
Frisco, TX 75034
no matter what the location
Delivery Methods
Analytics, Machine Learning and
Artificial Intelligence 101
Have you heard? Our program includes everything from data drift to feature
engineering so that you can be a pro in just a few weeks!
Software development, here we come!
Program Highlights
Benefits for High Schoolers
Ready - Set - Prep
Students who succeed on our certification exams will be able to prove to colleges (and to major corporations!) that they know the software in and out and that they will be a valuable asset to their software development programs.
We not only welcome students interested in pursuing a career in software development but also students who are unsure of what their future holds. Our thorough instruction will help students decide whether ML and AI are potential options for future careers.
One session dedicated to all the terminology and evolution (example: PC to server to VM to VM in cloud). We will split this session to cover server, network, database, analytics, ML and AI.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
What do we mean by data-driven decisioning? What has industry research established about businesses that are data-driven in their decision making vs. those that are not? Understand the difference between business intelligence (hindsight executive dashboard/reporting analytics), data science (hypothesis/diagnostics driven analytics), machine learning (predictive & prescriptive analytics) and artificial intelligence (cognitive intelligence).
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
What are the typical technologies and tools that data scientists use? Understand the difference between on-premise vs. cloud technology alternatives. We will learn how to setup a typical data science environment: both on-premise (on our laptops) as well as in the cloud employing a minimalist set of tools. Understand the difference between No-Code/GUI driven tools vs. Code-first approach.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand the evolution of Data Science-ML/AI methodologies from CRISP-DM to TDSP to CPMAI. Establish how these advanced analytics methodologies relate to Agile/Scrum (SAFe 4.0). We will demonstrate the importance of clearly understanding the business need before embarking on an advanced analytics exercise within the context of business process (batch vs. real-time), data assets and availability (structured, unstructured vs. streaming), the technology/application integration and success measures/KPIs.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Introduction to basic mathematical, statistical and computer science concepts that are frequently invoked by data scientists/machine learning engineers. Examples include: vectors, matrices, iterations, step-size, tolerance, statistical significance, inner and outer joins.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand what is involved with data collection and ingestion – from data profiling and manipulation to cleansing and validation, across numerical, categorical and boolean data types. We will also learn the importance of setting up train vs. validation vs. test samples – and typical methods employed to setup train vs. test (e.g. in-time vs. out-of-time samples).
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand what is involved with data exploration – from target/outcome definition and hypothesis design to data visualization and setting the stage for feature engineering.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand what is involved with feature engineering – from data transformations and feature proxies to interpreting the relationship to the target/outcome and impact on production data preparation pipelines.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand the difference between supervised and unsupervised learning algorithms – the pros and cons of each, and guidelines for where to employ each category of algorithms.
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand how to evaluate models, what are some of the most frequently used metrics employed in validating a model – the pros/cons of each metric, how to interpret the metrics – and how do these vary by the type of advanced analytic being pursued (classification vs. estimation vs. time-series vs. unstructured data/text/image analytics). Examine the difference between Model Validation and business Benefits Quantification (lift, incremental value, etc.).
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand the relevance of model monitoring, governance and ethical ML/AI in the context of operationalizing advanced analytics models to production. What are some of the regulatory considerations that need to be considered (FCRA, GDPR/CCPA, HIPAA, etc.) and what types of model performance monitoring should be considered (data drift, model drift, decision performance, etc.).
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.
Understand the difference between model deployment (batch, real-time vs. A/B testing) and model serving (associated technology architecture: server vs. serverless). Revisit CRISP-DM, TDSP/CPMAI to establish how DevOps and CI/CD fits into the analytics lifecycle and the implications on operationalized models (model upgrades, package/library updates, version control, etc.).
Hands-on lab on the covered topic, using actual grocery store/finance transaction data.