What is statistics for data science?

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.

Why learn statistics for data science?

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.

What are typical careers that use statistics for data science?

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.

How can online courses help me learn statistics for data science?

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.

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Statistics For Data Science

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Stanford University
Introduction to Statistics
習得できるスキル： Data Science, General Statistics, Probability & Statistics, Statistical Tests, Estimation, Basic Descriptive Statistics, Correlation And Dependence, Probability Distribution, Regression, Bayesian Statistics, Data Analysis, Data Visualization, Econometrics, Experiment, Machine Learning, Markov Model, Plot (Graphics), Statistical Analysis, Statistical Visualization
4.5
（1.9k件のレビュー）
Beginner · Course · 1-3 Months
Johns Hopkins University
Advanced Statistics for Data Science
習得できるスキル： Probability & Statistics, General Statistics, Mathematics, Probability Distribution, Regression, Linear Algebra, Bayesian Statistics, Experiment, Econometrics, Machine Learning, Basic Descriptive Statistics, Biostatistics, Calculus, Statistical Tests, Algebra, Artificial Neural Networks, Dimensionality Reduction, Machine Learning Algorithms, Statistical Machine Learning, Communication, Correlation And Dependence, Data Analysis, Estimation, Exploratory Data Analysis, Statistical Analysis
4.4
（688件のレビュー）
Advanced · Specialization · 3-6 Months
University of Michigan
Statistics with Python
習得できるスキル： Probability & Statistics, General Statistics, Statistical Programming, Python Programming, Data Science, Business Analysis, Regression, Computer Programming, Data Analysis, Statistical Analysis, Statistical Tests, Experiment, Econometrics, Machine Learning, Machine Learning Algorithms, Data Visualization, Statistical Visualization, Bayesian Statistics, Correlation And Dependence, Data Analysis Software, Estimation, Basic Descriptive Statistics, Mathematics, Plot (Graphics), Programming Principles
4.6
（3k件のレビュー）
Beginner · Specialization · 1-3 Months
IBM Skills Network
Statistics for Data Science with Python
習得できるスキル： General Statistics, Probability & Statistics, Data Analysis, Business Analysis, Statistical Analysis, Probability Distribution, Statistical Tests, Basic Descriptive Statistics, Correlation And Dependence, Data Visualization, Plot (Graphics), Regression, Statistical Visualization
4.5
（284件のレビュー）
Mixed · Course · 1-3 Months
無料
IBM Skills Network
IBM Data Science
習得できるスキル： Python Programming, Data Science, Data Analysis, Data Structures, Statistical Programming, Machine Learning, Data Mining, Regression, Machine Learning Algorithms, Data Visualization, General Statistics, Basic Descriptive Statistics, SQL, Applied Machine Learning, Statistical Analysis, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Databases, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Data Management, Database Theory, Data Visualization Software, R Programming, Statistical Machine Learning, Statistical Tests, Deep Learning, Probability & Statistics, Database Application, Extract, Transform, Load, Plot (Graphics), Devops Tools, SPSS, Estimation, Interactive Data Visualization, Algorithms, Database Administration, Geovisualization, Reinforcement Learning, Theoretical Computer Science, Big Data, Business Analysis, Computational Logic, Correlation And Dependence, Econometrics, Entrepreneurship, Marketing, Mathematical Theory & Analysis, Mathematics, Spreadsheet Software, Storytelling, Supply Chain Systems, Supply Chain and Logistics, Writing
4.6
（106.2k件のレビュー）
Beginner · Professional Certificate · 3-6 Months
Johns Hopkins University
Data Science: Statistics and Machine Learning
習得できるスキル： R Programming, Statistical Programming, General Statistics, Statistical Analysis, Data Analysis, Machine Learning, Probability & Statistics, Statistical Tests, Data Science, Machine Learning Software, Basic Descriptive Statistics, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Linear Algebra, Regression, Exploratory Data Analysis, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Probability Distribution, Plot (Graphics), Machine Learning Algorithms, Algorithms, Applied Machine Learning, Business Analysis
4.4
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Intermediate · Specialization · 3-6 Months
Imperial College London
Mathematics for Machine Learning
習得できるスキル： Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model
4.6
（13.4k件のレビュー）
Beginner · Specialization · 3-6 Months
無料
Eindhoven University of Technology
Improving your statistical inferences
習得できるスキル： Probability & Statistics, Statistical Tests, General Statistics, R Programming, Statistical Programming, Bayesian Statistics, Data Analysis, Probability Distribution, Statistical Analysis, Bayesian Network, Business Analysis, Experiment, Machine Learning
4.9
（750件のレビュー）
Intermediate · Course · 1-3 Months
University of Michigan
Django for Everybody
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4.7
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Intermediate · Specialization · 3-6 Months
University of California, Santa Cruz
Bayesian Statistics
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4.6
（3.3k件のレビュー）
Intermediate · Specialization · 3-6 Months

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要約して、statistics for data science の人気コース10選をご紹介します。

Introduction to Statistics: Stanford University
Advanced Statistics for Data Science: Johns Hopkins University
Statistics with Python: University of Michigan
Statistics for Data Science with Python: IBM Skills Network
Data Science Math Skills: Duke University
IBM Data Science: IBM Skills Network
Data Science: Statistics and Machine Learning: Johns Hopkins University
Mathematics for Machine Learning: Imperial College London
Improving your statistical inferences: Eindhoven University of Technology
Django for Everybody: University of Michigan

データサイエンスのための統計学に関するよくある質問

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.‎