Courses tagged with "Data Analysis & Statistics" (159)

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Starts : 2017-08-08
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edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX LouvainX Social Sciences

L'analyse de données quantitatives est devenue aujourd'hui une pratique incontournable dans tous les métiers liés aux sciences sociales. Ces analyses sont utilisées pour comprendre des phénomènes économiques et financiers, décrire la nature de la relation entre des personnes, des objets ou des événements, ou encore anticiper les conséquences d’une décision.

Ce cours vous permettra d’acquérir les premiers concepts nécessaires pour construire rigoureusement des modèles économétriques. Il est constitué de leçons construites à partir de cas pratiques originaux interrogeant la vie quotidienne. Mentionnons parmi d’autres:

  • Comment réussir une campagne de financement collaboratif ?
  • Quels sont les déterminants des écarts salariaux entre les hommes et les femmes ?
  • Existe-t-il encore un lien entre l’inflation et le chômage ?
  • Peut-on prédire un changement de niveau de risque sur les marchés financiers ?
  • Existe-t-il un lien entre le niveau de revenu et le sentiment d’être en bonne santé ?
  • Le salaire est-il la seule motivation économique des travailleurs ?
  • Le prix de vente des œuvres de Picasso correspond-il à sa valeur de catalogue ?

Ce cours contient huit leçons. Il permet tout d’abord de comprendre l’étendue et les limites de l’utilisation de l’économétrie en sciences sociales. La formation se poursuit en étudiant le modèle linéaire, tout en insistant sur la manière dont la construction d’un tel modèle permet d'identifier et de quantifier diverses relations entre les données mesurées. Il élabore également les propriétés statistiques de l'estimateur de moindres carrés, permettant ensuite de montrer comment il est possible de vérifier ou de tester des hypothèses économiques en pratique. Des leçons particulières sont consacrées à l’analyse de données fréquemment utilisées dans la pratique de l'économétrie, telles que les variables catégorielles et les séries temporelles.

Starts : 2014-02-04
No votes
edX Free Closed [?] Mathematics Data Analysis & Statistics Data Analysis & Statistics Data Analysis & Statistics Data Analysis & Statistics Data Analysis & Statistics Data Analysis & Statistics

An introduction to probabilistic models, including random processes and the basic elements of statistical inference.

Starts : 2016-09-21
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX UC BerkeleyX

Gain a deeper understanding of Spark by learning about its APIs, architecture, and common use cases.  This statistics and data analysis course will cover material relevant to both data engineers and data scientists.  You’ll learn how Spark efficiently transfers data across the network via its shuffle, details of memory management, optimizations to reduce compute costs, and more.  Learners will see several use cases for Spark and will work to solve a variety of real-world problems using public datasets.  After taking this course, you should have a thorough understanding of how Spark works and how you can best utilize its APIs to write efficient, scalable code.  You’ll also learn about a wide variety of Spark’s APIs, including the APIs in Spark Streaming. 

Starts : 2015-12-01
No votes
edX Free Closed [?] English Data Analysis & Statistics DelftX Economics & Finance EdX Math

This course covers cutting-edge topics of credit risk management in a rigorous yet inspiring way.

The playing field of credit risk professionals, consultants and managers is continually changing. Developments in financial markets and updates in regulatory frameworks make it a challenging field that makes it vital for you to stay on top of your game. But how?

The Advanced Credit Risk Management course from TU Delft, a course designed specifically for risk professionals, provides a unique opportunity to take the next step in credit risk management and helps you in contributing to the stability and economic sustainability of lending institutions.

This course is for anyone who wants or needs a deeper understanding of credit risk topics in order to advance current work tasks or support future professional development. The course balances between theory and practice to make it both challenging and valuable for your work. But the course is not just about increased knowledge: you will have the opportunity to interact with peers from different countries and institutions; you will receive feedback from the lecturer, and have the chance to choose together with other course members, one additional topic on which Dr. Cirillo will elaborate further. In addition, close collaboration with industry partners will give you the chance to be acquainted with different voices from the field, and discuss your views with other experts.

Key Benefits

For individuals:

  • Learn the latest, high-level knowledge on credit risk management and risk modelling.
  • Update one’s knowledge on the new regulatory frameworks.
  • Combine theory and practice to help you to stay on top of your profession.
  • Enhance your employability and career opportunities.
  • Lively interaction with international peers and networking opportunities.

For organizations:

  • Increase your organizational knowledge to include updated and cutting-edge credit risk management concepts.
  • Enhance your capability to implement the evolving Basel framework.
  • Improve your basis for sound decision-making in loan granting and risk assessment.
  • Add to your employees’ levels of competence and expand your talent pool.

Starts : 2017-05-25
No votes
edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX Environmental Studies IDBx

As a consumer, you know the price of the food that you consume daily. However, do you know why your food costs this amount and why it is different from the price it’s sold for in other countries? Well, agricultural public policies determine, among other things, the price you have to pay for your food. In fact, did you know that these policies also impact food security and climate change?

Do not miss this chance to compare agricultural policies in Latin America and the Caribbean, and to describe their influence in food security, climate change and regional competitiveness. For you to achieve this, we will introduce you to ‘AGRIMONITOR’, a database created by IDB that contains information about 23 countries in Latin America and the Caribbean. AGRIMONITOR will be your ‘right-hand tool’ in the analysis of all these relevant topics.

In addition to the use of AGRIMONITOR, in the course you will find content videos in which specialists from several organizations (IDB, FAO and OECD) will share their knowledge about the topics covered in the course, readings, IDB case studies in which we promote public policy analysis, suggested readings, practical activities that allow you to apply the content you have learnt, and discussion forums where you and your classmates will discuss several topics starting with guiding questions.

Furthermore, if you obtain 90 points out of 100 in the course, you will have the chance to participate in the “Researching with AGRIMONITOR” competition. The winner (or winners) will work on a research project for the IDB Environment, Rural Development and Disaster Risk Management Division, under the conditions stipulated by the Bank. US$10,000 will be available to finance research projects related to MOOC topics.

Join the course and examine which are the best agricultural public policies in your country.

AGRIMONITOR is waiting for you!

*The AGRIMONITOR course is also available in Spanish. To access the Spanish version, click here.

NOTE: We consider the English version and Spanish version of the course to be different courses. Subscription to the Spanish course does not mean that you will also have access to the English version. If you want access to both versions, you will have to subscribe to both versions. The same rule applies to obtain the course certificate. If you obtain the certificate in the Spanish version of the course, it does not mean that you will also obtain the certificate for the English version of the course.

Starts : 2017-05-25
No votes
edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX Environmental Studies IDBx

Como consumidor, seguro conoces muy bien el costo de todos los alimentos que ingieres a diario. Pero, ¿sabes por qué tus alimentos tienen ese precio y por qué éste es diferente al de otros países? Pues son las políticas públicas agropecuarias, entre otros factores, los que los determinan. ¿Y sabías, además, que estas políticas también inciden de manera muy importante en la seguridad alimentaria y el cambio climático?

No te pierdas la oportunidad de comparar las políticas agropecuarias de los países de América Latina y el Caribe y describir la influencia de estas políticas en la seguridad alimentaria, el cambio climático y la competitividad regional. Para ello te acercaremos a “AGRIMONITOR”: una base de datos creada por el BID, que contiene información de 23 países de América Latina y el Caribe, y que será tu mano derecha en el análisis de todos estos importantes temas.

Además de trabajar con AGRIMONITOR, en el curso encontrarás videos, en los que especialistas de diversas organizaciones (BID, FAO, OCDE, entre otras) exponen su conocimiento sobre los temas tratados en el curso; lecturas teóricas; casos reales del BID donde se promueve el análisis de las políticas agropecuarias; recomendaciones de bibliografía de consulta; actividades prácticas para ir aprendiendo a utilizar AGRIMONITOR y foros de discusión, donde se abren espacios para el aprendizaje grupal alrededor de una pregunta orientadora.

Además, si obtienes 90 puntos sobre 100 posibles durante el curso… ¡tendrás la oportunidad de participar en el concurso “Investiga con AGRIMONITOR”! El(los) ganador(es) trabajará(n) desarrollando investigación(es) para la División de Medio Ambiente, Desarrollo Rural y Administración de Riesgos por Desastres del Banco Interamericano de Desarrollo. Un monto de 10,000 dólares estará disponible para financiar proyectos de investigación sobre temas relacionados al MOOC.

*NOTA: para participar en el concurso, se requiere dominar idiomas español e inglés.

Ya lo sabes: súmate al curso y analiza cuáles son las mejores políticas públicas agropecuarias para tu país en el futuro.

¡AGRIMONITOR te está esperando!

*La preparación de este curso fue financiada por fondos de la iniciativa BID AGRIMONITOR y fondos del Programa Especial para el Desarrollo Institucional (SPID) del Banco Interamericano de Desarrollo (para mayor información, consulte la página web SPID).

*También contamos con una versión este curso en inglés. Para ingresar a la versión inglés, hacer clic en este enlace.

NOTA: el curso en español se considera como un curso diferente al inglés. El acceso a la versión en español no garantiza acceso a la versión en inglés. Si deseas acceso al curso en los dos idiomas, debes suscribirte en ambos por separado. La misma regla aplica para la obtención de certificados verificados. La obtención de un certificado verificado en idioma español no significa que podrás obtener el certificado en idioma inglés.

Starts : 2016-09-15
No votes
edX Free Closed [?] Business English Business & Management Data Analysis & Statistics DelftX Economics & Finance EdX

Imagine that you are a bank and a main part of your daily business is to lend money. Unfortunately, lending money is a risky business - there is no 100% guarantee that you will get all your money back. If the borrower defaults, you will face losses in your portfolio. Or, in a bit less extreme scenario, if the credit quality of your counterparty deteriorates according to some rating system, the loan will become more risky. These are typical situations in which credit risk manifests itself.

According to the Basel Accord, a global regulation framework for financial institutions, credit risk is one of the three fundamental risks a bank or any other regulated financial institution has to face when operating in the markets (the two other risks being market risk and operational risk). As the 2008 financial crisis has shown us, a correct understanding of credit risk and the ability to manage it are fundamental in today’s world.

This course offers you an introduction to credit risk modelling and hedging. We will approach credit risk from the point of view of banks, but most of the tools and models we will overview can be beneficial at the corporate level as well.

At the end of the course, you will be able to understand and correctly use the basic tools of credit risk management, both from a theoretical and, most of all, a practical point of view. This will be a quite unconventional course. For each methodology, we will analyse its strengths as well as its weaknesses. We will do this in a rigorous way, but also with fun: there is no need to be boring.

Thanks to the wonderful feedback of last year’s students, the course has been further improved.
Follow us on Twitter @CRMooc for updates and hints about the course.

FAQs

What is the estimated effort for course?
The total effort is 48 hours. You can decide for yourself when you will work on the course.

How much does it cost to take the course?

Nothing! The course is free.

Will the text of the lectures be available?

Yes. All of our lectures will have transcripts synced to the videos.

Do I need to watch the lectures live?

No. You can watch the lectures at your leisure.

Is this course related to campus courses of Delft University of Technology?

Yes, this course can be seen as an evolution of the WI3421TU Risk Management course, a compulsory course of the Minor Finance at TU Delft.

Starts : 2017-05-09
No votes
edX Free Closed [?] English BabsonX Business & Management Data Analysis & Statistics EdX

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

This course is part of the Business Principles and Entrepreneurial Thought XSeries.

Starts : 2017-04-04
No votes
edX Free Closed [?] English CurtinX Data Analysis & Statistics Education & Teacher Training EdX

Do you want to be more reflective in your teaching practice and wonder if there are technologies that can help? Are you curious about how data-driven, evidence-based teaching practices can improve your students’ learning? This is the course for you!

Analytics for the Classroom Teacher is an introduction to the emerging field of teaching and learning analytics from the perspective of a classroom teacher.

Experts from all over the world will provide an overview of the current state-of-the-art in teaching and learning analytics. You’ll learn how teachers, curriculum developers and policy makers are collecting and analysing data from the classroom to help guide decisions at all levels.

The course will then focus on the school teacher, and how data analytics can help you to make improvements in your classroom.

You’ll learn to use analytics to improve your lesson plans and your delivery of those plans, and discover more about your students' learning.

No previous knowledge in data-driven instruction, teaching and learning analytics is needed. Join us and a large community of innovative teachers from around the globe and become a pioneer of teaching and learning analytics in your school.

Starts : 2017-08-29
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Microsoft

All analytics work begins and ends with a story. Storytelling is the analytics professional’s missing link in delivering the essence of signals and insights from data to executives, management and more for real business results.

In this analytics storytelling course, you’ll learn effective strategies and tools to master data communication in the most impactful way possible—through well-crafted analytics stories.

Starts : 2017-07-01
No votes
edX Free Closed [?] English Business & Management Data Analysis & Statistics EdX Microsoft

This course is part of the Microsoft Professional Program Certificate in Big Data, and the Microsoft Professional Program Certificate in Data Science.

Excel is one of the most widely used solutions for analyzing and visualizing data. It now includes tools that enable the analysis of more data, with improved visualizations and more sophisticated business logics. In this data science course, you will get an introduction to the latest versions of these new tools in Excel 2016 from an expert on the Excel Product Team at Microsoft.

Learn how to import data from different sources, create mashups between data sources, and prepare data for analysis. After preparing the data, find out how business calculations can be expressed using the DAX calculation engine. See how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and even consumed on mobile devices.

Do you feel that the contents of this course is a bit too advanced for you and you need to fill some gaps in your Excel knowledge? Do you need a better understanding of how pivot tables, pivot charts and slicers work together, and help in creating dashboards? If so, check out DAT205x: Introduction to Data Analysis using Excel.

This course is also a part of the Microsoft Excel for the Data Analyst XSeries

Starts : 2017-07-01
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX Microsoft

This course is part of the Microsoft Professional Program Certificate in Big Data, and the Microsoft Professional Program Certificate in Data Science.

Power BI is quickly gaining popularity among professionals in data science as a cloud-based service that helps them easily visualize and share insights from their organizations’ data.

In this data science course, you will learn from the Power BI product team at Microsoft with a series of short, lecture-based videos, complete with demos, quizzes, and hands-on labs. You’ll walk through Power BI, end to end, starting from how to connect to and import your data, author reports using Power BI Desktop, and publish those reports to the Power BI service. Plus, learn to create dashboards and share with business users—on the web and on mobile devices.

Starts : 2017-07-01
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Microsoft

This course is part of the Microsoft Professional Program Certificate in Big Data, and the Microsoft Professional Program Certificate in Data Science

The open-source programming language R has for a long time been popular (particularly in academia) for data processing and statistical analysis. Among R's strengths are that it's a succinct programming language and has an extensive repository of third party libraries for performing all kinds of analyses. Together, these two features make it possible for a data scientist to very quickly go from raw data to summaries, charts, and even full-blown reports. However, one deficiency with R is that traditionally it uses a lot of memory, both because it needs to load a copy of the data in its entirety as a data.frame object, and also because processing the data often involves making further copies (sometimes referred to as copy-on-modify). This is one of the reasons R has been more reluctantly received by industry compared to academia.

The main component of Microsoft R Server (MRS) is the RevoScaleR package, which is an R library that offers a set of functionalities for processing large datasets without having to load them all at once in the memory. RevoScaleR offers a rich set of distributed statistical and machine learning algorithms, which get added to over time. Finally, RevoScaleR also offers a mechanism by which we can take code that we developed on our laptop and deploy it on a remote server such as SQL Server or Spark (where the infrastructure is very different under the hood), with minimal effort.

In this course, we will show you how to use MRS to run an analysis on a large dataset and provide some examples of how to deploy it on a Spark cluster or a SQL Server database. Upon completion, you will know how to use R for big-data problems.

Since RevoScaleR is an R package, we assume that the course participants are familiar with R. A solid understanding of R data structures (vectors, matrices, lists, data frames, environments) is required. For example, students should be able to confidently tell the difference between a list and a data frame, or what each object is generally a good representation for and how to subset it. Students should be familiar with basic programming concepts such as control flows, loops, functions and scope. Students should have a good understanding of how to write and debug R functions. Finally, students are expected to have a good understanding of data manipulation and data processing in R (e.g. functions such as merge, transform, subset, cbind, rbind, lapply, apply). Familiarity with 3rd party packages such as dplyr is also helpful.

Starts : 2015-02-23
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics DavidsonX EdX Math

From simulating complex phenomenon on supercomputers to storing the coordinates needed in modern 3D printing, data is a huge and growing part of our world. A major tool to manipulate and study this data is linear algebra. This course is part 1 of a 2-part course.  In this part, we’ll learn basics of matrix algebra with an emphasis on application. This class has a focus on computer graphics while also containing examples in data mining. We’ll learn to make an image transparent, fade from one image to another, and rotate a 3D wireframe model. We’ll also mine data; for example, we will find similar movies that one might enjoy seeing. In the topic of sports ranking, we’ll be ready to participate in March Madness and submit our own mathematically generated brackets to compete against millions of others. The lectures are developed to encourage you to explore and create your own ideas either through your own programming but also with online tools developed for the course. Come to this course ready to investigate your own ideas.

Courses offered via edX.org are not eligible for academic credit from Davidson College. A passing score in a DavidsonX course(s) will only be eligible for a verified certificate generated by edX.org.

Starts : 2015-04-06
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics DavidsonX EdX Math

Our world is in a data deluge with ever increasing sizes of datasets. Linear algebra is a tool to manage and analyze such data.

This course is part 2 of a 2-part course, with this part extending smoothly from the first. Note, however, that part 1, is not a prerequisite for part 2. In this part of the course, we'll develop the linear algebra more fully than part 1. This class has a focus on data mining with some applications of computer graphics. We'll discuss, in further depth than part 1, sports ranking and ways to rate teams from thousands of games. We’ll apply the methods to March Madness. We'll also learn methods behind web search, utilized by such companies as Google.  We'll also learn to cluster data to find similar groups and also how to compress images to lower the amount of storage used to store them. The tools that we learn can be applied to applications of your interest.  For instance, clustering data to find similar movies can be applied to find similar songs or friends. So, come to this course ready to investigate your own ideas.

Courses offered via edX.org are not eligible for academic credit from Davidson College. A passing score in a DavidsonX course(s) will only be eligible for a verified certificate generated by edX.org.

Starts : 2017-07-01
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX Microsoft

This course is part of the Microsoft Professional Program Certificate in Data Science.

In this data science course, you will explore the theory and practice of select advanced methods commonly used in data science.

In the first two modules, you will learn about common applications of specialized data types. Then, in the remaining two modules, you will focus on unstructured data. You will work with tools such as R, Python, and Azure Machine Learning to solve advanced data science problems.

Starts : 2016-08-15
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX UC BerkeleyX

Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.

This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.

This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.

Starts : 2017-12-01
No votes
edX Free Closed [?] English AdelaideX Computer Science Data Analysis & Statistics EdX

Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Hadoop, R and MOA (Massive Online Analysis).

Topics covered in this course include:

  • cloud-based big data analysis;
  • predictive analytics, including probabilistic and statistical models;
  • application of large-scale data analysis;
  • analysis of problem space and data needs;
  • understanding of ethical and social concerns of data mining.

By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.

Starts : 2018-04-01
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX UC San DiegoX

In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation.

The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.

In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.

You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).

In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.

Starts : 2017-06-19
No votes
edX Free Closed [?] English Data Analysis & Statistics Education & Teacher Training EdX PennX

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.

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