Massachusetts Institute of Technology is glad to announce “Data Analysis for Social Scientists” free online course. The course is aimed to tell you about methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest.
The course will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. The course will start on February 6, 2017.
Course At A Glance
- Length: 12 weeks
- Effort: 12 hours per week
- Subject: Data Analysis & Statistics
- Institution: Massachusetts Institute of Technology
- Languages: English
- Price: Free
- Session: The course will start on February 6, 2017.
Providers Details
About University: Massachusetts Institute of Technology — a coeducational, privately endowed research university founded in 1861 — is dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century.
Requirements
No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.
About This Course
- This statistics and data analysis course will introduce you to the essential notions of probability and statistics. The course provides instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.The language of the course is English.
- Data analysis is important for a research will be an understatement rather no research can survive without data analysis.Data analysis can be done by different methods as according to the needs and requirements of different domains like science, business, social science dissertation etc.
How to Join This Course
You should register yourself through the given link to join this free on-line course: https://courses.edx.org/register?course_id=course-v1%3AMITx%2B14.310x%2B1T2017&enrollment_action=enroll&email_opt_in=true
Course Format
Module 0: The Basics of R
- Introduction to the software R with exercises. Suggested resources for learning more on the web.
Module 1: Introduction
- Introduction to the power of data and data analysis, overview of what will be covered in the course.
Module 2: Fundamentals of Probability, Random Variables, Distributions And Joint Distributions
- Basics of probability and introduction to random variables.
- Discussion of distributions and joint distributions.
Module 3: Gathering and Collecting Data, Ethics, And Kernel Density Estimates
- Introduction to collecting data through surveys, web scraping, and other data collection methods.
- Principles and practical steps for protection of human subjects in research.
- Discussion of kernel density estimates.
Module 4: Joint, Marginal, and Conditional Distributions & Functions of Random Variables
- Builds on the basics from module 2 to cover joint, marginal, and conditional distributions.
- Similarly builds on the basics from module 2 to cover functions of random variables.
Module 5: Moments of A Random Variable, Applications To Auctions, & Intro To Regression
- Discussion of moments of a distribution, expectation, and variance.
- Application of some principles of probability to the analysis of auctions.
- Basics of regression analysis.
Module 6: Special Distributions, the Sample Mean, Central Limit Theorem, And Estimation
- Discussion of properties of special distributions with several examples.
- Statistics: Introduction to the sample mean, central limit theorem, and estimation.
Module 7: Assessing And Deriving Estimators- Confidence Intervals And Hypothesis Testing
- Deriving and assessing estimators.
- Constructing and interpreting confidence intervals.
- Introduction to hypothesis testing.
Module 8: Causality, Analysing Randomized Experiments, & Nonparametric Regression
- Understanding randomization in the context of experimentation.
- Introduction to nonparametric regression techniques.
Module 9: Single and Multivariate Linear Models
- In-depth discussion of the linear model and the multivariate linear model.
Module 10: Practical Issues in Running Regressions, And Omitted Variable Bias
- Covariates, fixed effects, and other functional forms.
- Introduction to regression discontinuity design.
Module 11: Intro To Machine Learning And Data Visualization
- Introduction to the use of machine learning for prediction. Covers tuning and training.
- Principles of data visualization with examples of well-crafted visual presentations of data.
Module 12: Endogeneity, Instrumental Variables, And Experimental Design
- Understanding the problem of endogeneity. Introduction to instrumental variables and two stage least squares, with a discussion of how to assess the validity of an instrument.
- Discussion of how to design an effective experiment, followed by an example from Indonesia.
Why Take This Course
- Advantage of course: This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively. The course will introduce you to the use of machine learning for prediction. Covers tuning and training.
Learning Outcomes
- Intuition behind probability and statistical analysis
- How to summarize and describe data
- A basic understanding of various methods of evaluating social programs
- How to present results in a compelling and truthful way
- Skills and tools for using R for data analysis
Instructors
- Esther Duflo: She is the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics at MIT.
- Sara Fisher Ellison: She is a Senior Lecturer in the MIT Economics Department.
Suggested Re eading
The basic knowledge of calculus and algebra is helpful to you to be entering in the course.
You Might Also Be Interested In
If you are interested you can also join “Business and Data Analysis Skills” course.
Conclusion
- After completing the course you will gain knowledge how to summarize and describe data
- You will get techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design
- After completing the course you will be able to answer questions of cultural, social, economic, and policy interest.
Detailed Information
For more information about the course, you can check the given link:
https://www.edx.org/course/data-analysis-social-scientists-mitx-14-310x-0#!
