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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

    • D

      DeepLearning.AI

      Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

      Skills you'll gain: Tensorflow, Computer Vision, Image Analysis, Keras (Neural Network Library), Deep Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML)

      4.8
      Rating, 4.8 out of 5 stars
      ·
      20K reviews

      Intermediate · Course · 1 - 4 Weeks

    • A

      Amazon Web Services

      Getting Started with Data Analytics on AWS

      Skills you'll gain: Descriptive Analytics, Analytics, Data Analysis, Dashboard, Data Literacy, AWS Identity and Access Management (IAM), Data Visualization, Amazon Web Services, Big Data, Amazon S3, Business Intelligence, Cloud Computing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      890 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Manchester

      Industrial Biotechnology

      Skills you'll gain: Process Engineering, Molecular Biology, Chemical Engineering, Life Sciences, Biochemistry, Bioinformatics, Biology, Pharmaceuticals, Environmental Engineering, Manufacturing Processes, Process Development, Microbiology

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Colorado Boulder

      Foundations of Data Structures and Algorithms

      Skills you'll gain: Theoretical Computer Science, Algorithms, Graph Theory, Data Structures, Operations Research, Public Key Cryptography Standards (PKCS), Cryptography, Programming Principles, Computational Thinking, Design Strategies, Applied Mathematics, Computer Science, Tree Maps, Encryption, Network Model, Linear Algebra, Combinatorics, Mathematical Modeling, Advanced Mathematics, Analysis

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      810 reviews

      Advanced · Specialization · 3 - 6 Months

    • Status: Free
      Free
      S

      Stanford University

      Child Nutrition and Cooking

      Skills you'll gain: Nutrition Education, Cooking, Nutrition and Diet, Food and Beverage, Food Safety and Sanitation, Child Health, Working With Children, Community Health, Parent Communication

      4.8
      Rating, 4.8 out of 5 stars
      ·
      10K reviews

      Beginner · Course · 1 - 3 Months

    • 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
      ·
      45 reviews

      Beginner · Course · 1 - 3 Months

    • 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
      ·
      337 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • G

      Google

      Foundations of User Experience (UX) Design

      Skills you'll gain: Design Thinking, User Research, User Experience Design, User Centered Design, Usability, Web Content Accessibility Guidelines, Cross Platform Development, Prototyping, Wireframing, Sprint Planning

      4.8
      Rating, 4.8 out of 5 stars
      ·
      73K reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      IBM AI Engineering

      Skills you'll gain: Prompt Engineering, Large Language Modeling, PyTorch (Machine Learning Library), Supervised Learning, Feature Engineering, Generative AI, Keras (Neural Network Library), Deep Learning, Jupyter, Natural Language Processing, Reinforcement Learning, Unsupervised Learning, Generative AI Agents, Scikit Learn (Machine Learning Library), Image Analysis, Data Manipulation, Tensorflow, Python Programming, Verification And Validation, Artificial Neural Networks

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      20K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • G

      Google

      Google Business Intelligence

      Skills you'll gain: Business Intelligence, Data Modeling, Dashboard, Database Design, Extract, Transform, Load, Data Integration, Stakeholder Engagement, Professional Networking, Data Warehousing, Data Presentation, Performance Tuning, Data Pipelines, Data Visualization Software, Business Reporting, Data Integrity, Business Analytics, Interviewing Skills, Professional Development, LinkedIn, Relationship Building

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.8K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • Status: Free
      Free
      Y

      Yale University

      Moral Foundations of Politics

      Skills you'll gain: Political Sciences, Policy Analysis, Social Justice, Social Sciences, European History, Law, Regulation, and Compliance, Governance, Ethical Standards And Conduct, Socioeconomics, Economics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      6K reviews

      Mixed · Course · 1 - 3 Months

    • D

      Duke University

      Excel to MySQL: Analytic Techniques for Business

      Skills you'll gain: Data Storytelling, Database Design, Dashboard, MySQL, Relational Databases, SQL, Tableau Software, Business Analytics, Business Metrics, Data Visualization Software, Analytics, Data Visualization, Business Process Improvement, Business Intelligence, Financial Modeling, Microsoft Excel, Business Process, Probability Distribution, Business Risk Management, Predictive Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      15K reviews

      Beginner · Specialization · 3 - 6 Months

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

    • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: DeepLearning.AI
    • Getting Started with Data Analytics on AWS: Amazon Web Services
    • Industrial Biotechnology: University of Manchester
    • Foundations of Data Structures and Algorithms: University of Colorado Boulder
    • Child Nutrition and Cooking: Stanford University
    • Statistics in Psychological Research: American Psychological Association
    • Python for Data Analysis: Pandas & NumPy: Coursera Project Network
    • Foundations of User Experience (UX) Design: Google
    • IBM AI Engineering: IBM
    • Google Business Intelligence: 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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