Code Monkey home page Code Monkey logo

Data-Science-For-Beginners's Introduction

Data Science for Beginners - A Curriculum

GitHub license GitHub contributors GitHub issues GitHub pull-requests PRs Welcome

GitHub watchers GitHub forks GitHub stars

Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.

Hearty thanks to our authors: Jasmine Greenaway, Dmitry Soshnikov, Nitya Narasimhan, Jalen McGee, Jen Looper, Maud Levy, Tiffany Souterre, Christopher Harrison.

πŸ™ Special thanks πŸ™ to our Microsoft Student Ambassador authors, reviewers and content contributors, notably Raymond Wangsa Putra, Ankita Singh, Rohit Yadav, Arpita Das, Mohamma Iftekher (Iftu) Ebne Jalal, Dishita Bhasin, Miguel Correa, Nawrin Tabassum, Sanya Sinha, Majd Safi, Sheena Narula, Anupam Mishra, Dibri Nsofor, Aditya Garg, Alondra Sanchez, Yogendrasingh Pawar, Max Blum, Samridhi Sharma, Tauqeer Ahmad, Aaryan Arora, ChhailBihari Dubey

 Sketchnote by (@sketchthedocs)
Data Science For Beginners - Sketchnote by @nitya

Getting Started

Teachers, we have included some suggestions on how to use this curriculum. We'd love your feedback in our discussion forum!

Students, to use this curriculum on your own, fork the entire repo complete the exercises on your own, starting with a pre-lecture quiz, then reading the lecture completing the rest of the activities. Try to create the projects by comprehending the lessons rather than copying the solution code; however that code is available in the /solutions folders in each project-oriented lesson. Another idea would be to form a study group with friends go through the content together. For further study, we recommend Microsoft Learn.

Pedagogy

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is project-based and that it includes frequent quizzes. 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.

In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 10 week cycle.

Find our Code of Conduct, Contributing, Translation guidelines. We welcome your constructive feedback!

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

A note about quizzes: All quizzes are contained in this app, for 40 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the quiz-app folder. They are gradually being localized.

Lessons

 Sketchnote by (@sketchthedocs)
Data Science For Beginners: Roadmap - Sketchnote by @nitya
Lesson Number Topic Lesson Grouping Learning Objectives Linked Lesson Author
01 Defining Data Science Introduction Learn the basic concepts behind data science and how it’s related to artificial intelligence, machine learning, and big data. lesson video Dmitry
02 Data Science Ethics Introduction Data Ethics Concepts, Challenges & Frameworks. lesson Nitya
03 Defining Data Introduction How data is classified and its common sources. lesson Jasmine
04 Introduction to Statistics & Probability Introduction The mathematical techniques of probability and statistics to understand data. lesson video Dmitry
05 Working with Relational Data Working With Data Introduction to relational data and the basics of exploring and analyzing relational data with the Structured Query Language, also known as SQL (pronounced β€œsee-quell”). lesson Christopher
06 Working with NoSQL Data Working With Data Introduction to non-relational data, its various types and the basics of exploring and analyzing document databases. lesson Jasmine
07 Working with Python Working With Data Basics of using Python for data exploration with libraries such as Pandas. Foundational understanding of Python programming is recommended. lesson video Dmitry
08 Data Preparation Working With Data Topics on data techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. lesson Jasmine
09 Visualizing Quantities Data Visualization Learn how to use Matplotlib to visualize bird data πŸ¦† lesson Jen
10 Visualizing Distributions of Data Data Visualization Visualizing observations and trends within an interval. lesson Jen
11 Visualizing Proportions Data Visualization Visualizing discrete and grouped percentages. lesson Jen
12 Visualizing Relationships Data Visualization Visualizing connections and correlations between sets of data and their variables. lesson Jen
13 Meaningful Visualizations Data Visualization Techniques and guidance for making your visualizations valuable for effective problem solving and insights. lesson Jen
14 Introduction to the Data Science lifecycle Lifecycle Introduction to the data science lifecycle and its first step of acquiring and extracting data lesson Jasmine
15 Analyzing Lifecycle This phase of the data science lifecycle focuses on techniques to analyze data. lesson Jasmine
16 Communication Lifecycle 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. lesson Jalen
17 Data Science in the Cloud Cloud Data This series of lessons introduces data science in the cloud and its benefits. lesson Tiffany and Maud
18 Data Science in the Cloud Cloud Data Training models using Low Code tools lesson Tiffany and Maud
19 Data Science in the Cloud Cloud Data Deploying models with Azure Machine Learning Studio lesson Tiffany and Maud
20 Data Science in the Wild In the Wild Data science driven projects in the real world lesson Nitya

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify on your local machine, then in the root folder of this repo, type docsify serve. The website will be served on port 3000 on your localhost: localhost:3000.

Note, notebooks will not be rendered via Docsify, so when you need to run a notebook, do that separately in VS Code running a Python kernel.

PDF

A PDF of all of the lessons can be found here

Help Wanted!

If you would like to translate all or part of the curriculum, please follow our Translations guide.

Other Curricula

Our team produces other curricula! Check out:

Data-Science-For-Beginners's People

Contributors

paladique avatar jlooper avatar Amagash avatar shwars avatar dependabot[bot] avatar anupamishra333 avatar nitya avatar JalenMcG avatar dishitarocks avatar maud-lv avatar RohitY24 avatar AnkitaSinghIE avatar microsoftopensource avatar ChungZH avatar ssanya942 avatar AdityaGarg00 avatar leestott avatar Iftu119 avatar RayWP avatar alondraSanchezM avatar iofall avatar dibrinsofor avatar Arpiiitaaa avatar Ghost911-s avatar Nawrin14 avatar SheenaNarula avatar vishvanathan-k avatar microsoft-github-operations[bot] avatar

Stargazers

Saulius Krasuckas avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    πŸ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. πŸ“ŠπŸ“ˆπŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❀️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.