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The data science certification program comprises the focus areas of both the SAS Certified Big Data Professional and the SAS Certified Advanced Analytics Professional programs, including:
SAS software covered
SAS® Certified Big Data Professional Curriculum:
Taught by certified instructors at High-Tech facilities across the country:
Self Paced Learning Platform
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To enroll in the program, you need at least six months of programming experience in SAS or another programming language. We also recommend that you have at least six months of experience using mathematics and/or statistics in a business environment. If you're just getting started or need to brush up on your skills, we recommend:
Statistics 1: Introduction to ANOVA, Regression or Logistic Regression – available as an instructor-led course or free online e-learning course.
And one of the following:
Module 1: Big Data Preparation, Statistics and Visual Exploration
Course 1: Big Data Challenges and Analysis-Driven Data
This course provides an overview of the challenges associated with big data and analysis-driven data.
Course 2: Exploring Data With SAS Visual Analytics
In this course, you'll learn how to use SAS Visual Analytics Explorer to explore in-memory tables from the SAS® LASR™ Analytic Server and perform advanced data analyses.
Course 3: Statistics 1: Introduction to ANOVA, Regression and Logistic Regression
This introductory SAS/STAT® course focuses on t-tests, ANOVA and linear regression, and includes a brief introduction to logistic regression.
Course 4: Preparing Data for Analysis and Reporting
In this course, you'll learn how to perform data management tasks, such as improving data quality, entity resolution and data monitoring.
Course 5: Crafting Compelling (and true) Data Stories
Storytelling is a necessary skill when talking to key stakeholders. Insights uncovered in your data can move mountains if the right people say yes. But how do you move someone from simply being curious, all the way to, "Let's do this!" In this course, you'll learn why storytelling is a skill you need to develop, when a story works and when it doesn't, and how to communicate data in a meaningful way.
Module 1 prepares you for the SAS Big Data Preparation, Statistics and Visual Exploration certification exam.
Module 2: Big Data Programming and Loading
Course 1: Introduction to SAS and Hadoop: Essentials
This course teaches you how to use SAS programming methods to read, write and manipulate Hadoop data. You'll learn how to use Base SAS methods to read and write raw data with the DATA step, manage the Hadoop Distributed File System (HDFS) and execute MapReduce and Pig code from SAS via the HADOOP procedure. You'll also learn how to use SAS/ACCESS® Interface to Hadoop methods that allow LIBNAME access and SQL pass-through techniques to read and write Hive or Impala table structures.
Course 2: DS2 Programming Essentials With Hadoop
This course focuses on DS2, a fourth-generation SAS proprietary language for advanced data manipulation, which enables parallel processing and storage of large data with reusable methods and packages.
Course 3: Hadoop Data Management With Hive, Pig and SAS
In this course, you will use processing methods to prepare structured and unstructured big data for analysis. You will learn to organize the data into structured tabular form using Apache Hive and Apache Pig. You will also learn SAS software technology and techniques that integrate with Hive and Pig, as well as how to use these open source capabilities by programming with Base SAS and SAS/ACCESS Interface to Hadoop, and with SAS Data Integration Studio.
Course 4: Getting Started With SAS In-Memory Statistics
This course focuses on accessing data on the SAS LASR Analytic Server and performing exploratory analysis and preparation. Topics include starting the server, loading data and manipulating data on the SAS LASR Analytic Server using the IMSTAT procedure. IMSTAT topics include deriving new temporary and permanent tables and columns, calculating summary statistics (e.g., mean, frequency and percentile), and creating filters and joins on in-memory data.
Module 2 prepares you for the SAS Big Data Programming and Loading certification exam.
Module 1: Predictive Modeling
Course 1: Applied Analytics Using SAS Enterprise Miner
This course covers the skills required to assemble analysis flow diagrams using SAS Enterprise Miner for both pattern discovery (segmentation, association and sequence analyses) and predictive modeling (decision trees, regression and neural network models).
Module 1 prepares you for the Predictive Modeling certification exam.
Module 2: Advanced Predictive Modeling
Course 1: Neural Network Modeling
This course helps you understand and apply two popular artificial neural network algorithms – multilayer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered.
Course 2: Predictive Modeling Using Logistic Regression
This course explores predictive modeling using SAS/STAT® software, with an emphasis on the LOGISTIC procedure.
Course 3: Data Mining Techniques: Predictive Analytics on Big Data
This course introduces applications and techniques for assaying and modeling large data. It presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models and mixture distribution models. You will perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics and SAS In-Memory Statistics.
Course 4: Using SAS to Put Open Source Models Into Production
This course introduces the basics for integrating R programming and Python scripts into SAS and SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.
Module 2 prepares you for the Advanced Predictive Modeling certification exam.
Module 3: Text Analytics, Time Series, Experimentation and Optimization
Course 1: Text Analytics Using SAS Text Miner
In this course, you will learn to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections, automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors.
Course 2: Time Series Modeling Essentials
In this course, you'll learn the fundamentals of modeling time series data, with a focus on the applied use of the three main model types for analyzing univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM).
Course 3: Experimentation in Data Science
This course explores the essentials of experimentation in data science, why experiments are central to any data science efforts, and how to design efficient and effective experiments.
Course 4: Optimization Concepts for Data Science
This course focuses on linear, nonlinear and efficiency optimization concepts. Participants will learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. Course demonstrations include examples of data envelopment analysis and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce concepts introduced in the course.
Module 3 prepares you for the Text Analytics, Time Series, Experimentation and Optimization certification exam.
The SAS Certified Data Science Professional program includes all five learning modules, comprising 18 courses.
SAS Certified Data Scientist self-paced e-learning program includes:
Please note that the self-paced program format requires you to purchase certification exams and practice exams separately.
License duration for the program: 365 days.
Yes, there are interest-free installments options available for courses. Please connect for more details