E.g., Tuesday, February 9, 2016
E.g., Tuesday, February 9, 2016
E.g., Tuesday, February 9, 2016
Jan 18th 2016

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.

Average: 5.5 (2 votes)
Jan 18th 2016

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 8.3 (3 votes)
Jan 18th 2016

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

Average: 7.5 (2 votes)
Jan 18th 2016

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 9 (1 vote)
Jan 18th 2016

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 3 (5 votes)
Jan 18th 2016

Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.

Average: 1 (1 vote)
Self Paced

In this course you will be introduced to the basic ideas behind the qualitative research in social science. You will learn about data collection, description, analysis and interpretation in qualitative research. Qualitative research often involves an iterative process. We will focus on the ingredients required for this process: data collection and analysis.

Average: 8 (4 votes)
Self Paced

Investigate, Visualize, and Summarize Data Using R.

Average: 3.8 (8 votes)
Self Paced

Use of available (mainly web-based) programs for analyzing biological data. This is an introductory course with a strong emphasis on hands-on methods. Some theory is introduced, but the main focus is on using extant bioinformatics tools to analyze data and generate biological hypotheses.

Average: 7.8 (4 votes)
Self Paced

The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.

Average: 4.7 (6 votes)
Self Paced

Data is essential for improving outcomes for students. Whether it is informing improved instruction, empowering parents and communities, or helping policymakers make decisions and target resources, our education system needs data in order to continuously improve. In order to create a culture of trust that enables effective data use, policymakers and education professionals must ensure that the public has confidences that state and local leaders act to protect student data privacy. This self-paced course will discuss the value data brings to improve education, offer recommendations for addressing privacy concerns while promoting effective data use, and explore lessons learned from existing and emerging policies in education and other sectors.

Average: 1 (1 vote)
Self Paced

The course gives you an introduction to using R.

Average: 5.4 (7 votes)
Self Paced

This course will guide you through analyzing your own Facebook data.

Average: 5 (4 votes)
Self Paced

This is an introductory course to Digital Signal Processing that can be taken at any time. The course deals with the fundamentals in addition to exploring techniques like filtering, correlation and Fourier analysis. There is an emphasis in applying DSP theory to practical problems.

Average: 4 (7 votes)
Self Paced

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.

Average: 3.1 (8 votes)
Self Paced

This course provides an introduction to various classes of derivative securities and we will learn how to price them using "risk-neutral pricing". In the follow-up to this course (FE & RM Part II) we will consider portfolio optimization, risk management and more advanced examples of derivatives pricing including, for example, real options and energy derivatives.

No votes yet
Self Paced

This course follows on from FE & RM Part I. We will consider portfolio optimization, risk management and some advanced examples of derivatives pricing that draw from structured credit, real options and energy derivatives. We will also cast a critical eye on how financial models are used in practice.

Average: 10 (2 votes)
Self Paced

A matemática é a ciência do raciocínio lógico e abstrato, estuda quantidades, medidas, espaços, estruturas e variações. Um trabalho matemático consiste em procurar por padrões, formular conjecturas e, por meio de deduções rigorosas a partir de axiomas e definições, estabelecer novos resultados.

No votes yet
Self Paced

Learn what it takes to become a data scientist.

Average: 7 (1 vote)

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