Statistics for Data Science
Statistics for Data Science
Statistics for Data Science
Those possibilities in Data Science are known as Statistical Analysis. Most of us can't help thinking about how could data in the form of text, images, videos, and other profoundly unstructured formats get easily processed by Machine Learning models. Statistics acts as a pathway to understand your data and process that for successful results.
Table of Contents
- Introduction
- Sample or population data?
- The fundamentals of descriptive statistics
- Measures of central tendency, asymmetry, and variability
- Practical example: descriptive statistics
- Distributions
- Estimators and estimates
- Confidence intervals: advanced topics
- Practical example: inferential statistics
- Hypothesis testing: Introduction
- Hypothesis testing: Let's start testing!
- Practical example: hypothesis testing
- The fundamentals of regression analysis
- Subtleties of regression analysis
- Assumptions for linear regression analysis
- Dealing with categorical data
- Practical example: regression analysis
Statistics for Data Science FAQs
Is data science a dying career?
There are no sharp upturns or downturns. This could suggest that data science will not just abruptly disappear in the near future. All things considered, there would be a slow decline after some time, of which there right now isn't really any proof.
What is the future of data science?
You can think about the data increase from IoT or social data at the edge. Assuming we look somewhat more ahead, the US Bureau of Labor Statistics predicts that by 2026—so around six years from now—there will be 11.5 million jobs in data science and analytics.
Why statistics is used in data science?
Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can search for meaningful trends and changes in Data. In simple words, Statistics can be used to get meaningful insights from data by performing mathematical computations on it.