{"id":17027297,"url":"https://github.com/ieshreya/data-science-resources","last_synced_at":"2025-10-03T18:54:27.432Z","repository":{"id":46528223,"uuid":"337489430","full_name":"ieshreya/Data-Science-Resources","owner":"ieshreya","description":"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!","archived":false,"fork":false,"pushed_at":"2021-10-06T11:02:37.000Z","size":70801,"stargazers_count":116,"open_issues_count":0,"forks_count":20,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-03-26T06:34:34.175Z","etag":null,"topics":["computer-science","data-science","python","resources"],"latest_commit_sha":null,"homepage":"https://ieshreya.github.io/Data-Science/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ieshreya.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-02-09T17:53:29.000Z","updated_at":"2025-02-06T16:11:41.000Z","dependencies_parsed_at":"2022-07-19T22:33:53.976Z","dependency_job_id":null,"html_url":"https://github.com/ieshreya/Data-Science-Resources","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ieshreya%2FData-Science-Resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ieshreya%2FData-Science-Resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ieshreya%2FData-Science-Resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ieshreya%2FData-Science-Resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ieshreya","download_url":"https://codeload.github.com/ieshreya/Data-Science-Resources/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248565003,"owners_count":21125413,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-science","data-science","python","resources"],"created_at":"2024-10-14T07:47:04.612Z","updated_at":"2025-10-03T18:54:22.391Z","avatar_url":"https://github.com/ieshreya.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Free Data Science Resources for Beginners\n```Repository Under Development```\n\nI'm currently learning Data Science. I build this repository for helping myself. But if it helps you anyhow, feel free to ⭐ it!\n\n---\n**Data Science Cheat Sheet**: [PDF here](https://github.com/ieshreya/Data-Science/blob/main/data/Data%20Science%20Cheat%20Sheet.pdf)\n\n---\n### Get Started With Python: Tutorials\n_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!_\n- Python 3 in one picture:  [Here](https://fossbytes.com/wp-content/uploads/2015/09/python-3-in-one-pic.png)\n- **Awesome Python**:  [Here](https://github.com/vinta/awesome-python)\n- **Jargon from the functional programming world in simple terms!**:  [Here](https://github.com/hemanth/functional-programming-jargon)\n- **Dive Into Python**:  [Here](http://www.diveintopython.net/index.html)\n- Learn Python Wiki on Reddit:  [Here](https://www.reddit.com/r/learnpython/wiki/index)\n- Learn 90% of Python in 90 Minutes:  [Here](https://www.slideshare.net/MattHarrison4/learn-90)\n- Highest Voted Python Questions:  [Here](http://stackoverflow.com/questions/tagged/python?sort=votes\u0026pageSize=50)\n- Python Basic Concepts:  [Here](https://github.com/gumption/Python_for_Data_Science/blob/master/3_Python_Basic_Concepts.ipynb)\n- Quick Reference to Python:  [Here](http://www.dataschool.io/python-quick-reference/)\n- The Elements of Python Style:  [Here](https://github.com/amontalenti/elements-of-python-style)\n- **What does the yield keyword do in Python?**:  [Here](http://stackoverflow.com/questions/231767/what-does-the-yield-keyword-do-in-python)\n- Parsing values from a JSON file in Python:  [Here](http://stackoverflow.com/questions/2835559/parsing-values-from-a-json-file-in-python)\n- **Python Quora FAQs**:  [Here](https://www.quora.com/topic/Python-programming-language-1)\n- time-complexity of various operations - list/dict - in current CPython:  [Here](https://wiki.python.org/moin/TimeComplexity)\n- Scripting in Python\n    - [Python Scripting Tutorial](http://www.dreamsyssoft.com/python-scripting-tutorial/intro-tutorial.php)\n    - [Scripting with Python](https://www.schrodinger.com//AcrobatFile.php?type=supportdocs\u0026type2=\u0026ident=404)\n    - [**Can I use Python as a bash replacement?**](http://stackoverflow.com/questions/209470/can-i-use-python-as-a-bash-replacement)\n\n---\n\n### Data Science with Python\n- **Data Science IPython Notebooks**:  [here](https://github.com/donnemartin/data-science-ipython-notebooks)\n- Awesome Python - Data Analysis:  [here](https://github.com/vinta/awesome-python#science-and-data-analysis)\n- Statistics\n  - Statistics and Data Science:  [here](https://github.com/svaksha/pythonidae/blob/master/Statistics.md)\n- **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)\n- Data Analysis and IPython Notebooks:  [here](https://github.com/kirang89/pycrumbs#data-analysis)\n- Python for Data Science: Basic Concepts:  [here](https://github.com/gumption/Python_for_Data_Science/blob/master/2_Data_Science_Basic_Concepts.ipynb)\n- Pycon India 2015 Notes:  [here](http://www.analyticsvidhya.com/blog/2015/10/notes-impressions-experience-excitement-pycon-india-2015/)\n- **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)\n- Data Exploration with Numpy cheat sheet:  [here](http://www.analyticsvidhya.com/blog/2015/07/11-steps-perform-data-analysis-pandas-python)\n- Querying Craiglist with Python:  [here](http://chrisholdgraf.com/querying-craigslist-with-python/?imm_mid=0d8940\u0026cmp=em-data-na-na-newsltr_20150916)\n- **An introduction to Numpy and Scipy**:  [here](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n- Create NBA Shot Charts:  [here](http://savvastjortjoglou.com/nba-shot-sharts.html)\n- PythoR- Python meets R:  [here](http://nipunbatra.github.io/2016/01/pythor/)\n- **How do I learn data analysis with Python?**:  [here](https://www.quora.com/How-do-I-learn-data-analysis-with-Python?redirected_qid=2464720)\n- 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)\n- **Which is better for data analysis: R or Python?**:  [here](https://www.quora.com/Which-is-better-for-data-analysis-R-or-Python)\n- **Web scraping in Python**:  [here](https://github.com/ujjwalkarn/Web-Scraping)\n- The Guide to Learning Python for Data Science:  [here](http://www.datasciencecentral.com/profiles/blogs/the-guide-to-learning-python-for-data-science-2)\n- Python For Data Science - A Cheat Sheet For Beginners:  [here](https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics)\n- Top voted Python data science questions:  [here](http://datascience.stackexchange.com/questions/tagged/python)\n- Awesome Python - Data Visualization:  [here](https://github.com/vinta/awesome-python#data-visualization)\n- Awesome Python - Map Reduce:  [here](https://github.com/vinta/awesome-python#mapreduce)\n\n---\n\n### Data Visualization\n\nCollection of the best libraries that I know for easy and powerful data visualizations.\n\n- [ggplot](http://ggplot.yhathq.com/) - ggplot for python ported by the team at yhat.\n- [matplotlib](http://matplotlib.org/) - Awesome plotting library for python.\n- [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.\n- [bokeh](http://bokeh.pydata.org/) - Interactive visualization library.\n- [d3py](https://github.com/mikedewar/d3py) - Another library for data viz.\n- [vincent](http://vincent.readthedocs.org/en/latest/) - Help with python for d3.\n- [seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/) - Clean statistical data visualization library.\n\nOther available Visualization Resources.\n\n- [Scott Murray's D3 Tutorials](alignedleft.com/tutorials/d3/) Tutorials from _Interactive Data Visualization for the Web_\n- [tributary.io](http://tributary.io) - live code visualization platform designed specifically for D3.js\n- [plot.ly](http://plot.ly) - A web visualization and data processing platform\n- [blockspring](http://blockspring.com) - Share code and visualizations through a single platform\n- [dot.append](http://enjalot.github.io/dot-append/) - Ian Johnson (enjalot) goes through several live-coding examples using D3\n- [Text Visualization Plots](#http://textvis.lnu.se/) - Interactive site with different types of text visualization for different problems.\n\n---\n\n### Data Structures and Algorithms\n* [Java Programming](https://java-programming.mooc.fi/)\n\n* [Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1)\n\n* [Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2)\n\n---\n\n### Databases\n[Database Management Essentials](https://www.coursera.org/learn/database-management)\n\n[Data Warehouse Concepts, Design, and Data Integration](https://www.coursera.org/learn/dwdesign)\n\n[Relational Database Support for Data Warehouses](https://www.coursera.org/learn/dwrelational)\n\n[Business Intelligence Concepts, Tools, and Applications](https://www.coursera.org/learn/business-intelligence-tools)\n\n[Design and Build a Data Warehouse for Business Intelligence Implementation](https://www.coursera.org/learn/data-warehouse-bi-building)\n\n[MongoDB for Developers Learning Path](https://university.mongodb.com/learning_paths/developer)\n\n---\n### Single Variable Calculus\n[Calculus 1A: Differentiation](https://www.edx.org/course/calculus-1a-differentiation-mitx-18-01-1x)\n\n[Calculus 1B: Integration](https://www.edx.org/course/calculus-1b-integration-mitx-18-01-2x)\n\n[Calculus 1C: Coordinate Systems \u0026 Infinite Series](https://www.edx.org/course/calculus-1c-coordinate-systems-infinite-mitx-18-01-3x)\n\n---\n### Linear Algebra\n[Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)\n\n[Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/)\n\n---\n### Multivariable Calculus\n[Multivariable Calculus](http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm)\n\n---\n### Statistics \u0026 Probability\n[Introduction to Probability](https://projects.iq.harvard.edu/stat110/home)\n\n[Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)\n\n[Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)\n\n---\n### Data Science Tools \u0026 Methods\n[Tools for Data Science](https://www.coursera.org/learn/open-source-tools-for-data-science)\n\n[Data Science Methodology](https://www.coursera.org/learn/data-science-methodology)\n\n[Data Science: Wrangling](https://www.edx.org/course/data-science-wrangling)\n\n---\n### Machine Learning/Data Mining\n[Machine Learning](https://www.coursera.org/learn/machine-learning)\n\n[Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)\n\n[Mining Massive Datasets](https://www.edx.org/course/mining-massive-datasets)\n\n[Process Mining](https://www.coursera.org/learn/process-mining)\n\n---\n### **Books That Can Help (pdfs)** 📗\n- R for Data Science (*Just Awesome!*): [Go here to read online (official website)](https://r4ds.had.co.nz/index.html)\n\n- Data Science: Theories, Models, Algorithms and Analytics: [here](https://github.com/ieshreya/Data-Science/blob/main/data/DSA_Book.pdf)\n\n- Data Science Cheat Sheet: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Data%20Science%20Cheat%20Sheet.pdf)\n\n- Fundamentals of Data Visualization: [here](https://github.com/ieshreya/Data-Science/blob/main/data/Fundamentals%20of%20Data%20Visualization.pdf)\n\n- 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)\n\n- 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)\n\n- 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)\n\n- 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)\n\n- Python Notes For Professionals: [here](https://github.com/ieshreya/Data-Science/blob/main/data/PythonNotesForProfessionals.pdf)\n\n- The Data Science Handbook: [here](https://github.com/ieshreya/Data-Science/blob/main/data/The%20Data%20Science%20Handbook.pdf)\n\n- Pratical Statistics For Data Scientists: [here](https://github.com/ieshreya/Data-Science/blob/main/data/pratical-statistics-for-data-scientists.pdf)\n\n- Mathematics, Probability and Statistics: [here](https://www.pdfdrive.com/mathematicsprobability-and-statisticsapplied-mathematics-e16657497.html)\n\n---\n### **Some Free Courses On Web** :octocat:\n1. Introduction to Docker (3 hours): [here](https://datastack.tv/docker-course.html)\n\n2. Scala at Light Speed (2 hours): [here](https://datastack.tv/scala-course.html)\n\n3. JavaScript for Data Science: [here](http://js4ds.org/)\n\n4. Learn Python (Codecademy):  [here](https://www.codecademy.com/learn/python#)\n5. Free Interactive Course: Intro to Python for Data Science (DataCamp):  [here](https://www.datacamp.com/courses/intro-to-python-for-data-science)\n6. Introduction to Computer Science and Programming Using Python (MIT):  [here](https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11)\n7. Python for Everybody:  [here](https://www.coursera.org/learn/python)\n8. Python Programming Essentials:  [here](https://www.coursera.org/learn/python-programming)\n9. Linear Algebra (OpenCourseWare): [here](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)\n10. Multivariable Calculus (OpenCourseWare): [here](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/)\n\n---\n### **Some Great Articles On Web** 🔖\n1. Becoming a Data Scientist – Curriculum via Metromap : [here](http://nirvacana.com/thoughts/2013/07/08/becoming-a-data-scientist/)\n2. 100+ Downloadable Free Machine Learning Books (*Must see*): [here](https://www.theinsaneapp.com/2020/12/download-free-machine-learning-books.html)\n3. Who is a Data Scientist? : [here](https://www.edureka.co/blog/who-is-a-data-scientist/)\n4. Building a Data Scientist Resume : [here](https://www.edureka.co/blog/data-scientist-resume/)\n5. Introduction to Data Visualization in Python : [here](https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed)\n6. An Intuitive Guide to Data Visualization in Python : [here](https://www.analyticsvidhya.com/blog/2021/02/an-intuitive-guide-to-visualization-in-python/)\n\n### **Some Blogs to Subscribe**\n1. Analytics India Magazine: [here](https://analyticsindiamag.com/)\n2. Data Quest: [here](https://www.dqindia.com/)\n3. Analytics Vidhya: [here](https://www.analyticsvidhya.com/)\n4. KDnuggets:[here](https://www.kdnuggets.com/topic/data-science)\n5. Data Science Central: [here](https://www.datasciencecentral.com/)\n6. SmartData Collective: [here](https://www.smartdatacollective.com/)\n7. What's The Big Data?: [here](https://whatsthebigdata.com/)\n8. No Free Hunch:[here](https://medium.com/kaggle-blog)\n---\n### **Some Awesome Repositories** 📌\n1. 100+ Books Collection Of Programming, Databases, Linux \u0026 Tools : [here](https://github.com/MrAlex6204/Books)\n\n2. Free Resources to get started with Data Science (*must see*): [here](https://github.com/therealsreehari/Learn-Data-Science-For-Free)\n\n3. 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)\n\n4. IPython notebooks for \"Python for Data Analysis\": [here](https://github.com/wesm/pydata-book)\n\n5. Python Machine Learning By Example(2nd Edition): [here](https://github.com/PacktPublishing/Python-Machine-Learning-By-Example-Second-Edition)\n\n6. Getting Started with TensorFlow: [here](https://github.com/PacktPublishing/Getting-Started-with-TensorFlow)\n\n7. Deep Learning with Keras: [here](https://github.com/PacktPublishing/Deep-Learning-with-Keras)\n\n8. Hands-On Data Science and Python Machine Learning: [here](https://github.com/PacktPublishing/Hands-On-Data-Science-and-Python-Machine-Learning)\n\n9. Learn Python by Building Data Science Applications: [here](https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications)\n\n10. **Data Engineer Roadmap**(Must see):[here](https://github.com/datastacktv/data-engineer-roadmap)\n\n11. JavaScript for Data Science: [here](https://github.com/software-tools-in-javascript/js4ds)\n\n12. *[MUST SEE]* Python Data Science Handbook: full text in Jupyter Notebooks: [here](https://github.com/shreyalive/PythonDataScienceHandbook)      \n                                                                               or read book online [here](http://jakevdp.github.io/PythonDataScienceHandbook)\n13. Data Science Specialization :  [here](https://github.com/DataScienceSpecialization/DataScienceSpecialization.github.io)                                                                              \n14. Data Science Projects Using Python and R : [here](https://github.com/WillKoehrsen/Data-Analysis)\n\n15. DataCamp data-science courses : [here](https://github.com/wblakecannon/DataCamp)\n\n---\n\n# Contributions by awesome learners\n\u003e This section includes all contributions submitted by data science learners out there - \n\n\n\u003c!--   Add your contributions below this comment. See the format below :\nContributed by: @username\n--- Add resources here ---\n--\u003e\n\nContributed by: [@braydenq](https://github.com/braydenq)\n### Data Science Courses:\n* [Coursera](https://www.coursera.org/specialization/jhudatascience/1) - Data Science Specialization at Coursera - many other courses available as well.\n* [Udacity](https://www.udacity.com/courses#!/data-science) - Online MOOCs that are the Data Science related courses. by I\n* [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.\n* [Coursera Machine Learning Course](https://www.coursera.org/course/ml) - Andrew Ng's pinnacle Machine Learning course.\n* [Edx](https://www.edx.org/course/mitx/mitx-6-00-2x-introduction-computational-2836#.VEANx9TF-tw) - EDX courses related to data science.\n\n\n```If you have some good resources, please contribute to this repository :)```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fieshreya%2Fdata-science-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fieshreya%2Fdata-science-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fieshreya%2Fdata-science-resources/lists"}