Data Science & Analytics courses

4.57

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You...

This course provides a comprehensive introduction to working with base graphics in R.

Train deep learning architectures such as Multi-layer perceptron, Convolutional Neural Networks and Recurrent Neural Networks for classificationValidate and compare different machine learning algorithmsPreprocess Electronic Health Records and represe...

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...

4.68

Distinguish between artificial intelligence and data science concepts. Protect data privacy with ethical and legal standards. Assess the potential risks of bias. Advocate for the importance of risk evaluation in emerging technologies.

4.8

You will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions.

4.1

Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres...

Time series data is fun, but challenging. When ordering matters, your datasets get large, and timestamp precision differences can foil your merges, building reliable data processing pipelines requires a principled approach with the right tools....

What powers the systems that we use every day without realizing, like fraud detection to keep our transactions secure or traffic signals that keep traffic flowing smoothly? The answer is streaming data (data that is...

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...

A good proportion of the data out there in the real world is inherently spatial. From the population recorded in the national census, to every shop in your neighborhood, the majority of datasets have a...

4.14

Learn how to apply business analytics and intelligence on the business data collected using Google Analytics in this hands-on course called 'Data Science for Business Analytics and Intelligence', created by The Click Reader. In this...

4.36

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures...

4.75

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You...

4.79

"Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay". You have been hearing about this a lot. You try to get more information on this and...

As with any fundamentals course, Introduction to Natural Language Processing in R is designed to equip you with the necessary tools to begin your adventures in analyzing text. Natural language processing (NLP) is a constantly...

3.96

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk...

4.44

With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves...

Why Learn Regular Expressions? Regular expressions, or regex for short, are one of the most powerful and applicable techniques in programming. We can use regular expressions to search for and find patterns in strings. They...

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on...