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The Data Science Professional Consulting course provides an easy-to-understand overview of Machine Learning and Artificial Intelligence. It explains how Machine Learning systems work behind the scenes, as well as their capabilities, limitations, common business applications and algorithm types. The course further covers Artificial Intelligence and neural networks, including how they can be used to process information and a step-by-step process for assembling an AI system.

Complete the Data Science Professional Consulting course and, optionally, get accredited as a Certified Data Science Consultant by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses both the Big Data Professional and Data Science Consultant certifications, upon passing the exam you will also receive official Big Data Professional and Data Science Consultant digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the Big Data Professional course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the Data Science Consultant track here.

The Data Science Professional Consulting course is comprised of the following 3 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Big Data Science & Analytics
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 3: Big Data Analysis & Technology Lab
  • Module 7: Fundamental Machine Learning
  • Module 10: Fundamental Predictive & Generative Artificial Intelligence (AI)

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Exam Details

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable watermarked PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

The course is comprised of a set of modules. Each module has a set of lessons and is further supplemented with exercises to help reinforce your understanding of key topics. Shown below are the digital contents and the topic outline for each course module:


Module 1: Fundamental Big Data Science & Analytics

This foundational course module 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 module content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises
  • Mind Map Poster

  • Symbol Legend Poster
  • Patterns and Mechanisms Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Understanding Big Data
  • Fundamental Big Data Terminology and Concepts
  • Big Data Business Drivers and Technology Drivers
  • Traditional Enterprise Technologies Related to Big Data
  • OLTP, OLAP, ETL and Data Warehouses in relation to Big Data
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Structured, Unstructured and Semi-Structured Data

  • Metadata and Data Veracity
  • Fundamental Analysis and Analytics
  • Quantitative and Qualitative Analysis
  • Machine Learning Types
  • Descriptive and Diagnostic Analytics
  • Predictive and Prescriptive Analytics
  • Business Intelligence and Big Data
  • Data Visualization and Big Data
  • Big Data Adoption and Planning Considerations

Module 2: Big Data Analysis & Technology Concepts

This course module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The module content intentionally keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises

  • Mind Map Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • Big Data Analysis Lifecycle (from Business Case Evaluation to Data Analysis and Visualization)
  • A/B Testing and Correlation
  • Regression and Heat Maps
  • Time Series Analysis
  • Network Analysis and Spatial Data Analysis
  • Classification and Clustering
  • Filtering, including Collaborative Filtering and Content-based Filtering
  • Sentiment Analysis and Text Analytics

  • Clusters and Processing Batch and Transactional Workloads
  • How Cloud Computing relates to Big Data
  • Foundational Big Data Technology Mechanisms
  • Big Data Storage Devices and Processing Engines
  • Resource Managers, Data Transfer Engines and Query Engines
  • Analytics Engines, Workflow Engines and Coordinate Engines

Module 3: Big Data Analysis & Technology Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab will help highlight areas that require further attention and will help prove proficiency in big data analysis and technology and practices as they are applied and combined to solve real-world problems.


Course Module Contents


  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Topics Covered

  • Reading Exercise 3.1: Case Study Background PLGM
  • Lab Exercise 3.2: Plan the Big Data BI Environment
  • Lab Exercise 3.3: Analyze Customer Loyalty Data
  • Lab Exercise 3.4: Alleviate Customer Dissatisfaction
  • Lab Exercise 3.5: Improve PLGM’s On-Line Sales
  • Reading Exercise 3.6: Case Study Background LHL
  • Lab Exercise 3.7: Plan the Data Integration and Reporting Environment

  • Lab Exercise 3.8: Develop a Treatment Personalization Capability
  • Lab Exercise 3.9: Enhance LHL’s Research Capability
  • Reading Exercise 3.10: Case Study Background SWP
  • Lab Exercise 3.11: Smart Meter Data Analysis
  • Lab Exercise 3.12: Enhance Electricity Demand Prediction Capability
  • Lab Exercise 3.13: Asset Management and Risk Identification Capability

Module 7: Fundamental Machine Learning

This course module provides an easy-to-understand overview of machine learning for anyone interested in how it works, what it can and cannot do and how it is commonly utilized in support of business goals. The module covers common algorithm types and further explains how machine learning systems work behind the scenes. The base module materials are accompanied with an informational supplement covering a range of common algorithms and practices.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises
  • Mind Map Poster
  • Symbol Legend Poster

  • Machine Learning Algorithms and Practices Reference Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Machine Learning Business and Technology Drivers
  • Machine Learning Benefits and Challenges
  • Machine Learning Usage Scenarios
  • Datasets, Structured, Unstructured and Semi Structured Data
  • Models, Algorithms, Model Training and Learning
  • How Machine Learning Works
  • Collecting and Pre-Processing Training Data
  • Algorithm and Model Selection
  • Training Models and Deploy Trained Models
  • Machine Learning Algorithms and Practices

  • Supervised Learning, Classification, Decision Tree
  • Regression, Ensemble Methods, Dimension Reduction
  • Unsupervised Learning and Clustering
  • Semi-Supervised and Reinforcement Learning
  • Machine Learning Best Practices
  • How Machine Learning Systems Work
  • Common Machine Learning Mechanisms
  • How Mechanisms Are Used in Model Training
  • Machine Learning and Deep Learning, Artificial Intelligence (AI)

Module 10: Fundamental Predictive & Generative Artificial Intelligence (AI)

This course module covers the fundamentals of predictive AI and generative AI, by providing essential coverage of benefits, challenges, concepts and common practices. All of the content is authored in easy-to-understand, plain English.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises
  • Mind Map Poster
  • Symbol Legend Poster
  • Neural Networks and Neuron Types Mapping Poster
  • Problem Types and Neural Networks Mapping Poster

  • Neural Networks and Practices Mapping Poster
  • Problem Types and Practices Mapping Poster
  • Neural Networks Supplement
  • Algorithms and Practices Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Predictive AI and Generative AI Business and Technology Drivers
  • Predictive and Generative AI Benefits
  • Common Risks and Challenges of Using Predictive AI and Generative AI
  • Business Problem Categories Addressed by Predictive AI and Generative AI

  • Types of Predictive AI and Generative AI
  • Understanding Models, Algorithms and Neural Networks
  • Understanding Model Training
  • Step-by-Step Training Loop Process
  • Supervised Learning, Unsupervised Learning, Continuous Learning

Learn About Arcitura: Take the Video Tour

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About Arcitura

About Arcitura Courses

About Arcitura Certifications

What’s in an Arcitura Course

Comprehensive
Coverage

Each course provides a comprehensive curriculum with 2-3 modules and 20-40 hours of training.

More Than Just
Video Lessons

In addition to standard video lessons, courses include full-color workbooks and reference posters for all lessons.

Interactive & Graded
Challenges

Courses also include interactive and graded exercises, interactive and graded self-tests and other supplements.

The Arcitura Difference

EACH COURSE

  • is authored by a dedicated courseware development team
  • has a self-test, accreditation exam and professional certification
  • is available via two different eLearning platforms

ALL COURSES

  • undergo a common development process
  • are authored to be consistent in quality, structure and style
  • share a common vocabulary and symbol notation
  • are authored in collaboration with subject matter experts

Take Your Skills Anywhere

Regardless of whether you are an individual looking to boost your career or an organization looking to up-skill a team, Arcitura courses and certifications provide a sound investment.

Because both courses and accreditations are vendor-neutral, they empower you with skills and credentials that you can take to wherever you need to go.

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