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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Student Testimonials
How It Works
Each course is 2 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 exercises 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.
Duration: 3 hours
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.
Duration: 3 hours
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.
Duration: 3 hours
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.
Duration: 3 hours
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 Learning
Course 5: Learn Data Wrangling
Data wrangling is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain. In preparation for the ODSC conference, our specially designed course on “Data Wrangling with Python” offers attendees a hands-on experience to master the essential techniques. From cleaning and transforming raw data to making it ready for analysis, this course will equip you with the skills needed to handle real-world data challenges. As part of a comprehensive series leading up to the conference, this course not only lays the foundation for more advanced AI topics but also aligns with the industry’s most popular coding language.
Upon completion of this short course attendees will be fully equipped with the knowledge and skills to manage the data lifecycle and turn raw data into actionable insights, setting the stage for advanced data analysis and AI applications.
Duration: 3 hours
Outline
Module 1:
Introduction
Module 2:
Data Cleaning
Module 3:
Data Transformation
Module 4:
Data Manipulation
- Introduction to Data Wrangling
- Importance and role of data wrangling in the data analysis process.
- Overview of data cleaning, transformation, and reshaping.
- Data sources
- Techniques for obtaining data
- Handling missing data.
- Dealing with outliers and duplicates.
- Addressing data quality issues
- Reshaping data
- Pivoting, melting, and stacking
- Handling categorical variables
- Converting between data types
- Normalization and scaling
- The Pandas Library
- Filtering, sorting, and aggregating data
- Data Integration and Joining
- Combining data
- Merging and joining datasets
Course 6: Learn LLMs & Prompt Engineering
In the rapidly evolving field of AI, the “LLMs, Prompt Engineering, and Generative AI” course stands as a cutting-edge offering, designed to equip learners with the latest advancements in Large Language Models (LLMs), prompt engineering, and generative AI techniques. This course delves into the architecture and functioning of LLMs, the art of crafting effective prompts to guide AI responses, and the principles behind generating creative and coherent content. As these components are becoming integral to the AI stack, understanding them is essential for anyone looking to innovate, optimize, and excel in AI-driven applications.
Whether you’re a researcher, developer, or AI enthusiast, this course will provide you with the insights and hands-on experience needed to harness the power of these transformative technologies and stay at the forefront of the AI revolution.
Duration: 3 hours
Outline
Module 1:
LLM Basics
Module 2:
Prompt Engineering
Module 3:
ChatGPT API
Module 4:
Fine Tuning LLMs
- Large Language Models (LLMs)
- Transformer architecture
- Applications of LLMs
- Using LLMs out of the box
- The process flow of chaining
- Text summarization
- Question answering
- Text similarity
- Fundamentals
- Prompt engineering examples
- Manipulation prompt
- Prompt engineering guardrails
- Impact responses from prompting
- Temperature – predictable versus creative outputs.
- Tokens & Prompting
- Iterative Prompt Development
- Evaluate OF prompt effectiveness
- Guiding model behavior
- Build your own Chatbot
- Common shortfall of prompting
- Hallucinations, Fairness, Biases, & Jailbreaking
- Fine-tuning introduction
- When to fine-tune
- Model stages
- Classification
Topic Modeling, Sentiment analysis, and Entity recognition examples - Pre-training
- Hardware and data considerations
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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Participate at ODSC West 2023
As part of the global data science community we value inclusivity, diversity, and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.