Foundations of Data Literacy
This online program is designed for flexible learning at your own pace and teaches you how to use data to make informed data-driven decisions. It features an engaging microlearning approach, combining interactive activities with bite-sized videos, short articles, mini-quizzes, and a real-world application activity to enhance your understanding.
Key Topics Include:
- Learn foundational data concepts and how to develop effective metrics.
- Understand how to choose and improve data visualizations for clarity.
- Discover the steps to make and communicate decisions based on data.
- Explore common mistakes in real-world data analysis and when to seek expert help.
The Foundations of Data Literacy course teaches you how to use data to make informed data-driven decisions. Connect the dots between the data understanding already attained and tie it together with your professional expertise to create powerful stories and make informed decisions.
Python & Math Fundamentals
The course is for beginners aiming for a data science career and to build foundational computer programming and math skills. It pairs a microlearning approach with interactive activities and exercises that allow learners to practice coding as they learn. Learn how to write Python code and get hands-on experience with tools like GitHub and Command Line, all delivered in a self-paced, on-demand format.
Key Topics Include:
- Learn how to write Python code and solve problems using linear algebra, calculus, probability, and statistics.
- Get hands-on experience with tools like GitHub and Command Line.
- Work through a course-long coding project divided into milestones for each module.
Our Python & Math Fundamentals course was designed for you, the beginner looking for an introduction to the building blocks essential to developing data science skills or forging a new career in the field. It is well known that the field of data science is very technical, and this course will equip you with the necessary basic skills in computer programming and mathematics. Learners need no prior experience, and the course will walk you through the installation of Python on your device.
Prerequisites
- There are no prerequisites for this course - if you're an absolute beginner or just interested in data science, this course is for you!
- Students only need a device on which Anaconda (for Python 3) can be installed.
Course Topics
- Introduction to Coding
- Python: Getting Started
- Command Line
- Python: Sequences and Loops
- Python: Functions and Packages
- Python: More Data Types and Comprehensions
- Git and GitHub
- Probability
- Statistics
- Linear Algebra
- Calculus
Course Includes
- A microlearning approach that pairs engaging and memorable content with interactive activities.
- Bite-sized videos that provide clear explanations and illustrative examples.
- Knowledge checks that help learners check their understanding as they go.
- Exercises that allow learners to practice coding as they learn.
- Assessments to determine comprehension of course material.
- A course-long coding project divided into milestones for each module.
Exploratory Data Analysis
This course delivers the skills, tools, and strategies needed to advance in the data science and analytics field. Students will learn exploratory data analysis, examine the tools used to extract meaningful insights, and take a deep dive into Python and SQL. This course is ideal for aspiring data analysts, business intelligence analysts, business analysts, and associate data scientists. In this short, immersive course, students gain skills they can apply right away at work.
Prerequisites:
Students should have a basic understanding of the following:
- General Python programming concepts, such as data types, conditional statements, loops, and functions
- Math fundamentals like probability and distribution, linear algebra, and statistics
Key Topics Include:
- How to explore, manipulate, and describe tabular datasets using Pandas and NumPy
- The use of Matplotlib and Seaborn to visualize variables and the relationships between them
- How to work with database tools to connect to and query from relational databases
The Exploratory Data Analysis (EDA) program will provide you with the skills, tools, and strategies needed to explore data. We’ll start with the basics and get you acquainted with exploratory data analysis and the tools used to extract meaningful insights, namely: SQL and Python libraries. We’ll then dive deeper into Python and SQL. Throughout the course you’ll build on a project where you will extract information and insights from a messy dataset, write code in Jupyter notebooks, perform EDA using Python, and visualize results. You will also pull and clean data using a relational database.
Prerequisites
Students should have a basic understanding of the following:
- General Python programming concepts, such as data types, conditional statements, loops, and functions
- Math fundamentals like probability and distribution, linear algebra, and statistics
Course Topics
- Introduction to Databases and Basic SQL Queries
- Creating and Working with Multiple Tables
- Introduction to Pandas
- Git
- Advanced Pandas
- Data Cleaning
- Matplotlib
- Seaborn
- Importing Data to Python
- Intermediate SQL Queries and SQL in Python
- NumPy
- Object-Oriented Programming
- Complexity
Course Includes:
- A microlearning approach that pairs engaging and memorable content with interactive activities.
- Bite-sized videos that provide clear explanations and illustrative examples.
- Knowledge checks that help learners check their understanding as they go.
- Exercises that allow learners to practice coding as they learn.
- Assessments to determine comprehension of course material.
- A course-long coding project divided into milestones for each module.
