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Measuring the Success of a Patient Safety or Quality Improvement Project (Patient Safety VI)
by Matt Austin- 4.6
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Approx. 6 hours to complete
Equally important, they will learn which data sources provide the most meaningful information and tools for how and where to locate them. Interpreting Data and Telling Data's Story From Data to Action and Iteration Use of Quantitative & Qualitative Data in QI Projects Using Data & Measures to Evaluate the Success of an Improvement Project...
Marketing analytics: Know your customers
by Ray Welling- 4.7
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Approx. 20 hours to complete
Rapid advancements in technology mean more powerful data and analytics can inform marketing decisions. Types of customer data 1 Which types of data are relevant? 3 How digital technology has transformed customer data collection Tools for analysing customer data 1 Making data useful: the DIKW pyramid Practice task - Connecting with customer using data...
Fundamentals of Social Media Advertising
by Daniel Kob- 4.9
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Approx. 9 hours to complete
How Data is Collected Online Different Data Formats Intro to Data Protection Policies Company Data Policies The Role of Data in Social Media Advertising Practice Quiz...
Follow a Machine Learning Workflow
by Renée Cummings- 0.0
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Approx. 20 hours to complete
The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. Data Structure Terminology Data Quality Issues Data Sources Guidelines for Using Visualizations to Analyze Data...
Tidy Data: Updated Data Processing With tidyr and dplyr in R
by Minerva Singh- 4.5
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4.5 hours on-demand video
Learn Data Preprocessing, Data Wrangling and Data Visualisation With the Two Most Happening R Data Science Packages THIS IS YOUR ROADMAP TO LEARNING & BECOMING HIGHLY PROFICIENT IN DATA PREPROCESSING, DATA WRANGLING, & DATA VISUALIZATION USING TWO OF THE MOST IN-DEMAND R DATA SCIENCE PACKAGES!...
$12.99
Database Design and Basic SQL in PostgreSQL
by Charles Russell Severance- 4.8
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Approx. 13 hours to complete
Using SQL techniques and common commands (INSERT INTO, WHERE, ORDER BY, ON DELETE CASCADE, etc) will enable you to create tables, column types and define the schema of your data in PostgreSQL. You will learn about data modeling and how to represent one-to-many and many-to-many relationships in PostgreSQL. Data Types in PostgreSQL...
Data Science in Real Life
by Brian Caffo, PhD , Jeff Leek, PhD , Roger D. Peng, PhD- 4.4
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Approx. 7 hours to complete
Have you ever had the perfect data science experience? The data pull went perfectly. How does one manage a team facing real data analyses? This is a focused course designed to rapidly get you up to speed on doing data science in real life. 1, Describe the “perfect” data science experience...
Bioconductor for Genomic Data Science
by Kasper Daniel Hansen, PhD- 3.9
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Approx. 9 hours to complete
This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University. rtracklayer - Data Import Basic Data Types Getting data into Bioconductor...
Intro to Machine Learning with TensorFlow
by Cezanne Camacho , Mat Leonard , Luis Serrano , Dan Romuald Mbanga , Jennifer Staab , Sean Carrell , Josh Bernhard , Jay Alammar , Andrew Paster , Juan Delgado , Michael Virgo- 0.0
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3 months
This program is designed to teach you foundational machine learning skills that data scientists and machine learning engineers use day-to-day. Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms in TensorFlow and scikit-learn....
Intro to Machine Learning with PyTorch
by Cezanne Camacho , Mat Leonard , Luis Serrano , Dan Romuald Mbanga , Jennifer Staab , Sean Carrell , Josh Bernhard , Jay Alammar , Andrew Paster- 0.0
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3 months
This program is designed to teach you foundational machine learning skills that data scientists and machine learning engineers use day-to-day. Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms in PyTorch and scikit-learn....
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