SAS Courses

Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeMachine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice. This means that two different species must cooperate in harmony: the business leader and the quant. This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within - and successfully produces value for - business operations. If you're more a quant than a business leader, you'll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don't usually go there. But know this: The soft skills are often the hard ones.After this course, you will be able to:- Apply ML: Identify the opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and much more.- Plan ML: Determine the way in which machine learning will be operationally integrated and deployed, and the staffing and data requirements to get there. - Greenlight ML: Forecast the effectiveness of a machine learning project and then internally sell it, gaining buy-in from your colleagues.- Lead ML: Manage a machine learning project, from the generation of predictive models to their launch.- Prep data for ML: Oversee the data preparation, which is directly informed by business priorities.- Evaluate ML: Report on the performance of predictive models in business terms, such as profit and ROI.- Regulate ML: Manage ethical pitfalls, such as when predictive models reveal sensitive information about individuals, including whether they're pregnant, will quit their job, or may be arrested - aka AI ethics.NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike by contextualizing the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. There are no exercises involving coding or the use of machine learning software.WHO IT'S FOR. This concentrated entry-level program is for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll do so in the role of enterprise leader or quant. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants - as well as data scientists.LIKE A UNIVERSITY COURSE. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course.IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel - a winner of teaching awards when he was a professor at Columbia University - this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning. VENDOR-NEUTRAL. This specialization includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with. PREREQUISITES. Before this course, learners should take the first of this specialization's three courses, "The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats."

SAS Statistical Business Analyst

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis program is for those who want to enhance their predictive and statistical modeling skills to drive data-informed business outcomes. If modeling data for business outcomes is relevant in your job role or industry, this certificate is a valuable indication of your proficiency.

Doing More with SAS Programming

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course is for business analysts and SAS programmers who want to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data. The course builds on the concepts that are presented in the Getting Started with SAS Programming course and is not recommended for beginning SAS software users.In this course you learn how to understand and control DATA step processing, create an accumulating column and process data in groups, manipulate data with functions, convert column type, create custom formats, concatenate and merge tables, process repetitive code, and restructure tables. This course addresses Base SAS software.Before attending this course, you should be able to write DATA step code to access data, subset rows and columns, compute new columns, and process data conditionally. You should also be able to sort tables using the SORT procedure andapply SAS formats.Course Overview and Data SetupIn this module you get an overview of what you learn in this course and you set up the software and data you use for activities and practices in the course.Controlling DATA Step ProcessingIn this module, we dig deeper into the DATA step. You learn how the DATA step processes data behind the scenes. Then you use this knowledge to control when and where the DATA step outputs rows to new tables.Summarizing DataIn this module, you learn new syntax that enables you to alter the default behavior of the DATA step to solve a problem. First you learn to create an accumulating column, or in other words generate a running total.Then you learn to process data in groups, so you can perform an action when each group begins or ends. Manipulating Data with FunctionsIn this module, you learn to use some new functions that enable you to manipulate numeric, date, and character values. In addition, you learn to use functions that change a column from one data type to another.Creating and Using Custom FormatsIn this module, you learn to create and use custom formats to enhance the way your data is displayed in a table or report.Combining TablesIn this module, we take a comprehensive look at combining tables by using the DATA step. You learn to concatenate tables, merge tables, and identify matching and nonmatching rows.Processing Repetitive CodeIn this module, you learn to save time by taking advantage of iterative processing with DO loops. First you learn to create an iterative DO Loop, then you learn to create conditional DO loops.Restructuring TablesIn this module, you learn techniques that can be used to transpose or restructure a table. First you learn to restructure data with the DATA step. Then you learn to restructure data by using the TRANSPOSE procedure.

Preparing for the SAS Programming Certification Exam

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.Course Overview and Data SetupIn this module you get an overview of this course and set up the data you need for practices and activities.Review of Getting Started with SAS Programming, Part 1This module is a review of the first three modules of the Getting Started with SAS Programming course. Lectures demonstrate the concepts you learned, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Review of Getting Started with SAS Programming, Part 2This module reviews the preparing, analyzing and exporting modules of the Getting Started with SAS Programming course. Lectures demonstrate the concepts you learned, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Review of Doing More with SAS Programming, Part 1This module is a review of the first four modules of the Doing More with SAS Programming course. Lectures demonstrate the concepts you learned about for preparing data, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Review of Doing More with SAS Programming, Part 2This module is a review of the last three modules of the Doing More with SAS Programming course. Lectures demonstrate the concepts you learned about for preparing data, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.

Building a Large-Scale, Automated Forecasting System

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course you learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Then you learn how to improve overall baseline forecasting performance by modifying default processes in the system.This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time. The courses is primarily syntax based, so analysts taking this course need some familiarity with coding. Experience with an object-oriented language is helpful, as is familiarity with manipulating large tables.Specialization Overview (Review)In this module you get an overview of the courses in this specialization and what you can expect. Note: This same module appears in each course in this specialization.Course OverviewIntroduction to Large-Scale ForecastingIn this modules you'll get an overview of the functionality used in the course. We'll describe how objects and methods in the Automatic Time Series Modeling, or ATSM, package in SAS Visual Forecasting can be combined to solve the large-scale forecasting problem. We'll also describe how the configuration of objects and information flows change depending on what stage of the automatic forecasting process you are in.Exploring and Processing Timestamped DataIn this module we'll use the TSMODEL procedure to perform time series accumulation and missing value interpretation. We'll use packages for PROC TSMODEL, which are blocks of code that can be inserted within the flow of your PROC TSMODEL code to perform specialized tasks for both data preparation and analysis. Then, we'll discuss time series hierarchies and how to use a BY statement in PROC TSMODEL to create a hierarchy.Automatic Forecasting: Model Specification and SelectionIn this module, we'll use the ATSM package in PROC TSMODEL to perform automatic forecasting, model selection, and specification. We'll walk through the process for declaring and using the many different ATSM objects and discuss how and where each object fits within the automatic forecasting process.Creating Custom Models and Managing Model ListsThis module describes and illustrates functionality for creating your own custom models in the forecasting system. We'll provide step-by-step instructions for building a custom specification and then modifying the automatic model selection process to include your model as a candidate for all series in a given level of the data hierarchy.Event Variables in the Forecasting SystemIn this module, we'll generate event variables three different ways. First, we'll use the ATSM package to create and implement predefined event variables. Second, we'll create event variables using the HPFEVENTS procedure. Third, we'll perform conditional BY-group processing for event variable creation. Next, we'll use and identify ARIMAX and ESM models, produce model selection lists, and select a champion model. Using the selected champion model and passing the predefined event variables to the TSMODEL procedure, we'll generate automatic forecasts and output model estimates and fit statistics.Reconciling Statistical ForecastsReconciling statistical forecasts occurs after the automatic model generation, selection, and forecasting processes are done. In this module, we describe the reconciliation process and illustrate system tools and options for reconciling statistical forecasts we generated earlier in the course.Setting Up the Forecasting System and Generating Best ForecastsThis module covers a variety of topics. First, we'll discuss system tools and best practices that have the potential to improve the precision of your system forecasts. These include best practices like honest assessment for champion model selection and system tools like outlier detection and combined model forecasts. Next, we'll describe options and best practices associated with rolling the system forward in time.Course ReviewIn this module you test your understanding of the course material.

Structured Query Language (SQL) using SAS

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeCourse DescriptionIn this course, you learn about Structured Query Language (SQL) and how it can be used in SAS programs to create reports and query your data. “By the end of this course, a learner will be able to…”●Query and subset data.●Summarize and present data.●Combine tables using joins and set operators.●Create and modify tables and views.●Create data-driven macro variables using a query.●Access DBMS data with SAS/ACCESS technology.Course Overview and Data SetupIn this module you get an overview of what you learn in this course and you set up the software and data you use for activities and practices in the course.EssentialsIn this module, you learn about the Structured Query Language (SQL) and begin exploring data using the SQL procedure in SAS.PROC SQL FundamentalsIn this module, you learn the fundamentals of SQL by using the SELECT, FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses. You generate simple queries, group and summarize data, create and manage tables, and retrieve information about your SAS session using DICTIONARY tables.SQL JoinsIn this module, you learn about joining data horizontally from multiple tables using the Cartesian product. You learn how to perform INNER, OUTER and complex joins.SubqueriesIn this module, you learn about using subqueries, or a query within a query. You begin by using a subquery in the WHERE or HAVING clause to dynamically subset your data, then you use a query in the FROM clause (In-Line view) to act as a virtual table. Finally, you use a subquery in the SELECT clause to perform dynamic calculations.Set OperatorsIn this module, you learn to concatenate tables vertically using the INTERSECT, EXCEPT, UNION and OUTER UNION set operators. You learn the difference between the set operators, as well as how to use modifiers to adjust the default behavior.Using and Creating Macro Variables in SQLIn this module, you learn about creating and utilizing user-defined macro variables to dynamically write programs that are easily maintained. In addition, you learn to create data-driven macro variables using the SQL procedure, and how to apply the newly created macro variables to your program.Accessing DBMS Data with SAS/ACCESSIn this module, you learn about SAS/ACCESS technology to retrieve data from third party database management systems (DBMS). You learn about accessing data from a DBMS through the SQL Pass-Through Facility, which allows you to use the specific DBMS implementation of SQL, and the SAS/ACCESS LIBNAME statement, which translates SAS SQL to native DBMS SQL. Finally, you learn about the FEDSQL procedure to use vendor neutral SQL to push as much processing into the DBMS as possible.Case Study (Honors) and Certification Practice ExamIn this case study, you solve a real-world business problem by applying concepts that you learned in this course.

Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeMachine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S.If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better.And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it's time to demystify the predictive power of data – and how to scientifically tap it.This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers.With this course, you'll learn what works and what doesn't – the good, the bad, and the fuzzy: – How predictive modeling algorithms work, including decision trees, logistic regression, and neural networks– Treacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlations– How to interpret a predictive model in detail and explain how it works– Advanced methods such as ensembles and uplift modeling (aka persuasion modeling)– How to pick a tool, selecting from the many machine learning software options– How to evaluate a predictive model, reporting on its performance in business terms– How to screen a predictive model for potential bias against protected classes – aka AI ethicsIN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike with expansive coverage of the state-of-the-art techniques and the most pernicious pitfalls. There are no exercises involving coding or the use of machine learning software. However, for one of the assessments, you'll perform a hands-on exercise, creating a predictive model by hand in Excel or Google Sheets and visualizing how it improves before your eyes.BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology with a strong conceptual framework and covers topics that are generally omitted from even the most technical of courses, including uplift modeling (aka persuasion modeling) and some particularly treacherous pitfalls.VENDOR-NEUTRAL. This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.PREREQUISITES. Before this course, learners should take the first two of this specialization's three courses, "The Power of Machine Learning" and "Launching Machine Learning."

Advanced SAS Programming Techniques

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you learn advanced techniques within the DATA step and procedures to manipulate data.“By the end of this course, a learner will be able to…”●Use additional functions (LAG, FINDC/FINDW, and COUNT/COUNTC/COUNTW).●Perform pattern matching using PRX functions.●Process repetitive code, rotate data, and perform table lookups using arrays.●Perform table lookups and sort data using hash and hash iterator objects.●Create numeric templates using the FORMAT procedure.●Create custom functions using the FCMP procedure.Course Overview, Review, and Data SetupIn this module, you'll set up software and data for this course. Then you'll review the concepts of SAS DATA step processing and how to process data sets.Using Advanced FunctionsIn this module, you'll use advanced functions to compare data between multiple rows in a SAS table, find and count substrings within a column, and clean and standardize data. You'll also explore CALL routines, Perl regular expressions, and how to use advanced functions to modify and analyze storm, weather, and population data.Defining and Processing ArraysIn this module, you'll learn how to use arrays to simplify your code. You'll use arrays to process repetitive code, rotate data, and perform table lookups.Defining and Processing Hash ObjectsIn this module, you'll learn how to declare a hash object, instantiate or create an instance of the object, and initialize its lookup keys and data. You'll use the hash object to store and retrieve data, create an output table from the data in the hash object, and create a hash iterator object to process the data in a particular order.Using Utility ProceduresIn this module, you'll learn how to use PROC FORMAT's PICTURE statement to create a custom template to display large numbers, dates, and times. You'll also use PROC FCMP, the function compiler procedure, to create custom functions and CALL routines.

Performing Network, Path, and Text Analyses in SAS Visual Analytics

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you learn about the data structure needed for network, path, and text analytics and how to create network analysis, path analysis, and text analytics in SAS Visual Analytics.Course OverviewIn this module, you learn about the business scenario that you will follow for this course and where the files are located in SAS Viya for Learners.Performing Network AnalysisIn this module, you learn more about network analysis in Visual Analytics.Performing Path AnalysisIn this module, you learn more about path analysis in Visual Analytics.Performing Text AnalyticsIn this module, you learn more about text analytics in Visual Analytics.

Introduction to Statistical Analysis: Hypothesis Testing

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.Course Overview and Data SetupIn this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course. Introduction and Review of ConceptsIn this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.ANOVA and RegressionIn this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.More Complex Linear ModelsIn this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

Modeling Time Series and Sequential Data

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations on creating combined (or ensemble) and hybrid model forecasts.This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time. This course uses a variety of different software tools. Familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, as well as open-source tools for sequential data handling and modeling, is helpful but not required. The lessons on Bayesian analysis and machine learning models assume some prior knowledge of these topics. One way that students can acquire this background is by completing these SAS Education courses: Bayesian Analyses Using SAS and Machine Learning Using SAS Viya.

Creating Advanced Reports with SAS Visual Analytics

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you learn how to create advanced data items, filters, and parameters in SAS Visual Analytics.Course OverviewIn this module, you learn about the business scenario that you will follow for this course and where the files are located in SAS Viya for Learners.Creating Advanced Data ItemsIn this module, you learn how to create and use advanced data items such as calculated items and aggregated measures in Visual Analytics.Creating Advanced FiltersIn this module, you learn how to create advanced static and interactive filters in Visual Analytics.Using Parameters to Create Advanced ReportsIn this module, you learn how to use parameters in Visual Analytics to make your reports more dynamic.

The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIt's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value.And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively.This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers:– How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations– A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees– Reporting on the predictive performance of machine learning and the profit it generates– What your data needs to look like before applying machine learning– Avoiding the hype and false promises of “artificial intelligence”– AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected classNO HANDS-ON AND NO HEAVY MATH. This concentrated entry-level program is totally accessible to business leaders – and yet totally vital to data scientists who want to secure their business relevance. It's for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll play a role on the business side or the technical side. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists.BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact.LIKE A UNIVERSITY COURSE. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course.IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.VENDOR-NEUTRAL. This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

Practical SAS Programming and Certification Review

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.Course Overview and Data SetupIn this module you get an overview of this course and set up the data you need for practices and activities.Review of Getting Started with SAS Programming, Part 1This module is a review of the first three modules of the Getting Started with SAS Programming course. Lectures demonstrate the concepts you learned, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Review of Getting Started with SAS Programming, Part 2This module reviews the preparing, analyzing and exporting modules of the Getting Started with SAS Programming course. Lectures demonstrate the concepts you learned, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Case Study/Programming Assignment: Analyze TSA Claims DataThis module enables you to apply what you learned in Getting Started with SAS Programming to a real programming problem.Review of Doing More with SAS Programming, Part 1This module is a review of the first four modules of the Doing More with SAS Programming course. Lectures demonstrate the concepts you learned about for preparing data, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Review of Doing More with SAS Programming, Part 2This module is a review of the last three modules of the Doing More with SAS Programming course. Lectures demonstrate the concepts you learned about for preparing data, and readings from the SAS Certification Prep Guide reinforce those concepts. The review and programming questions assess your understanding of the material.Case Study: Preparing World Tourism Data

SAS Advanced Programmer

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWhen you complete this professional certificate program, you will have experience in SAS programming using SAS 9 and will be able to process data using Structured Query Language in the SAS environment, use the SAS macro facility to design, write, and debug dynamic macro programs, and use advanced DATA step techniques and procedures to manipulate data. These skills prepare you for the SAS Advanced Programming Professional certification exam.