Coursera

4.67

By the end of this course, you will be able to: Approach complex industrial and business research problems and address them through a rigorous, statistically sound experimental strategyUse modern software to effectively plan experimentsAnalyze the...

4.76

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

4.02

Identify and use core technologies required to support effective MLOps. Configure and provision Google Cloud architectures for reliable and effective MLOps environments. Adopt the best CI/CD practices in the context of ML systems. Implement reliable...

4.72

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating...

4.63

In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of...

4.58

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial...

3.88

It’s no secret that machine learning is one of the fastest-growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a...

4.49

Describe the various roles that make up a Data Science teamManage a Data Science team onboardingKnow relevant questions for interviewing data scientistsUnderstand how to encourage and empower Data Science teams

4.33

This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team...

4.85

Understand the basics of SELECT statementsUnderstand how and why to filter resultsExplore grouping and aggregation to answer analytic questionsWork with sorting and limiting results

4.64

In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data wi...

4.6

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for...

3.97

Design basic quantitative trading strategies Use Keras and Tensorflow to build machine learning modelsBuild a pair trading strategy prediction model and back test it. Build a momentum-based trading model and back test it.

4.64

Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or...

4.63

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from...

4.86

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these...

4.79

In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on...

4.26

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining....

4.77

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a...

4.84

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In...