{"id":18926634,"url":"https://github.com/durgeshsamariya/data-science-roadmap","last_synced_at":"2025-10-17T01:44:23.300Z","repository":{"id":47605425,"uuid":"330666818","full_name":"durgeshsamariya/data-science-roadmap","owner":"durgeshsamariya","description":"Roadmap to learn Data Science and related areas.","archived":false,"fork":false,"pushed_at":"2023-03-29T11:41:12.000Z","size":15,"stargazers_count":268,"open_issues_count":3,"forks_count":47,"subscribers_count":16,"default_branch":"main","last_synced_at":"2024-12-31T18:42:08.610Z","etag":null,"topics":["data-science","data-science-resources","learn-data-science","roadmap"],"latest_commit_sha":null,"homepage":"","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/durgeshsamariya.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":"2021-01-18T12:58:17.000Z","updated_at":"2024-11-15T19:03:00.000Z","dependencies_parsed_at":"2022-08-12T13:50:09.661Z","dependency_job_id":null,"html_url":"https://github.com/durgeshsamariya/data-science-roadmap","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/durgeshsamariya%2Fdata-science-roadmap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/durgeshsamariya%2Fdata-science-roadmap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/durgeshsamariya%2Fdata-science-roadmap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/durgeshsamariya%2Fdata-science-roadmap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/durgeshsamariya","download_url":"https://codeload.github.com/durgeshsamariya/data-science-roadmap/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239922622,"owners_count":19718988,"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":["data-science","data-science-resources","learn-data-science","roadmap"],"created_at":"2024-11-08T11:16:50.636Z","updated_at":"2025-10-17T01:44:18.264Z","avatar_url":"https://github.com/durgeshsamariya.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Science Roadmap\n\nSelf learning **Data Science** curriculum.\n\n### About\nThis repository intendend to provide a complete **Data Science** learning path to those who intersted in learning Data Science. In this repository, I gave preference to free resource. However, some valuable paid courses also included.\n\n### Explanation\n- :tv: Video content.\n- :dollar: Paid content.\n- :newspaper: Online article.\n- :file_folder: GitHub repo.\n\n### Content\n- [Statistics \u0026 Probability](#statistics-\u0026-probability)\n    - [Descriptive Statistics](#descriptive-statistics)\n    - [Probability](#probability)\n    - [Combinations and Permutations](#combinations-and-permutations)\n    - [Distributions](#distributions)\n    - [Confidence Intervals](#confidence-intervals)\n    - [Hypothesis](#hypothesis)\n- [Linear Algebra](#linear-algebra)\n    - [Vectors and Spaces](#vectors-and-spaces)\n    - [Dot Product](#dot-product)\n    - [Matrix Transformations](#matrix-transformations)\n    - [Eigenvalues and Eigenvectors](#eigenvalues-and-eigenvectors)\n    - [Integrals](#integrals)\n- [Python Programming](#python-programming)\n- [Numpy](#numpy)\n- [Pandas](#pandas)\n- [To-Do](#to-do)\n- [Contribution guideline](#contribution-guideline)\n\n# Statistics \u0026 Probability\n\n### Descriptive Statistics\n- Intro to Descriptive Statistics [Article 1](https://towardsdatascience.com/descriptive-statistics-f2beeaf7a8df) **or** [Article 2](https://towardsdatascience.com/intro-to-descriptive-statistics-252e9c464ac9)\n\n### Probability\n- :tv: [Theoretical probability](https://www.khanacademy.org/math/statistics-probability/probability-library/basic-theoretical-probability/v/basic-probability)\n- :tv: [Sample spaces](https://www.khanacademy.org/math/statistics-probability/probability-library/probability-sample-spaces/v/events-and-outcomes-3)\n- :tv: [Set operations](https://www.khanacademy.org/math/statistics-probability/probability-library/basic-set-ops/v/intersection-and-union-of-sets)\n- :tv: [Addition rule](https://www.khanacademy.org/math/statistics-probability/probability-library/addition-rule-lib/v/probability-with-playing-cards-and-venn-diagrams)\n- :tv: [Multiplication rule for independent events](https://www.khanacademy.org/math/statistics-probability/probability-library/multiplication-rule-independent/v/compound-sample-spaces)\n- :tv: [Multiplication rule for dependent events](https://www.khanacademy.org/math/statistics-probability/probability-library/multiplication-rule-dependent/v/introduction-to-dependent-probability)\n- :tv: [Conditional probability and independence](https://www.khanacademy.org/math/statistics-probability/probability-library/conditional-probability-independence/v/calculating-conditional-probability)\n\n### Combinations and Permutations\n- :tv: [Counting principle and factorial](https://www.khanacademy.org/math/statistics-probability/counting-permutations-and-combinations/counting-principle-factorial/v/tree-diagram-to-count-outcomes)\n- :tv: [Permutations](https://www.khanacademy.org/math/statistics-probability/counting-permutations-and-combinations/permutation-lib/v/permutation-formula)\n- :tv: [Combinations](https://www.khanacademy.org/math/statistics-probability/counting-permutations-and-combinations/combinations-lib/v/introduction-to-combinations)\n\n### Distributions\n- :tv: [Normal distribution and the Empirical rule](https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/normal-distributions-library/v/ck12-org-normal-distribution-problems-qualitative-sense-of-normal-distributions)\n- :tv: [Introduction to Sampling Distributions](https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library/what-is-a-sampling-distribution/v/introduction-to-sampling-distributions)\n- :tv: [Sampling distribution of a sample proportion](https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library/sample-proportions/v/sampling-distribution-of-sample-proportion-part-1)\n- :tv: [Sampling distribution of a sample mean](https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library/sample-means/v/statistics-sample-vs-population-mean)\n\n### Confidence Intervals\n- :tv: [Confidence Intervals](https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample/introduction-to-confidence-intervals/v/confidence-intervals-and-margin-of-error)\n- :tv: [Estimating Sample Proportions](https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample/estimating-population-proportion/v/confidence-interval-example)\n- :tv: [Estimating Sample Means](https://www.khanacademy.org/math/statistics-probability/confidence-intervals-one-sample/estimating-population-mean/v/introduction-to-t-statistics)\n\n### Hypothesis\n- :tv: [Hypothesis Testing](https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/idea-of-significance-tests/v/simple-hypothesis-testing)\n- :tv: [Error probabilities and power](https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/error-probabilities-and-power/v/introduction-to-type-i-and-type-ii-errors)\n- :tv: [Tests about a population proportion](https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/tests-about-population-proportion/v/constructing-hypotheses-for-a-significance-test)\n- :tv: [Tests about a population mean](https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/tests-about-population-mean/v/writing-hypotheses-for-significance-test-about-means)\n\n# Linear Algebra\n\n### Vectors and Spaces\n- :tv: [Vectors](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/vectors/v/vector-introduction-linear-algebra)\n- :tv: [Linear Combinations and Spans](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/linear-combinations/v/linear-combinations-and-span)\n- :tv: [Linear Dependence and Independence](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/linear-independence/v/linear-algebra-introduction-to-linear-independence)\n- :tv: [Subspaces and the basis for a subspace](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/subspace-basis/v/linear-subspaces)\n\n### Dot Product\n- :tv: [Vector dot and cross products](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/dot-cross-products/v/vector-dot-product-and-vector-length)\n\n### Matrix Transformations\n- :tv: [Functions and Linear Transformations](https://www.khanacademy.org/math/linear-algebra/matrix-transformations/linear-transformations/v/a-more-formal-understanding-of-functions)\n- :tv: [Transformations and Matrix Multiplications](https://www.khanacademy.org/math/linear-algebra/matrix-transformations/composition-of-transformations/v/compositions-of-linear-transformations-1)\n- :tv: [Inverse Functions and Transformations](https://www.khanacademy.org/math/linear-algebra/matrix-transformations/inverse-transformations/v/linear-algebra-introduction-to-the-inverse-of-a-function)\n- :tv: [Inverses and Determinants](https://www.khanacademy.org/math/linear-algebra/matrix-transformations/inverse-of-matrices/v/linear-algebra-deriving-a-method-for-determining-inverses)\n- :tv: [Transpose of a Matrix](https://www.khanacademy.org/math/linear-algebra/matrix-transformations/matrix-transpose/v/linear-algebra-transpose-of-a-matrix)\n\n### Eigenvalues and Eigenvectors\n- :tv: [Eigenvalues and Eigenvectors](https://www.mathsisfun.com/algebra/eigenvalue.html)\n\n### Integrals\n- :tv: [Approximation with Riemann Sums](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-riemann-sums/v/simple-riemann-approximation-using-rectangles)\n- :tv: [Definite Integrals with Riemann Sums](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-definite-integral-definition/v/riemann-sums-and-integrals)\n- :tv: [The Fundamental Theorem of Calculus and Accumulation Functions](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-ftc-part-1/v/fundamental-theorem-of-calculus)\n- :tv: [Properties of Definite Integrals](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-integral-prop/v/negative-definite-integrals)\n- :tv: [The Fundamental Theorem of Calculus and Definite Integrals](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-ftc-part-2/v/connecting-the-first-and-second-fundamental-theorems-of-calculus)\n- :tv: [Reverse Power Rule](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-reverse-power-rule/v/indefinite-integrals-of-x-raised-to-a-power)\n- :tv: [Indefinite Integrals of Common Functions](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-common-indefinite-integrals/v/antiderivative-of-x-1)\n- :tv: [Definite Integrals of Common Functions](https://www.khanacademy.org/math/integral-calculus/ic-integration/ic-common-definite-integrals/v/reverse-power-rule-for-definite-integrals)\n\n# Python Programming\n- :newspaper: [Learn Python Tutorial](https://www.learnpython.org)\n### Basics\n- :newspaper: [Hello, World!](https://www.learnpython.org/en/Hello%2C_World%21)\n- :newspaper: [Variables and Types](https://www.learnpython.org/en/Variables_and_Types)\n- :newspaper: [Lists](https://www.learnpython.org/en/Lists)\n- :newspaper: [Basic Operators](https://www.learnpython.org/en/Basic_Operators)\n- :newspaper: [String Formatting](https://www.learnpython.org/en/String_Formatting)\n- :newspaper: [Basic String Operations](https://www.learnpython.org/en/Basic_String_Operations)\n- :newspaper: [Conditions](https://www.learnpython.org/en/Conditions)\n- :newspaper: [Loops](https://www.learnpython.org/en/Loops)\n- :newspaper: [Functions](https://www.learnpython.org/en/Functions)\n- :newspaper: [Classes and Objects](https://www.learnpython.org/en/Classes_and_Objects)\n- :newspaper: [Dictionaries](https://www.learnpython.org/en/Dictionaries)\n- :newspaper: [Modules and Packages](https://www.learnpython.org/en/Modules_and_Packages)\n\n### Advanced\n- :newspaper: [Generators](https://www.learnpython.org/en/Generators)\n- :newspaper: [List Comprehensions](https://www.learnpython.org/en/List_Comprehensions)\n- :newspaper: [Multiple Function Arguments](https://www.learnpython.org/en/Multiple_Function_Arguments)\n- :newspaper: [Regular Expressions](https://www.learnpython.org/en/Regular_Expressions)\n- :newspaper: [Exception Handling](https://www.learnpython.org/en/Exception_Handling)\n- :newspaper: [Sets](https://www.learnpython.org/en/Sets)\n- :newspaper: [Serialization](https://www.learnpython.org/en/Serialization)\n- :newspaper: [Partial functions](https://www.learnpython.org/en/Partial_functions)\n- :newspaper: [Code Introspection](https://www.learnpython.org/en/Code_Introspection)\n- :newspaper: [Closures](https://www.learnpython.org/en/Closures)\n- :newspaper: [Decorators](https://www.learnpython.org/en/Decorators)\n- :newspaper: [Map, Filter, Reduce](https://www.learnpython.org/en/Map%2C_Filter%2C_Reduce)\n\n### More Resources\n- :newspaper: [Python 3 Tutorial](https://www.tutorialspoint.com/python3/)\n- :tv: Introduction to Python [Video 1](https://www.youtube.com/watch?v=_uQrJ0TkZlc\u0026t=86s) **or** [Video 2](https://www.youtube.com/watch?v=rfscVS0vtbw)\n\n# Numpy\n\n### Basics\n- :newspaper: [An example](https://numpy.org/doc/stable/user/quickstart.html#an-example)\n- :newspaper: [Array Creation](https://numpy.org/doc/stable/user/quickstart.html#array-creation)\n- :newspaper: [Printing Arrays](https://numpy.org/doc/stable/user/quickstart.html#printing-arrays)\n- :newspaper: [Basic Operations](https://numpy.org/doc/stable/user/quickstart.html#basic-operations)\n- :newspaper: [Universal Functions](https://numpy.org/doc/stable/user/quickstart.html#universal-functions)\n- :newspaper: [Indexing, Slicing and Iterating](https://numpy.org/doc/stable/user/quickstart.html#indexing-slicing-and-iterating)\n\n### Shape Manipulation\n- :newspaper: [Changing the shape of an array](https://numpy.org/doc/stable/user/quickstart.html#changing-the-shape-of-an-array)\n- :newspaper: [Stacking together different arrays](https://numpy.org/doc/stable/user/quickstart.html#stacking-together-different-arrays)\n- :newspaper: [Splitting one array into several smaller ones](https://numpy.org/doc/stable/user/quickstart.html#splitting-one-array-into-several-smaller-ones)\n\n### Copies and Views\n- :newspaper: [No Copy at All](https://numpy.org/doc/stable/user/quickstart.html#no-copy-at-all)\n- :newspaper: [View or Shallow Copy](https://numpy.org/doc/stable/user/quickstart.html#view-or-shallow-copy)\n- :newspaper: [Deep Copy](https://numpy.org/doc/stable/user/quickstart.html#deep-copy)\n- :newspaper: [Functions and Methods Overview](https://numpy.org/doc/stable/user/quickstart.html#functions-and-methods-overview)\n\n### Less Basic\n- :newspaper: [Broadcasting rules](https://numpy.org/doc/stable/user/quickstart.html#broadcasting-rules)\n\n### Advanced indexing and index tricks\n- :newspaper: [Indexing with Arrays of Indices](https://numpy.org/doc/stable/user/quickstart.html#indexing-with-arrays-of-indices)\n- :newspaper: [Indexing with Boolean Arrays](https://numpy.org/doc/stable/user/quickstart.html#indexing-with-boolean-arrays)\n- :newspaper: [The ix_() function](https://numpy.org/doc/stable/user/quickstart.html#the-ix-function)\n- :newspaper: [Indexing with strings](https://numpy.org/doc/stable/user/quickstart.html#indexing-with-strings)\n\n### Linear Algebra\n- :newspaper: [Simple Array Operations](https://numpy.org/doc/stable/user/quickstart.html#simple-array-operations)\n\n### More Resources\n- :newspaper: [NumPy: the absolute basics for beginners](https://numpy.org/doc/stable/user/absolute_beginners.html)\n- :newspaper: [NumPy Tutorial](https://www.tutorialspoint.com/numpy/)\n- :newspaper: [The Ultimate Beginner’s Guide to NumPy](https://towardsdatascience.com/the-ultimate-beginners-guide-to-numpy-f5a2f99aef54)\n- :newspaper: [The Ultimate NumPy Tutorial for Data Science Beginners](https://www.analyticsvidhya.com/blog/2020/04/the-ultimate-numpy-tutorial-for-data-science-beginners/)\n- :newspaper: [NumPy Tutorial: Your First Steps Into Data Science in Python](https://realpython.com/numpy-tutorial/)\n- :newspaper: [101 NumPy Exercises for Data Analysis (Python)](https://www.machinelearningplus.com/python/101-numpy-exercises-python/)\n- :tv: [Complete Python NumPy Tutorial](https://www.youtube.com/watch?v=GB9ByFAIAH4\u0026t=210s)\n- :file_folder: [Python Numpy Tutorial (with Jupyter and Colab)](https://cs231n.github.io/python-numpy-tutorial/)\n- :file_folder: [100 numpy exercises](https://github.com/rougier/numpy-100)\n\n# Pandas\n- :newspaper: [10 minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html)\n- :newspaper: [Intro to data structures](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html)\n- :newspaper: [Essential basic functionality](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html)\n- :newspaper: [IO tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)\n- :newspaper: [Indexing and selecting data](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)\n- :newspaper: [MultiIndex / advanced indexing](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html)\n- :newspaper: [Merge, join, concatenate and compare](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)\n- :newspaper: [Reshaping and pivot tables](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html)\n- :newspaper: [Working with text data](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html)\n- :newspaper: [Duplicate Labels](https://pandas.pydata.org/pandas-docs/stable/user_guide/duplicates.html)\n- :newspaper: [Categorical data](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html)\n- :newspaper: [Nullable integer data type](https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html)\n- :newspaper: [Nullable Boolean data type](https://pandas.pydata.org/pandas-docs/stable/user_guide/boolean.html)\n- :newspaper: [Visualization using pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)\n- :newspaper: [Computational tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html)\n- :newspaper: [Group by: split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html)\n- :newspaper: [Windowing Operations](https://pandas.pydata.org/pandas-docs/stable/user_guide/window.html)\n- :newspaper: [Time series / date functionality](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html)\n- :newspaper: [Time deltas](https://pandas.pydata.org/pandas-docs/stable/user_guide/timedeltas.html)\n- :newspaper: [Styling](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)\n- :newspaper: [Options and settings](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html)\n- :newspaper: [Cookbook](https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html)\n\n### More Resources\n- :newspaper: [Learn Pandas Tutorials | Kaggle](https://www.kaggle.com/learn/pandas)\n- :tv: [Python Pandas Tutorial](https://www.youtube.com/watch?v=jgrE0IlsZ0M)\n- :tv: [Complete Python Pandas Data Science Tutorial](https://www.youtube.com/watch?v=vmEHCJofslg)\n- :newspaper: [101 Pandas Exercises for Data Analysis](https://www.machinelearningplus.com/python/101-pandas-exercises-python/)\n- :file_folder: [pandas_exercises](https://github.com/guipsamora/pandas_exercises)\n\n# Matplotlib\n\n### Matplotlib Official Tutorials\n- :newspaper: [Sample plots in Matplotlib](https://matplotlib.org/2.2.2/tutorials/introductory/sample_plots.html#sphx-glr-tutorials-introductory-sample-plots-py)\n- :newspaper: [Customizing Matplotlib with style sheets and rcParams](https://matplotlib.org/2.2.2/tutorials/introductory/customizing.html#sphx-glr-tutorials-introductory-customizing-py)\n- :newspaper: [Styling with cycler](https://matplotlib.org/2.2.2/tutorials/intermediate/color_cycle.html#sphx-glr-tutorials-intermediate-color-cycle-py)\n- :newspaper: [Legend guide](https://matplotlib.org/2.2.2/tutorials/intermediate/legend_guide.html#sphx-glr-tutorials-intermediate-legend-guide-py)\n- :newspaper: [Specifying Colors](https://matplotlib.org/2.2.2/tutorials/colors/colors.html#sphx-glr-tutorials-colors-colors-py)\n- :newspaper: [Annotations](https://matplotlib.org/2.2.2/tutorials/text/annotations.html#id23)\n\n### Other Resources\n- :newspaper: [Introduction to Matplotlib — Data Visualization in Python](https://heartbeat.fritz.ai/introduction-to-matplotlib-data-visualization-in-python-d9143287ae39)\n- :newspaper: [Python Plotting With Matplotlib (Guide)](https://realpython.com/python-matplotlib-guide/)\n- :newspaper: [Matplotlib Tutorial](https://www.tutorialspoint.com/matplotlib/index.htm)\n- :newspaper: [Python Graph Gallery](https://python-graph-gallery.com/matplotlib/)\n- :tv: [Python Matplotlib Tutorial | Edureka](https://www.youtube.com/watch?v=yZTBMMdPOww)\n- :tv: [Matplotlib tutorial | Simplilearn](https://www.youtube.com/watch?v=OKJyGzgWP6c)\n\n## To-Do\n- [ ] Seaborn\n- [ ] Exploratory Data Analysis (EDA)\n- [ ] SQL\n- [ ] Machine Learning Concepts\n- [ ] Scikit-Learn\n- [ ] Projects\n- [ ] Translation in different language\n- [ ] Cheatsheets\n\n## FAQ\n1. Which programming languages should I use?\nPython and R. However, I added materials on Python.\n\n2. How to contribute?\nCheck out [contribution guidelines](#contribution-guidelines).\n\n\n## Contribution guideline\nYou can [open an issue](https://github.com/durgeshsamariya/data-science-roadmap/issues) and give your suggestions as to how I can improve this guide, or what I can do to improve the learning experience.\n\nYou can also **fork this repo** and send a **pull request** to fix any mistakes that you have found.\n\nIf you want to suggest a new resource, send a pull request adding such resource to the extras section. The extras section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdurgeshsamariya%2Fdata-science-roadmap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdurgeshsamariya%2Fdata-science-roadmap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdurgeshsamariya%2Fdata-science-roadmap/lists"}