Course is closed
Lead Instructor(s)
TBD Summer 2024
Live Virtual
Course Length
4 half-days
Course Fee
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Course is closed
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Extract more value from your organization’s data by learning to translate strategic business questions into specific analyses and execute them within an analytics platform. In this applied course, you will acquire the analytics and AI tools you need to build predictive machine learning models in no-code environments, anticipate model pitfalls and shortcomings, and lead efforts to make data science more accessible within your organization. 

This course may be taken individually or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence.

Course Overview

Whether you’re a manager, domain expert, or other organizational decision-maker, your role requires making data-drive choices in days or even hours, not months. These constraints don’t allow time for careful construction of a data processing pipeline and model in Python to guide decision-making. Compounding the challenges is the fact that your position focuses on business outcomes—not Python fluency, APIs, or the latest machine learning and statistic libraries. 

In this course, you will acquire the no-code analytics and AI tools you need to become a “citizen data scientist”—a professional who is equipped to perform analytical tasks while working in an area of expertise outside statistics and analytics. This knowledge will negate the need for hundreds of lines of code and enable you to focus on contextualizing questions and communicating the results to drive positive change. The tools also provide a platform to bring together managers, domain experts, and data scientists.

Alongside a group of global peers, you will master key data science concepts and learn frameworks for translating organizational challenges into data science questions without code. Whether refining a product pricing strategy, predicting risk factor, or accelerating production on an assembly line, the right questions can maximize the effectiveness of your response:

  • DESCRIPTIVE: What happened? Take data and drill down to visualize and describe the current state. 
  • PREDICTIVE: Why did it happen? Identify the drivers of change, and their level of importance. 
  • PRESCRIPTIVE: How do I change what happens? Figure out how we can use these learnings to alter future outcomes. 

Through interactive lectures and hands-on labs, this course will prepare you to answer these questions and create actionable insights for your company. You will leave equipped with an overview of current no-code tools and a guide toward expanding data science access in your organization.

Learning Outcomes
  • Understand the core concepts of data science and explain how they apply to real world challenges 
  • Examine the primary principles underlining descriptive, predictive, and prescriptive analytics
  • Enhance your ability to explore and understand your organization’s unique data 
  • Learn how to build machine learning models to make predictions
  • Translate analytic outputs into feasible and actionable changes within an organization 
  • Discover how the no-code tool eco-system can be used to democratize data science in organizations 

Program Outline

This course will run for 4 half-days Monday through Thursday, 10:00am-2:30pm EDT.

Day One

  • Democratizing data science: Challenges and opportunities
  • Descriptive analytics: How to prepare and explore your data
  • Data exploration lab

Day Two

  • Predictive analytics, model key outcomes
  • ML model lab
  • Forecasting: Working with time series data
  • Forecasting lab

Day Three

  • Process and event data and feature engineering
  • Process mining and modeling lab
  • Prescriptive analytics: Techniques for data-driven decision making
  • Deploying models and data products

Day Four

  • Deploying models and data products
  • Data product lab
  • No-code tool ecosystem
  • Strategies to democratize data science within an organization
  • Closing remarks
Who Should Attend

This course is designed for professionals across industries who have an interest in data, but limited knowledge of statistics and programming. Ideal participants are from mid- to large- organizations that would benefit from increasing the number of people who are able to engage with data firsthand and build analytic outputs.

Potential job titles include, but are not limited to: 

  • Managers who need to make data-driven decisions that drive teams and projects forward 
  • Domain Experts who have access to large quantities of data but lack the tools and expertise to act on it 
  • Business Analysts who need analytical strategies for making predictions based on large quantities of data
  • Strategy or Finance Manager/Directors who want to increase their understanding of their organization’s data to drive positive change
  • VPs of Marketing or Operations who make high-level organizational decisions that would benefit from data-driven guidance 
  • IT Directors who are looking to complement their knowledge and experience with no-code analysis platforms 
  • Data Engineers/DB Analysts who want to get more from their analytic outputs 
  • Software Development Engineers who would benefit from translating data into actionable strategies and projects
Download the Course Brochure
No Code Analytics and AI

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 30%|Latest Developments: Recent advances and future trends - 30%|Industry Applications: Linking theory and real-world - 40%
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 40%|Discussion: Guided discussion reinforcing lectures and lab work - 20%|Labs: Demonstrations, experiments, simulations - 40%

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 35%|Specialized: Assumes experience in practice area or field - 35%|Advanced: In-depth explorations at the graduate level - 30%