统计培训
统计培训，Statistics培训
客户评论
它涵盖了广泛的信息
Continental AG / Abteilung: CF IT Finance
课程名称: A practical introduction to Data Analysis and Big Data
Translated by
It covered a broad range of information.
很多知识理论和实践。
Anna Alechno
课程名称: Forecasting with R
Translated by
A lot of knowldege - theoretical and practical
他的知识和实践例证。
Irina Tulgara
课程名称: Forecasting with R
Translated by
his knowlede and practical exemples
概述并理解这个话题有多大。
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
课程名称: Forecasting with R
Translated by
Overview and understanding how big the topic is
技术介绍。
Continental AG / Abteilung: CF IT Finance
课程名称: A practical introduction to Data Analysis and Big Data
Translated by
presentation of technologies
总的来说内容很好
Sameer Rohadia
课程名称: A practical introduction to Data Analysis and Big Data
Translated by
Overall the Content was good.
举例说明是最有帮助的
Sean Kaukas
课程名称: Introduction to R
Translated by
Hands on examples were the most helpful.
实践练习非常有益。
Vascutek Ltd
课程名称: Minitab for Statistical Data Analysis
Translated by
The practical exercises were extremely beneficial
例子的使用，尽管这些例子都以相当快的速度被证明。
Vascutek Ltd
课程名称: Minitab for Statistical Data Analysis
Translated by
The use of examples, though even these were demonstrated at some considerable speed.
第二天，我们做了很多量规R＆R和临界性评估的例子。
Vascutek Ltd
课程名称: Minitab for Statistical Data Analysis
Translated by
2nd day we done lots of examples of gauge R&R & Criticality assessments
练习使用Minitab。
Vascutek Ltd
课程名称: Minitab for Statistical Data Analysis
Translated by
exercises - use of Minitab
对R和良好的主题范围很好的概述。培训师很高兴回答所有问题。
Symphony EYC
课程名称: R
Translated by
Good overview of R and good range of topics. Trainer was happy to answer all questions
对培训师的了解。
Stephanie Seiermann
课程名称: R
Translated by
Knowledge of the trainer
课程简介
| 代码 | 名字 | 时长 | 概览 |
|---|---|---|---|
| excelstatsda | Excel For Statistical Data Analysis | 14小时 | Audience Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel. Course Objectives This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research. This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them. |
| sspsspas | Statistics with SPSS Predictive Analytics Software | 14小时 | Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques. |
| StaEcoMod | Statistical and Econometric Modelling | 21小时 | The objective of the course is to enable participants to gain a mastery of the fundamentals of statistical and econometric modelling. |
| sixsigmagbfast | Six Sigma Green Belt - Fast Track | 28小时 | Green Belts participate in and lead Lean and Six Sigma projects from within their regular job function. They can tackle projects as part of a cross functional team or projects scoped within their normal job. Each session of Green Belt training is separated by 3 or 4 weeks when the Green Belts apply their training to their improvement projects. We recommend supporting the Green Belts on their projects in between training sessions and holding stage gate reviews along with leadership and Lean Six Sigma Champions to ensure DMAIC methodology is being rigorously applied. Week 1 Foundation: covers the fundamentals of the Lean Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling participants to take part and lead waste and defect reduction projects and initiatives. Week 2 Practitioner: provides additional data analysis and lean tools for participants to lead well scoped process improvement projects related to their regular job function. This is the fast track program that paces up the original training dividing the training two blocks - each of 2 days duration. This training is for individuals who wants to grasp quick concepts. |
| stats1 | Statistics Level 1 | 14小时 | This course has been created for people who require general statistics skills. This course can be tailored to a specific area of expertise like market research, biology, manufacturing, public sector research, etc... |
| mrkanar | Marketing Analytics using R | 21小时 | Audience: Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals. Overview: The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech. Format: Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course. |
| statsqa | Statistical Quality Analysis | 7小时 | This course covers the fundamentals of statistical process control and how these quality tools can provide the necessary evidence to improve and control processes. Know when and where to use the various types of control charts available in Minitab for your own processes. And learn how to use capability analysis tools to evaluate your processes. |
| intermediaterforfinance | Intermediate R for Finance | 21小时 | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples. By the end of this training, participants will be able to: Implement advanced R programming techniques Use R to manipulate their data to perform more advanced financial operations Audience Programmers Finance professionals IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| stats2 | Statistics Level 2 | 28小时 | This training course covers advanced statistics. It explains most of the tools commonly used in research, analysis and forecasting. It provides short explanations of the theory behind the formulas. This course does not relate to any specific field of knowledge, but can be tailored if all the delegates have the same background and goals. Some basic computer tools are used during this course (notably Excel and OpenOffice) |
| rneuralnet | Neural Network in R | 14小时 | This course is an introduction to applying neural networks in real world problems using R-project software. |
| dataar | Data Analytics With R | 21小时 | R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on |
| mlfinancer | Machine Learning for Finance (with R) | 28小时 | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| sixsigmayb | Six Sigma Yellow Belt | 21小时 | Yellow Belt covers the basics of the Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling delegates to take part and lead team based waste and defect reduction projects and initiatives. In addition emphasis is placed on applying the problem solving tools into daily roles. At the end of the course you will be equipped to look at your immediate team and role, determine what can be improved and create a business improvement project on a selected opportunity that is aligned to customer requirements. You will be able to analyse the process using visualization tools and identify the waste (non-value adding) components and work to eliminate these from the process. You will apply root cause analysis techniques to identify the underlying causes of defects in the process. The course uses simulations, case study exercises and work based projects to enable delegates to 'learn through doing'. Notes: This course has a minimum class size of 4. And if requested this course can be delivered in 2 days with some reductions to the course content and level of detail in some areas, notably Customer needs; Graphical analysis and Process handover. |
| apacheh | Administrator Training for Apache Hadoop | 35小时 | Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration. |
| datavisR1 | Introduction to Data Visualization with R | 28小时 | This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions |
| dlfinancewithr | Deep Learning for Finance (with R) | 28小时 | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use R to create deep learning models for finance Build their own deep learning stock price prediction model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mtstatda | Minitab for Statistical Data Analysis | 14小时 | The course is aimed at anyone interested in statistical analysis. It provides familiarity with Minitab and will increase the effectiveness and efficiency of your data analysis and improve your knowledge of statistics. |
| statdm | Statistical Thinking for Decision Makers | 7小时 | This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization. For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not. If you need longer course which covers the very basics of statistical thinking have a look at 5 day "Statistics for Managers" training. |
| dsbda | Data Science for Big Data Analytics | 35小时 | Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28小时 | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| statsman | Statistics for Managers | 35小时 | This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them. The course uses a lot of pictures, diagrams, computer simulations, anecdotes and sense of humour to explain concepts and pitfalls of statistics. |
| rprogadv | Advanced R Programming | 7小时 | This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills. The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience |
| Piwik | Getting started with Piwik | 21小时 | Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course Part lecture, part discussion, heavy hands-on practice |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28小时 | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| statsres | Statistics for Researchers | 35小时 | This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines. It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs. In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...) In the case of public courses, mixed examples are used. Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion. This course can be delivered as a blended course i.e. with homework and assignments. |
| 67795 | Numerical Methods | 14小时 | This course is for data scientists and statisticians that have some familiarity with numerical methods and have at least one programming language from R, Python, Octave, and some C++ options. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose of this course is to give a practical introduction in numerical methods to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| datamodeling | Pattern Recognition | 35小时 | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| shinyrhtml | Shiny, R and HTML: Merging Data Science and Web Development | 7小时 | Shiny is an open source R package that provides a web framework for building interactive web applications using R. In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML. By the end of this training, participants will be able to: Build interactive web applications with R using Shiny Audience Data scientists Web developers Statisticians Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlintro | Introduction to Machine Learning | 7小时 | This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. |
| kdd | Knowledge Discover in Databases (KDD) | 21小时 | Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience Data analysts or anyone interested in learning how to interpret data to solve problems Format of the course After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations. |
| tableaupython | Tableau with Python | 14小时 | Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: Integrate Tableau and Python using TabPy API Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pgmt | The Practitioner’s Guide to Multivariate Techniques | 14小时 | The introduction of the digital computer, and now the widespread availability of computer packages, has opened up a hitherto difficult area of statistics; multivariate analysis. Previously the formidable computing effort associated with these procedures presented a real barrier. That barrier has now disappeared and the analyst can therefore concentrate on an appreciation and an interpretation of the findings. |
| appliedml | Applied Machine Learning | 14小时 | This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| druid | Druid: Build a fast, real-time data analysis system | 21小时 | Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience Application developers Software engineers Technical consultants DevOps professionals Architecture engineers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| rsttatan | R for Statistical Analysis | 14小时 | Organization – Lab work and practical examples on real data |
| sixsigmagb | Six Sigma Green Belt | 70小时 | Green Belts participate in and lead Lean and Six Sigma projects from within their regular job function. They can tackle projects as part of a cross functional team or projects scoped within their normal job. Each session of Green Belt training is separated by 3 or 4 weeks when the Green Belts apply their training to their improvement projects. We recommend supporting the Green Belts on their projects in between training sessions and holding stage gate reviews along with leadership and Lean Six Sigma Champions to ensure DMAIC methodology is being rigorously applied. Week 1 Foundation: covers the fundamentals of the Lean Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling participants to take part and lead waste and defect reduction projects and initiatives. Week 2 Practitioner: provides additional data analysis and lean tools for participants to lead well scoped process improvement projects related to their regular job function. |
| MLFWR1 | Machine Learning Fundamentals with R | 14小时 | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| nlpwithr | NLP: Natural Language Processing with R | 21小时 | It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| tableaufordev | Tableau for Developers | 21小时 | Tableau is a business intelligence and data visualization software. Tableau offers a wide array of developer tools and APIs for integration, customization, automation, and extension of Tableau features for organization-specific needs. In this instructor-led, live training, participants will learn how to extend the capabilities of Tableau to fit the specific needs of their organization. By the end of this training, participants will be able to: Install and configure Tableau (Desktop, Server, Online) Understand the fundamentals of developing with Tableau Create and publish data visualizations and interactions with Tableau Use Tableau's developer tools and APIs to customize and extend the capabilities of Tableau for their organization Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To request a customized training for this course, please contact us to arrange. |
| sixsigmabb | Six Sigma Black Belt | 84小时 | Six Sigma is a data driven approach that tackles variation to improve the performance of products, services and processes, combining practical problem solving and the best scientific approaches found in experimentation and optimisation of systems. The approach has been widely and successfully applied in industry, notably by Motorola, AlliedSignal & General Electric. Black Belt is a qualification for improvement managers in a Six Sigma organisation. You will learn the tools and techniques to take an improvement project through the Define, Measure, Analyse, Improve and Control phases (DMAIC). These techniques include Process Mapping, Measurement System Evaluation, Regression Analysis, Design of Experiments, Statistical Tolerancing, Monte Carlo Simulation and Lean Thinking. The content of the course takes the participants through the DMAIC phases as well as introducing subjects such as Lean Thinking, Design for Six Sigma and discussing important leadership issues and experiences in deploying a Six Sigma programme. Week 1 Foundation: covers the fundamentals of the Lean Six Sigma Define Measure Analyse Improve Control (DMAIC) approach enabling participants to take part and lead waste and defect reduction projects and initiatives. Week 2 Practitioner: provides additional data analysis and lean tools for participants to lead well scoped process improvement projects related to their regular job function. Week 3 Expert: provides regression, design of experiment and data analysis techniques to enable participants to tackle complex problem solving projects that require understanding of the relationships between multiple variables. The trainer has 16 years experience with Six Sigma and as well as leading the deployment of Six Sigma at a number of businesses he has trained and coached over 300 Black Belts. Here are a few comments from previous participants: “Probably the most valuable course I will ever pass” “The content was very well delivered. The examples very relevant. Thank you” “The course was excellent and I am able to use part of it to coach my lean teams here” (Company supervisor who attended with KTP associate) |
| bigdatar | Programming with Big Data in R | 21小时 | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| BigData_ | 数据分析和大数据的实用介绍 | 35小时 | 参与者完成此次培训后，将会对大数据及其相关技术、方法、工具有一个实际和真实的理解。 参与者将有机会通过动手练习将这些知识付诸实践。小组互动和讲师反馈是课堂的重要组成部分。 本课程首先介绍大数据的基本概念，然后讲解用于执行数据分析的编程语言和方法，最后我们会讨论可启用大数据存储、分布式处理及可扩展性的工具和基础架构。 受众 开发人员/程序员 IT顾问 课程形式 部分讲座、部分讨论、实操、偶尔测评进度 |
| dataminr | Data Mining with R | 14小时 | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| kylin | Apache Kylin: From classic OLAP to real-time data warehouse | 14小时 | Apache Kylin is an extreme, distributed analytics engine for big data. In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse. By the end of this training, participants will be able to: Consume real-time streaming data using Kylin Utilize Apache Kylin's powerful features, including snowflake schema support, a rich SQL interface, spark cubing and subsecond query latency Note We use the latest version of Kylin (as of this writing, Apache Kylin v2.0) Audience Big data engineers Big Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| xcelsius | Xcelsius | 14小时 | Description: In this Xcelsius Training course, students will use Xcelsius Present to create interactive visualizations for presenting complex data in a simple way, and to conduct analysis to make critical decisions. Students will also create complete dashboards that present business, project, and human resources information, all consolidated and presented in a user-friendly manner. Finally, students will publish dashboards into various file formats such as Adobe Flash, Microsoft Office PowerPoint, Adobe PDF, and also to the web. Objectives: Upon successful completion of this course, students will be able to: Explore the Xcelsius workspace and an already created dashboard. Create simple visualizations. Conduct data analysis using Xcelsius components that give dynamic functionality to the specified data. Create a Project Management dashboard. Create a dashboard to consolidate and present the Human Resources information of an organization. Finalize dashboards and export them to different file formats. Audience: This course is designed for professionals who conduct data analysis and need to present robust and timely data in an interactive display. |
| mrkfct | Market Forecasting | 14小时 | Audience This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting. Description This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data. It uses standard tools like Microsoft Excel or some Open Source programs (notably R project). The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...) |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7小时 | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| octaveda | Octave for Data Analysis | 14小时 | Audience: This course is for data scientists and statisticians that have some familiarity statistical methods and would like to use the Octave programming language at work. The purpose of this course is to give a practical introduction in Octave programming to participants interested in using this programming language at work. |
| mlbankingr | 机器学习用于银行业务（使用R） | 28小时 | 在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。R将被用作编程语言。 参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。 受众 开发人员 数据科学家 具有技术背景的银行专业人士 课程形式 部分讲座、部分讨论、练习和大量实操 |
| samr | Statistic analysis in market research | 28小时 | Goal: Improving consumer behavior researcher workshop products and services Addressees The researchers, market analysts, managers and employees of marketing departments, sales departments primarily pharmaceutical and FMCG, students of socio-economic and everyone interested in market research |
| surveyrste | Survey Research, Sampling Techniques & Estimation | 14小时 | This 2-day course provides a comprehensive demonstration of the fundamenals of survey research, sampling techniques & estimation. |
| excelafd | Analysing Financial Data in Excel | 14小时 | Audience Financial or market analysts, managers, accountants Course Objectives Facilitate and automate all kinds of financial analysis with Microsoft Excel |
| advspsspas | Advanced Statistics using SPSS Predictive Analytics Software | 28小时 | Goal: Mastering the skill work independently with the program SPSS for advanced use, dialog boxes, and command language syntax for the selected analytical techniques. The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and advanced level and learn the selected statistical models. Training takes universal analysis problems and it is dedicated to a specific industry |
| datashrinkgov | Data Shrinkage for Government | 14小时 | The objective of the course is to enable participants to gain a mastery of the fundamentals of data shrinkage for government. |
| rforfinance | R Programming for Finance | 28小时 | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the R programming language Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate deploy and optimize an R application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| rintro | Introduction to R | 21小时 | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (http://www.r-project.org) both on a command line and in a graphical user interface (GUI). |
| rdataana | R for Data Analysis and Research | 7小时 | Audience managers developers scientists students Format of the course on-line instruction and discussion OR face-to-face workshops |
| frcr | Forecasting with R | 14小时 | This course allows delegate to fully automate the process of forecasting with R |
Upcoming Courses
| 课程 | 日期 | 价格【远程 / 传统课堂】 |
|---|---|---|
| Market Forecasting - 新东方大厦 | 星期一, 2018-06-11 09:30 | ￥17750 / ￥18950 |
| Forecasting with R - Beijing Digital Building | 星期三, 2018-06-13 09:30 | ￥20530 / ￥21730 |
| Statistics with SPSS Predictive Analytics Software - 深圳 - 新世界中心 | 星期四, 2018-06-14 09:30 | ￥17750 / ￥22130 |
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