Data-Science-For-Beginners

10 Weeks, 20 Lessons, Data Science for All!

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Quick Overview

Data Science For Beginners is a comprehensive curriculum developed by Microsoft to introduce beginners to the field of data science. It consists of 10 weeks of lessons covering various aspects of data science, including data analysis, visualization, machine learning, and ethics. The course is designed to be accessible to learners with no prior experience in data science.

Pros

Free and open-source curriculum with high-quality content from industry experts
Covers a wide range of data science topics, providing a well-rounded introduction to the field
Includes hands-on projects and exercises to reinforce learning
Available in multiple languages, making it accessible to a global audience

Cons

May be overwhelming for absolute beginners due to the breadth of topics covered
Some lessons may require additional resources or tools not provided in the repository
Limited depth in some advanced topics, as it's designed for beginners
Lack of formal certification or accreditation upon completion

Getting Started

To get started with the Data Science For Beginners curriculum:

Visit the GitHub repository: https://github.com/microsoft/Data-Science-For-Beginners
Clone or download the repository to your local machine
Navigate to the README.md file in the root directory for an overview of the course structure
Start with the first lesson in the 1-Introduction folder
Follow the lessons sequentially, completing exercises and projects as you go
Join the discussion on GitHub Issues or contribute to the project if you find areas for improvement

Note: This is not a code library, so there are no code examples or quick start instructions provided.

Competitor Comparisons

ML-For-Beginners

68,986

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Pros of ML-For-Beginners

More focused on practical machine learning techniques and algorithms
Includes hands-on projects with popular ML frameworks like scikit-learn and TensorFlow
Covers a wider range of ML topics, including deep learning and neural networks

Cons of ML-For-Beginners

Less emphasis on data preprocessing and exploratory data analysis
May be more challenging for absolute beginners without prior programming experience
Fewer lessons on statistical concepts and probability theory

Code Comparison

ML-For-Beginners:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression()
model.fit(X_train, y_train)

Data-Science-For-Beginners:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
df.describe()
plt.scatter(df['x'], df['y'])
plt.show()

The code snippets highlight the different focus areas of the two repositories. ML-For-Beginners emphasizes machine learning model implementation, while Data-Science-For-Beginners concentrates more on data exploration and visualization techniques.

PythonDataScienceHandbook

42,701

Python Data Science Handbook: full text in Jupyter Notebooks

Pros of PythonDataScienceHandbook

Comprehensive coverage of Python data science libraries (NumPy, Pandas, Matplotlib, Scikit-Learn)
In-depth explanations with practical examples and visualizations
Available as a free online resource and Jupyter notebooks

Cons of PythonDataScienceHandbook

Focuses primarily on Python libraries rather than broader data science concepts
Less structured curriculum compared to Data-Science-For-Beginners
May be overwhelming for absolute beginners due to its depth

Code Comparison

PythonDataScienceHandbook:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.show()

Data-Science-For-Beginners:

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())
print(df.describe())

The PythonDataScienceHandbook example demonstrates more advanced visualization, while Data-Science-For-Beginners focuses on basic data manipulation and exploration.

handson-ml

25,157

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

Pros of handson-ml

More comprehensive coverage of machine learning topics
Includes practical examples and Jupyter notebooks for hands-on learning
Regularly updated with new content and improvements

Cons of handson-ml

Steeper learning curve for beginners
Less focus on foundational data science concepts
Requires more prior knowledge in programming and mathematics

Code Comparison

handson-ml:

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

Data-Science-For-Beginners:

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('data.csv')
plt.scatter(data['x'], data['y'])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

The handson-ml repository provides more advanced machine learning code examples, focusing on model implementation and preprocessing. In contrast, Data-Science-For-Beginners offers simpler code snippets that introduce basic data manipulation and visualization concepts, making it more accessible for newcomers to the field.

TensorFlow-Examples

43,354

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Pros of TensorFlow-Examples

Focuses specifically on TensorFlow, providing in-depth examples for this popular machine learning library
Includes a wide range of examples from basic to advanced, covering various neural network architectures
Regularly updated to incorporate new TensorFlow features and best practices

Cons of TensorFlow-Examples

Limited to TensorFlow, not covering broader data science concepts or other libraries
May be overwhelming for absolute beginners without prior programming or machine learning knowledge
Lacks structured curriculum or learning path compared to Data-Science-For-Beginners

Code Comparison

TensorFlow-Examples (Neural Network example):

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

Data-Science-For-Beginners (Python basics example):

# Define a list of fruits
fruits = ["apple", "banana", "cherry"]

# Print each fruit using a for loop
for fruit in fruits:
    print(fruit)

The TensorFlow-Examples code demonstrates a more advanced, specific implementation, while Data-Science-For-Beginners focuses on fundamental programming concepts.

data-science-ipython-notebooks

27,106

Pros of data-science-ipython-notebooks

Extensive collection of Jupyter notebooks covering various data science topics
Practical examples and code snippets for immediate application
Includes advanced topics like deep learning and big data

Cons of data-science-ipython-notebooks

Less structured curriculum compared to Data-Science-For-Beginners
May be overwhelming for absolute beginners due to its breadth
Not as frequently updated as Data-Science-For-Beginners

Code Comparison

data-science-ipython-notebooks:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

Data-Science-For-Beginners:

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

Both repositories use popular data science libraries, but data-science-ipython-notebooks tends to include more advanced techniques and a wider variety of libraries across its notebooks. Data-Science-For-Beginners focuses on foundational concepts and libraries, making it more suitable for beginners.

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README

Data Science for Beginners - A Curriculum

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.

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


Data Science For Beginners - Sketchnote by @nitya

Announcement - New Curriculum on Generative AI was just released!

We just released a 12 lesson curriculum on generative AI. Come learn things like:

prompting and prompt engineering
text and image app generation
search apps

As usual, there's a lesson, assignments to complete, knowledge checks and challenges.

Check it out:

https://aka.ms/genai-beginners

Are you a student?

Get started with the following resources:

Student Hub page In this page, you will find beginner resources, Student packs and even ways to get a free cert voucher. This is one page you want to bookmark and check from time to time as we switch out content at least monthly.
Microsoft Learn Student Ambassadors Join a global community of student ambassadors, this could be your way into Microsoft.

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 and complete the exercises on your own, starting with a pre-lecture quiz. Then read the lecture and complete 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 and go through the content together. For further study, we recommend Microsoft Learn.

Meet the Team

Gif by Mohit Jaisal

ð¥ Click the image above for a video about the project the folks who created it!

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 the Quiz-App folder, for 40 total quizzes of three questions each. They are linked from within the lessons, but the quiz app can be run locally or deployed to Azure; follow the instruction in the quiz-app folder. They are gradually being localized.

Lessons


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 itâs 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 âsee-quellâ).	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 ð¦	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

GitHub Codespaces

Follow these steps to open this sample in a Codespace:

Click the Code drop-down menu and select the Open with Codespaces option.
Select + New codespace at the bottom on the pane. For more info, check out the GitHub documentation.

VSCode Remote - Containers

Follow these steps to open this repo in a container using your local machine and VSCode using the VS Code Remote - Containers extension:

If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the getting started documentation.

To use this repository, you can either open the repository in an isolated Docker volume:

Note: Under the hood, this will use the Remote-Containers: Clone Repository in Container Volume... command to clone the source code in a Docker volume instead of the local filesystem. Volumes are the preferred mechanism for persisting container data.

Or open a locally cloned or downloaded version of the repository:

Clone this repository to your local filesystem.
Press F1 and select the Remote-Containers: Open Folder in Container... command.
Select the cloned copy of this folder, wait for the container to start, and try things out.

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.

Help Wanted!

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

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Our team produces other curricula! Check out:

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