Data Science: Inference and Modeling

Add your review
Product is rated as #69 in category Data Science
Learner rating8

You’ll see how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and how to do this using R.

Last updated on June 17, 2021 11:20 am

Introduction

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

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 it will also 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, you will be able to put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.

What you will learn from this course on Inference and Modeling?

  • Concepts that are 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.

Prerequisites

  • Data Science: Probability or basic knowledge of probability theory.

Syllabus

1. Inference and Modeling: Introduction and Welcome

2. Section 1: Parameters and Estimates

3. Section 2: The Central Limit Theorem in Practice

4. Section 3: Confidence Intervals and p-Values

5. Section 4: Inference and Modeling: Statistical Models

6. Section 5: Bayesian Statistics

7. Section 6: Election Forecasting

8. Section 7: Inference and Modeling: Association Tests

9. Inference and Modeling: Course Wrap-up: Brexit

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

$99.00

Add to wishlistAdded to wishlistRemoved from wishlist 0
Add to compare
  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Data Science Data Science with 'R' Statistics
Expert Score
8
Learner rating
8
PROS: Insight of two concepts that are ubiquitous - confidence intervals, and p-values. You will learn about Bayesian modeling. It is easy, quick, and packed with great material. You get quite a good efficiency between time spent and concepts/practical knowledge learnt on the subject.
CONS: Beginners should have prior knowledge of Probability.

Description

Introduction

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

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 it will also 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, you will be able to put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.

What you will learn from this course on Inference and Modeling?

  • Concepts that are 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.

Prerequisites

  • Data Science: Probability or basic knowledge of probability theory.

Syllabus

1. Inference and Modeling: Introduction and Welcome

2. Section 1: Parameters and Estimates

3. Section 2: The Central Limit Theorem in Practice

4. Section 3: Confidence Intervals and p-Values

5. Section 4: Inference and Modeling: Statistical Models

6. Section 5: Bayesian Statistics

7. Section 6: Election Forecasting

8. Section 7: Inference and Modeling: Association Tests

9. Inference and Modeling: Course Wrap-up: Brexit

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Data Science Data Science with 'R' Statistics

User Reviews

0.0 out of 5
0
0
0
0
0
Write a review

There are no reviews yet.

Be the first to review “Data Science: Inference and Modeling”

Your email address will not be published. Required fields are marked *

Data Science: Inference and Modeling
Data Science: Inference and Modeling

$99.00

courseonline.info
courseonline.info
Logo
Compare items
  • Total (0)
Compare
0