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    • Statistics For Data Science

    Statistics for Data Science Courses Online

    Master statistics for data science applications. Learn about statistical techniques, data analysis, and machine learning models.

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    Explore the Statistics for Data Science Course Catalog

    • M

      McMaster University

      DNA Decoded

      Skills you'll gain: Molecular Biology, Medical Science and Research, Biochemistry, Laboratory Testing, Biology, Microbiology, Science and Research

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Virginia

      How Things Work: An Introduction to Physics

      Skills you'll gain: Mechanics, Torque (Physics), Physics, Energy and Utilities, Experimentation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3K reviews

      Intermediate · Course · 1 - 3 Months

    • I

      IBM

      Data Warehouse Fundamentals

      Skills you'll gain: Data Warehousing, Extract, Transform, Load, Star Schema, IBM DB2, Data Modeling, Snowflake Schema, PostgreSQL, Database Design, Data Architecture, Data Mart, Database Systems, Data Lakes, Data Validation, Data Integration, SQL, Data Cleansing, Query Languages, Data Quality

      4.5
      Rating, 4.5 out of 5 stars
      ·
      227 reviews

      Intermediate · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Security in Google Cloud

      Skills you'll gain: Google Cloud Platform, Cloud Infrastructure, Identity and Access Management, Data Loss Prevention, Distributed Denial-Of-Service (DDoS) Attacks, Prompt Engineering, Kubernetes, Cloud Security, Containerization, OAuth, Cloud Storage, Cloud Services, Application Deployment, Virtual Machines, Security Controls, Cloud Computing, Application Development, Data Storage Technologies, Threat Management, Virtual Private Networks (VPN)

      4.7
      Rating, 4.7 out of 5 stars
      ·
      49K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free
      Free
      D

      Duke University

      Copyright for Educators & Librarians

      Skills you'll gain: Intellectual Property, Educational Materials, Law, Regulation, and Compliance, Education and Training, Legal Research

      4.8
      Rating, 4.8 out of 5 stars
      ·
      595 reviews

      Mixed · Course · 1 - 3 Months

    • G

      Google Cloud

      Architecting with Google Compute Engine

      Skills you'll gain: Cloud Infrastructure, Google Cloud Platform, Identity and Access Management, Prompt Engineering, Kubernetes, Application Deployment, Cloud Storage, Infrastructure As A Service (IaaS), Containerization, Cloud Computing, CI/CD, Cloud Computing Architecture, Cloud Security, Cloud Services, Load Balancing, Service Level Agreement, Infrastructure as Code (IaC), Virtual Machines, Microservices, Service Level

      4.7
      Rating, 4.7 out of 5 stars
      ·
      58K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      AI for Good

      Skills you'll gain: Disaster Recovery, Data Analysis, Jupyter, Exploratory Data Analysis, Data Ethics, Image Analysis, Emergency Response, Artificial Intelligence, Machine Learning, Predictive Modeling, Applied Machine Learning, Mitigation, Text Mining, Environmental Monitoring, Natural Language Processing, Unstructured Data, Information Privacy, Deep Learning, Data Processing, Forecasting

      4.7
      Rating, 4.7 out of 5 stars
      ·
      459 reviews

      Beginner · Specialization · 1 - 3 Months

    • I

      IBM

      IBM Cybersecurity Analyst

      Skills you'll gain: Cyber Threat Hunting, Incident Response, IT Service Management, Identity and Access Management, Cloud Computing, Information Technology Infrastructure Library, Endpoint Security, Malware Protection, Penetration Testing, Linux, Computer Security Incident Management, Database Management, Network Security, Networking Hardware, TCP/IP, Cybersecurity, Intrusion Detection and Prevention, Cyber Threat Intelligence, Interviewing Skills, Cyber Security Assessment

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      26K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Introduction to R Programming for Data Science

      Skills you'll gain: Data Manipulation, Web Scraping, R Programming, Data Analysis, Data Science, Data Structures, Data Import/Export, Exploratory Data Analysis, Programming Principles, Jupyter, Integrated Development Environments

      4.5
      Rating, 4.5 out of 5 stars
      ·
      562 reviews

      Beginner · Course · 1 - 3 Months

    • G

      Google

      Analyze Data to Answer Questions

      Skills you'll gain: Data Validation, Spreadsheet Software, Data Analysis, Analytics, Data Manipulation, Excel Formulas, Data Compilation, Pivot Tables And Charts, SQL, Data Integration, Data Transformation, User Feedback

      4.6
      Rating, 4.6 out of 5 stars
      ·
      12K reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Introduction to HTML5

      Skills you'll gain: Hypertext Markup Language (HTML), Web Content Accessibility Guidelines, Web Design and Development, Usability, Semantic Web, Cascading Style Sheets (CSS), Cloud Hosting, Web Servers, Data Validation, Web Development Tools

      4.7
      Rating, 4.7 out of 5 stars
      ·
      28K reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of California San Diego

      Data Structures and Performance

      Skills you'll gain: Unit Testing, Data Structures, Development Testing, Java Programming, Software Testing, Performance Tuning, Debugging, Computer Programming, Algorithms, Object Oriented Programming (OOP), Theoretical Computer Science

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.2K reviews

      Intermediate · Course · 1 - 3 Months

    Statistics For Data Science learners also search

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    1…262728…834

    In summary, here are 10 of our most popular statistics for data science courses

    • DNA Decoded: McMaster University
    • How Things Work: An Introduction to Physics: University of Virginia
    • Data Warehouse Fundamentals: IBM
    • Security in Google Cloud: Google Cloud
    • Copyright for Educators & Librarians: Duke University
    • Architecting with Google Compute Engine: Google Cloud
    • AI for Good: DeepLearning.AI
    • IBM Cybersecurity Analyst: IBM
    • Introduction to R Programming for Data Science: IBM
    • Analyze Data to Answer Questions: Google

    Frequently Asked Questions about 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.‎

    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.‎

    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.‎

    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.‎

    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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