Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/ieshreya/data-science-resources

Free self-taught educational resources for Data Science! I'm currently learning Data Science. I build this repository for helping myself. But if it helps you anyhow, feel free to star it!
https://github.com/ieshreya/data-science-resources
computer-science data-science python resources
Last synced: 2 months ago
JSON representation
Free self-taught educational resources for Data Science! I'm currently learning Data Science. I build this repository for helping myself. But if it helps you anyhow, feel free to star it!
Host: GitHub
URL: https://github.com/ieshreya/data-science-resources
Owner: ieshreya
Created: 2021-02-09T17:53:29.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2021-10-06T11:02:37.000Z (about 3 years ago)
Last Synced: 2023-06-02T08:25:39.393Z (over 1 year ago)
Topics: computer-science, data-science, python, resources
Homepage: https://ieshreya.github.io/Data-Science/
Size: 67.5 MB
Stars: 97
Watchers: 6
Forks: 14
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        # Free Data Science Resources for Beginners

```Repository Under Development```

I'm currently learning Data Science. I build this repository for helping myself. But if it helps you anyhow, feel free to ⭐ it!

---

**Data Science Cheat Sheet**: [PDF here](https://github.com/ieshreya/Data-Science/blob/main/data/Data%20Science%20Cheat%20Sheet.pdf)

---

### Get Started With Python: Tutorials

_Python is one of the best language used by data scientist for various data science projects/application. If you're new to programming, here are some awesome free resources to get started in your Python Journey!_

- Python 3 in one picture:  [Here](https://fossbytes.com/wp-content/uploads/2015/09/python-3-in-one-pic.png)

- **Awesome Python**:  [Here](https://github.com/vinta/awesome-python)

- **Jargon from the functional programming world in simple terms!**:  [Here](https://github.com/hemanth/functional-programming-jargon)

- **Dive Into Python**:  [Here](http://www.diveintopython.net/index.html)

- Learn Python Wiki on Reddit:  [Here](https://www.reddit.com/r/learnpython/wiki/index)

- Learn 90% of Python in 90 Minutes:  [Here](https://www.slideshare.net/MattHarrison4/learn-90)

- Highest Voted Python Questions:  [Here](http://stackoverflow.com/questions/tagged/python?sort=votes&pageSize=50)

- Python Basic Concepts:  [Here](https://github.com/gumption/Python_for_Data_Science/blob/master/3_Python_Basic_Concepts.ipynb)

- Quick Reference to Python:  [Here](http://www.dataschool.io/python-quick-reference/)

- The Elements of Python Style:  [Here](https://github.com/amontalenti/elements-of-python-style)

- **What does the yield keyword do in Python?**:  [Here](http://stackoverflow.com/questions/231767/what-does-the-yield-keyword-do-in-python)

- Parsing values from a JSON file in Python:  [Here](http://stackoverflow.com/questions/2835559/parsing-values-from-a-json-file-in-python)

- **Python Quora FAQs**:  [Here](https://www.quora.com/topic/Python-programming-language-1)

- time-complexity of various operations - list/dict - in current CPython:  [Here](https://wiki.python.org/moin/TimeComplexity)

- Scripting in Python

    - [Python Scripting Tutorial](http://www.dreamsyssoft.com/python-scripting-tutorial/intro-tutorial.php)

    - [Scripting with Python](https://www.schrodinger.com//AcrobatFile.php?type=supportdocs&type2=&ident=404)

    - [**Can I use Python as a bash replacement?**](http://stackoverflow.com/questions/209470/can-i-use-python-as-a-bash-replacement)

---

### Data Science with Python

- **Data Science IPython Notebooks**:  [here](https://github.com/donnemartin/data-science-ipython-notebooks)

- Awesome Python - Data Analysis:  [here](https://github.com/vinta/awesome-python#science-and-data-analysis)

- Statistics

  - Statistics and Data Science:  [here](https://github.com/svaksha/pythonidae/blob/master/Statistics.md)

- **An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy**:  [here](http://www.kdnuggets.com/2016/06/intro-scientific-python-numpy.html)

- Data Analysis and IPython Notebooks:  [here](https://github.com/kirang89/pycrumbs#data-analysis)

- Python for Data Science: Basic Concepts:  [here](https://github.com/gumption/Python_for_Data_Science/blob/master/2_Data_Science_Basic_Concepts.ipynb)

- Pycon India 2015 Notes:  [here](http://www.analyticsvidhya.com/blog/2015/10/notes-impressions-experience-excitement-pycon-india-2015/)

- **5 important Python Data Science advancements of 2015**:  [here](https://medium.com/@elgehelge/the-5-most-important-python-data-science-advancements-of-2015-a136482da89b#.sp2c1la9z)

- Data Exploration with Numpy cheat sheet:  [here](http://www.analyticsvidhya.com/blog/2015/07/11-steps-perform-data-analysis-pandas-python)

- Querying Craiglist with Python:  [here](http://chrisholdgraf.com/querying-craigslist-with-python/?imm_mid=0d8940&cmp=em-data-na-na-newsltr_20150916)

- **An introduction to Numpy and Scipy**:  [here](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)

- Create NBA Shot Charts:  [here](http://savvastjortjoglou.com/nba-shot-sharts.html)

- PythoR- Python meets R:  [here](http://nipunbatra.github.io/2016/01/pythor/)

- **How do I learn data analysis with Python?**:  [here](https://www.quora.com/How-do-I-learn-data-analysis-with-Python?redirected_qid=2464720)

- What are some interesting things to do with Python?:  [here](https://www.quora.com/Python-programming-language-What-are-some-interesting-things-to-do-with-Python?redirected_qid=2324227)

- **Which is better for data analysis: R or Python?**:  [here](https://www.quora.com/Which-is-better-for-data-analysis-R-or-Python)

- **Web scraping in Python**:  [here](https://github.com/ujjwalkarn/Web-Scraping)

- The Guide to Learning Python for Data Science:  [here](http://www.datasciencecentral.com/profiles/blogs/the-guide-to-learning-python-for-data-science-2)

- Python For Data Science - A Cheat Sheet For Beginners:  [here](https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics)

- Top voted Python data science questions:  [here](http://datascience.stackexchange.com/questions/tagged/python)

- Awesome Python - Data Visualization:  [here](https://github.com/vinta/awesome-python#data-visualization)

- Awesome Python - Map Reduce:  [here](https://github.com/vinta/awesome-python#mapreduce)

---

### Data Visualization

Collection of the best libraries that I know for easy and powerful data visualizations.

- [ggplot](http://ggplot.yhathq.com/) - ggplot for python ported by the team at yhat.

- [matplotlib](http://matplotlib.org/) - Awesome plotting library for python.

- [d3](http://d3js.org/) - Mike Bostock's viz library - the de facto gold standard for polished visualization - in js, steep learning curve but beautiful outcomes.

- [bokeh](http://bokeh.pydata.org/) - Interactive visualization library.

- [d3py](https://github.com/mikedewar/d3py) - Another library for data viz.

- [vincent](http://vincent.readthedocs.org/en/latest/) - Help with python for d3.

- [seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/) - Clean statistical data visualization library.

Other available Visualization Resources.

- [Scott Murray's D3 Tutorials](alignedleft.com/tutorials/d3/) Tutorials from _Interactive Data Visualization for the Web_

- [tributary.io](http://tributary.io) - live code visualization platform designed specifically for D3.js

- [plot.ly](http://plot.ly) - A web visualization and data processing platform

- [blockspring](http://blockspring.com) - Share code and visualizations through a single platform

- [dot.append](http://enjalot.github.io/dot-append/) - Ian Johnson (enjalot) goes through several live-coding examples using D3

- [Text Visualization Plots](#http://textvis.lnu.se/) - Interactive site with different types of text visualization for different problems.

---

### Data Structures and Algorithms

* [Java Programming](https://java-programming.mooc.fi/)

* [Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1)

* [Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2)

---

### Databases

[Database Management Essentials](https://www.coursera.org/learn/database-management)

[Data Warehouse Concepts, Design, and Data Integration](https://www.coursera.org/learn/dwdesign)

[Relational Database Support for Data Warehouses](https://www.coursera.org/learn/dwrelational)

[Business Intelligence Concepts, Tools, and Applications](https://www.coursera.org/learn/business-intelligence-tools)

[Design and Build a Data Warehouse for Business Intelligence Implementation](https://www.coursera.org/learn/data-warehouse-bi-building)

[MongoDB for Developers Learning Path](https://university.mongodb.com/learning_paths/developer)

---

### Single Variable Calculus

[Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x)

[Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x)

[Calculus 1C: Coordinate Systems & Infinite Series](https://www.edx.org/course/calculus-1c-coordinate-systems-infinite-mitx-18-01-3x)

---

### Linear Algebra

[Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

[Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/)

---

### Multivariable Calculus

[Multivariable Calculus](http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm)

---

### Statistics & Probability

[Introduction to Probability](https://projects.iq.harvard.edu/stat110/home)

[Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)

[Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)

---

### Data Science Tools & Methods

[Tools for Data Science](https://www.coursera.org/learn/open-source-tools-for-data-science)

[Data Science Methodology](https://www.coursera.org/learn/data-science-methodology)

[Data Science: Wrangling](https://www.edx.org/course/data-science-wrangling)

---

### Machine Learning/Data Mining

[Machine Learning](https://www.coursera.org/learn/machine-learning)

[Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)

[Mining Massive Datasets](https://www.edx.org/course/mining-massive-datasets)

[Process Mining](https://www.coursera.org/learn/process-mining)

---

### **Books That Can Help (pdfs)** 📗

- R for Data Science (*Just Awesome!*): [Go here to read online (official website)](https://r4ds.had.co.nz/index.html)

- Data Science: Theories, Models, Algorithms and Analytics: [here](https://github.com/ieshreya/Data-Science/blob/main/data/DSA_Book.pdf)

- Data Science Cheat Sheet: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Data%20Science%20Cheat%20Sheet.pdf)

- Fundamentals of Data Visualization: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Fundamentals%20of%20Data%20Visualization.pdf)

- Learning Python OOP 5th Edition O'reilly: [here](https://github.com/ieshreya/Data-Science/blob/main/data/LEARNING%20PYTHON%20%20POWERFUL%20OBJECT-ORIENTED%20PROGRAMMING%2C%205TH%20EDITION-OREILLY%20(%20PDFDrive%20).pdf)

- Naked Statistics_ Stripping the Dread from the Data: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Naked%20Statistics_%20Stripping%20the%20Dread%20from%20the%20Data%20(%20PDFDrive%20).pdf)

- Practical Data Science with R - Nina Zumel John Mount: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Practical%20Data%20Science%20with%20R%20-%20Nina%20Zumel%20John%20Mount.pdf)

- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017, O’Reilly): [here](https://github.com/ieshreya/Data-Science/blob/main/data/Python%20for%20Data%20Analysis.%20Data%20Wrangling%20with%20Pandas%2C%20NumPy%2C%20and%20IPython%20(2017%2C%20O%E2%80%99Reilly).pdf)

- Python Notes For Professionals: [here](https://github.com/ieshreya/Data-Science/blob/main/data/PythonNotesForProfessionals.pdf)

- The Data Science Handbook: [here](https://github.com/ieshreya/Data-Science/blob/main/data/The%20Data%20Science%20Handbook.pdf)

- Pratical Statistics For Data Scientists: [here](https://github.com/ieshreya/Data-Science/blob/main/data/pratical-statistics-for-data-scientists.pdf)

- Mathematics, Probability and Statistics: [here](https://www.pdfdrive.com/mathematicsprobability-and-statisticsapplied-mathematics-e16657497.html)

---

### **Some Free Courses On Web** :octocat:

1. Introduction to Docker (3 hours): [here](https://datastack.tv/docker-course.html)

2. Scala at Light Speed (2 hours): [here](https://datastack.tv/scala-course.html)

3. JavaScript for Data Science: [here](http://js4ds.org/)

4. Learn Python (Codecademy):  [here](https://www.codecademy.com/learn/python#)

5. Free Interactive Course: Intro to Python for Data Science (DataCamp):  [here](https://www.datacamp.com/courses/intro-to-python-for-data-science)

6. Introduction to Computer Science and Programming Using Python (MIT):  [here](https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11)

7. Python for Everybody:  [here](https://www.coursera.org/learn/python)

8. Python Programming Essentials:  [here](https://www.coursera.org/learn/python-programming)

9. Linear Algebra (OpenCourseWare): [here](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)

10. Multivariable Calculus (OpenCourseWare): [here](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/)

---

### **Some Great Articles On Web** 🔖

1. Becoming a Data Scientist – Curriculum via Metromap : [here](http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/)

2. 100+ Downloadable Free Machine Learning Books (*Must see*): [here](https://www.theinsaneapp.com/2020/12/download-free-machine-learning-books.html)

3. Who is a Data Scientist? : [here](https://www.edureka.co/blog/who-is-a-data-scientist/)

4. Building a Data Scientist Resume : [here](https://www.edureka.co/blog/data-scientist-resume/)

5. Introduction to Data Visualization in Python : [here](https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed)

6. An Intuitive Guide to Data Visualization in Python : [here](https://www.analyticsvidhya.com/blog/2021/02/an-intuitive-guide-to-visualization-in-python/)

### **Some Blogs to Subscribe**

1. Analytics India Magazine: [here](https://analyticsindiamag.com/)

2. Data Quest: [here](https://www.dqindia.com/)

3. Analytics Vidhya: [here](https://www.analyticsvidhya.com/)

4. KDnuggets:[here](https://www.kdnuggets.com/topic/data-science)

5. Data Science Central: [here](https://www.datasciencecentral.com/)

6. SmartData Collective: [here](https://www.smartdatacollective.com/)

7. What's The Big Data?: [here](https://whatsthebigdata.com/)

8. No Free Hunch:[here](https://medium.com/kaggle-blog)

---

### **Some Awesome Repositories** 📌

1. 100+ Books Collection Of Programming, Databases, Linux & Tools : [here](https://github.com/MrAlex6204/Books)

2. Free Resources to get started with Data Science (*must see*): [here](https://github.com/therealsreehari/Learn-Data-Science-For-Free)

3. 500 + Artificial Intelligence Projects: [here](https://github.com/therealsreehari/Learn-Data-Science-For-Free#500--%F0%9D%97%94%F0%9D%97%BF%F0%9D%98%81%F0%9D%97%B6%F0%9D%97%B3%F0%9D%97%B6%F0%9D%97%B0%F0%9D%97%B6%F0%9D%97%AE%F0%9D%97%B9-%F0%9D%97%9C%F0%9D%97%BB%F0%9D%98%81%F0%9D%97%B2%F0%9D%97%B9%F0%9D%97%B9%F0%9D%97%B6%F0%9D%97%B4%F0%9D%97%B2%F0%9D%97%BB%F0%9D%97%B0%F0%9D%97%B2-%F0%9D%97%A3%F0%9D%97%BF%F0%9D%97%BC%F0%9D%97%B7%F0%9D%97%B2%F0%9D%97%B0%F0%9D%98%81-%F0%9D%97%9F%F0%9D%97%B6%F0%9D%98%80%F0%9D%98%81-%F0%9D%98%84%F0%9D%97%B6%F0%9D%98%81%F0%9D%97%B5-%F0%9D%97%B0%F0%9D%97%BC%F0%9D%97%B1%F0%9D%97%B2)

4. IPython notebooks for "Python for Data Analysis": [here](https://github.com/wesm/pydata-book)

5. Python Machine Learning By Example(2nd Edition): [here](https://github.com/PacktPublishing/Python-Machine-Learning-By-Example-Second-Edition)

6. Getting Started with TensorFlow: [here](https://github.com/PacktPublishing/Getting-Started-with-TensorFlow)

7. Deep Learning with Keras: [here](https://github.com/PacktPublishing/Deep-Learning-with-Keras)

8. Hands-On Data Science and Python Machine Learning: [here](https://github.com/PacktPublishing/Hands-On-Data-Science-and-Python-Machine-Learning)

9. Learn Python by Building Data Science Applications: [here](https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications)

10. **Data Engineer Roadmap**(Must see):[here](https://github.com/datastacktv/data-engineer-roadmap)

11. JavaScript for Data Science: [here](https://github.com/software-tools-in-javascript/js4ds)

12. *[MUST SEE]* Python Data Science Handbook: full text in Jupyter Notebooks: [here](https://github.com/shreyalive/PythonDataScienceHandbook)      

                                                                               or read book online [here](http://jakevdp.github.io/PythonDataScienceHandbook)

13. Data Science Specialization :  [here](https://github.com/DataScienceSpecialization/DataScienceSpecialization.github.io)                                                                              

14. Data Science Projects Using Python and R : [here](https://github.com/WillKoehrsen/Data-Analysis)

15. DataCamp data-science courses : [here](https://github.com/wblakecannon/DataCamp)

---

# Contributions by awesome learners

> This section includes all contributions submitted by data science learners out there - 

Contributed by: [@braydenq](https://github.com/braydenq)

### Data Science Courses:

* [Coursera](https://www.coursera.org/specialization/jhudatascience/1) - Data Science Specialization at Coursera - many other courses available as well.

* [Udacity](https://www.udacity.com/courses#!/data-science) - Online MOOCs that are the Data Science related courses. by I

* [Data Science Bootcamps](http://yet-another-data-blog.blogspot.com/2014/04/data-science-bootcamp-landscape-full.html) - A collection of all bootcamps currently on the market as of April 5, 2014 by Ikechukwu Okonkwo.

* [Coursera Machine Learning Course](https://www.coursera.org/course/ml) - Andrew Ng's pinnacle Machine Learning course.

* [Edx](https://www.edx.org/course/mitx/mitx-6-00-2x-introduction-computational-2836#.VEANx9TF-tw) - EDX courses related to data science.

```If you have some good resources, please contribute to this repository :)```