parties aggregated information that does not personally identify you. Similarly, any research findings will be reported at the aggregate level and will not expose your personal identity.

Please read the edX Privacy Policy for more information regarding the processing, transmission, and use of data collected through the edX platform.

Nondiscrimination/anti-harassment statement
Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

About this course

Manufacturing systems are complex systems that require analytical analysis. Managers and practitioners use a wide variety of methods to analyze and optimize the performance of manufacturing systems and control costs.

In this course, part of the Principles of Manufacturing MicroMasters program, you will learn about Multi-Part-Type Manufacturing Systems. We will discuss Material Requirements Planning (MRP), Multi-Stage Control and Scheduling as well as Simulation and Quality.

This course will enable you to develop an intuition about stochastic production lines. You will understand the importance and cost of inventory buffers, run basic simulation and optimizations and develop a policy to manage production systems.

The topics that we cover will provide the basis for you to continue into the manufacturing field in roles such as an operations manager and supply chain manager.

This course should be taken in sequence following Introduction to Manufacturing Systems.

Develop the skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing & Design program.

What you’ll learn

• Understand the application of stochastic production line models
• Issues to consider in the design and use of simulations
• Material requirements planning (MRP) to better manage manufacturing processes

Prerequisites

Must have completed Introduction to Manufacturing Systems I. In addition to having knowledge and comfortability with undergraduate-level calculus, probability and statistics.

About This Course:

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn basic algorithmic techniques and ideas for computational problems, which arise in practical applications such as sorting and searching, divide and conquer, greedy algorithms and dynamic programming.

This course will cover theories, including:

• how to sort data and how it helps for searching;
• how to break a large problem into pieces and solve them recursively;
• when it makes sense to proceed greedily;
• how dynamic programming is used in genomic studies.

You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

What You’ll Learn:

• Essential algorithmic techniques – greedy algorithms, divide and conquer, binary search, sorting, dynamic programming
• Best practices of implementing algorithms efficiently
• Ways of testing and debugging programs

Prerequisites:

• Basic knowledge of at least one programming language: loops, arrays, stacks, recursion.
• Basic knowledge of mathematics: proof by induction, proof by contradiction.

Who can take this course?

All

About This Course:

Randomness is inherent in all processes including manufacturing. The fundamental concepts taught in this course will help learners develop powerful statistical process control methods that are the foundation of world-class manufacturing quality.

As part of the Principles of Manufacturing MicroMasters program, this course will introduce statistical methods that apply to any unit manufacturing process. We will cover the following topics:

• Recognizing inherent variability in continuous production
• Identifying sources of process output variation
• Describing variation in a structured manner
• Applying basic probability and statistics concepts to characterize process variation
• Differentiating between design specifications and process capability
• Synthesizing novel approaches to unfamiliar situations by extending the core material (i.e. go beyond the “standard” uses).
• Assessing the appropriateness of various statistical methods for a variety of problems

Develop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing & Design program.

What You’ll Learn:

• Variation modeling using the theory of Random Processes
• Statistical Process Control (SPC) foundations and applications
• Xbar, EWMA, CUSUM and discrete event methods for detecting process problems
• Methods for analyzing process changes by looking at general process physics
• How to apply these methods to achieve world-class quality in unit manufacturing processes

Prerequisites:

• Engineering Undergraduate preparation
• Some knowledge of basic manufacturing processes
• Knowledge or probability theory is helpful but not necessary