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- Statistics For Data Science

- Use Tableau for Your Data Science Workflow: University of California, Irvine
- Essential Linear Algebra for Data Science: University of Colorado Boulder
- Essential Causal Inference Techniques for Data Science: Coursera Project Network
- SQL for Data Science Capstone Project: University of California, Davis
- Tidyverse Skills for Data Science in R: Johns Hopkins University
- Cybersecurity for Data Science: University of Colorado Boulder
- Statistics for Machine Learning for Investment Professionals: CFA Institute
- Advanced Linear Models for Data Science 2: Statistical Linear Models: Johns Hopkins University
- AutoML tools for data science: Coursera Project Network
- IBM Data Analyst: IBM Skills Network

Statistics for data science refers to the mathematical analysis used to sort, analyze, interpret, and present data. It includes concepts like probability distribution, regression, and over or under-sampling. Descriptive statistics organizes data based on characteristics of the data set, such as normal distribution, central tendency, variability, and standard deviation. Inferential statistics incorporates the use of probability theory to infer characteristics of the data set.

Learning statistics for data science can lead to career opportunities in data science and related fields. As organizations increasingly rely on data to make decisions, they tend to seek out analysts who understand how to work with data and present it to stakeholders. Learning statistics for data science can also provide a good salary. As of 2020, the median pay for computer and information research scientists in the US is $122,840 and the job market remains positive, according to the Bureau of Labor Statistics. Mathematicians and statisticians have a similar job outlook and a median salary of $92,030 per year.

Data analysis, data architects, data scientists, and information officers typically use statistics for data science in their regular work. Data science is a broad field, and statistics can be useful in other roles that require analyzing and presenting data. This includes data warehouse analysts, data visualization developers, database managers, and machine learning engineers. Additional related fields include financial analysts, teachers, and researchers working for universities and corporate settings.

Through online courses, you can learn the fundamentals of statistics for data science, including the theories and techniques statisticians use in their work. Some courses explore fundamental concepts like Bayes’ Theorem and probability theory. Others present methods for calculating and evaluating data sets. You can brush up on your knowledge of programs statisticians use, like Excel and Python, or examine the application of statistics specific fields.

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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