Statistics & Data Analysis

 

 


 

Data science is one of today’s fastest-growing fields. Become a Data Scientist in 2016 with Coursera.



Customize your search:

E.g., 2017-02-27
E.g., 2017-02-27
E.g., 2017-02-27
Mar 21st 2017

En un mundo globalizado y cada vez más dinámico, la toma de decisiones correctas de forma ágil y eficiente es una actividad esencial en muchos ámbitos de nuestra actividad diaria. Para cualquier sector empresarial es fundamental contar con profesionales que sean capaces de combinar grandes cantidades de datos e información para llevar a cabo procesos de toma de decisiones a partir de evidencias objetivas. El curso va dirigido a todas aquellas personas que deseen obtener una visión introductoria y práctica sobre análisis de datos y big data. En particular, el MOOC se centra en conceptos, métodos y herramientas básicas para el procesado, análisis y construcción de modelos estadísticos con datos de muy diversa índole.

Average: 7 (1 vote)
Mar 13th 2017

In today’s digital age, mobile devices have become a de facto tool for business, not only performing as communication devices, but as enablers of business activities. In this space, the mobile workforce needs efficient ways to interact with the overload of information delivered to it. In most cases, key data is delivered in the form of PDF or spreadsheet attachments that make consumption on mobile devices cumbersome. To solve the problem, SAP BusinessObjects Roambi, a data visualization and publishing platform, was designed for mobile devices from the ground up with the end user in mind. The visualizations are designed to use the mobile device screen size effectively and enable the user to interact with the reports with engaging functionality and gestures commonly used in other mobile apps.

No votes yet
Self Paced

Understand key aspects of business operations and lean management including capacity, productivity, quality, and supply chain. Have you ever wondered about the right methods to improve productivity, configure your supply chain or address the demand on hand? In recent years, businesses have strived to improve productivity and quality, reduce costs and delivery times, and embrace flexibility and innovation. These strategies are part of the Operations Management (OM) activities that service and manufacturing organizations engage in.

Average: 7 (2 votes)
Feb 28th 2017

Tell your story with charts and maps on the web, using easy-to-learn free tools: Google Sheets, Tableau, Highcharts, Carto, Leaflet, GitHub. Tell your story and show it with data. In this data visualization course, you will learn how to design interactive charts and customized maps for your website.

No votes yet
Feb 27th 2017

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery.

Average: 7.7 (3 votes)
Feb 27th 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.

Average: 6 (1 vote)
Feb 27th 2017

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Average: 5 (1 vote)
Feb 27th 2017

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

No votes yet
Feb 27th 2017

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Average: 1.7 (3 votes)
Feb 27th 2017

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics.

Average: 9 (5 votes)
Feb 27th 2017

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery.

No votes yet
Feb 27th 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: 6 (4 votes)
Feb 27th 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)
Feb 27th 2017

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

Average: 8 (1 vote)
Feb 27th 2017

Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

Average: 8 (7 votes)
Feb 27th 2017

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Average: 7.7 (7 votes)
Feb 27th 2017

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.

Average: 3.5 (4 votes)
Feb 27th 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)
Feb 27th 2017

This course will help you become scientifically literate so that you can make better choices for yourself and the world. Unlike other courses on statistics and scientific methods, we explore global challenges - such as poverty or climate change - and then discuss how key approaches of statistics and scientific methods can help tackle these challenges. We present these approaches in a non-mathematical and easily accessible way. You will leave the course being able to recognize which efforts to do good in this world actually work, and you will have used your science literacy to make some personal changes in your life.

Average: 4 (1 vote)
Feb 27th 2017

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts.

Average: 5.5 (19 votes)

Pages