Coursera

4.33

This course is an introduction to 3D scientific data visualization, with an emphasis on science communication and cinematic design for appealing to broad audiences. You will develop visualization literacy, through being able to interpret/analyze (re...

4.46

In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation...

3.66

This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression...

4.46

While telling stories with data has been part of the news practice since its earliest days, it is in the midst of a renaissance. Graphics desks which used to be deemed as “the art department,”...

3.58

This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. By the end of this course, students will...

4.24

Understand how text is handled in PythonApply basic natural language processing methodsWrite code that groups documents by topicDescribe the nltk framework for manipulating text

4.66

Define networks and discover the languages networks use. Analyze a social network through data wrangling and visualizing a network. Discuss what mechanisms generate networks. Examine social networks analysis using case studies.

5

In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise...

3.84

You will learn how to create and manage data pipelines in the cloud using Azure Data Factory and Azure Synapse Pipeline.

4.62

This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case...

4.88

Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values. Compare and contrast predictive models using simple linear, multiple linear, and polynomial regression...

4.51

In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will...

3.68

Understand the structure and techniques used in reinforcement learning (RL) strategies. Understand the benefits of using RL vs. other learning methods. Describe the steps required to develop and test an RL trading strategy. Describe the...

4.62

Combine the data and follow the best practices to present your story Create calculated fields for KPIs to build a figure that will be used to measure progress in the data Assemble a dashboard Analyze...

4.61

Assess the feasibility of your own ML use case and its ability to meaningfully impact your business. Identify the requirements to build, train, and evaluate an ML model. Define data characteristics and biases that affect...

4.55

Create a chart using Tableau Create dates using calculated fields Customize table calculations Customize and create dual layer maps

4.59

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations s...

4.76

In this course, you learn how to use SAS Visual Analytics on SAS Viya to modify data for analysis, perform data discovery and analysis, and create interactive reports.

4.56

Organize data analysis to help make it more reproducibleWrite up a reproducible data analysis using knitrDetermine the reproducibility of analysis projectPublish reproducible web documents using Markdown

4.29

This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profilin...