Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 1: Literacy and Foundations
Data Science for Beginners
Data Science for Beginners is a curriculum of 20 lessons that focus on the foundations of Data Science and requires no prior knowledge to get started. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. The project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
Each lesson includes:
- Optional sketchnote
- Optional supplemental video
- Pre-lesson warmup quiz
- Written lesson
- For project-based lessons, step-by-step guides on how to build the project
- Knowledge checks
- A challenge
- Supplemental reading
- Assignment
- Post-lesson quiz
Courses in this program
1) Data Science for Beginners
Data Science for Beginners focuses on foundational concepts and practical applications of Data Science.
By the end of this series, students will have learned basic principles of data science, including ethical concepts, data preparation, different ways of working with data, data visualization, data analysis, real-world use cases of data science, and more.
Estimated effort 10 weeks
English language
Introduction
01
Defining Data Science
Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data.
02
Data Science Ethics
Data Ethics Concepts, Challenges & Frameworks.
03
Defining Data
How data is classified and its common sources.
04
Introduction to Statistics & Probability
The mathematical techniques of probability and statistics to understand data.
Working With Data
05
Working with Relational Data
Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced “see-quell”).
06
Working with NoSQL Data
Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases.
07
Working with Python
Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended.
08
Data Preparation
Topics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data.
Data Visualization
09
Visualizing Quantities
Learn how to use Matplotlib to visualize bird data.
10
Visualizing Distributions of Data
Visualizing observations and trends within an interval.
11
Visualizing Proportions
Visualizing discrete and grouped percentages.
12
Visualizing Relationships
Visualizing connections and correlations between sets of data and their variables.
13
Meaningful Visualizations
Techniques and guidance for making your visualizations valuable for effective problem solving and insights.
Lifecycle
14
Introduction to the Data Science lifecycle
Introduction to the data science lifecycle and its first step of acquiring and extracting data.
15
Analyzing
This phase of the data science lifecycle focuses on techniques to analyze data.
16
Communication
This phase of the data science lifecycle focuses on presenting the insights from the data in a way that makes it easier for decision makers to understand.
Cloud Data
17
Data Science in the Cloud
This series of lessons introduces data science in the cloud and its benefits.
18
Data Science in the Cloud
Training models using Low Code tools.
19
Data Science in the Cloud
Deploying models with Azure Machine Learning Studio.
In the Wild
20
Data Science in the Wild
Data science driven projects in the real world.