Learn Advance Your Future.

Learn the fundamentals of programming, data structures, web development, and data science using Python - The hottest language.

Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Wharton's acclaimed accounting professors, you'll learn how data is used to assess what drives financial performance and to forecast future financial scenarios.

In this course, you will learn how to use accounting to facilitate and align decisions made by owners, managers, and employees. You will learn how accountants create, organize, interpret, and communicate information that improves internal processes, and allows organizations to identify and leverage opportunities to create value within the supply chain and with customers.

This course is the first course in a five-course Financial Reporting Specialization that covers the collection, processing, and communication of accounting information (via financial reports) about economic entities to interested parties (i.e., managers and external stakeholders such as stockholders and creditors).

In this course, you will get hands-on instruction of advanced Excel 2013 functions. You'll learn to use PowerPivot to build databases and data models. We'll show you how to perform different types of scenario and simulation analysis and you'll have an opportunity to practice these skills by leveraging some of Excel's built in tools including, solver, data tables, scenario manager and goal seek.

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take.

This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

This course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis.

In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists.

This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus.

Digital Analytics for Marketing Professionals: Marketing Analytics in Practice is the second in a two-part series of complementary courses and focuses on the skills and practical abilities analysts need to be successful in today's digital business world.

This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing.

This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.