Data Analysis courses

This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you...

4.83

Welcome to this course on Data Analytics for Lean Six Sigma. In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this...

4.6

Describe what a methodology is and why data scientists need a methodology. Describe the six stages in the Cross Industry Process for Data Mining (CRISP-DM) methodology including Business Understanding and Data Understanding. Describe some of...

4.76

In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of...

Missing data is part of any real world data analysis. It can crop up in unexpected places, making analyses challenging to understand. In this course, you will learn how to use tidyverse tools and the...

4.28

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph...

4.78

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you...

4.42

At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed...

This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum anal...

4.51

Understand techniques such as lambdas and manipulating csv filesDescribe common Python functionality and features used for data scienceQuery DataFrame structures for cleaning and processingExplain distributions, sampling, and t-tests

In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine....

4.45

This is the fourth course in the Data Warehouse for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will gain the knowledge and skills for using data warehouses...

4.77

AI Ethics research is an emerging field, and to prove our skills, we need to demonstrate our critical thinking and analytical ability. Since it's not reasonable to jump into a full research paper with our...

4.58

This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate...

This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets...

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as...

4.52

Understand critical programming language conceptsConfigure statistical programming softwareMake use of R loop functions and debugging toolsCollect detailed information using R profiler

4.32

The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience...

You have experience working with databases or, perhaps, you have heard how useful databases can be for organizing your data. Have you ever wanted to create and manage your own databases but thought such knowledge...

Create charts to describe and compare the composition of data setsIllustrate the distribution of data through visualizationsCreate specialized visualizations such as heat maps, correlograms, and mosaic plotsUse R Markdown to create documents, reports...