Big Data Science Certified Professional (BDSCP)

Transcription

1 Big Data Science Certified Professional (BDSCP) Course Catalog Provided by Arcitura TM Education

2 Step 1: Get Trained Take instructor-led workshops or purchase Self-Study Kits Step 2: Get Tested Take eams anywhere in the world via Prometric testing centers Step 3: Get Certified Get recognized by attaining one or more industry certifications

3 How to Get Trained Public Workshops Visit for calendar. On-Site Training Contact for details. Self-Study Visit for details. How to Get Tested Eams are available world-wide via the Prometric testing network. For details, visit Eams can also be proctored on-site as part of private on-site and select public workshops. How to Get Certified Receiving passing grades on the eams that correspond to a certification track results in the automatic issuance of an official certificate. The matri at the center of this course catalog shows how eams relate to certifications.

4 The Big Data Science Certified Professional (BDSCP) program is comprised of a comprehensive curriculum of course modules, eams and industry certifications providing IT professionals with the opportunity to obtain formal accreditation in recognition of proficiency in specialized areas of Big Data practice and technology. The BDSCP curriculum is strictly vendor-neutral and aligned with the Big Data industry as a whole. Its academic coverage of contemporary Big Data topics ensures that skills developed through study are applicable to different commercial Big Data vendor tools and environments. This program was developed in cooperation with best-selling author Thomas Erl and several organizations and subjectmatter eperts. To receive automatic updates about new courses, eams and certifications, send a blank to notify@arcitura.com. BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

5 data mining is closely related to machine learning quantitative analysis are comprised of is a type of Module 1: Fundamental Big Data Official Relationship Map Supplement automate data analysis is a type of qualitative analysis analytics analyze large datasets uses is used for uses business intelligence reports KPIs can process Hadoop use is an open-source implementation of is used for ETL commodity hardware generally uses Big Data solution provides storage for NoSQL database uses data stored in uses uses feeds data into data warehouse interfaces with/ provides OLAP OLTP Big Data Science School TM Copyright Arcitura Education Inc. business justification data procurement organizational prerequisites privacy provenance security limited realtime support distinct performance challenges distinct governance requirements distinct methodology cloud computing metadata semi-structured analytics business intelligence dataset data analysis key performance indicator (KPI) structured unstructured Value Variety Velocity Veracity Volume Module 1: Fundamental Big Data Official Mind Map Supplement Big Data Types Adoption & Planning Considerations Terminology & Concepts Big Data Characteristics Big Data Drivers Data Visualization tool features analytics & data science digitization affordable technology & commodity hardware social media hyper-connected communities & devices cloud computing Module 1 Fundamental Big Data aggregation drill-down filter roll-up what-if analysis Enterprise Technologies online transaction processing (OLTP) online analytical processing (OLAP) etract-transform-load (ETL) data warehouse data mart Hadoop quantitative analysis Data Analysis types qualitative analysis data mining Business Intelligence types Big Data Sources Analytics types traditional BI Big Data BI human-generated machine-generated descriptive diagnostic predictive prescriptive Big Data Science School TM Copyright Arcitura Education Inc. Module 1 Fundamental Big Data (Eam B90.01) This foundational course provides a high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges and adoption issues. The following primary topics are covered: Fundamental Terminology and Concepts A Brief History of Big Data Business Drivers leading to Big Data Innovations Characteristics of Big Data Benefits of Adopting Big Data Challenges and Limitations of Big Data Basic Big Data Analytics Big Data and Traditional Business Intelligence and Data Warehouses Big Data Visualization Common Adoption Issues Planning for Big Data Initiatives New Roles Introduced by Big Data Projects Emerging Trends For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIGDATASCIENCESCHOOL.COM

6 eternal datasets internal datasets alerts applications business process optimization A/B Testing Correlation Regression Natural Language Processing Sentiment Analysis Tet Analytics Classification Clustering Outlier Detection Filtering Heat Maps Network Analysis Spatial Data Analysis Time Series Analysis Module 2: Big Data Analysis & Technology Concepts Official Mind Map Supplement Business Case Evaluation Data Identification Data Acquisition & Filtering Data Etraction Data Validation & Cleansing Data Aggregation & Representation via of Data Analysis Data Visualization Utilization of Analysis Results Statistical Analysis Semantic Analysis Machine Learning Visual Analysis Big Data Analysis Lifecycle Stages Big Data Analysis Techniques Module 2 Big Data Analysis & Technology Concepts Big Data Technology Components & Concepts Big Data Mechanisms Clusters File Systems & Distributed File Systems NoSQL Distributed Data Processing Parallel Data Processing batch Processing Workloads types transactional Cloud Computing Analytics Engine Coordination Engine Data Transfer Engine types Processing Engine Query Engine Resource Manager Storage Device Workflow Engine Ingress Egress Big Data Science School TM Copyright Arcitura Education Inc. 2 Module 2 Big Data Analysis & Technology Concepts (Eam B90.02) This course eplores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The following primary topics are covered: The Big Data Analysis Lifecycle (from Dataset Identification to Integration, Analysis and Visualization) Common Analysis and Analytics Techniques (including A/B Testing, Regression, Correlation, Tet Analytics, Sentiment Analysis, Time Series Analysis, Network Analysis, Spatial Analysis) Automated Recommendation, Classification, Clustering, Machine Language, Natural Language, Semantics, Data Visualization and Visual Analysis Assessing Hierarchies, Part-to-Whole Relationships, Plotting Connections and Relationships, Mapping Geo- Spatial Data Foundational Big Data Technology Mechanisms, Big Data and Cloud Computing Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE) Big Data Processing (Parallel Data Processing, Distributed Data Processing, Shared-Everything/Nothing Architecture, SCV) For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

7 Heat Maps Network Analysis Spatial Data Analysis Time Series Analysis Classification Clustering Outlier Detection Filtering Natural Language Processing Sentiment Analysis Tet Analytics A/B Testing Correlation Regression business justification data procurement organizational prerequisites privacy provenance security limited realtime support distinct performance challenges distinct governance requirements distinct methodology cloud computing analytics business intelligence dataset data analysis key performance indicator (KPI) Visual Analysis Machine Learning Semantic Analysis Statistical Analysis Module 3: Big Data Analysis & Technology Lab Official Mind Map Supplement alerts applications business process optimization Adoption & Planning Considerations Terminology & Concepts metadata semi-structured structured unstructured eternal datasets internal datasets Big Data Analysis Techniques Big Data Types value variety velocity veracity volume via Business Case Evaluation Data Identification of Data Acquisition & Filtering Data Etraction Data Validation & Cleansing Data Aggregation & Representation Data Analysis Data Visualization Utilization of Analysis Results Big Data Characteristics Big Data Analysis Lifecycle Stages Module 3 Big Data Analysis & Technology Lab Data Visualization tool features Analytics Engine Coordination Engine Ingress Data Transfer Engine types Egress Processing Engine Big Data Mechanics Query Engine Resource Manager Storage Device Workflow Engine Clusters File Systems & Distributed File Systems NoSQL Big Data Technology Components & Concepts Distributed Data Processing Parallel Data Processing batch Processing Workloads types transactional Cloud Computing Business Intelligence aggregation drill-down filter roll-up what-if analysis types Big Data Drivers Enterprise Technologies Data Analytics types Big Data Sources descriptive diagnostic Analytics types predictive prescriptive traditional BI Big Data BI analytics & data science digitization affordable technology & commodity hardware social media hyper-connected communities & devices cloud computing online transaction processing (OLTP) online analytical processing (OLAP) etract-transform-load (ETL) data warehouse data mart Hadoop quantitative analysis qualitative analysis data mining human-generated machine-generated Big Data Science School TM Copyright Arcitura Education Inc. Module 3 Big Data Analysis & Technology Lab (Eam B90.03) This course module presents participants with a series of eercises and problems designed to test their ability to apply knowledge of topics covered previously in course modules 1 and 2. Completing this lab will help foster cross-topic proficiency and will assist in highlighting areas that require further attention. As a hands-on lab, this course provides a set of detailed eercises that require participants to solve a number of inter-related problems, with the goal of fostering a comprehensive understanding of how Big Data environments work from both front and back-ends, and how they are used to solve real-world analysis and analytics problems. For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all eercises are carried out completely and accurately. Attendees can voluntarily have eercises reviewed and graded as part of the class completion. For individual completion of this course as part of the Module 3 Self- Study Kit, a number of supplements are provided to help participants carry out eercises with guidance and numerous resource references. For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIGDATASCIENCESCHOOL.COM

8 forward selection backward elimination decision tree induction feature etraction binning clustering bar chart line graph histogram frequency polygon scatter plot stem and leaf plot cross-tabulation measures of central tendency measures of variation or dispersion measures of association Chebyshev s inequality rule empirical rule bo and whisker plot quantile-quantile (q-q) plot high-volume high-velocity high-variety high-veracity high-value lattice plot dimensionality reduction data discretization univariate analysis bivariate analysis multivariate analysis time series analysis Visualization Module 4: Fundamental Big Data Analysis & Science Official Mind Map Supplement numerical summaries rules plots data reduction analysis types Big Data Dataset Categories Eploratory Data Analysis Statistics Mathematics Module 4 Fundamental Big Data Analysis & Science Statistics Analysis descriptive statistics inferential statistics correlation covariance hypothesis testing mean median mode robustness range quantile quintile quartile percentile population bias variance standard deviation z-score distributions frequency probability discrete continuous sampling binomial geometric Poisson normal uniform Statistics Variable Categories null hypothesis alternative hypothesis statistical significance p-value type I error type II error standard error statistical estimator confidence interval skewness nominal ordinal binary quantitative independent random Big Data Science School TM Copyright Arcitura Education Inc. Module 4 Fundamental Big Data Analysis & Science (Eam B90.04) This course provides an in-depth overview of essential topic areas pertaining to data science and analysis techniques relevant and unique to Big Data, with an emphasis on how analysis and analytics need to be carried out individually and collectively in support of the distinct characteristics, requirements and challenges associated with Big Data datasets. The following primary topics are covered: Data Science, Data Mining and Data Modeling Big Data Dataset Categories Eploratory Data Analysis (EDA) (including numerical summaries, rules, data reduction) EDA Analysis Types (including univariate, bivariate, multivariate) Essential Statistics (including variable categories, relevant mathematics) Statistics Analysis (including descriptive, inferential, correlation, covariance, hypothesis testing) Data Munging and Machine Learning Variables and Basic Mathematical Notations Statistical Measures and Statistical Inference Distributions and Data Processing Techniques Data Discretization, Binning and Clustering Visualization Techniques and Numerical Summaries Correlation for Big Data Time Series Analysis for Big Data For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

9 k-means cluster-based local outlier factors (CBLOF) non-clustering inverse document frequency (IDF) term frequency inverse document frequency (TFIDF) Module 5: Advanced Big Data Analysis & Science Official Mind Map Supplement global contetual collective parametric non-parametric distance-based/unsupervised supervised assign update reassignment semi-supervised clustering actionable trivial ineplicable k-means stages tet representation bag of words term frequency cosine distance n-grams named entity etraction outlier types statistical techniques association rules Apriori algorithm clustering tet analytics Outlier Detection Module 5 Advanced Big Data Analysis & Science Pattern Identification Classification Model Evaluation Measures Statistical Models logistic regression decision trees pre-pruning post-pruning feature splitting entropy information gain classification rules rule-based model naïve Bayes Bayes theorem Laplace smoothing sensitivity specificity recall precision accuracy error rate f-score confusion matri cross-validation bias-variance one rule (1R) algorithm k nearest neighbor (knn) linear regression mean squared error error term residual coefficient of determination R 2 standard error of estimate Big Data Science School TM Copyright Arcitura Education Inc. Module 5 Advanced Big Data Analysis & Science (Eam B90.05) This course delves into a range of advanced data analysis practices and analysis techniques that are eplored within the contet of Big Data. The course content focuses on topics that enable participants to develop a thorough understanding of statistical, modeling and analysis techniques for data patterns, clusters and tet analytics, as well as the identification of outliers and errors that affect the significance and accuracy of predictions made on Big Data datasets. The following primary topics are covered: Statistical Models, Model Evaluation Measures (including cross-validation, bias-variance, confusion matri, f-score) Machine Learning Algorithms, Pattern Identification (including association rules, Apriori algorithm) Advanced Statistical Techniques (including parametric vs. non-parametric, clustering vs. non-clustering distancebased, supervised vs. semi-supervised) Linear Regression and Logistic Regression for Big Data Decision Trees for Big Data Classification Rules for Big Data K Nearest Neighbor (knn) for Big Data Naïve Bayes for Big Data Association Rules for Big Data K-Means for Big Data Tet Analytics for Big Data Outlier Detection for Big Data For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIGDATASCIENCESCHOOL.COM

10 B90.01 Fundamental Big Data B90.02 Big Data Analysis & Technology Concepts B90.03 Big Data Analysis & Technology Lab B90.04 Fundamental Big Data Analysis & Science B90.05 Advanced Big Data Analysis & Science B90.06 Big Data Analysis & Science Lab Big Data Science Professional (BDSCP) Certification Matri Several eams provide credit for more than one certification. These views can help you plan certification paths and discover how eams you ve passed may already be giving you credit toward additional certifications. This matri is available online at along with information about BDSCP eams, courses and certifications. Certified Big Data Science Professional Certified Big Data Scientist Certified Big Data Consultant Certified Big Data Engineer Certified Big Data Architect Certified Big Data Governance Specialist

11 B90.07 Fundamental Big Data Engineering B90.08 Advanced Big Data Engineering B90.09 Big Data Engineering Lab B90.10 Fundamental Big Data Architecture B90.11 Advanced Big Data Architecture B90.12 Big Data Architecture Lab B90.13 Fundamental Big Data Governance B90.14 Advanced Big Data Governance B90.15 Big Data Governance Lab To view this matri online, visit:

12 inverse document frequency (IDF) term frequency inverse document frequency (TFIDF) k-means cluster-based local outlier factors (CBLOF) sensitivity specificity recall precision accuracy error rate f-score confusion matri cross-validation bias-variance non-clustering assign update reassignment Model Evaluation Measures linear regression mean squared error error term residual coefficient of determination R 2 standard error of estimate tet representation bag of words term frequency cosine distance n-grams named entity etraction global contetual collective parametric non-parametric distance-based/unsupervised supervised semi-supervised clustering actionable trivial ineplicable k-means stages Statistical Models Module 6: Big Data Analysis & Science Lab Official Mind Map Supplement association rules Apriori algorithm clustering outlier types statistical techniques one rule (1R) algorithm k nearest neighbor (knn) tet analytics Pattern Identification Outlier Detection logistic regression decision trees pre-pruning post-pruning feature splitting entropy information gain classification rules rule-based model naïve Bayes Bayes theorem Laplace smoothing Classification Module 6 Big Data Analysis & Science Lab Visualization Big Data Dataset Categories bar chart line graph histogram frequency polygon scatter plot stem and leaf plot cross-tabulation high-volume measures of central tendency high-velocity numerical summaries measures of variation or dispersion high-variety measures of association high-veracity high-value Chebyshev s inequality rule rules empirical rule bo and whisker plot plots quantile-quantile (q-q) plot forward selection Eploratory Data Analysis lattice plot backward elimination decision tree induction dimensionality reduction feature etraction data reduction binning data discretization clustering univariate analysis bivariate analysis analysis types multivariate analysis time series analysis mean median mode robustness range quantile quintile Statistics Mathematics quartile percentile population frequency bias probability variance discrete standard deviation standard error continuous z-score statistical estimator sampling distributions confidence interval binomial skewness geometric Poisson normal uniform descriptive statistics inferential statistics null hypothesis Statistics Analysis correlation alternative hypothesis covariance statistical significance hypothesis testing p-value nominal type I error ordinal type II error binary Statistics Variable Categories quantitative independent random Big Data Science School TM Copyright Arcitura Education Inc. Module 6 Big Data Analysis & Science Lab (Eam B90.06) This course module covers a series of eercises and problems designed to test the participant s ability to apply knowledge of topics covered previously in course modules 4 and 5. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and science practices as they are applied and combined to solve real-world problems. As a hands-on lab, this course incorporates a set of detailed eercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data analysis techniques can be applied to solve problems in Big Data environments and used to make significant, relevant predictions that offer increased business value. For more information about course materials provided during instructor-led workshops and as part of self-study kits, visit: BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

13 Certified Big Data Science Professional A Certified Big Data Science Professional has demonstrated proficiency in the analysis practices and technology concepts and mechanisms that comprise and are featured in contemporary Big Data environments and tools. The following course modules are part of the official Big Data Science Professional Certification curriculum: Module 1: Fundamental Big Data Foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues. Module 2: Big Data Analysis & Technology Concepts Eplores contemporary analysis practices, technologies and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions. Module 3: Big Data Analysis & Technology Lab A hands-on lab providing a series of real-world eercises for assessing and establishing Big Data environments, and for solving problems using Big Data analysis techniques and tools. Workshops & Self-Study Attend an instructor-led workshop, or purchase the official Big Data Science Professional Certification Self-Study Kit Bundle. BIGDATASCIENCESCHOOL.COM

14 Certified Big Data Scientist A Certified Big Data Scientist has demonstrated proficiency in the application of techniques and tools required for eploring large volumes of comple data and the communication of the analysis results. The courses in this certification track focus on the application of numerous contemporary analysis and analytics techniques. In addition to Modules 1 and 2, the following courses are part of this certification: Module 4: Fundamental Big Data Analysis & Science Essential coverage of Big Data analysis algorithms, as well as the application of analytics, data mining and basic mathematical and statistical techniques. Module 5: Advanced Big Data Analysis & Science An in-depth course that covers the application of a range of advanced analysis techniques, including machine learning algorithms, data visualization and various forms of data preparation and querying. Module 6: Big Data Analysis & Science Lab A case study-based lab providing a series of real-world eercises that require participants to apply Big Data analysis and analytics techniques to fulfill requirements and solve problems. Workshops & Self-Study Attend an instructor-led workshop or purchase the official Big Data Scientist Certification Self-Study Kit Bundle. BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

15 Certified Big Data Consultant A Certified Big Data Consultant has demonstrated proficiency in the most common Big Data analysis and analytics concepts and techniques, as well as contemporary Big Data technologies, tools and solution environments. In addition to Modules 1 and 2, the following courses are part of this certification: Module 3: Big Data Analysis & Technology Lab A hands-on lab providing a series of real-world eercises for assessing and establishing Big Data environments, and for solving problems using Big Data analysis techniques and tools. Module 4: Fundamental Big Data Analysis & Science Essential coverage of Big Data analysis algorithms, as well as the application of analytics, data mining and basic mathematical and statistical techniques. Module 7: Fundamental Big Data Engineering Focuses on the hands-on usage of the Hadoop and MapReduce frameworks, HDFS, Hive, Pig, Sqoop, Flume and NoSQL databases. Workshops & Self-Study Attend an instructor-led workshop or purchase the official Big Data Consultant Certification Self-Study Kit Bundle. BIGDATASCIENCESCHOOL.COM

16 Certified Big Data Engineer A Certified Big Data Engineer has demonstrated proficiency in utilizing, configuring and programming an established Big Data solution (using Hadoop, MapReduce and other tools) to customize and optimize features in support of Big Data Scientists and general business requirements. In addition to Modules 1 and 2, the following courses are part of this certification: Module 7: Fundamental Big Data Engineering Focuses on the hands-on usage of the Hadoop and MapReduce frameworks, HDFS, Hive, Pig, Sqoop, Flume and NoSQL databases. Module 8: Advanced Big Data Engineering Builds upon Module 7 to delve into advanced development, testing and debugging techniques, as well as the application of Big Data design patterns. Module 9: Big Data Engineering Lab A hands-on lab during which participants carry out a series of eercises based upon the tools and technologies covered in preceding course modules. Workshops & Self-Study Attend an instructor-led workshop or purchase the official Big Data Engineer Certification Self-Study Kit Bundle. BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

17 Certified Big Data Architect A Certified Big Data Architect has demonstrated proficiency in the design, implementation and integration of Big Data solutions within IT enterprise and cloud-based environments. The courses in this certification track focus on a drill-down perspective of Big Data platforms and environments via the definition of mechanisms and architectural design patterns. In addition to Modules 1 and 2, the following courses are part of this certification: Module 10: Fundamental Big Data Architecture Coverage of the Hadoop stack, data pipelines and other technology architecture layers, mechanisms and components, and associated design patterns. Module 11: Advanced Big Data Architecture Drill-down of Big Data solution environments, additional advanced design patterns and coverage of cloud implementations and various enterprise integration considerations. Module 12: Big Data Architecture Lab A hands-on lab in which a set of realworld eercises challenge participants to build and integrate Big Data solutions within IT enterprise and cloudbased environments. Workshops & Self-Study Attend an instructor-led workshop or purchase the official Big Data Architect Certification Self-Study Kit Bundle. BIGDATASCIENCESCHOOL.COM

18 Certified Big Data Governance Specialist A Certified Big Data Governance Specialist has demonstrated proficiency in establishing and administering Big Data governance frameworks that standardize and regulate the Big Data lifecycle, the bodies of data processed by Big Data solutions, as well as the Big Data environments themselves. In addition to Modules 1 and 2, the following courses are part of this certification: Module 13: Fundamental Big Data Governance Introduces Big Data governance frameworks, and covers the basics of governing high-volume, multi-source data and Big Data technology environments. Module 14: Advanced Big Data Governance Steps through the Big Data lifecycle to cover specific precepts, processes and associated policies for regulating disparate bodies of data and Big Data solution environments. Module 15: Big Data Governance Lab A hands-on lab during which participants are required to work with Big Data governance precepts, processes and policies to address a series of real-world governance concerns. Workshops & Self-Study Attend an instructor-led workshop or purchase the official Big Data Governance Specialist Certification Self-Study Kit Bundle. BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP)

19 this document is protected by copyright and legal privacy and confidentiality regulations. do not redistribute without permission. Certified Trainer Guide TM Arcitura The Big Data Science Certified Professional program is operated and overseen by Arcitura TM Education Inc., a global provider of vendorneutral IT training and accreditation. To learn more, visit: Education Arcitura TM Community Connect with the Arcitura TM Community via Facebook, Twitter, LinkedIn and YouTube. Eplore the ever-growing network of schools, practitioners, instructors, academic institutions, authors and events. Becoming a Certified Trainer, Training Partner or Reseller Arcitura TM provides comprehensive programs dedicated to the development of certified trainers and different types of partnerships. TA Certified Trainer Guide To learn more, contact: partners@arcitura.com BIGDATASCIENCESCHOOL.COM

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