Online Course – Data Science Fundamentals: Certified Professional Inference in Statistical Inference from Johns Hopkins University and the University of Colorado Boulder

Build your statistics skills for data science. Master the statistics required for data science.

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קורס אונליין - יסודות מדע הנתונים: התמחות מקצועית מוסמכת בהסקת סטטיסטיקה מאוניברסיטת Johns Hopkins ואוניברסיטת Colorado Boulder

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Communication skills
  • Teamwork
  • Troubleshooting
  • Time management
  • Critical thinking
  • Leadership skills
  • Technological skills
  • project management
  • Research skills
  • Ability to display information

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Statistician
  • Data Scientist
  • Data software developer
  • Information Systems Analyst
  • Researcher in the field of statistics
  • Probability expert
  • Data Analyst

Focus – a three-part course series

This program is designed to provide the student with a solid foundation in probability theory to prepare them for more comprehensive studies in statistics. The program will introduce the student to the fundamentals of statistics and statistical theory and provide them with the skills required to perform basic statistical analyses of data systems in the R programming language.

specialization

  • This specialization is available as an academic credit within the Master of Science in Data Science (MS-DS) degree offered by CU Boulder on the Coursera platform.
  • The MS-DS is an interdisciplinary degree that connects faculty from different institutions such as applied mathematics, computer science, information science, and more.
  • With performance-based admissions requirements and no application process, the MS-DS is suitable for individuals with a broad background in disciplines such as computer science, information science, mathematics, and statistics.
  • For more information about the MS-DS program, visit: https://www.coursera.org/degrees/master-of-science-data-science-boulder .

Applied Learning Project

  • Students will practice new probability skills, including basic statistical analysis of data sets, by completing exercises in Jupyter Notebooks.
  • Additionally, students will test their knowledge by completing Benchmark tests during the courses.

Details of the courses that make up the specialization

Probability Theory: A Foundation for Data Science

Course 1 • 40 hours • 4.5 (218 ratings)

Course Details

What you’ll learn

  • Explain why probability is important to statistics and data science.
  • To see the relationship between conditional and independent events in a statistical experiment.
  • Calculate the expectation and variance of several random variables and develop intuition on the subject.

Skills you will gain

  • Category: Bay trial
  • Bay trial
  • Category: Continuous random variables
  • Continuous random variables
  • Category: Probability
  • probability
  • Category: Discrete random variables
  • Discrete random variables
  • Category: Central Boundary Law
  • The central limit theorem

Finding values ​​in statistics in data science

Course 2 • 28 hours • 4.1 (76 ratings)

Course Details

What you’ll learn

  • Identify features of good “estimates” and compare competing estimates.
  • Construct valid estimates using maximum likelihood and the method of moments techniques.
  • Construct and interpret confidence intervals for one and two population means, one and two population proportions, and population variances.

Statistical inference and hypothesis testing in data science applications

Course 3 • 36 hours • 4.7 (46 ratings)

Course Details

What you’ll learn

  • Define a complex hypothesis and significance level for testing with a complex null hypothesis.
  • Define a test statistic, significance level, and rejection region for hypothesis testing. Give the shape of the rejection region.
  • Perform tests regarding the true variability of the population.
  • Calculate the sampling distributions for the sample mean and sample minimum of the exponential distribution.

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