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.
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.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.
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Understand the components of a machine learning algorithm and how to apply multiple basic machine learning tools. Build and Evaluate Predictors on real data.
Basics of creating data products using Shiny, R packages, and interactive graphics. Focuses on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
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applied before modeling to inform development of complex
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Statement of Accomplishment: Data Science Specialization II - R ProgrammingFolco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 2nd Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
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.
Statement of Accomplishment: Data Science Specialization IV - Exploratory Dat...Folco Bombardieri
Statement of Accomplishment for the "R Programming" Course from Coursera - 4th Course of the Data Science Specialization series (offered by Johns Hopkins University)
Duration: 4 weeks
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
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Coursera Getting and Cleaning Data 2014
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
SEPTEMBER 08, 2014
MALOY MANNA
HAS SUCCESSFULLY COMPLETED THE JOHNS HOPKINS UNIVERSITY'S OFFERING OF
Getting and Cleaning Data
This course covers obtaining 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, codebooks, &
processed 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.