Coursera

5

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide...

4

Identify the core functionalities of data modeling in the data mining pipelineApply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work. Evaluate data modeling techniques, determine...

4.27

This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course...

3.68

Who is this course for ? This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques...

4.68

Distinguish between artificial intelligence and data science concepts. Protect data privacy with ethical and legal standards. Assess the potential risks of bias. Advocate for the importance of risk evaluation in emerging technologies.

4.7

We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations in the real world. Whether it's...

4.09

This course is part of HSE University Master of Business Analytics and Master of Finance degree programs. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted...

4.77

In this Capstone Project, you'll bring together all the new skills and insights you've learned through the four courses. You'll be given a 'mock' client problem and a data set. You'll need to analyze the...

4.29

The application of predictive modeling to professional and academic workApplications of classification analysis: decision treesApplications of regression analysis (linear and logistic)

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...

4.9

Business analysts need to be able to prescribe optimal solution to problems. But analytics courses are often focused on training students in data analysis and visualization, not so much in helping them figure out how...

4.42

In the final capstone project you will apply the skills you learned by building a large data-intensive application using real-world data. You will implement a complete application processing several gigabytes of data. This application will...

4.38

This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems. The course will introduce you to take everyday and complex business proble...

4.22

This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs),...

4.31

In this Google Cloud Labs Series, learners get hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get...

and practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools.

4.72

“By the end of this Course, you will be able to…” • Evaluate effective leadership styles for leadership in nursing informatics in clinical or academic contexts to improve leadership success. • Discover core values that...

4.1

Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres...

4.2

​Develop a project proposal and select your data​Perform descriptive statistics as part of your exploratory analysisDevelop metrics and perform advanced techniques in SQLP​resent your findings and make recommendations

5

Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values. Conduct exploratory data analysis using descriptive statistics, data grouping, data analysis and corr...