Data Analysis

 

 


 

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E.g., 2017-06-29
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Jun 5th 2017

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos. Además también abordarás la utilización de uno de los IDEs más populares entre la comunidad de usuarios de R, llamado RStudio.

Average: 6.8 (6 votes)
Jun 5th 2017

You'll begin this course by looking at some advanced Excel skills - including index formulas, logical text and nested functions. You'll also look at data connections to external databases, and Visual Basic for Applications (the programming language behind Excel). Once you're comfortable with that, you'll move on to preparing a spreadsheet for a client - giving it a clean design and making it easy to use and reproduce.

Average: 10 (3 votes)
Jun 5th 2017

The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have know how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future.

Average: 5.7 (21 votes)
Jun 5th 2017

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Average: 6.5 (6 votes)
Jun 5th 2017

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This course is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics.

Average: 7.7 (10 votes)
Jun 5th 2017

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions.

Average: 8 (2 votes)
Jun 5th 2017

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Average: 7.9 (11 votes)
Jun 5th 2017

Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialisation. 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.

Average: 4.5 (24 votes)
Jun 5th 2017

This four-module course introduces users to Julia as a first language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics and many more.

Average: 5.5 (4 votes)
Jun 5th 2017

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.

Average: 5.6 (24 votes)
Jun 5th 2017

Using economic theory and data analysis, explore the economic lives of the poor, and the ways to design and implement effective development policy. In this course, we will study the different facets of human development in topics such as education, health, gender, the family, land relations, risk, informal and formal norms, public policy, and institutions.

Average: 8 (1 vote)
Jun 5th 2017

Learn how to both design randomized evaluations and implement them in the field to measure the impact of social programs. A randomized evaluation, also known as a randomized controlled trial (RCT), field experiment or field trial, is a type of impact evaluation that uses random assignment to allocate resources, run programs, or apply policies as part of the study design.

No votes yet
Jun 5th 2017

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!

Average: 7.4 (5 votes)
Jun 5th 2017

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Average: 6 (6 votes)
Jun 5th 2017

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.

Average: 7.1 (11 votes)
Jun 5th 2017

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala.

No votes yet
Jun 5th 2017

The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions.

Average: 8.8 (4 votes)
Jun 5th 2017

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.

Average: 10 (1 vote)
Jun 5th 2017

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary.

Average: 6.7 (3 votes)
Jun 5th 2017

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Average: 8.6 (7 votes)

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