Coursera

4.27

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar ta...

4.59

Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn...

4.57

Develop basic applications and interactive graphics using GoogleVisUse Leaflet to create interactive annotated mapsBuild an R Markdown presentation that includes a data visualizationCreate a data product that tells a story to a mass audience

4.8

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

4.12

D​istinguish between tidy and non-tidy dataDescribe how non-tidy data can be transformed into tidy dataD​escribe the Tidyverse ecosystem of packagesO​rganize and initialize a data science project

4.59

D​escribe different data formatsA​pply Tidyverse functions to import data into R from external formatsO​btain data from a web API

4.32

In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of...

3.75

In this Google Cloud Lab, you will learn how to create the core components of a simple vision-based system to analyze a tennis serve using the Video Intelligence API and a pretrained AutoML Vision Object...

4.91

Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today....

4.41

Welcome to the specialization course Relational Database Systems. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple...

4.66

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will...

4.67

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest ne...

4.39

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data...

4.8

BigQuery is Google's fully managed, NoOps, low-cost analytics database. With BigQuery you can query terabytes and terabytes of data without managing infrastructure or needing a database administrator. BigQuery uses SQL and takes advantage of the...

4.52

The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook....

4.43

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical...

4.25

Explore data using unsupervised machine learning. Solve complex supervised learning problems using tree-based models. Apply hyperparameter tuning and cross-validation strategies to improve model performance.

3.15

The basics of Probability, Bayesian statistics, modeling and inference. You will also get a hands-on introduction to using Python for computational statistics using Scikit-learn, SciPy and Numpy.

4.76

The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports, by showing that data analytics could be used to increase team winning percentage. This course shows how to program data...

4.57

In this course, we will cover the basics of visualization and how it fits into the Data Science workflow. We will focus on the main concepts behind the purpose of visualization and the design principles...