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.

What are the benefits of taking an online Statistics for Data Science course?

Online Statistics for Data Science courses offer a convenient and flexible way to enhance your knowledge or learn new Statistics for Data Science skills. Choose from a wide range of Statistics for Data Science courses offered by top universities and industry leaders tailored to various skill levels.

What Statistics for Data Science courses are best for training and upskilling employees or the workforce?

When looking to enhance your workforce's skills in Statistics for Data Science, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here .

Top Statistics For Data Science Courses - Learn Statistics For Data Science Online

Explore the Statistics for Data Science Course Catalog

Johns Hopkins University
Genomic Data Science
Skills you'll gain: Bioinformatics, Unix Commands, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Unix, Data Science, Data Management, Statistical Methods, Molecular Biology, Command-Line Interface, Statistical Hypothesis Testing, Linux Commands, Data Analysis Software, Statistical Modeling, Data Structures, Data Analysis, R Programming, Computational Thinking, Jupyter
4.5
Rating, 4.5 out of 5 stars
6.6K reviews
Intermediate · Specialization · 3 - 6 Months
Microsoft
Preparing Data for Analysis with Microsoft Excel
Skills you'll gain: Excel Formulas, Microsoft Excel, Spreadsheet Software, Power BI, Data Analysis, Productivity Software, Data Cleansing, Data Manipulation, Data Management
4.7
Rating, 4.7 out of 5 stars
4.4K reviews
Beginner · Course · 1 - 4 Weeks
DeepLearning.AI
Calculus for Machine Learning and Data Science
Skills you'll gain: Applied Mathematics, Calculus, Numerical Analysis, Mathematical Modeling, Machine Learning, Python Programming, Regression Analysis, Artificial Neural Networks, Deep Learning, Derivatives
4.8
Rating, 4.8 out of 5 stars
841 reviews
Intermediate · Course · 1 - 4 Weeks
DeepLearning.AI
Linear Algebra for Machine Learning and Data Science
Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Machine Learning Methods, Jupyter, Data Manipulation, Data Science, Applied Mathematics, Python Programming, Image Analysis, Artificial Intelligence
4.6
Rating, 4.6 out of 5 stars
2K reviews
Intermediate · Course · 1 - 4 Weeks
Status: New
IBM
IBM Data Architecture
Skills you'll gain: NoSQL, Data Warehousing, SQL, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Data Security, Linux Commands, Data Migration, Database Design, Data Governance, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Shell Script, Database Architecture and Administration, Data Store
4.6
Rating, 4.6 out of 5 stars
6.6K reviews
Beginner · Professional Certificate · 3 - 6 Months
Google Cloud
Preparing for Google Cloud Certification: Cloud Data Engineer
Skills you'll gain: Data Pipelines, Dataflow, Google Cloud Platform, Real Time Data, Data Maintenance, Data Lakes, Data Storage, MLOps (Machine Learning Operations), Data Analysis, Dashboard, Data Warehousing, Data Processing, Extract, Transform, Load, Cloud Engineering, Data Infrastructure, Cloud Infrastructure, Cloud Storage, Big Data, Tensorflow, Unstructured Data
4.6
Rating, 4.6 out of 5 stars
19K reviews
Intermediate · Professional Certificate · 3 - 6 Months
University of California, Irvine
Data Science Fundamentals
Skills you'll gain: Data Ethics, Predictive Modeling, Unsupervised Learning, Natural Language Processing, Predictive Analytics, Data Analysis, Classification And Regression Tree (CART), Regression Analysis, Data Mining, Statistical Analysis, Data Science, Social Media, Text Mining, Anomaly Detection, Business Analytics, Decision Tree Learning, Statistical Modeling, Big Data, Analytics, Cloud Computing
4.3
Rating, 4.3 out of 5 stars
239 reviews
Beginner · Specialization · 3 - 6 Months
Duke University
Data Analysis with R
Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Exploratory Data Analysis, Regression Analysis, Statistical Reporting, Probability Distribution, Statistical Methods, Data Analysis Software, R Programming, Bayesian Statistics, Statistical Analysis, Data Analysis, Statistical Software, Statistical Modeling, Probability & Statistics, Probability, Statistics, Correlation Analysis, Data Literacy
4.7
Rating, 4.7 out of 5 stars
7.5K reviews
Beginner · Specialization · 3 - 6 Months
Status: Free
Coursera Instructor Network
Data Science for Professionals
Skills you'll gain: Predictive Modeling, Statistical Analysis, Data Manipulation, Data Analysis, Data Science, Pandas (Python Package), Data Visualization Software, Statistics, Data Cleansing, Data Processing, Data-Driven Decision-Making, Business Analytics, Exploratory Data Analysis, Machine Learning
4.5
Rating, 4.5 out of 5 stars
14 reviews
Beginner · Course · 1 - 4 Weeks
Johns Hopkins University
Python for Genomic Data Science
Skills you'll gain: Bioinformatics, Data Structures, Jupyter, Python Programming, Programming Principles, Scripting Languages, Scripting, Data Processing, Computer Programming, Data Manipulation, File Management
4.3
Rating, 4.3 out of 5 stars
1.8K reviews
Mixed · Course · 1 - 4 Weeks
Multiple educators
DeepLearning.AI Data Engineering
Skills you'll gain: Apache Airflow, Data Modeling, Data Pipelines, Data Storage, Data Storage Technologies, Data Architecture, Data Transformation, Requirements Analysis, Data Processing, Data Warehousing, Query Languages, Apache Hadoop, Extract, Transform, Load, Data Lakes, Amazon Web Services, Apache Spark, Database Systems, Data Integration, Infrastructure as Code (IaC), Terraform
4.8
Rating, 4.8 out of 5 stars
440 reviews
Intermediate · Professional Certificate · 3 - 6 Months
IBM
Data Engineering Foundations
Skills you'll gain: Web Scraping, Database Design, SQL, MySQL, Data Transformation, Data Store, Extract, Transform, Load, IBM DB2, Relational Databases, Data Architecture, Jupyter, Data Pipelines, Big Data, Data Warehousing, Data Governance, Data Manipulation, Stored Procedure, Databases, Automation, Python Programming
4.6
Rating, 4.6 out of 5 stars
56K reviews
Beginner · Specialization · 3 - 6 Months