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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1070 résultats pour ''statistics for data science'

Gratuit
Stanford University
Introduction to Statistics
Compétences que vous acquerrez : Experiment, Inference, Data Analysis, Hypothesis Testing, Plot (Graphics), Statistical Hypothesis Testing, General Statistics, Basic Descriptive Statistics, Probability & Statistics, Statistical Tests, Econometrics, Bayesian Statistics, Markov Model, Machine Learning, Regression, Correlation And Dependence, Data Visualization, Probability Distribution, Statistical Analysis, Statistical Inference
4.5
(1.2k avis)
Beginner · Course · 1-3 Months
University of Michigan
Statistics with Python
Compétences que vous acquerrez : Basic Descriptive Statistics, Bayesian Statistics, Business Analysis, Computer Programming, Correlation And Dependence, Data Analysis, Data Visualization, Econometrics, Experiment, General Statistics, Machine Learning, Machine Learning Algorithms, Modeling, Plot (Graphics), Probability & Statistics, Programming Principles, Python Programming, Regression, Statistical Analysis, Statistical Inference, Statistical Programming, Statistical Tests, Statistical Visualization
4.6
(2.8k avis)
Beginner · Specialization · 1-3 Months
IBM Skills Network
Statistics for Data Science with Python
Compétences que vous acquerrez : Basic Descriptive Statistics, Plot (Graphics), Statistical Tests, Probability & Statistics, Statistical Visualization, Business Analysis, Regression, Correlation And Dependence, Statistical Analysis, Data Visualization, Probability Distribution, General Statistics, Data Analysis
4.6
(217 avis)
Mixed · Course · 1-3 Months
Gratuit
Johns Hopkins University
Advanced Statistics for Data Science
Compétences que vous acquerrez : Algebra, Artificial Neural Networks, Bayesian Statistics, Biostatistics, Calculus, Communication, Dimensionality Reduction, Econometrics, Experiment, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, Regression, Statistical Machine Learning, Statistical Tests
4.4
(660 avis)
Advanced · Specialization · 3-6 Months
Johns Hopkins University
Data Science: Statistics and Machine Learning
Compétences que vous acquerrez : Algorithms, Analysis, Applied Machine Learning, Business Analysis, Data Analysis, Data Visualization, Econometrics, General Statistics, Inference, Interactive Data Visualization, Interactivity, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Plot (Graphics), Probability & Statistics, Probability Distribution, R Programming, Regression, Regression Analysis, Statistical Analysis, Statistical Programming, Statistical Tests, Theoretical Computer Science
4.4
(7k avis)
Intermediate · Specialization · 3-6 Months
Gratuit
Eindhoven University of Technology
Improving your statistical inferences
Compétences que vous acquerrez : General Statistics, Hypothesis Testing, Probability & Statistics, Experiment, Euler'S Totient Function, Data Analysis, Statistical Tests, Statistical Hypothesis Testing, Hypothesis, Bayesian Network, Bayesian Statistics, Statistical Analysis, Business Analysis, Analysis, Inference, Statistical Inference, Machine Learning, Bayesian
4.9
(724 avis)
Intermediate · Course · 1-3 Months
Imperial College London
Mathematics for Machine Learning
Compétences que vous acquerrez : Algebra, Algorithms, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Lambda Calculus, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Matrices, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science
4.6
(12.8k avis)
Beginner · Specialization · 3-6 Months
Duke University
Data Analysis with R
Compétences que vous acquerrez : Bayesian Statistics, Business Analysis, Data Analysis, Data Mining, Data Visualization, Econometrics, Experiment, Exploratory Data Analysis, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Plot (Graphics), Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Regression Analysis, Statistical Analysis, Statistical Programming, Statistical Tests
4.7
(6.6k avis)
Beginner · Specialization · 3-6 Months
Google
Google Data Analytics
Compétences que vous acquerrez : Algorithms, Analysis, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Type, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Distributed Computing Architecture, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, Full-Stack Web Development, General Statistics, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Metadata, Network Security, Other Programming Languages, Plot (Graphics), Presentation, Privacy, Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Tableau Software, Theoretical Computer Science, Visual Design, Web Development
4.8
(70.1k avis)
Beginner · Professional Certificate · 3-6 Months

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En résumé, voici 10 de nos cours statistics for data science les plus populaires

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

Questions fréquentes sur Statistiques pour la science des données

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.‎

Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.