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Complete the Digital Transformation: Advanced Data Science course and, optionally, get accredited as a Certified Digital Transformation Data Scientist 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 multiple certifications, upon passing the exam you will also receive official Digital Transformation Specialist, Digital Transformation Data Science Professional and Digital Transformation Data Scientist 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 Modules 1, 2, 9, 10 and 11 in this certification track, you can purchase a partial course (or a partial bundle) with only the modules specific to the Digital Transformation Data Scientist track here.

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Course Outlines (PDF)

The Digital Transformation: Advanced Data Science course is comprised of the following 8 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Digital Transformation
  • Module 2: Digital Transformation in Practice
  • Module 9: Fundamental Big Data Analysis & Analytics
  • Module 10: Fundamental Machine Learning
  • Module 11: Fundamental AI
  • Module 12: Advanced Big Data Analysis & Analytics
  • Module 13: Advanced Machine Learning
  • Module 14: Advanced 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.

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 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.

Shown below are the digital contents and the topic outline for each course module:


Lifetime Access Guarantee

Access to the interactive eLearning platform (1) and the study kit platform (3) is dependent on third-party platform providers. As a result, Arcitura cannot make any guarantees or warranties regarding the behavior or availability of the platform, nor can Arcitura guarantee a specific duration for which these platforms will remain available. When your course is set up, your access will have no expiration date and will be provided for a minimum of one year and indefinitely thereafter, for as long as the platforms remain available under the conditions by which they were contracted by Arcitura. The PDF documents (3) that will be made available for you to download are yours to keep forever.

Module 1: Fundamental Digital Transformation

This course module provides an easy-to-understand introduction to Digital Transformation and how it relates to business, technology, data and people. Coverage includes the benefits, risks and challenges of Digital Transformation, as well as its business and technology drivers.


Course Module Contents

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

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

Topics Covered

  • Understanding Digital Transformation
  • Benefits of Digital Transformation
  • Challenges of Digital Transformation
  • Digital Transformation Business and Technology Drivers
  • Understanding Customer-Centricity
  • Product-Centric vs. Customer-Centric Relationships
  • Relationship-Value Actions and Warmth
  • Omni-Channel Customer Interactions
  • Customer Journeys and Customer Data Intelligence

  • Data Intelligence Basics
  • Data Origins and Data Sources
  • Data Collection Methods and Data Utilization Types
  • Intelligent Decision-Making
  • Computer-Assisted Manual Decision-Making and Conditional Automated Decision-Making
  • Intelligent Manual Decision-Making vs. Intelligent Automated Decision-Making
  • Direct-Driven Automated Decision-Making and Periodic Automated Decision-Making
  • Realtime Automated Decision-Making

Module 2: Digital Transformation in Practice

This course module delves into Digital Transformation automation environments by exploring the key contemporary technologies used to build Digital Transformation Automation solutions, including AI, RPA, IoT, machine learning, blockchain, cloud computing and big data.


Course Module Contents

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

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

Topics Covered

  • Distributed Solution Design Basics
  • Data Ingress Basics, including File Pull, File Push, API Pull, API Push and Data Streaming
  • An Introduction to Digital Transformation Automation Technologies
  • Cloud Computing Basics and Cloud Computing as part of Digital Transformation Solutions
  • Common Cloud Computing Risks and Challenges
  • Blockchain Basics and Blockchain as part of Digital Transformation Solutions
  • Common Blockchain Risks and Challenges
  • Internet of Things (IoT) Basics and IoT as part of Digital Transformation Solutions
  • Common IoT Risks and Challenges
  • Robotic Process Automation (RPA) and RPA as part of Digital Transformation Solutions
  • Common RPA Risks and Challenges

  • An Introduction to Digital Transformation Data Science Technologies
  • Big Data and Data Analytics and Big Data as part of Digital Transformation Solutions
  • Common Big Data Risks and Challenges
  • Machine Learning Basics and Machine Learning as part of Digital Transformation Solutions
  • Common Machine Learning Risks and Challenges
  • Artificial Intelligence (AI) Basics and AI as part of Digital Transformation Solutions
  • Common AI Risks and Challenges
  • Inside a Customer-Centric Digital Transformation Solution (a comprehensive, step-by-step exploration)
  • Mapping Individual Digital Transformation Technologies to Solution Processing
  • Tracking how Data Intelligence is Collected and Used in a Digital Transformation Solution

Module 9: Fundamental Big Data Analysis & Analytics

This foundational course module provides an overview of essential big data science topics and explores a range of the most relevant contemporary analysis practices, technologies and tools for big data environments. Topics include common analysis functions and features offered by big data solutions, as well as an exploration of the big data analysis lifecycle.


Course Module Contents

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

  • Analysis and Analytics Relationships Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Understanding Big Data
  • Fundamental Terminology & Concepts
  • Big Data Business & Technology Drivers
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Fundamental Analysis and Analytics
  • Business Intelligence & Big Data
  • Data Visualization & Big Data

  • The Big Data Analysis Lifecycle
  • A/B Testing, Correlation, Regression
  • Time Series Analysis, Heat Maps
  • Network Analysis, Spatial Data Analysis
  • Classification, Clustering
  • Filtering (including collaborative filtering & content-based filtering)
  • Sentiment Analysis, Text Analytics

Module 10: 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)
  • Mind Map Poster
  • Symbol Legend Poster
  • Patterns and Mechanisms 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 11: Fundamental AI

This course module provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The module provides concrete coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks, how they exist, how they work and how they can be used to process information.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • 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
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • AI Business and Technology Drivers, AI Benefits and Challenges
  • Business Problem Categories Addressed by AI, AI Types (Narrow, General, Symbolic, Non-Symbolic, etc.)
  • Common AI Learning Approaches and Algorithms
  • Supervised Learning, Unsupervised Learning, Continuous Learning
  • Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
  • Common AI Functional Designs, Computer Vision, Pattern Recognition
  • Robotics, Natural Language Processing (NLP)
  • Speech Recognition, Natural Language Understanding (NLU)
  • Frictionless Integration, Fault Tolerance Model Integration
  • Neural Networks, Neurons, Layers, Links, Weights
  • Understanding AI Models and Training Models and Neural Networks
  • Understanding how Models and Neural Networks Exist

  • Loss, Hyperparameters, Learning Rate, Bias, Epoch
  • Activation Functions (Sigmoid, Tanh, ReLU, Leaky RelU, Softmax, Softplus)
  • Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, Convolution, Pool, Output, Match Input, etc.)
  • Fundamental and Specialized Neural Network Architectures
  • Perceptron, Feedforward, Deep Feedforward, AutoEncoder, Recurrent, Long/Short Term Memory
  • Boltzmann Machine, Restricted Boltzmann Machine, Deep Belief Network
  • Deep Convolutional Network, Extreme Learning Machine, Deep Residual Network
  • Support Vector Machine, Kohonen Network, Hopfield Network
  • Generative Adversarial Network, Liquid State Machine, How to Build an AI System (Step-by-Step)
  • Common AI System Design Principles and Common AI Project Best Practices

Module 12: Advanced Big Data Analysis & Analytics

This course module provides an in-depth overview of essential and advanced 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.


Course Module Contents

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

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

Topics Covered

  • Exploratory Data Analysis, Essential Statistics, including Variable Categories and Relevant Mathematics
  • Statistics Analysis, including Descriptive, Inferential, Covariance, Hypothesis Testing, etc.
  • Measures of Variation or Dispersion, Interquartile Range & Outliers, Z-Score, etc.
  • Probability, Frequency, Statistical Estimators, Confidence Interval, etc.
  • Variables and Basic Mathematical Notations, Statistical Measures and Statistical Inference
  • Confirmatory Data Analysis (CDA)
  • Data Discretization, Binning and Clustering
  • Visualization Techniques, including Bar Graph, Line Graph, Histogram, Frequency Polygons, etc.
  • Prediction Linear Regression, Mean Squared Error and Coefficient of Determination R2, etc.
  • Numerical Summaries, Modeling, Model Evaluation, Model Fitting and Model Overfitting

  • Statistical Models, Model Evaluation Measures
  • Cross-Validation, Bias-Variance, Confusion Matrix and F-Score
  • Association Rules and Apriori Algorithm
  • Data Reduction, Dimensionality Feature Selection
  • Feature Extraction, Data Discretization (Binning and Clustering)
  • Parametric vs. Non-Parametric, Clustering vs. Non-Clustering
  • Distance-Based, Supervised vs. Semi-Supervised
  • Linear Regression and Logistic Regression for Big Data
  • Logistics Regression, Naïve Bayes, Laplace Smoothing, etc.
  • Decision Trees for Big Data
  • Pattern Identification, Association Rules, Apriori Algorithm
  • Time Series Analysis, Trend, Seasonality, Nearest Neighbor (kNN), K-means
  • Text Analytics for Big Data and Outlier Detection for Big Data
  • Statistical, Distance-Based, Supervised and Semi-Supervised Techniques

Module 13: Advanced Machine Learning

This course module delves into the many algorithms, methods and models of contemporary machine learning practices to explore how a range of different business problems can be solved by utilizing and combining proven machine learning techniques.


Course Module Contents

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

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

Topics Covered

  • Data Exploration Patterns
  • Central Tendency Computation, Variability Computation
  • Associativity Computation, Graphical Summary Computation
  • Data Reduction Patterns
  • Feature Selection, Feature Extraction
  • Data Wrangling Patterns
  • Feature Imputation, Feature Encoding
  • Feature Discretization, Feature Standardization
  • Supervised Learning Patterns

  • Numerical Prediction, Category Prediction
  • Unsupervised Learning Patterns
  • Category Discovery, Pattern Discovery
  • Model Evaluation Patterns, Baseline Modeling
  • Training Performance Evaluation, Prediction Performance Evaluation
  • Model Optimization Patterns
  • Ensemble Learning, Frequent Model Retraining
  • Lightweight Model Implementation, Incremental Model Learning

Module 14: Advanced AI

This course module covers a series of practices for preparing and working with data for training and running contemporary AI systems and neural networks. It further provides techniques for designing and optimizing neural networks, including approaches for measuring and tuning neural network model performance. The practices and techniques are documented as design patterns that can be applied individually or in different combinations to address a range of common AI system problems and requirements.


Course Module Contents

  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Mind Map Poster
  • Neural Networks and Design Patterns Mapping Poster

  • Problem Types and Design Patterns Mapping Poster
  • Practices and Design Patterns Mapping Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics Covered

  • Data Wrangling Patterns for Preparing Data for Neural Network Input
  • Feature Encoding for Converting Categorical Features
  • Feature Imputation for Inferring Feature Values
  • Feature Scaling for Training Datasets with Broad Features
  • Text Representation for Converting Data while Preserving Semantic and Syntactic Properties
  • Dimensionality Reduction to Reduce Feature Space for Neural Network Input
  • Supervised Learning Patterns for Training Neural Network Models
  • Supervised Network Configuration for Establishing the Number of Neurons in Network Layers
  • Image Identification for using a Convolutional Neural Network
  • Sequence Identification for using a Long Short Term Memory Neural Network
  • Unsupervised Learning Patterns for Training Neural Network Models

  • Pattern Identification for Visually Identifying Patterns via a Self Organizing Map
  • Content Filtering for Generating Recommendations
  • Model Evaluation Patterns for Measuring Neural Network Performance
  • Training Performance Evaluation for Assessing Neural Network Performance
  • Prediction Performance Evaluation for Predicting Neural Network Performance in Production
  • Baseline Modeling for Assessing and Comparing Complex Neural Networks
  • Model Optimization Patterns for Refining and Adapting Neural Networks
  • Overfitting Avoidance for Tuning a Neural Network
  • Frequent Model Retraining for Keeping a Neural Network in Synch with Current Data
  • Transfer Learning for Accelerating Neural Network Training

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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 2-8 modules and 20-80 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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Contact info@arcitura.com or 604-904-4100 during PT working hours.