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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

    • A

      American Psychological Association

      Statistics in Psychological Research

      Skills you'll gain: Descriptive Statistics, Plot (Graphics), Graphing, Statistical Visualization, Statistical Hypothesis Testing, Probability & Statistics, Statistical Inference, Quantitative Research, Statistics, Sample Size Determination, Data Analysis, Scatter Plots, Psychology, Research Design, Experimentation, Scientific Methods

      4.6
      Rating, 4.6 out of 5 stars
      ·
      44 reviews

      Beginner · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Data Science in Real Life

      Skills you'll gain: Data Quality, Data Management, Technical Communication, Data Analysis, Data-Driven Decision-Making, Statistical Analysis, Data Science, Statistical Machine Learning, Statistical Inference, A/B Testing, Statistical Modeling

      4.5
      Rating, 4.5 out of 5 stars
      ·
      2.4K reviews

      Mixed · Course · 1 - 4 Weeks

    • C

      Coursera Project Network

      Python for Data Analysis: Pandas & NumPy

      Skills you'll gain: Pandas (Python Package), NumPy, Data Analysis, Data Science, Python Programming, Data Structures, Data Manipulation, Computer Programming

      4.5
      Rating, 4.5 out of 5 stars
      ·
      333 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Digital Marketing & E-commerce

      Skills you'll gain: Media Planning, Search Engine Marketing, Email Marketing, Data Storytelling, Social Media Strategy, Search Engine Optimization, Social Media Marketing, Content Creation, Order Fulfillment, Google Ads, Online Advertising, A/B Testing, Social Media Campaigns, Target Audience, Digital Marketing, E-Commerce, Loyalty Programs, Customer Retention, Customer experience strategy (CX), Campaign Management

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      39K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • O

      Olay

      Introduction to Cosmetic Science and Ingredients

      Skills you'll gain: Laboratory Experience, Quality Control, Product Development, Packaging and Labeling, Quality Assurance, Laboratory Equipment, Prototyping, Product Quality (QA/QC), New Product Development, Statistical Process Controls, Product Design, Good Manufacturing Practices, Quality Management, Quality Management Systems, Product Testing, Laboratory Testing, Chemistry, Safety Training, Personal Care, Safety Assurance

      4.7
      Rating, 4.7 out of 5 stars
      ·
      380 reviews

      Beginner · Specialization · 3 - 6 Months

    • S

      Snowflake

      Intro to Snowflake for Devs, Data Scientists, Data Engineers

      Skills you'll gain: Data Manipulation, Data Lakes, Data Warehousing, SQL, Data Pipelines, Cloud Applications, Extract, Transform, Load, Application Development, Artificial Intelligence and Machine Learning (AI/ML), Role-Based Access Control (RBAC), Stored Procedure, Generative AI, Large Language Modeling

      4.8
      Rating, 4.8 out of 5 stars
      ·
      77 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Python 3 Programming

      Skills you'll gain: Unified Modeling Language, JSON, Object Oriented Programming (OOP), Software Design, Debugging, Object Oriented Design, Data Processing, Web Scraping, Unit Testing, Programming Principles, Data Import/Export, Restful API, Python Programming, Image Analysis, Data Manipulation, Jupyter, Maintainability, Data Structures, Software Engineering, File Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      23K reviews

      Beginner · Specialization · 3 - 6 Months

    • C

      Coursera Project Network

      Data Science Coding Challenge: Loan Default Prediction

      Skills you'll gain: Applied Machine Learning, Jupyter, Data Processing, Predictive Modeling, Machine Learning, Predictive Analytics, Data Manipulation, Data Science, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      90 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • U

      University of Pennsylvania

      Computational Thinking for Problem Solving

      Skills you'll gain: Computational Thinking, Algorithms, Pseudocode, Python Programming, Data Structures, Computer Hardware, Computer Programming, Analysis, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.4K reviews

      Beginner · Course · 1 - 4 Weeks

    • D

      Duke University

      Pandas for Data Science

      Skills you'll gain: Pandas (Python Package), Data Cleansing, Data Manipulation, NumPy, Query Languages, Data Integration, Python Programming, Data Import/Export, Data Analysis, Debugging

      4.3
      Rating, 4.3 out of 5 stars
      ·
      8 reviews

      Beginner · Course · 1 - 4 Weeks

    • V

      Vanderbilt University

      Prompt Engineering for Law

      Skills you'll gain: Prompt Engineering, ChatGPT, Generative AI, Productivity, AI Personalization, Law Practice Management Software, OpenAI, Artificial Intelligence, Personalized Service, Large Language Modeling, Legal Writing, Legal Research, Data Ethics, Expense Management, Creative Thinking, Ingenuity, Productivity Software, Legal Support, Brainstorming, Travel Arrangements

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6K reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: New
      New
      I

      IBM

      IBM Generative AI Engineering

      Skills you'll gain: Prompt Engineering, Generative AI, Data Wrangling, Large Language Modeling, Unit Testing, Supervised Learning, Feature Engineering, Keras (Neural Network Library), Deep Learning, ChatGPT, Natural Language Processing, Data Cleansing, Jupyter, Data Analysis, Unsupervised Learning, Data Manipulation, PyTorch (Machine Learning Library), Artificial Intelligence, Data Import/Export, Data Ethics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      85K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    Statistics For Data Science learners also search

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

    • Statistics in Psychological Research: American Psychological Association
    • Data Science in Real Life: Johns Hopkins University
    • Python for Data Analysis: Pandas & NumPy: Coursera Project Network
    • Google Digital Marketing & E-commerce: Google
    • Introduction to Cosmetic Science and Ingredients: Olay
    • Intro to Snowflake for Devs, Data Scientists, Data Engineers: Snowflake
    • Python 3 Programming: University of Michigan
    • Data Science Coding Challenge: Loan Default Prediction: Coursera Project Network
    • Computational Thinking for Problem Solving: University of Pennsylvania
    • Pandas for Data Science: Duke University

    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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