Isabelle Claire Valette successfully completed the Coursera course Practical Machine Learning from Johns Hopkins University with distinction on April 04, 2015. The course taught students the components of machine learning algorithms and how to apply basic machine learning tools to build and evaluate predictors using real data. The course was instructed by Jeffrey Leek, Roger Peng, and Brian Caffo from the Johns Hopkins Bloomberg School of Public Health.
Understand the components of a machine learning algorithm and how to apply multiple basic machine learning tools. Build and Evaluate Predictors on real data.
Understand the components of a machine learning algorithm and how to apply multiple basic machine learning tools. Build and Evaluate Predictors on real data.
Overview of the data, questions, & tools that data analysts &
scientists work with. It is a conceptual introduction to the ideas behind turning data into knowledge as well as a practical introduction to tools like version control, markdown, git, GitHub, R, and RStudio.
Overview of the data, questions, & tools that data analysts &
scientists work with. It is a conceptual introduction to the ideas behind turning data into knowledge as well as a practical introduction to tools like version control, markdown, git, GitHub, R, and RStudio.
Statement of Accomplishment: Data Science Specialization VI - Statistical Inf...Folco Bombardieri
Statement of Accomplishment for the "Statistical Inference" Course from Coursera - 6th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Obtain data from the web, APIs, databases, and colleagues in various formats, as well as the basics of cleaning and “tidying” data. It also covers the components of a complete data set: raw data, processing instructions, code-books, &
processed data.
Statement of Accomplishment: Data Science Specialization III - Getting and Cl...Folco Bombardieri
Statement of Accomplishment for the "Getting and Cleaning Data" Course from Coursera - 3rd Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
APRIL 04, 2015
ISABELLE CLAIRE VALETTE
HAS SUCCESSFULLY COMPLETED THE JOHNS HOPKINS UNIVERSITY'S OFFERING OF
Practical Machine Learning
Upon completion of this course students understand the
components of a machine learning algorithm and how to apply
multiple basic machine learning tools. Students also learn to
apply these tools to build and evaluate predictors on real data.
JEFFREY LEEK, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
ROGER D. PENG, PHD
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
BRIAN CAFFO, PHD, MS
DEPARTMENT OF BIOSTATISTICS, JOHNS HOPKINS
BLOOMBERG SCHOOL OF PUBLIC HEALTH
PLEASE NOTE: THE ONLINE OFFERING OF THIS CLASS DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT
THE JOHNS HOPKINS UNIVERSITY. THIS STATEMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A STUDENT AT THE JOHNS
HOPKINS UNIVERSITY IN ANY WAY. IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY GRADE; IT DOES NOT CONFER JOHNS HOPKINS
UNIVERSITY CREDIT; IT DOES NOT CONFER A JOHNS HOPKINS UNIVERSITY DEGREE; AND IT DOES NOT VERIFY THE IDENTITY OF THE
STUDENT.