This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use Google Cloud Client Libraries for .NET to query BigQuery public datasets with C#.

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the AI-900 Microsoft Azure AI Fundamentals certification exam. You will refresh your knowledge of fundamental principles of machine learning on Microsoft Azure. You will go back over the main consideration of AI workloads and the features of computer vision, Natural Language Processing (NLP), and conversational AI workloads on Azure. In short, you will recap all the core concepts and skills that are measured by the exam. You will test your knowledge in a series of practice exams​ mapped to all the main topics covered in the AI-900 exam, ensuring you’re well prepared for certification success. You will prepare to pass the certification exam by taking practice tests with similar formats and content. You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the AI-900 proctored exam. By the end of this course, you will be ready to sign-up for and take the AZ-900 exam.​ This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology, general technology concepts, including concepts of machine learning and artificial intelligence.

In this course, you will discover how to solve business problems with machine learning, no coding required. You will explore Amazon SageMaker Canvas, a visual point-and-click interface that allows you to generate accurate ML predictions without requiring any machine learning experience or having to write a single line of code. At the end of the course, you will walk away understanding how to make better business decisions using no-code machine learning.

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making. For instance,: Is a consumer going to default on a loan or not? Will the company make a profit? Should we extend into a certain sector of the market? 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 course is designed to prepare you to enter the job market as a data scientist. It provides guidance about the regular functions and tasks of data scientists and their place in the data ecosystem, as well as the opportunities of the profession and some options for career development. It explains practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. Let seasoned professionals share their experience to help you get ahead of the competition.

This is a self-paced lab that takes place in the Google Cloud console. The Apps Script CLI, or clasp, is a tool that lets you create, edit, and deploy Apps Script projects locally and create and publish web apps and add-ons for products like Sheets, Docs, Forms, and Slides from the command line.

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash

In this 1-hour long project-based course, you will learn how to explore presidential concession speeches by US presidential candidates over time, looking specifically at speech length and top words and examining variation by Democrat and Republican candidates. You will learn how to import textual data stored in raw text files, turn these files into a corpus (a collection of textual documents) and tokenize the text all using the software package quanteda. You will also learn how to extract useful information from filenames and how to use this information to generate visualizations of textual data using the stringr and ggplot2 packages. 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.

The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods. This course is written for individuals who: - Have some familiarity with R or Python - have written some scripts. - Have not had formal training in computational methods. - Have limited or no familiar with GitHub, Docker, or package management tools. Motivation Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones et al, 2017). Reproducibility in cancer informatics (as with other fields) is still not monitored or incentivized despite that it is fundamental to the scientific method. Despite the lack of incentive, many researchers strive for reproducibility in their own work but often lack the skills or training to do so effectively. Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved. Reproducible analyses are more likely to be understood, applied, and replicated by others. This helps expedite the scientific process by helping researchers avoid false positive dead ends. Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing. Curriculum This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub. The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these activities as they follow along with the course material to help increase the reproducibility of their analyses. **Goal of this course:** Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an "ease into it" approach. We attempt to give learners doable, incremental steps to increase the reproducibility of their analyses. **What is not the goal** This course is meant to introduce learners to the reproducibility tools, but _it does not necessarily represent the absolute end-all, be-all best practices for the use of these tools_. In other words, this course gives a starting point with these tools, but not an ending point. The advanced version of this course is the next step toward incrementally "better practices". How to use the course This course is designed with busy professional learners in mind -- who may have to pick up and put down the course when their schedule allows. Each exercise has the option for you to continue along with the example files as you've been editing them in each chapter, OR you can download fresh chapter files that have been edited in accordance with the relative part of the course. This way, if you decide to skip a chapter or find that your own files you've been working on no longer make sense, you have a fresh starting point at each exercise.