# What are the best online statistics courses?

## What are the best online statistics courses?

A Quick Look: Best Statistics Online Courses

• Statistics for Data Science and Business Analysis by Udemy.
• Basic Statistics by the University of Amsterdam.
• Everyday Statistics with Eddie Davila by LinkedIn Learning.
• Python Statistics Essential Training by LinkedIn Learning.
• Statistics with R — Intermediate Level by Udemy.

How do you handle missing data in statistics?

Best techniques to handle missing data

1. Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
2. Use regression analysis to systematically eliminate data.
3. Data scientists can use data imputation techniques.

### How do I learn statistics for data analysis?

1. Step 1: Learn Descriptive Statistics. Udacity course on descriptive statistics from Udacity.
2. Step 2: Learn Inferential statistics. Undergo the course on Inferential statistics from Udacity.
3. Step 3: Predictive Model (Learning ANOVA, Linear and Logistic Regression on SAS)
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What percentage of missing data is acceptable?

Proportion of missing data Yet, there is no established cutoff from the literature regarding an acceptable percentage of missing data in a data set for valid statistical inferences. For example, Schafer ( 1999 ) asserted that a missing rate of 5\% or less is inconsequential.

## What happens when dataset includes records with missing data?

If it’s a large dataset and a very small percentage of data is missing the effect may not be detectable at all. In any case, generally missing data creates imbalanced observations, cause biased estimates, and in extreme cases, can even lead to invalid conclusions.

What is the best strategy for handling missing data?

Imputation – or filling-in missing values according to some rule – is typically the best strategy for handling missing data. There are many ways to approach this, ranging from simple to complex. A few potential options are discussed below: Simply using the mean or median in place of the missing value is a straight-forward method of imputation.

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### How does missing data affect your data science project?

Missing or incomplete data can have a huge negative impact on any data science project. This is particularly relevant for companies in the early stages of developing solid data collection and management systems.

What are the different types of missing data?

There are three main categories of missing data: Missing Completely at Random (MCAR): In this scenario, the missing data are unrelated to the observation being studied or the other variables in the data set. Essentially, there are no systemic differences between the observations with and without missing data.

## How to handle missing data in a time series analysis?

For time-series data, a straight-forward option is to use the last observed value for any missing data. Similarly, we could use the first observation after the missing value, which is referred to as Next Observation Carried Backward (NOCB).