Former Major League Baseball (MLB) player Matt Kata joins MathWorks to introduce you to data analysis using baseball statistics. By analyzing historic batting statistics, you will explore player careers and answer the question: When do great hitters peak in their career? In this project, you will work in MATLAB, a programming environment used by millions of engineers and scientists, and now MLB players! You’ll have access to pitching, batting, and defensive statistics dating back to 1871, enabling you to explore and answer a wide variety of questions. You will compute statistics like On-base Plus Slugging (OPS), visualize results, and filter data to highlight players that meet criteria you specify, such as the number of home runs. Whether you’re analyzing sports data, financial markets, or electric engine performance, you can apply the data analysis skills you learn in this project to many other fields and applications. So, step up to the plate and take a swing at MATLAB for data analysis.

In this 2-hour long project-based course, you will learn how to understand the predictions of your model, feature relations, visualize and interpret feature & model relation with statistics and much more.

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business. This third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course. Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this project you will explore how MySQL fits into information systems. You will become familiar with MySQL features as you explore database management system options and participate in hands-on exercises using MySQL Workbench to create and populate a table in a MySQL relational database. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

By the end of this project, you will be able to apply data analysis to predict career longevity for NBA Rookie using python. Determining whether a player’s career will flourish or not became a science based on the player’s stats. Throughout the project, you will be able to analyze players’ stats and build your own binary classification model using Scikit-learn to predict if the NBA rookie will last for 5 years in the league if provided with some stats such as Games played, assists, steals and turnovers …. etc. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Welcome to this Project about “Two Major Models of running Containers in AWS”. This Project will be focusing on one of the many types of computing in AWS, called “Container Computing”. To understand the benefits of AWS Products and services, which relates to Containers, you need to understand what ‘Containers’ are and what benefits ‘Container Computing’ Provides. In this Project, you are going to get a chance to make a ‘Container’. If you are looking to deploy applications across multiple machines and platforms, Containers and virtual machines (VMs) are two of the top approaches in use today. Both can help your IT team become more agile and responsive to business demands. Both are used to host applications. Before containers came along, the “virtual machine” was the technology of choice for optimizing server capacity, but Virtual Machines had some drawbacks. I f you run a virtual machine, say with 4 GB RAM and 4 GB disk, the operating system can easily consume 60 - 70% of the disk and much of the available memory, leaving relatively little for application which run in those Virtual Machine. But Containerization handles things in a different way. Instead of running a whole ‘operating System’ for each application, containers run as a process. So if you could run 5 applications using ‘Virtualization’, you will be able to run 15 applications using ‘Containerization’. This Project will help you to learn different ways of running “containers” in AWS. There are two different ways to run “containers” in AWS. One is using “EC2 instance” and other using “ECS Fargate”.So this Project has two major parts. In the first part, you will learn to create a docker image and test that image by running on a container, and once you verified your docker image works, you are going to upload it to “DockerHub”, which is a popular online resource for uploading docker images for others to access, either public or specific private individuals.In the second part of this project, you will learn to create an ECS cluster with Fargate cluster mode, and will deploy the container we created, into Fargate Cluster. So you are going to get some practical experience of how to deploy real container into a Fargate Cluster. There is a lot to get through though. so let’s get started!! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is the sixth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. You’ll learn how to visualize and present your data findings as you complete the data analysis process. This course will show you how data visualizations, such as visual dashboards, can help bring your data to life. You’ll also explore Tableau, a data visualization platform that will help you create effective visualizations for your presentations. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, you will: - Examine the importance of data visualization. - Learn how to form a compelling narrative through data stories. - Gain an understanding of how to use Tableau to create dashboards and dashboard filters. - Discover how to use Tableau to create effective visualizations. - Explore the principles and practices involved with effective presentations. - Learn how to consider potential limitations associated with the data in your presentations. - Understand how to apply best practices to a Q&A with your audience.

Welcome to "Simulation of Manufacturing Process Using R Simmer". This is a project-based course which should take about 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will gain introductiory knowledge of Discrete Event Simulation, Manufacturing Process Analysis, be able to use R Studio and Simmer library, create statistical variables required for simulation, define process trajectory, define and assign resources, define arrivals (eg. incoming customers / work units), run simulation in R, store results in data frames, plot charts and interpret the results.