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I follow Massive Open Online Courses and passed +20 courses and counting .. Description and duration are as described by the course provider
[Design and Interpretation of Clinical Trials]({{site.url}}/certificates/Design and Interpretation of Clinical Trials.pdf) by Johns Hopkins University on Coursera. Certificate earned on July 15, 2016
Duration: Approx. 11 hours to complete Suggested: 4 hours/week
Description: The course will explain the basic principles for design of randomized clinical trials and how they should be reported. In the first part of the course, students will be introduced to terminology used in clinical trials and the several common designs used for clinical trials, such as parallel and cross-over designs. We will also explain some of the mechanics of clinical trials, like randomization and blinding of treatment. In the second half of the course, we will explain how clinical trials are analyzed and interpreted. Finally, we will review the essential ethical consideration involved in conducting experiments on people.
[Introduction to Systematic Review and Meta-Analysis]({{site.url}}/certificates/Introduction to Systematic Review and Meta-Analysis.pdf) by Johns Hopkins University on Coursera. Certificate earned on July 19, 2016
Duration: Approx. 17 hours to complete Suggested: 6 weeks of study, 4-6 hours/week
Description: Upon successfully completing this course, participants will be able to: - Describe the steps in conducting a systematic review - Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework - Describe the process used to collect and extract data from reports of clinical trials - Describe methods to critically assess the risk of bias of clinical trials - Describe and interpret the results of meta-analyses
[The Data Scientist’s Toolbox]({{site.url}}/certificates/The Data Scientist’s Toolbox.pdf) by Johns Hopkins University on Coursera. Certificate earned on March 30, 2016
Duration: Approx. 8 hours to complete Suggested: 1-4 hours/week
Description: In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
[Framework for Data Collection and Analysis]({{site.url}}/certificates/Framework for Data Collection and Analysis.pdf) by University of Maryland, College Park. Certificate earned on September 22, 2016
Duration: Approx. 8 hours to complete Suggested: 4 weeks of study, 1-2 hours/week
Description: This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.
[R Programming]({{site.url}}/certificates/R Programming.pdf) by Johns Hopkins University on Coursera. Certificate earned on March 31, 2016
Duration: Approx. 20 hours to complete Suggested: 7 hours/week
Description: 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.
[Getting and Cleaning Data]({{site.url}}/certificates/Getting and Cleaning Data.pdf) by Johns Hopkins University on Coursera. Certificate earned on March 31, 2016
Duration: Approx. 14 hours to complete Suggested: 5 hours/week
Description: 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.
[Exploratory Data Analysis]({{site.url}}/certificates/Exploratory Data Analysis.pdf) by Johns Hopkins University on Coursera. Certificate earned on May 27, 2016
Duration: Approx. 15 hours to complete Suggested: 5 hours/week
Description: 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.
[Statistical Inference]({{site.url}}/certificates/Statistical Inference.pdf) by Johns Hopkins University on Coursera. Certificate earned on March 16, 2016
Duration: Approx. 16 hours to complete Suggested: 5 hours/week
Description: 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.
[Bayesian Statistics: From Concept to Data Analysis]({{site.url}}/certificates/Bayesian Statistics: From Concept to Data Analysis.pdf) by University of California, Santa Cruz on Coursera. Certificate earned on October 2, 2016
Duration: Approx. 21 hours to complete Suggested: Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics.
Description: This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.
[Programming for Everybody (Getting Started with Python]({{site.url}}/certificates/Getting Started with Python.pdf) ) by University of Michigan on Coursera. Certificate earned on March 24, 2016
Duration: Approx. 12 hours to complete Suggested: 2-4 hours/week
Description: This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
[Python Data Structures]({{site.url}}/certificates/Python Data Structures.pdf) by University of Michigan on Coursera. Certificate earned on May 6, 2016
Duration: Approx. 10 hours to complete Suggested: 2-4 hours/week
Description: This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.
[Using Python to Access Web Data]({{site.url}}/certificates/Using Python to Access Web Data.pdf) by University of Michigan on Coursera. Certificate earned on May 22, 2016
Duration: Approx. 15 hours to complete Suggested: 6 weeks of study, 2-4 hours/week
Description: This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
[Using Databases with Python]({{site.url}}/certificates/Using Databases with Python.pdf) by University of Michigan on Coursera. Certificate earned on June 9, 2016
Duration: Approx. 12 hours to complete Suggested: 5 weeks of study, 2-3 hours/week
Description: This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-13 of the textbook and the first three courses in this specialization. This course covers Python 3.
[Machine Learning Foundations: A Case Study Approach]({{site.url}}/certificates/Machine Learning Foundations: A Case Study Approach.pdf) by University of Washington on Coursera. Certificate earned on March 26, 2016
Duration: Approx. 24 hours to complete Suggested: 6 weeks of study, 5-8 hours/week
Description: This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
[Statistical Learning course]({{site.url}}/certificates/Statistical Learning.pdf) provided by Stanford online, taught by Trevor and Hastie. statement of accomplishment with distinction earned on April 6, 2017
Duration: Estimated Effort 5 hours per week 7 weeks
Description: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical)
[Grammar and Punctuation]({{site.url}}/certificates/Grammer and punctuation.pdf) by University of California, Irvine on Coursera. Certificate earned on February 21, 2016
Duration: Approx. 9 hours to complete Suggested: 4 weeks of study, 4-5 hours/week
Description: After completing this course, you will be able to: - identify the correct verb tenses to use - use commas effectively - utilize several different sentence types - write more effectively in English
[Getting Started with Essay Writing]({{site.url}}/certificates/Getting Started with Essay Writing.pdf) by University of California, Irvine on Coursera. Certificate earned on November 14, 2016
Duration: Approx. 16 hours to complete Suggested: 4 weeks of study, 1-3 hours/week
Description: This is the second course in the Academic English: Writing specialization. By introducing you to three types of academic essays, this course will especially help prepare you for work in college classes, but anyone who wants to improve his or her writing skills can benefit from this course. After completing this course, you will be able to: - create effective thesis statements for your essays - plan and write compare/contrast, cause/effect, and argument essays - write well-developed body paragraphs
[Introduction to Genomic Technologies]({{site.url}}/certificates/Introduction to Genomic Technologies.pdf) by Johns Hopkins University on Coursera. Certificate earned on February 13, 2016
Duration: Approx. 8 hours to complete Suggested: 8 hours/week
Description: This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.
[Genomic Data Science with Galaxy]({{site.url}}/certificates/Genomic Data Science with Galaxy.pdf) by Johns Hopkins University on Coursera. Certificate earned on March 16, 2016
Duration: Approx. 11 hours to complete Suggested: 3 hours/week
Description: Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.