Source: Coursera
Data analytics is a data science. The purpose of data analytics is to generate insights from data by connecting patterns and trends with organizational goals. Comparing data assets against organizational hypotheses is a common use case of data analytics, and the practice tends to be focused on business and strategy. Data analytics deals less in AI, machine learning, and predictive modeling, and more with viewing historical data in context.
With this in mind, we’ve compiled this list of the best data analytics courses on Coursera if you’re looking to grow your skills for work or play. Coursera is one of the top online education platforms in the world, partnering with more than 200 universities and companies to provide a range of learning opportunities. The platform touts more than 77 million learners around the globe. As you can see below, we broke the best data analytics courses on Coursera down into categories based on the recommended proficiency level. Each section also features our inclusion criteria. Click GO TO TRAINING to learn more and register.
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Note: We included top-rated Coursera data analytics training via the Level selection to make your search easier.
Description: This course presents a gentle introduction to the concepts of data analysis, the role of a Data Analyst, and the tools that are used to perform daily functions. You will gain an understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining. You will then learn the soft skills that are required to effectively communicate your data to stakeholders, and how mastering these skills can give you the option to become a data-driven decision-maker.
Description: Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more.
Description: This course is designed to provide you with basic working knowledge for using Excel spreadsheets for Data Analysis. It covers some of the first steps for working with spreadsheets and their usage in the process of analyzing data. It includes plenty of videos, demos, and examples for you to learn, followed by step-by-step instructions for you to apply and practice on a live spreadsheet.
Description: In this course, you’ll get an introduction to Data Analytics and its role in business decisions. You’ll learn why data is important and how it has evolved. You’ll be introduced to “Big Data” and how it is used. You’ll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you’ll have a chance to put your knowledge to work in a simulated business setting.
Description: In this course, you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course, you are able to analyze and interpret data gathered within such a project. You will be able to use Minitab to analyze the data. I will also briefly explain what Lean Six Sigma is.
Description: This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. The course is divided into three sections. In the first section, instructors bridge accountancy to analytics. In the second, teachers emphasize the importance of assembling data. In the third, and largest section of the course, learners explore how Excel and Tableau can be used to analyze big data.
Description: This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.
Description: This course will teach you the core building blocks of statistical analysis – types of variables, common distributions, hypothesis testing – but, more than that, it will enable you to take a data set you’ve never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions.
Note: We included top-rated Coursera data analytics training via the Level selection to make your search easier.
Description: This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide businesses and managers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the ideal analytic tool for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data; and utilize data in decision making for their agencies, organizations or clients.
Description: This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time-series data and how to evaluate the risk-reward trade-off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leveraged in other domains.
Description: After taking this course, you will be able to utilize various Application Programming Interface (API) services to collect data, process the collected data, analyze unstructured data, and use different tools for collecting, analyzing, and exploring social media data for research and development purposes. The course will have a series of small assignments or mini-projects that involve data collection, analysis, and presentation involving various social media sources using the techniques learned in the class.
Description: This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. You will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. You will also compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach.
Note: We included top-rated Coursera data analytics training via the Level selection to make your search easier.
Description: This course will teach you how to warehouse your data efficiently using Hive, Spark SQL and Spark DataFframes, work with large graphs, such as social graphs or networks, and optimize your Spark applications for maximum performance.
Note: We included top-rated Coursera data analytics training via the Level selection to make your search easier.
Description: This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.
Description: In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics.
Description: The course is designed keeping in mind two kinds of learners – those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy-to-understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.
Description: This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. All advanced concepts are demonstrated in individual Excel spreadsheet templates that you can use to answer relevant questions.
