Learn core data, SQL, and Python Programming concepts and how they are applied to machine learning

These primer courses can be taken stand-alone or as part of our Mini-Bootcamp series. This foundations series is built from the ground up to boost your understanding of data-centric AI.

Meet your instructor

Software Engineer & AI Expert

Sheamus McGovern

Founder of ODSC and Software Architect specializing in, complex multi-platform systems across multiple industries including finance, healthcare, and education.

Course Curriculum

  • 1

    Data Primer Course

    • Slides

    • On-Demand Recording

  • 2

    SQL Primer Course

    • Slides

    • On-Demand Recording

  • 3

    Python Programming Course

    • Slides

    • On-Demand Recording

  • 4

    Learn AI course

    • Slides

    • On-Demand Recording

Course Outline - Data Primer Course

Data is the essential building block of Data Science, Machine Learning, and AI. This course is the first in the series and is designed to teach you the foundational skills and knowledge required to understand, work with, and analyze data. It covers topics such as data collection, organization, profiling, and transformation as well as basic analysis. This course is aimed at helping people begin their AI journey and gain valuable insights that we will build up in subsequent SQL, programming, and AI courses.

Introduction to Data

  • What is Data
  • Why Data is Important
  • The Data Life Cycle
  • Understanding Data Types
  • Data Centric AI

Data Collection

  • Data Collection
  • Sourcing Data
  • External Data
  • Licencing Data
  • Data Collection Tools

Data Transformation

  • Data Transformation
  • Data Enrichment
  • Correlations and Outliers
  • Data Quality
  • Data Transformation Tools

Data Analysis

  • Data Profiling
  • Describing a Dataset
  • Data Shaping and Shaping Examples
  • Data Analysis Tools

Course Outline - SQL Primer

This SQL coding course teaches students the basics of Structured Query Language, which is a standard programming language used for managing and manipulating data and an essential tool in AI.  The course covers topics such as database design and normalization, data wrangling, aggregate functions, subqueries, and join operations, and students will learn how to design and write SQL code to solve real-world problems. Upon completion, students will have a strong foundation in SQL and be able to use it effectively to extract insights from data. 

The ability to effectively access, retrieve, and manipulate data using SQL is essential for data cleaning, pre-processing, and exploration, which are crucial steps in any data science or machine learning project. Additionally, SQL is widely used in industry, making it a valuable skill for professionals in the field. This course builds upon the earlier data course in the series.

Data Wrangling

  • Introduction to Data Wrangling
  • Why SQL for Data Wrangling?
  • Data Lifecycle Review
  • SQL Data Types
  • Sourcing & Collecting Data

Tables and Databases

  • Data Storage
  • Popular Databases
  • Tables and Databases
  • Relational Data Design 
  • Data Normalization
  • Foreign and Primary Keys

SQL Syntax

  • Introduction to SQL Syntax
  • SQL Query Syntax
  • Understanding SQL CRUD (Create, Read, Update, Delete)
  • Filtering Data with SQL
  • Data Profiling with SQL

Data Manipulation

  • Subqueries in SQL
  • Loading and Inserting Data
  • Transaction Control  
  • Aggregate Functions and Groups
  • Join Operations
  • Updating Data with SQL

Course Outline - Learn Programming

The Python language is one of the most popular programming languages in data science and machine learning as it offers a number of powerful and accessible libraries and frameworks specifically designed for these fields. This programming course is designed to give participants a quick introduction to the basics of coding using the Python language. 

It covers topics such as data structures, control structures, functions, modules, and file handling. This course aims to provide a basic foundation in Python and help participants develop the skills needed to progress in the field of data science and machine learning.


  • Introduction 
  • Basic concepts
  • Variables & data types
  • Operators
  • Control structures,
  • Functions

Data Structures

  • Data Structures
  • Arrays
  • Lists
  • Tuples
  • Dictionaries;
  • Manipulating structures

Functions and Modules

  • Defining functions

  • Calling functions

  • Passing & returning values

  • built-in functions

  • Importing modules.

  • File I/O:

OOP & Libraries

  • Object-oriented programming

  • Defining classes and objects

  • Inheritance.

  • Exception handling

  • External libraries

Coming Up!

1. AI Primer, 2. Data Wrangling with Python, 3. LLMs, Prompt Engineering and Generative AI