Coursera

4.59

Set up R, R-Studio, Github and other useful toolsUnderstand the data, problems, and tools that data analysts useExplain essential study design conceptsCreate a Github repository

4.61

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor cont...

4.83

Welcome to this course on Data Analytics for Lean Six Sigma. In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this...

4.82

This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning,...

4.58

Frame a business use case as a machine learning problem. Gain a broad perspective of machine learning and where it can be usedConvert a candidate use case to be driven by machine learningRecognize biases that...

4.34

Explain why probability is important to statistics and data science. See the relationship between conditional and independent events in a statistical experiment. Calculate the expectation and variance of several random variables and develop some intu...

4.8

In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool...

4.78

In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. b) Apply object detection models such as regional-CNN and ResNet...

3.51

Validate and clean a datasetAssess and create datasets to answer your questionsSolve problems using SQLBuild a simple testing framework to touch on AB Testing

4.56

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how...

4.68

This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be...

4.3

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial...

4.52

Understand critical programming language conceptsConfigure statistical programming softwareMake use of R loop functions and debugging toolsCollect detailed information using R profiler

4.57

Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how...

4.75

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment-learning from the agent’s own experience. Learning from actual experience is striking...

4.63

We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact...

4.59

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic...

4.67

Properly identify various data types and understand the different uses for each Create data visualizations and numerical summaries with PythonCommunicate statistical ideas clearly and concisely to a broad audienceIdentify appropriate analytic techni...

4.49

Install Tableau Public Software and create a visualization Examine and navigate the Tableau Public workspace Practice and connect to different data sources Examine ways to define your project

4.77

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as...