You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.

By the end of this project, you will learn how to build a spam detector using machine learning & launch it as a serverless API using AWS Elastic Beanstalk technology. You will be using the Flask python framework to create the API, basic machine learning methods to build the spam detector & AWS desktop management console to deploy the spam detector into the AWS cloud servers. Additionally, you will learn more about how to switch between different versions of your web application & also, monitoring your AWS servers using Elastic Beanstalk Desktop Management Console. Note: To avoid distraction for set up during the course, we would recommend that you create an Amazon AWS account beforehand. Amazon AWS provides a free tier option for 1 year & the course materials will utilize services that fall under the free tier option.

This course is for everyone. In the new world we live in, coding is a universally valuable skill, whether you're a scientist, artist, or a humanist. Algorithms are everywhere, and we all have to understand how they work. The C language is particularly well suited as an introduction to coding: It's a tried-and-true language, and it allows you to understand computing processes at a deep level. No prior knowledge of coding is needed for this course. We'll start at the beginning. The time estimated time commitment for this course is five hours a week for five weeks.

In this one-hour, project-based course, you'll learn how to create and add Trees using Unity Tree Editor. You'll learn how to add and design branches by configuring the settings in the tree component, customize the leaf materials and add a wind effect using Unity's Wind Zone object. The guided project will introduce you to the following Unity concepts: -Tree -Terrain -Materials -Wind Zone This is Part 2 of a three-part series on creating landscape settings. Part 1 covers Terrain related settings. Part 3 continues in adding Terrain related objects such as flowers, bushes, and lake. 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.

Throughout this guided project we are going to create a simple Parallel Coordinates Plot (PCP) using d3 js. PCP is one of the most common data visualization techniques used to visualize high-dimensional datasets. In this guided project you will create a simple PCP step by step. We will also cover some important topics in data visualization such as Linear and Ordinal scaling to best visualize our data. Having the knowledge of javascript programming language and the basics of d3 js are the two most important prerequisites to get the most out of this guided project.

In this 2-hour long project-based course, you will learn how to predict mRNA Vaccine Degradation Rates at various positions of the molecule. Our model will predict likely degradation rates at each base of an RNA molecule which will be useful to develop models and design rules for RNA degradation. We will look at how to build a Bidirectional Gated Recurrent Units Neural Network which can predict the degradation for multiple scenarios at each of the base. We will cover how to train the model and evaluate on a test set. We will then finally make predictions using the trained model and compare it with the original degradation rates. 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 introduction on fundamental programming concepts and terminology. This project is intended for learners who have never programmed before. You will be exposed to the basic coding structures such as assignment statements, variables, constants, decision making statements, loops, and arrays in C# Console applications. By successfully completing the hands-on practices assigned, you will understand why and when to apply these basic programming concepts and be better prepared for taking other computer programming courses in any language.

In this 1.5 hours guided project, you will build a world encyclopedia app from scratch using AngularJS, HTML and JavaScript. You will learn how to take advantage of existing API via HTTP and also learn about more advanced Angular features such as services and routing. (To fit the project within the time limit, some code for areas that are not our focus will be provided). Prerequisites: Basic Angular, HTML, JavaScript, and CSS. Taking my class Make a Bill Splitter app with AngularJS is highly recommended if you want to get a foundation of fundamental AngularJS.

This project will teach you easy and simple examples on Flexbox properties so that one can understand and master the basics of Flexbox so that these can be applied in building modern web layouts in Front End part of the Web application process. This project covers all Flexbox properties like Flexbox Container Properties and Flexbox Item Properties. 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 set up an environment for Big Data Development using Visual Studio Code, MongoDB and Apache Spark. You will then use the environment to process a large dataset from NOAA showing hourly precipitation rates for a ten year period from the state of Wisconsin. MongoDB is a widely used NoSQL database well suited for very large datasets or Big Data. It is highly scalable and adaptable as well. Apache Spark is used for efficient in-memory processing of Big Data.