从 Excel 到 MySQL:商业分析技术 专项课程

于 4月 03 开始

从 Excel 到 MySQL:商业分析技术 专项课程


Drive business process change by identifying & analyzing key metrics in 4 industry-relevant courses.


Formulate data questions, explore and visualize large datasets, and inform strategic decisions. In this Specialization, you’ll learn to frame business challenges as data questions. You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights. In the final Capstone Project, you’ll apply your skills to explore and justify improvements to a real-world business process. The Capstone Project focuses on optimizing revenues from residential property, and Airbnb, our Capstone’s official Sponsor, provided input on the project design. Airbnb is the world’s largest marketplace connecting property-owner hosts with travelers to facilitate short-term rental transactions. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion.



5 courses






Beginner Specialization.
No prior experience required.
  1. 第 1 门课程


    即将开课的班次:4月 3 — 5月 8。
    该课程为 4 周长 估计需要每周学习 3-5 个小时
    English, Chinese (Simplified)


    在该课程中 您将学习使用数据分析使公司更好的盈利且更具竞争力的最佳实践 您将能够识别最关键的业务指标 并将其与单纯的数据区分开来 你将会对至关重要但不同角色的业务分析师 业务数据分析师和数据科学家在不同公司的角色扮演有个清晰的概念 并且 将了解这些高需求的职位被雇佣和成功所需要的技能 最后 您将能够使用该课程提供的一个清单 对每个公司如何有效地接受大数据文化给予评分 像亚马逊(Amazon)优步(Uber)和空中食宿(Airbnb)等数码公司正在通过创造性地利用大数据转换整个行业 你将了解为何这些公司如此具有破坏性 他们如何使用数据分析方法超越传统公司
  2. 第 2 门课程

    Mastering Data Analysis in Excel

    即将开课的班次:4月 3 — 5月 22。
    6 weeks, 3 - 5 hours per week


    In business, data and algorithms create economic value when they reduce uncertainty about financially important outcomes. This course teaches the concepts and mathematical methods behind the most powerful and universal metrics used by Data Scientists to evaluate the uncertainty-reduction – or information gain - predictive models provide. We focus on the two most common types of predictive model - binary classification and linear regression - and you will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer. These metrics are applicable to any form of model that uses new information to improve predictions cast in the form of a known probability distribution – the standard way of representing forecasts in data science. In addition, you will learn proper methodology to avoid common data-analytic pitfalls when forecasting – such as being “fooled by randomness” and over-fitting “noise” as if it were “signal.” Uniquely among data-analytics offerings, this course empowers you to understand and apply quite advanced information theory methods – Bayesian Logical Data Analysis - in business practice, without needing any calculus or matrix algebra, or any knowledge of Matlab or R or software programming. You will be able to answer all homework and quiz questions either by using basic algebra, or with the special custom Microsoft Excel Templates provided. Nor is any prior experience with Excel required; we will cover in detail at the beginning everything you need to know about using Excel to succeed in the course itself. If you already know Excel, you can skip that part. Be aware that this is not a broad general Excel skills course; it focuses on use of Excel to calculate information-related metrics, and to solve real business problems, such as developing your own predictive analytics model for which credit card applicants a bank should accept and which reject as too risky. Real problems are complicated! Personally I think learning to solve real problems is also a great way to learn Excel. We use specific tools in the Excel toolbox to build something useful, and you can always go back and learn more tools in the toolbox – more Excel functions – if and when you ever need them. This course requires some mathematical background: you should already know how to solve for an unknown using algebra; and have a basic familiarity with sigma (summation) notation; the concept of logarithms and working with bases other than base 10 (including base 2, and the natural logarithm and base “e”); and probability theory concepts such as calculating conditional, product, and joint probabilities. These concepts are assumed in the course rather than taught. All the “new” math taught in the course is summarized in a downloadable PDF document - "Mathematical Supplement" – please refer to it to decide if the difficulty level of this course seems right for you.
  3. 第 3 门课程

    使用 Tableau 展示可视化数据

    即将开课的班次:4月 3 — 5月 15。
    5 weeks, 6-8 hours per week


    One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses. By the end, you will know how to structure your data analysis projects to ensure the fruits of your hard labor yield results for your stakeholders. You will also know how to streamline your analyses and highlight their implications efficiently using visualizations in Tableau, the most popular visualization program in the business world. Using other Tableau features, you will be able to make effective visualizations that harness the human brain’s innate perceptual and cognitive tendencies to convey conclusions directly and clearly. Finally, you will be practiced in designing and persuasively presenting business “data stories” that use these visualizations, capitalizing on business-tested methods and design principles.
  4. 第 4 门课程


    即将开课的班次:3月 27 — 5月 8。
    English, Russian


  5. 第 5 门课程

    从 Excel 到 MySQL:商业分析技术毕业项目

    即将开课的班次:5月 8 — 7月 3。
    8 weeks of study, 8-10 hours/week


    In this final course you will complete a Capstone Project using data analysis to recommend a method for improving profits for your company, Watershed Property Management, Inc. Watershed is responsible for managing thousands of residential rental properties throughout the United States. Your job is to persuade Watershed’s management team to pursue a new strategy for managing its properties that will increase their profits. To do this, you will: (1) Elicit information about important variables relevant to your analysis; (2) Draw upon your new MySQL database skills to extract relevant data from a real estate database; (3) Implement data analysis in Excel to identify the best opportunities for Watershed to increase revenue and maximize profits, while managing any new risks; (4) Create a Tableau dashboard to show Watershed executives the results of a sensitivity analysis; and (5) Articulate a significant and innovative business process change for Watershed based on your data analysis, that you will recommend to company executives. Airbnb, our Capstone’s official Sponsor, provided input on the project design. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion. "Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone."


  • 杜克大学

    Duke University is consistently ranked as a top research institution, with graduate and professional schools among the leaders in their fields.

    Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

  • Jana Schaich Borg

    Jana Schaich Borg

    Assistant Research Professor
  • Daniel Egger

    Daniel Egger

    Executive in Residence and Director, Center for Quantitative Modeling


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