# Data Science: Inference and Modeling

**#69**in category Data Science

Learner rating | 8 |
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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.

## 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

## 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

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