Micro-Masters Program in Statistics and Data Science

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  • Course platform: EDX
  • Offered by: Massachusetts Institute of technology
  • Level: Advanced
  • Price: Paid Course (paid Certificate)
  • Class length Approx.1year, 2 months (10-14 hrs/week)

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Micro-Masters Program in Statistics and Data Science

About Data Science

About Data Science Micro masters program EDX

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data both structured and unstructured. Data science is related to computer science but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.

What does a Data Scientist do?

What does a Data Scientist do EDX Image

A Data Scientist is a person who should have multiple skills and has to play multiple roles in his day to day work.

The main aim of the Data Scientists is to analyze the Big Data for extracting the meaning out of it for their organization. To achieve this, they need to perform various activities such as sometimes they have to work as a mathematician, sometimes as an analyst, sometimes as a computer scientist, and much more.

What you will learn from this course?

Upon completion of this course you will gain proficiency in the following points:

  • Master the foundations of data science, statistics, and machine learning
  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
  • Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
  • Finishing this Micro-Masters program on Statistics and Data Science will prepare you for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer

Who can take this course?

Some of the learners from one or more of the following countries or regions will not be able to register for some or all of the courses in this program: Iran, Cuba, and the Crimea region of Ukraine. Please check the individual course About Pages for a specific direction. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

What is a Micro-Masters Program?

Micro-Masters programs are a series of graduate-level courses from top universities designed to advance your career. Micro-Masters program certificates showcase deep learning and in-demand skills to employers and can help you get started on a path toward completing an advanced degree.

Syllabus

There are 5 Courses in the Statistics and Data Science professional certificate program:

1. Probability- The Science of Uncertainty and Data

In this course, you will be able to build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference.

This course covers all of the basic probability concepts, including:

  • Multiple discrete or continuous random variables, expectations, and conditional distributions.
  • Laws of large numbers and main tools of Bayesian inference methods with an introduction to random processes (Poisson processes and Markov chains).
  • Probability models and axioms.
  • Conditioning and independence.
  • Discrete random variables.

2. Data Analysis in Social Science- Assessing your knowledge

 In this course, you will learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge, also it will introduce you to the essential notions of probability and statistics. It will cover techniques in modern data analysis which includes:

  • Estimation, regression, and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning.
  • Data visualization- It will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, it will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

3. Fundamental of Statistics

Fundamental of Statistics will help you to develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing, and prediction. In this master-program, you will gain expertise in the following concepts such as:

  • Construct estimators using the method of moments and maximum likelihood, and decide how to choose between them.
  • Quantify uncertainty using confidence intervals and hypothesis testing.
  • Choose between different models using the goodness of fit test.
  • Make predictions using linear, nonlinear, and generalized linear models.
  • Perform dimension reduction using principal component analysis (PCA).

4. Machine Learning with Python: Linear Models to Deep Learning

In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. This course will cover the following details such as:

  • Representation, over-fitting, regularization, generalization, VC dimension;
  • Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
  • On-line algorithms, support vector machines, and neural networks/deep learning.

This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT Ph.D. or a Master’s at other universities. (For more visit: https://micromasters.mit.edu/ds/).

5. Capstone Exam for Statistics and Data Science

This capstone exam is the final part of the MITx Micro-Masters Program in Statistics and Data Science. Upon completion of the four courses in this program, you will be able to take this virtually-proctored exam to earn your Micro-Masters credential and demonstrate your proficiency in data science or accelerate your path towards an MIT Ph.D. or a Master’s at other universities. In this course, you will solidify and demonstrate your knowledge and abilities in probability, data analysis, statistics, and machine learning in this culminating assessment

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FAQ

Specification: Micro-Masters Program in Statistics and Data Science

Course Platform EDX
Level Advanced
Class length <2 years
Program details Microdegree
Enrollment Paid Course (paid certificate)
Course Subjects Data Analytics, Data Science
Offered by University Massachusetts Institute of Technology

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Micro-Masters Program in Statistics and Data Science
Micro-Masters Program in Statistics and Data Science