Data Analysis

 

 


 

Customize your search:

E.g., 2017-01-16
E.g., 2017-01-16
E.g., 2017-01-16
Jan 16th 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)
Jan 16th 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: 7.8 (4 votes)
Jan 16th 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: 4 (4 votes)
Jan 16th 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: 7.5 (2 votes)
Jan 16th 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)
Jan 16th 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)
Jan 16th 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)
Jan 16th 2017

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.

Average: 7.2 (5 votes)
Jan 16th 2017

This course explores Excel as a tool for solving business problems. In this course you will learn the basic functions of excel through guided demonstration. Each week you will build on your excel skills and be provided an opportunity to practice what you’ve learned. Finally, you will have a chance to put your knowledge to work in a final project.

Average: 3 (6 votes)
Jan 16th 2017

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Average: 5.8 (9 votes)
Jan 16th 2017

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.

No votes yet
Jan 16th 2017

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

No votes yet
Jan 16th 2017

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 (4 votes)
Jan 16th 2017

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.

Average: 7.8 (4 votes)
Jan 16th 2017

This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis.

Average: 7.3 (4 votes)
Jan 16th 2017

This course builds on the theory and foundations of marketing analytics and focuses on practical application by demystifying the use of data in marketing and helping you realize the power of visualizing data with artful use of numbers found in the digital space.

Average: 10 (1 vote)
Jan 16th 2017

Organizations large and small are inundated with data about consumer choices. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. This course gives you the tools to measure brand and customer assets, perform regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns.

Average: 3.5 (2 votes)
Jan 16th 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: 4 (2 votes)
Jan 16th 2017

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

No votes yet
Jan 16th 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: 5.8 (20 votes)

Pages