Analytics, Machine Learning, and Artificial Intelligence 101
This course leads students towards path to success in the cutting-edge areas of analytics, machine learning and artificial intelligence. The course will cover the technical knowledge and the skills that will give you an edge in the university and business worlds! This course will also allow the students to gain the necessary expertise in the fast-growing fields of analytics, machine learning and AI.
Frisco
3550 Parkwood Blvd, #500,
Frisco, TX 75034
Online
High-quality education
no matter what the location

Delivery Methods

Virtual live group classes
Virtual live group classes
In-person group classes
In-person group classes

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

Thorough Instruction
Thorough Instruction
Our comprehensive curriculum includes everything from data collection to model validation, so participants will be educated in a variety of areas.
Weekly Labs
Weekly Labs
Along with lectures and projects, we will offer one lab each week to give participants a hands-on experience with the software. This way, they will be able to put their knowledge into practice and continue to develop their skills.
Numerous Make-Up Sessions
Numerous Make-Up Sessions
We will host webinars weekly - one for lecture and one for lab - and we will post recordings of these classes for those who are unable to attend that day.
Capstone Project Work
Capstone Project Work
Our projects will allow students and adults to explore their creative side and go outside the box to overcome challenges and find solutions to common software development problems.
College Preparation
College Preparation
High school students who participate in this program will be able to take a certification exam and, if they succeed, include it on their college and internship applications.
Career Preparation
Career Preparation
Adults who participate in this program and pass the certification exam can include this on job applications, thus enhancing their professional marketability and giving them an edge in the software industry.

Benefits for High Schoolers

Computer Science Preparation
Computer Science Preparation
Students who are interested in taking Computer Science 1, Computer Science 3, or Computer Science AP will get a head start in understanding code analysis and software development.
Internship Opportunities
Internship Opportunities
We believe that many students coming out of our classes will receive the preparation needed to take on internships at corporate companies. These internships will certainly help students stand out on their college applications.
Certification Exams
Certification Exams
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.
Career Planning
Career Planning
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.

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.

01. Terminology Fit
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

02. Getting Started, Data-driven Decisions, Data Science vs. ML vs. AI
A
Lecture

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).

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

03. Technologies, Tools, Libraries, Languages: GUIs vs. Code-first
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

04. CRISP-DM/TDSP/CPMAI, Understanding the Business Need
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

05. Statistics, Linear Algebra, Computer Science Prerequisites
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

06. Data Collection, Data Validation, Cleansing and Manipulation
A
Lecture

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).

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

07. Data Exploration, Visualization and Hypothesis Design
A
Lecture

Understand what is involved with data exploration – from target/outcome definition and hypothesis design to data visualization and setting the stage for feature engineering.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

08. Feature Engineering, Interpretation, Data Preparation Pipelines
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

09. Building Supervised and Unsupervised Learning Models
A
Lecture

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.

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

10. Model Validation and Benefits Quantification Methodologies
A
Lecture

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.).

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

11. Model Monitoring, Model Governance and Ethical-ML/AI
A
Lecture

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.).

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.

12. Model Deployment and Serving Architectures & DevOps and CI/CD Considerations for ML/AI
A
Lecture

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.).

B
Lab

Hands-on lab on the covered topic, using actual grocery store/finance transaction data.