Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. This Data Literacy training is designed to give you a high level overview of the key topics in Data Science and Machine Learning.
In this course you’ll learn the fundamental concepts relating to data, allowing you to understand what makes data suitable for data analysis, visualization and machine learning.
Then we’ll give you a quick overview of important statistical topics, such as mean, standard deviation, and the normal distribution.
Afterwards you will learn the different ways data scientists are able to visualize data to convey their ideas in a clear manner.
We’ll also teach you about the machine learning process, acquiring data, cleaning data, and an overview of the train/test split philosophy that supervised learning adheres to.
Then we’ll show you some examples of regression and classification algorithms, as well as how to evaluate their results.
We’ll also explore what the future holds by taking a peek at the bleeding edge of AI and ML, including DALLE-2 and GPT-3!
- Data and Opportunities
- Data Quality
- Understanding Big Data
- Data Measurements
- Understanding Central Tendency
- Understanding Dispersion
- Understanding Data Analysis
- Tour of Data Visualizations
- Probability and Uncertainty
- Testing theories and hypotheses
- Probability and Statistics Overview
- Machine Learning Overview
- Understanding Machine Learning Concepts
- Supervised Learning Overview
- Unsupervised Learning Overview
- Dimensionality Reduction Overview
- The Future of Data, ML, and AI
- Overview of Deep Learning Concepts
- What’s next for AI and ML
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