Pre-Bootcamp Primer Courses

Data, Coding, and AI preparation courses for ODSC Mini-Bootcamps

ODSC Bootcamp Primer Courses

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

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  • Available On-Demand 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.
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  • Available On-Demand SQL Primer Course

    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,
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  • April 6th, 2023 Programming Primer Course with Python

    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.
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  • April 26, 2023 AI Primer Course

    This AI literacy course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models.
    Learn More
Certificate Award
Available On-DemandAvailable On-DemandApril 6th, 2023April 26, 2023

Student Testimonials

“A very interesting and informative, well worth attending.”

Tim A., Ph.D. Researcher

“Excellent, no critics.”

Sami B., Researcher

“I learned a lot. Great job”

Isaac O., Data Scientist

How It Works

  • Each course is 2.5 to 3.5 hours long and includes extra materials

  • The primer series is taught live and then available on demand.

  • If you miss the live course, each session is available on-demand as soon as you register. 

  • Each course includes exercises to improve learning outcomes.

  • Coding expercises allow you to learn hands-on skills.

  • Learn at your own pace. Courses can be taken alongside additional Ai+ courses.

What You Will Learn

You will learn core data, SQL, and Python Programming concepts and how they are applied to machine learning

Course 1: Learn Data

Now Available On-Demand

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.

Outline

Module 1:
Introduction to Data 

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

Module 2:
Data Collection

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

Module 3:
Data Transformation

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

Module 4:
Data Analysis

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

Course 2: Learn SQL

Now Available On-Demand

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.

Outline

Module 1:
Data Wrangling

Module 2:
Tables & Databases  

Module 3:
SQL Syntax

Module 4:
Data Manipulation

  • Introduction to Data Wrangling
  • Why SQL for Data Wrangling?
  • Data Lifecycle Review
  • SQL Data Types
  • Sourcing & Collecting Data
  • Data Storage
  • Popular Databases
  • Tables and Databases
  • Relational Data Design 
  • Data Normalization
  • Foreign and Primary Keys
  • Introduction to SQL Syntax
  • SQL Query Syntax
  • Understanding SQL CRUD (Create, Read, Update, Delete)
  • Filtering Data with SQL
  • Data Profiling with SQL
  • Subqueries in SQL
  • Loading and Inserting Data
  • Transaction Control  
  • Aggregate Functions and Groups
  • Join Operations
  • Updating Data with SQL

Course 3: 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.

Outline

Module 1:
Introduction 

Module 2:
Data structures:

Module 3:
Functions and modules

Module 4:
OOP & Libraries

  • Introduction 
  • Basic concepts
  • Variables & data types
  • Operators
  • Control structures,
  • Functions
  • Data Structures
  • Arrays
  • Lists
  • Tuples
  • Dictionaries;
  • Manipulating structures

  • Defining functions

  • Calling functions

  • Passing & returning values

  • built-in functions

  • Importing modules.

  • File I/O:

  • Object-oriented programming

  • Defining classes and objects

  • Inheritance.

  • Exception handling

  • External libraries

Course 4: Learn AI

This AI literacy course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning including supervised, and unsupervised learning, as well as some of the most common models such as regression and k-means clustering. 

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science such as ODSC East’s Mini-Bootcamp.

Outline

Module 1:
Introduction

Module 2:
Types of ML  

Module 3:
Supervised Learning

Module 4:
Unsupervised Learninng

  • An Overview of AI
  • The AI Stack
  • Machine Learning Definitions
  • ML vs Traditional Programming
  • Algorithms and Models
  • Machine Learning Workflow
  • Independent vs Dependent Variables
  • Feature Selection
  • Data Labeling
  • Training & Testing Models
  • Structured and Unstructured Data
  • Type of Machine Learning
  • Supervised Machine Learning
  • Popular ML Algorithms
  • Classification Models
  • Regression Models
  • Which Model to Use?
  • Feature Extraction
  • Unsupervised Machine Learning
  • Supervised vs Unsupervised ML
  • K-Means Cluster Models
  • Deep Learning Overview 
  • Deep Learning vs Machine Learnin

Prerequisites

As these are primer courses, no prior experience is necessary. Individual setup prerequisites will be provided prior to each course.

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PLANS

Primer Course Access

  Ai+ Foundations & Certification  Courses 

Certification of Completion 

AI+ Premium Subscription

ODSC East 2023 Mini-Bootcamp Pass


$Free


With Bootcamp Pass 

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$FREE


WITH AI+ Plan

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$149

PER COURSE


DISCOUNTED FROM $199

$499  for ALL 4 

 
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