Coursera

4.41

This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow...

4.27

Explain the primary types of data analysisDefine the phases of the data analysis processIdentify tools and skills required to conduct data analysis

3.77

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response...

5

This course will cover the more complex concepts that become involved when working beyond simple datasets. Exploring the connection between visual aspects and data understanding, we will examine how those concepts work together through data...

4.48

This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow...

5

This course is designed for business professionals that want to learn how to analyze data to gain insight, use statistical analysis methods to explore the underlying distribution of data, use visualizations such as histograms, scatter...

3.42

This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction...

4.84

Participate in the application of machine learning, helping select between and evaluate technical approachesInterpret a predictive model for a manager or executive, explaining how it works and how well it predictsCircumvent the most common technical...

4.25

Train and evaluate linear regression models. Train binary and multi-class classification models. Evaluate and tune classification models to improve their performance. Train and evaluate clustering models to find useful patterns in unsupervised data.

4.72

In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks! The opportunities for learning are practically endless in a social network. Who...

4

I​mplement and evaluate machine learning models (neural networks, random forests, etc. ) on scientific data in Python

3.55

Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately...

3.9

The goal of this course is to understand the foundations of Big Data and the data that is being generated in the health domain and how the use of technology would help to integrate and...

4.62

This course will assist you with recreating work that a previous coworker completed, revisiting a project you abandoned some time ago, or simply reproducing a document with a consistent format and workflow. Incomplete information about...

4

Collect and prepare a dataset to use for training and testing a machine learning model. Analyze a dataset to gain insights. Set up and train a machine learning model as needed to meet business requirements....

3.5

In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining...

Create charts to describe and compare the composition of data setsIllustrate the distribution of data through visualizationsCreate specialized visualizations such as heat maps, correlograms, and mosaic plotsUse R Markdown to create documents, reports...

5

Describe how machine learning applications can address real-world investment problemsExplain machine learning concepts and techniques to a non-expert audienceUtilize the language of data science when working with data scientists and data engineersApp...

5

Communicate results of a model via web apps, and implement an d test pipelines that automate the model training, tuning and deployment process.

With this course, you will begin to take the first steps in the world of data analysis. You will see in detail the main concepts and processes that make up this discipline. The main goal...