Kamran Ahmed
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--- |
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jsonUrl: '/jsons/roadmaps/ai-data-scientist.json' |
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pdfUrl: '/pdfs/roadmaps/ai-data-scientist.pdf' |
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order: 6 |
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briefTitle: 'AI and Data Scientist' |
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briefDescription: 'Step by step guide to becoming an AI and Data Scientist in 2023' |
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title: 'AI and Data Scientist Roadmap' |
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description: 'Step by step guide to becoming an AI and Data Scientist in 2023' |
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hasTopics: true |
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isNew: true |
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dimensions: |
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width: 968 |
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height: 2243.96 |
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schema: |
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headline: 'AI and Data Scientist Roadmap' |
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description: 'Learn how to become an AI and Data Scientist with this interactive step by step guide in 2023. We also have resources and short descriptions attached to the roadmap items so you can get everything you want to learn in one place.' |
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imageUrl: 'https://roadmap.sh/roadmaps/ai-data-scientist.png' |
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datePublished: '2023-08-17' |
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dateModified: '2023-08-17' |
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seo: |
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title: 'AI and Data Scientist Roadmap' |
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description: 'Learn to become an AI and Data Scientist using this roadmap. Community driven, articles, resources, guides, interview questions, quizzes for modern backend development.' |
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keywords: |
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- 'ai and data scientist roadmap 2023' |
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- 'ai and data scientist roadmap 2023' |
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- 'guide to becoming an ai and data scientist' |
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- 'ai and data scientist roadmap' |
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- 'ai scientist' |
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- 'ai scientist roadmap' |
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- 'data scientist roadmap' |
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- 'ai skills' |
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- 'data scientist skills' |
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- 'ai engineer roadmap' |
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- 'ai skills test' |
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- 'data scientist skills test' |
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- 'ai and data scientist roadmap' |
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- 'become an ai and data scientist' |
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- 'ai and data scientist career path' |
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- 'ai career path' |
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- 'data scientist career path' |
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- 'skills for ai engineer' |
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- 'skills for data scientist' |
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- 'learn ai for developers' |
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- 'ai and data scientist quiz' |
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- 'ai and data scientist interview questions' |
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relatedRoadmaps: |
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- 'python' |
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- 'backend' |
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- 'devops' |
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sitemap: |
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priority: 1 |
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changefreq: 'monthly' |
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tags: |
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- 'roadmap' |
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- 'main-sitemap' |
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- 'role-roadmap' |
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--- |
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# AB Testing |
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|
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- [Practitioner’s Guide to Statistical Tests](https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b) |
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- [Step by Step Process for Planning an A/B Test](https://towardsdatascience.com/step-by-step-for-planning-an-a-b-test-ef3c93143c0b) |
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# Classic/Advanced ML |
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|
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- [Open Machine Learning Course](https://mlcourse.ai/book/topic01/topic01_intro.html) |
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- [Coursera: Machine Learning Spcialization](https://www.coursera.org/specializations/machine-learning-introduction#courses) |
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- [Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) |
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- [Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop](https://github.com/gerdm/prml) |
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|
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# Data Understanding, Analysis and Visualization |
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|
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- [Exploratory Data Analysis With Python and Pandas](https://www.coursera.org/projects/exploratory-data-analysis-python-pandas) |
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- [Exploratory Data Analysis for Machine Learning](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning#syllabus) |
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- [Exploratory Data Analysis with Seaborn](https://www.coursera.org/projects/exploratory-data-analysis-seaborn) |
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|
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# MLOps |
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|
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- [Machine Learning Engineering for Production (MLOps) Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses) |
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|
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# Differential Calculus |
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|
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- [Algebra and Differential Calculus for Data Science](https://coursera.org/learn/algebra-and-differential-calculus-for-data-science#syllabus) |
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|
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# Econometrics Pre-requisites |
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|
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- [10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
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|
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# Fully Connected NN, CNN, RNN, LSTM, Transformers, Transfer Learning |
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|
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- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) |
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- [Attention is All you Need](https://arxiv.org/pdf/1706.03762.pdf) |
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- [Deep Learning Book](https://www.deeplearningbook.org/) |
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- [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning#courses) |
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|
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# Hypothesis Testing |
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|
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- [Introduction to Statistical Analysis: Hypothesis Testing](https://www.coursera.org/learn/statistical-analysis-hypothesis-testing-sas#syllabus) |
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|
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# Increasing Test Sensitivity |
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|
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- [Minimum Detectable Effect (MDE)](https://splitmetrics.com/resources/minimum-detectable-effect-mde/) |
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- [Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://kdd.org/kdd2016/papers/files/adp0945-xieA.pdf) |
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- [Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data](https://exp-platform.com/Documents/2013-02-CUPED-ImprovingSensitivityOfControlledExperiments.pdf) |
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- [How Booking.com increases the power of online experiments with CUPED](https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d) |
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- [Improving Experimental Power through Control Using Predictions as Covariate — CUPAC](https://doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/) |
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- [Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://www.researchgate.net/publication/305997925_Improving_the_Sensitivity_of_Online_Controlled_Experiments_Case_Studies_at_Netflix) |
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|
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# |
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# Data Structures and Algorithms |
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|
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- [Learn Algorithms](https://leetcode.com/explore/learn/) |
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- [Leetcode - Study Plans](https://leetcode.com/studyplan/) |
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- [Algorithms Specialization](https://coursera.org/specializations/algorithms#courses) |
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# Python |
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|
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- [Kaggle — Python](https://www.kaggle.com/learn/python) |
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- [Google's Python Class](https://developers.google.com/edu/python) |
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|
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# SQL |
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|
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- [SQL Tutorial](https://www.sqltutorial.org/) |
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|
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# Learn Algebra, Calculus, Mathematical Analysis |
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|
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- [Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning#courses) |
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|
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# Probability and Sampling |
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|
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- [Probability and Statistics: To p or not to p?](https://www.coursera.org/learn/probability-statistics#syllabus) |
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|
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# Ratio Metrics |
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|
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- [Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas](https://arxiv.org/pdf/1803.06336.pdf) |
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- [Approximations for Mean and Variance of a Ratio](https://www.stat.cmu.edu/~hseltman/files/ratio.pdf) |
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|
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# Regressions, Time series, Fitting Distributions |
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|
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- [10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
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- [Dougherty Intro to Econometrics 4th edition](https://www.academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small) |
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- [Econometrics: Methods and Applications](https://www.coursera.org/learn/erasmus-econometrics#syllabus) |
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- [Kaggle - Learn Time Series](https://www.kaggle.com/learn/time-series) |
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- [Time series Basics : Exploring traditional TS](https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series) |
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- [How to Create an ARIMA Model for Time Series Forecasting in Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python) |
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- [11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/) |
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- [Blockchain.com Data Scientist TakeHome Test](https://github.com/stalkermustang/bcdc_ds_takehome) |
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- [Linear Regression for Business Statistics](https://www.coursera.org/learn/linear-regression-business-statistics#about) |
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|
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# Statistics, CLT |
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|
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- [Introduction to Statistics](https://coursera.org/learn/stanford-statistics#syllabus) |
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|
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