About this 전문분야
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매주 11시간 권장


자막: 영어, 아랍어

귀하가 습득할 기술

Microsoft ExcelLinear RegressionStatistical Hypothesis Testing

100% 온라인 강좌

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 일정

유연한 마감을 설정하고 유지 관리합니다.

초급 단계

완료하는 데 약 2개월 필요

매주 11시간 권장


자막: 영어, 아랍어

How the 전문분야 Works

강좌 수강

Coursera 전문 분야는 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 전문 분야에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 하나의 전문 분야에 속하는 강좌에 등록하면 해당 전문 분야 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료해도 됩니다. — 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.

실습 프로젝트

모든 전문 분야에는 실습 프로젝트가 포함되어 있습니다. 전문 분야를 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 전문 분야에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우 각 강좌를 완료해야 프로젝트를 시작할 수 있습니다.

수료증 취득

모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

how it works

이 전문분야에는 5개의 강좌가 있습니다.


Introduction to Data Analysis Using Excel

2,734개의 평가
636개의 리뷰
The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them. To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. ________________________________________ WEEK 1 Module 1: Introduction to Spreadsheets In this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel. Topics covered include: • Reading data into Excel using various formats • Basic functions in Excel, arithmetic as well as various logical functions • Formatting rows and columns • Using formulas in Excel and their copy and paste using absolute and relative referencing ________________________________________ WEEK 2 Module 2: Spreadsheet Functions to Organize Data This module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. Topics covered include: • IF and the nested IF functions • VLOOKUP and HLOOKUP • The RANDBETWEEN function ________________________________________ WEEK 3 Module 3: Introduction to Filtering, Pivot Tables, and Charts This module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel. Topics covered include: • VLOOKUP across worksheets • Data filtering in Excel • Use of Pivot tables with categorical as well as numerical data • Introduction to the charting capability of Excel ________________________________________ WEEK 4 Module 4: Advanced Graphing and Charting This module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own. Topics covered include • Line, Bar and Pie charts • Pivot charts • Scatter plots • Histograms...

Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions

851개의 평가
140개의 리뷰
The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course. To successfully complete course assignments, students must have access to Microsoft Excel. ________________________________________ WEEK 1 Module 1: Basic Data Descriptors In this module you will get to understand, calculate and interpret various descriptive or summary measures of data. These descriptive measures summarize and present data using a few numbers. Appropriate Excel functions to do these calculations are introduced and demonstrated. Topics covered include: • Categories of descriptive data • Measures of central tendency, the mean, median, mode, and their interpretations and calculations • Measures of spread-in-data, the range, interquartile-range, standard deviation and variance • Box plots • Interpreting the standard deviation measure using the rule-of-thumb and Chebyshev’s theorem ________________________________________ WEEK 2 Module 2: Descriptive Measures of Association, Probability, and Statistical Distributions This module presents the covariance and correlation measures and their respective Excel functions. You get to understand the notion of causation versus correlation. The module then introduces the notion of probability and random variables and starts introducing statistical distributions. Topics covered include: • Measures of association, the covariance and correlation measures; causation versus correlation • Probability and random variables; discrete versus continuous data • Introduction to statistical distributions ________________________________________ WEEK 3 Module 3: The Normal Distribution This module introduces the Normal distribution and the Excel function to calculate probabilities and various outcomes from the distribution. Topics covered include: • Probability density function and area under the curve as a measure of probability • The Normal distribution (bell curve), NORM.DIST, NORM.INV functions in Excel ________________________________________ WEEK 4 Module 4: Working with Distributions, Normal, Binomial, Poisson In this module, you'll see various applications of the Normal distribution. You will also get introduced to the Binomial and Poisson distributions. The Central Limit Theorem is introduced and explained in the context of understanding sample data versus population data and the link between the two. Topics covered include: • Various applications of the Normal distribution • The Binomial and Poisson distributions • Sample versus population data; the Central Limit Theorem...

Business Applications of Hypothesis Testing and Confidence Interval Estimation

466개의 평가
70개의 리뷰
Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes. This is the third course in the specialization "Business Statistics and Analysis" and the course advances your knowledge about Business Statistics by introducing you to Confidence Intervals and Hypothesis Testing. We first conceptually understand these tools and their business application. We then introduce various calculations to constructing confidence intervals and to conduct different kinds of Hypothesis Tests. These are done by easy to understand applications. To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. Please note that earlier versions of Microsoft Excel (2007 and earlier) will not be compatible to some Excel functions covered in this course. WEEK 1 Module 1: Confidence Interval - Introduction In this module you will get to conceptually understand what a confidence interval is and how is its constructed. We will introduce the various building blocks for the confidence interval such as the t-distribution, the t-statistic, the z-statistic and their various excel formulas. We will then use these building blocks to construct confidence intervals. Topics covered include: • Introducing the t-distribution, the T.DIST and T.INV excel functions • Conceptual understanding of a Confidence Interval • The z-statistic and the t-statistic • Constructing a Confidence Interval using z-statistic and t-statistic WEEK 2 Module 2: Confidence Interval - Applications This module presents various business applications of the confidence interval including an application where we use the confidence interval to calculate an appropriate sample size. We also introduce with an application, the confidence interval for a population proportion. Towards the close of module we start introducing the concept of Hypothesis Testing. Topics covered include: • Applications of Confidence Interval • Confidence Interval for a Population Proportion • Sample Size Calculation • Hypothesis Testing, An Introduction WEEK 3 Module 3: Hypothesis Testing This module introduces Hypothesis Testing. You get to understand the logic behind hypothesis tests. The four steps for conducting a hypothesis test are introduced and you get to apply them for hypothesis tests for a population mean as well as population proportion. You will understand the difference between single tail hypothesis tests and two tail hypothesis tests and also the Type I and Type II errors associated with hypothesis tests and ways to reduce such errors. Topics covered include: • The Logic of Hypothesis Testing • The Four Steps for conducting a Hypothesis Test • Single Tail and Two Tail Hypothesis Tests • Guidelines, Formulas and an Application of Hypothesis Test • Hypothesis Test for a Population Proportion • Type I and Type II Errors in a Hypothesis WEEK 4 Module 4: Hypothesis Test - Differences in Mean In this module, you'll apply Hypothesis Tests to test the difference between two different data, such hypothesis tests are called difference in means tests. We will introduce the three kinds of difference in means test and apply them to various business applications. We will also introduce the Excel dialog box to conduct such hypothesis tests. Topics covered include: • Introducing the Difference-In-Means Hypothesis Test • Applications of the Difference-In-Means Hypothesis Test • The Equal & Unequal Variance Assumption and the Paired t-test for difference in means. • Some more applications...

Linear Regression for Business Statistics

467개의 평가
73개의 리뷰
Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel. The focus of the course is on understanding and application, rather than detailed mathematical derivations. Note: This course uses the ‘Data Analysis’ tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac. WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model. Topics covered include: • Introducing the Linear Regression • Building a Regression Model and estimating it using Excel • Making inferences using the estimated model • Using the Regression model to make predictions • Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the ‘Dummy variable regression’ which is used to incorporate categorical variables in a regression. Topics covered include: • Hypothesis testing in a Linear Regression • ‘Goodness of Fit’ measures (R-square, adjusted R-square) • Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it. Topics covered include: • Dummy variable Regression (using Categorical variables in a Regression) • Interpretation of coefficients and p-values in the presence of Dummy variables • Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as ‘Interaction variables’ is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. Topics covered include: • Mean centering of variables in a Regression model • Building confidence bounds for predictions using a Regression model • Interaction effects in a Regression • Transformation of variables • The log-log and semi-log regression models...

Business Statistics and Analysis Capstone

122개의 평가
25개의 리뷰
The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst. Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand. Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. The aim being to mimic a real life application as close as possible....



Sharad Borle

Associate Professor of Management
Jones Graduate School of Business

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Rice University is consistently ranked among the top 20 universities in the U.S. and the top 100 in the world. Rice has highly respected schools of Architecture, Business, Continuing Studies, Engineering, Humanities, Music, Natural Sciences and Social Sciences and is home to the Baker Institute for Public Policy....

자주 묻는 질문

  • 네! 시작하려면 관심 있는 강좌 카드를 클릭하여 등록합니다. 강좌를 등록하고 완료하면 공유할 수 있는 인증서를 얻거나 강좌를 청강하여 강좌 자료를 무료로 볼 수 있습니다. 전문 분야 과정에 있는 강좌에 등록하면, 전체 전문 분야에 등록하게 됩니다. 학습자 대시보드에서 진행 사항을 추적할 수 있습니다.

  • 이 강좌는 100% 온라인으로 진행되므로 강의실에 직접 참석할 필요가 없습니다. 웹 또는 모바일 장치를 통해 언제 어디서든 강의, 읽기 자료, 과제에 접근할 수 있습니다.

  • 이 전문 분야는 대학 학점을 제공하지 않지만, 일부 대학에서 선택적으로 전문 분야 인증서를 학점으로 인정할 수도 있습니다. 자세한 내용은 해당 기관에 문의하세요.

  • The Specialization is 16 weeks long, plus the additional Capstone Project time.

  • The specialization will be at an introductory level.

  • It is recommended that the courses are taken in the order Course 1 "Introduction to Data Analysis Using Excel", Course 2 "Basic Data Descriptors and Data Distributions and Application to Business Decisions", Course 3 "Business Applications of Hypothesis Testing and Confidence Interval Estimation", and then Course 4 "Linear Regression and Its Application to Business".

  • You will be able to comfortably use spreadsheets to analyze business data in terms of various descriptive and graphical measures.

    You will also be able to conduct statistical analysis of data to test various business propositions using hypothesis testing, and learn to estimate confidence intervals to facilitate business decisions under uncertain circumstances.

    You will be able to translate a business decision in terms of a cause and effect regression model, estimate the model using a spreadsheet, and draw inferences regarding operational and strategic implications of the estimated model.

    Overall, you will begin the pathway towards transforming yourself into thoughtful, data-driven management decision maker.

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