General-Dev-Tools

• this is a repo that contains general development tools that i use, like, or want to explore
• currently has two sections:
  1. general resources
  2. training and general ed

General Resources

• Geocoding / geographical

  • Keplr: dd for for geocoding: https://kepler.gl/
  • Pandas Moving -> https://anitagraser.github.io/movingpandas/
  • GeoPandas
  • geopy
  • geog (https://pypi.org/project/geog/)
  • Collection of python geospatial _>
    • https://github.com/giswqs/python-geospatial
    • https://ishanjain-ai.medium.com/22-python-libraries-for-geospatial-data-analysis-f498959101bf?source=userActivityShare-4726d4325967-1629049273
    • Analysis example -> https://towardsdatascience.com/clustering-geospatial-data-f0584f0b04ec
  • Article about using SingleStore https://medium.com/@VeryFatBoy/using-singlestore-as-a-geospatial-database-28ddf92684af
  • Enrichment of geographic data:
    • General data by zipcode
      • zipcode / zcta / judicial crosswalk for ACS data: https://github.com/censusreporter/acs-aggregate/tree/master/crosswalks
      • zipcode to FIPS crosswalk: https://www.huduser.gov/portal/datasets/usps_crosswalk.html (select ZIP-COUNT in dropdown)
      • https://censusreporter.org/
      • https://github.com/censusreporter/census-api/blob/master/API.md
        • Demographics
        • Economics
        • Families
        • Housing
        • Social
        • Health insurance
        • Poverty (food stamps / SNAP)
    • Historical
      • Weather
        • Historical weather data -> https://www.visualcrossing.com/weather-api
      • COVID
        • https://apidocs.covidactnow.org/#register // historical covid with state or FIP code
    • Health + Disparities ->
      • https://www.neighborhoodatlas.medicine.wisc.edu/
        • Created my own github repo for easy access to data files: https://github.com/hantswilliams/countyhealthrankings
      • https://www.countyhealthrankings.org/
    • Food Environments (USDA)
      • https://www.ers.usda.gov/data-products/food-environment-atlas/go-to-the-atlas/
    • Food Retail Stores
      • Example NY -> https://catalog.data.gov/ne/dataset/retail-food-stores
  • Regulated Angencies
    • https://www.ttb.gov/ (wine, alcohol, fuel, guns, etc...)
    • Examples:
      • Washington DC liquer -> https://opendata.dc.gov/datasets/liquor-licenses/explore?location=38.902497%2C-77.008884%2C12.73

• Data Storage

  • De-centralized cloud storage
    • https://www.storj.io/
      • Very cool - I would want to do this for encrypting messages in future
  • Data reading/writing made easy (GCP, Azure, AWS -> )
    • SMart open - https://github.com/RaRe-Technologies/smart_open

• AWS specific / labs

  • Data Wrangler - https://github.com/awslabs/aws-data-wrangler
  • Labs- https://github.com/awslabs

• Deployement

  • Quick deployment of localhost test -> https://ngrok.com/

• Diagrams:

  • AWS diagram creator https://alanblackmore.medium.com/aws-diagram-creator-8f596052952c

• Data

  • Onboarding and Ingestion (ETL/ELT)
    • Flatfile Data Onboarding platform // https://flatfile.io/
    • Fivetran Cloud data integration platform // https://fivetran.com/
    • Matillion Cloud data integration platform
    • Apache Gobblin Open Source distributed data integration framework
    • Singer "Open Source standard for writing scripts that move data" // https://www.singer.io/
    • Meltano Open Source ELT for the DataOps
    • Airbyte Open Source data integration platform // https://airbyte.io/
    • Stitch Simple, extensible Cloud ETL platform (Talend) // https://www.stitchdata.com/
    • Hevo No-code data pipeline as a service
    • Apache Hop Open Source data integration platform project
    • Meroxa Real-time data ingestion infrastructure
    • Portable Cloud Hosted ELT Platform
    • Talend, StreamSets, Alooma (Google), Xplenty, Striim, Panoply, Stambia, HVR
  • Transformations
    • dbt -> transformations / https://www.getdbt.com/
  • apache beam
  • Data Lineage
    • Pachyderm - Pachyderm
  • Data management
    • Dataframe.ai - https://josephmoon.medium.com/dataframe-ai-a-comprehensive-data-context-management-tool-for-modern-data-teams-df47c8a1ce17 (dataframe.ai)
    • Have also built a search-like tool called WHALE -> https://github.com/hyperqueryhq/whale
  • Data Catalogs
    • Good comparison - - data hub / atlas / amunddsen -> https://medium.com/@gosin/finding-the-right-data-catalog-solution-a265a4b3c0c3
    • https://medium.com/hipay-tech/setting-up-a-data-discovery-tool-why-and-which-solution-to-choose-5e03fcbed458
    • Open Meta Data (https://blog.open-metadata.org/openmetadata-0-8-0-release-ca09bd2fbf54)

• APIs

  • Multiple/APIs services (get multiple)
    • Public API search (new): https://publicapis.sznm.dev/
    • https://listt.xyz/
    • https://m3o.com/
    • Communication focused: https://www.nylas.com/
  • Payments
    • Stripe Connect - https://stripe.com/connect
    • Stripe Payment Link - https://stripe.com/payments/payment-links
  • Security
    • Typing biometric - https://www.typingdna.com/
  • Scheduling
    • https://onsched.com/
    • https://www.timekit.io/
    • https://www.vyte.in/en/scheduling-api
  • Speech/NLP
    • https://www.assemblyai.com/
  • Food
    • https://chompthis.com/api/
  • News
    • https://newsapi.org/
  • Signatures
    • https://www.concordnow.com/

• Frontend

  • Multi-deployment
    • flutter: https://flutter.dev/
    • flutterflow - https://flutterflow.io/

• Authorization

  • Authorizer - https://medium.com/@SamaniLakhan/introducing-authorizer-9b89edb810fd

• Automation

  • Airflow

• Notebook tools

  • Hex // https://github.com/louislam/uptime-kuma
  • Deepnote //
  • PolyNote - https://medium.com/dataseries/netflixs-polynote-is-a-new-open-source-framework-to-build-better-data-science-notebooks-4bdab6b8d0ae
  • Resources / collections:
    • https://github.com/ml-tooling/best-of-jupyter#notebook-environments

• Statistics and Data Manipulation

  • Summary Statistics - Python Sidecar - https://levelup.gitconnected.com/sidetable-an-efficient-tool-to-summarize-pandas-dataframe-330958528a82
  • Test selection
    • http://www.sciences.ch/tmp/data_science_map/MindMap_Statistical_Tests_EN_2022_06_22_v0_2_r1230.html
  • Dates
    • Holidays / etc… https://danilzherebtsov.medium.com/ultimate-python-dateparser-with-holidays-paydays-and-all-the-good-stuff-fffe270a236
      • https://verstack.readthedocs.io/en/latest/#dateparser
  • Timeseries
    • https://unit8co.github.io/darts/index.html / Darts
  • Pinguin - stats - https://github.com/raphaelvallat/pingouin
  • Repeated Measures (highly reputable professor): https://www.drizopoulos.com/courses/EMC/CE08.pdf
  • Some of the best book(s)
    • Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition
  • Data checks/schema/types:
    • https://pandera.readthedocs.io/en/stable/schema_inference.html
    • https://pydantic-docs.helpmanual.io/
    • https://mypy.readthedocs.io/en/stable/index.html

• Linux

  • Command cheetsheets
    • xmind: https://www.xmind.net/m/WwtB/

• Visualization

  • Superset
  • Streamlit
    • Streamlit with Sweetviz - https://discuss.streamlit.io/t/this-is-how-to-use-sweetviz-with-streamlit/10897

• SaaS starter kits

  • Next JS
    • Enterprise: https://nextlessjs.com/
  • Free (same author): https://github.com/ixartz/Next-js-Boilerplate
  • Codebase generator (you get to choose backend, frontend framework, deployemnt, etc...)
    • https://divjoy.com/
  • Other paid:
    • https://saasrock.com/
    • https://serverless.page/
    • https://bedrock.mxstbr.com/
    • https://rocketapp.me/
    • https://reactapp.dev/
    • https://usegravity.app/
    • https://docs.usegravity.app/
    • https://shipsaas.com/
  • Free
    • https://github.com/async-labs/saas
    • https://github.com/Saas-Starter-Kit/SAAS-Starter-Kit-Pro
    • https://nextacular.co/ (https://github.com/nextacular/nextacular)
  • Hants Ordering:
    • Free:
      • https://nextacular.co/
        • Billing: Stripe
        • Documentation: Limited // work in progress // https://docs.nextacular.co/
        • Deployment: vercel (auto SSL)
        • Databases: only relational (SQL/PostgreSQL/Aurora)
        • Pros: multi-domain; DB -> relational; teams + workspaces; strip; tailwind; email handling
      • https://www.saasstarterkit.com/
        • Billing: Stripe
        • Documentation: Good // mostly flushed out // https://docs.saasstarterkit.com/docs/intro/welcome/
        • Deployment: on your own
        • Databases: relational (postgres) and non-relational (mongoDB)
        • Pros: ML example buil in; on-boarding; docker; stripe; AWS APIs;
    • Paid:
      • https://bedrock.mxstbr.com/ (396 p/project)
      • https://serverless.page/ (199 lifetime)
      • https://reactapp.dev/ (19 lifetime)
      • https://saasrock.com/ (149 lifetime)
      • https://nextlessjs.com/ (699 p/project)

• Low Code / No code

  • Backend
    • Supabase
    • Parse - https://parseplatform.org/
    • Appwrite - https://appwrite.io/
    • Nhost - https://nhost.io
    • Hasura
  • Pocketbase - https://github.com/pocketbase/pocketbase
  • Frontend
    • AppGyver

• Data bases DBs

  • Modern:
    • FaunaDB - https://medium.com/codesphere-cloud/is-faunadb-the-next-big-database-technology-4c5a67915d6e
    • SingleStore - https://www.singlestore.com
    • Apache Druid / would view this as a competitor to SingleStore - https://druid.apache.org/
    • RethinkDB - live DB - https://rethinkdb.com
    • Firebase
    • DyanmoDB

• Frontend - Styles

  • Fonts / Styles
    • https://medium.com/@anthonyjdella/why-you-should-use-a-developer-font-b19d5269d767
  • Examples of good stuff
    • https://www.reallygoodux.io

• Opensource alternatives:

  • Airflow:
    • https://nocodb.com
    • More airflow like ones -> https://medium.com/swlh/top-3-low-and-no-code-databases-for-creative-entrepreneurs-b10f069c36b3
  • Calendly:
    • https://cal.com

• Documentation

  • Docusaurus - https://docusaurus.io
  • Docz - https://www.docz.site
  • API documentation
    • readme.com / https://docs.readme.com/
  • redoc (redocly) / https://redocly.com/ - https://github.com/Redocly/redoc

• UUID - url friendly

  • https://javascript.plainenglish.io/introducing-a-url-friendly-unique-identifier-nanoid-fbf4d2162322

• Search tools:

  • Meili search tool
  • Typesense https://typesense.org/
  • DeepHaven - https://deephaven.io
  • Elastic search - https://diawahad.medium.com/elasticsearch-the-open-source-distributed-restful-json-based-search-engine-ready-for-the-big-640430fd655b

• Testing

  • Chaos Engineering
    • https://www.gremlin.com/product/

• Monitoring tools

  • Uptime Kuma - https://github.com/louislam/uptime-kuma

• Security

  • Cloud checks
    • Scout Suite (cloud specific)
  • Identity management
    • https://www.strongdm.com
  • Secrets management
    • Doppler - https://www.doppler.com
    • Vault
  • Dealing with multi-cloud account / going back and forth
    • https://www.leapp.cloud/ // https://github.com/Noovolari/leapp
  • Pen testing
    • Metasploit (now managed by rapid7) - https://GitHub.com/rapid7/metasploit-framework
    • VM hackable/volunerable machine testing - metasploitable - https://github.com/rapid7/metasploitable3
    • Onion browser - https://github.com/OnionBrowser/OnionBrowser
    • python based
      • https://github.com/brinhosa/awesome-pentest-tools-in-colab
      • https://github.com/dloss/python-pentest-tools
    • non-python based
      • https://github.com/enaqx/awesome-pentest (most starred)
    • https://github.com/coreb1t/awesome-pentest-cheat-sheets
    • https://github.com/jesusprubio/awesome-nodejs-pentest
    • https://github.com/CyberSecurityUP/Awesome-Cloud-PenTest
    • https://github.com/CyberSecurityUP/Awesome-PenTest-Practice

• CAAS (cloud as a service?):

  • my own (based on pulumi and aws):
  • Openstack: https://www.openstack.org/software/
  • Cloudstack: https://cloudstack.apache.org/
  • OpenNebula: https://opennebula.io/evaluate-opennebula/#try_now

• IAAS:

  • Pulumi
    • https://betterprogramming.pub/pulumi-a-true-infrastructure-as-code-paradigm-ac07c530e219
  • Terraform related tools:
    • Infracost // https://github.com/infracost/infracost
    • Brainbooard - automatically create terraform // https://www.brainboard.co/
    • Checkgov - Checkov - looks for config errors
  • Ansible
    • devOps examples: https://github.com/geerlingguy/ansible-for-devops
    • playbook best examples: https://github.com/ansible/ansible-examples

• Data stacks

  • Article - https://www.datafold.com/blog/the-modern-data-stack-open-source-edition
  • Github - good list - > https://github.com/victorcouste/data-tools#ingestion

• Webscraping

  • Stealth:
    • https://uxdesign.cc/stealth-web-scraping-in-python-avoid-blocking-like-a-ninja-8cb76db119ae

• Kubernet tools

  • Helm - Helm
  • Knative - https://knative.dev/docs/
  • Kubeflow - Kubeflow
  • Crossplane - https://crossplane.io

• ML

  • Primary: tensorflow (google)

  • Primary: pyTorch (facebook)

    • https://github.com/pytorch/pytorch

  • Cheat sheet

    • https://github.com/afshinea/stanford-cs-229-machine-learning

  • Labeling tool

    • https://labelstud.io
    • https://dataneuron.ai/product

  • Bias

    • https://github.com/dreji18/Fairness-in-AI

  • Drift/Monitoring

    • https://www.nannyml.com/

  • Scary Usecases

    • https://github.com/daviddao/awful-ai

  • Explainable

    • https://github.com/MAIF/shapash
    • https://github.com/oegedijk/explainerdashboard
    • https://github.com/SelfExplainML/PiML-Toolbox

  • Forecasting

    • https://github.com/Nixtla/neuralforecast

  • Features

    • Feather: https://github.com/linkedin/feathr

  • NLP - langauge translation

    • meta open source FairSeq: https://github.com/facebookresearch/fairseq/tree/nllb

  • NLP

    • Spacy
    • NER annotation - https://github.com/vopani/waveton/tree/main/apps/data_apps/ner_annotation

  • NLP + Science Journals

    • Pubmed Analysis - https://github.com/bepnye/EBM-NLP
    • Taking articles and converting to JSON - > https://github.com/allenai/s2orc-doc2json
    • Similarities across papers - https://github.com/stephenleo/stripnet
    • Analyze PDFs -> tables, figures, etc... = https://github.com/Layout-Parser/layout-parser
    • NLP with graph and human search - https://github.com/boopalanjayaraman/athena - https://www.tigergraph.com/graph-for-all/winners/third-place-most-impactful/

  • Premade ML/AI

    • https://huggingface.co/
    • https://cloud.blobcity.com/code/explore
    • https://github.com/trekhleb/homemade-machine-learning
    • https://modelzoo.co/
    • Speech and audio: https://github.com/balavenkatesh3322/audio-pretrained-model
      • NLP: https://github.com/balavenkatesh3322/NLP-pretrained-model
      • CV: https://github.com/balavenkatesh3322/CV-pretrained-model

  • AutoML

    • MLJar
    • https://auto.gluon.ai/stable/index.html
    • http://epistasislab.github.io/tpot/
    • https://github.com/mljar/mljar-supervised
    • AWS solution - AWS Sagemaker Autopilot - https://aws.amazon.com/sagemaker/autopilot/

  • Recommendation

    • Microsoft best practices - https://github.com/microsoft/recommenders
    • Suprise - http://surpriselib.com/
    • Collaborative
      • Good walkthrough of collaborative - https://sunjackson.github.io/2016/05/30/3cca3bba88363e21bbe6e536e4178018/
      • https://github.com/benfred/implicit

  • Session Based Recommendations

    • Competition: https://recsys.acm.org/challenges/, http://www.recsyschallenge.com/2022/
    • Python Notebooks for Session-based:
      • NextItNet, GRU4Rec, Caser https://github.com/microsoft/recommenders/blob/main/examples/00_quick_start/sequential_recsys_amazondataset.ipynb
      • SR-GNN https://colab.research.google.com/drive/1XwQ0njqSZL8vbHJMnRRHlH4ar0kYYFVz?usp=sharing
      • SASRec https://github.com/microsoft/recommenders/blob/main/examples/00_quick_start/sasrec_amazon.ipynb
      • Transformer4Rec https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/main/examples/tutorial/03-Session-based-recsys.ipynb
    • Python Libraries for Session-based:
      • https://recbole.io/docs/user_guide/model_intro.html#sequential-recommendation
      • https://github.com/maciejkula/spotlight
      • https://github.com/NVIDIA-Merlin/Transformers4Rec
      • https://github.com/microsoft/recommenders

  • Computer Vision

    • Text to Pictures -> https://github.com/lucidrains/DALLE2-pytorch
    • Text to Picture (Free) -> https://github.com/Yutong-Zhou-cv/Awesome-Text-to-Image
    • Image augmentation (FB) -> https://github.com/facebookresearch/AugLy
    • Bunch of random faces for training -> https://github.com/microsoft/FaceSynthetics
    • Simple facial recognition -> https://github.com/ageitgey/face_recognition
    • Real time tracking -> https://github.com/tryolabs/norfair
    • Item tracking --> DEEPSORT --> https://learnopencv.com/understanding-multiple-object-tracking-using-deepsort/
    • Digital Cloning Examples
      • Toolkit: https://github.com/titanlambda/identity-cloning-toolkit-ICT
      • Voice cloning: https://github.com/CorentinJ/Real-Time-Voice-Cloning
      • Voice cloning: https://github.com/BenAAndrew/Voice-Cloning-App
      • Paid service: https://www.synthesia.io/

  • Github - Awesome ML repos

    • General ML - https://github.com/EthicalML/awesome-production-machine-learning

    • General jupyter resources 1 - https://github.com/markusschanta/awesome-jupyter

    • General jupyter resources 2 - https://github.com/ml-tooling/best-of-jupyter

    • Deep learning (fakes, audio video, pose, etc...) - https://github.com/tugstugi/dl-colab-notebooks

  • Notebook examples

    • General Notebooks/starter templates (GoogleSheets,Airtable,Sendgrid,Slack,etc..) https://github.com/jupyter-naas/awesome-notebooks
    • https://github.com/trekhleb/homemade-machine-learning
    • https://github.com/lazyprogrammer/machine_learning_examples
    • https://github.com/susanli2016/Machine-Learning-with-Python
    • Tensorflow -- https://github.com/aymericdamien/TensorFlow-Examples
    • Pytorch -- https://github.com/pytorch/examples
    • AWS sagemaker examples -- https://github.com/aws/amazon-sagemaker-examples

• Datasets for testing:

  • https://towardsdatascience.com/data-repositories-for-almost-every-type-of-data-science-project-7aa2f98128b

• Free Cloud Resources

  • ORACLE - https://www.oracle.com/cloud/free/#always-free
  • VERCEL - has always free
  • HEROKU

Training and General Ed

• Resources

  • Blogs
    • https://www.chubbydeveloper.com/
    • Medium
  • Podcasts:

• Training and learning stuff:

  • Arch design / good book - https://bytebytego.com/ - system design the big archcive - https://media-exp2.licdn.com/dms/document/C561FAQHBrAeW02s_yw/feedshare-document-pdf-analyzed/0/1655057292746?e=1655942400&v=beta&t=PhyXPS-R-LcFIPYLrkfgiEXoiLKxNhSwIRg2nlClaSA

  • Quick read on architecture -> https://orkhanscience.medium.com/software-architecture-patterns-5-mins-read-e9e3c8eb47d2

  • ML learning with scikit learn: https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn

  • More Ai Visual Exapmle -> http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

  • Ml fun example -> https://medium.com/mlearning-ai/if-i-buy-a-diaper-i-will-surely-pick-up-a-beer-e692895a0c65

  • SSL Tunneling -> https://goteleport.com/blog/ssh-tunneling-explained/

  • Stanford Ml systems -> https://stanford-cs329s.github.io/syllabus.html

  • Explainable Ai -> https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/

  • ML cheatsheet - https://github.com/soulmachine/machine-learning-cheat-sheet

  • Kaggle Allstar book: https://github.com/abhishekkrthakur/approachingalmost

  • MIT - Assortment of tools / instructions; https://github.com/shervinea/mit-15-003-data-science-tools

  • Powerful python: https://powerfulpython.com/bootcamp/

  • Interpretable ML: https://christophm.github.io/interpretable-ml-book/index.html

  • Resource of resources: https://www.linkedin.com/posts/vipulppatel_ultimate-guide-to-ai-data-science-machine-activity-6860243081434828800--w_7

  • Udemy course I like: https://www.udemy.com/user/kirilleremenko/

  • Sql training - forget what I found this:

    • SQL Fiddle - a playground environment that let’s you create tables and run SQL queries in the browser (http://sqlfiddle.com/)
    • SQL Bolt - An interactive tutorial great for beginners (https://sqlbolt.com/)
    • Select Star SQL - An interactive tutorial (https://selectstarsql.com/)
    • SQL Murder Mystery - For intermediate/advanced SQL. Solve a murder mystery by running SQL queries (https://lnkd.in/en3-VnT9)
    • SQL Indexing for Devs - Indexing is an important concept for making SQL queries more efficient. This blog series provides a good introduction (https://lnkd.in/egBCqJPa)
    • SQL Zoo - Another interactive tutorial (https://lnkd.in/eeGAxE7q)
    • The SQL Tutorial for Data Analysis - Another great tutorial that segments topics by beginner, intermediate, and advanced (https://lnkd.in/eUq7VvMp)
    • Other sql to review -
      • Databases & SQL for DS - IBM - https://www.coursera.org/learn/sql-data-science
      • Learn SQL Basics for Ds - https://www.coursera.org/specializations/learn-sql-basics-data-science
      • SQL Cookbook - O'Reilly
      • SQL 57 Practice Problems – Sylvia Vasilik
      • SQL for Data Analytics – by Packt

  • Python Basics / Intro level

    • Official Python: https://bugs.python.org/file47781/Tutorial_EDIT.pdf
    • Pandas tutorials: https://pandastutor.com/index.html

  • Good jupyter notebooks for learning // https://github.com/milaan9?tab=repositories (02_python_datatypes; 04_python_dictionaries)

    • Stanford:
      • https://web.stanford.edu/class/archive/cs/cs106a/cs106a.1206/lectures/4-Variables/4-Variables.pdf
    • https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1194/readings/python-review.pdf
    • https://stanfordpython.com/#/lectures
    • Stanford: https://stanfordpython.com/lecture/lecture-2-full.pdf
    • Cloud computing intro: https://web.stanford.edu/class/cs349d/docs/L01_overview.pdf
    • Harvard:
      • http://tdc-www.harvard.edu/Python.pdf
    • UCSF:
      • https://www.library.ucsf.edu/ask-an-expert/data-science/programming/
      • Python: https://swcarpentry.github.io/python-novice-inflammation/
    • ECU:
      • https://ofe.ecu.edu/wp-content/pv-uploads/sites/277/2021/05/Python_intro_2021OFE.pdf
    • Duke:
      • https://courses.cs.duke.edu/compsci260/fall14/resources/python.tutorial.1.2014.pdf
    • Emertxe Information Technologies: https://www.slideshare.net/EmertxeSlides/presentations/5
      • Datatypes: https://www.slideshare.net/EmertxeSlides/python-data-types
    • Other python to review:
      • 2022 Complete Python Bootcamp - https://www.udemy.com/course/complete-python-bootcamp
      • Complete Python Developer - https://www.udemy.com/course/complete-python-developer-zero-to-mastery
      • Python Crash Course – 2nd Edition book by Eric Matthews
      • Python Cookbook: Recipes for Mastering Python 3 by O’Reilly
      • Elements of Programming Interviews in Python: The insider’s Guide by Adnan, Amit

  • stats to review:

    • Intro to Statistics - https://www.udacity.com/course/intro-to-statistics--st101
    • Intro to Inferential Statistics - https://www.udacity.com/course/intro-to-inferential-statistics--ud201
    • Statistics and probability by Khan Academy - https://www.khanacademy.org/math/statistics-probability
    • Statistics in Plain English by Timothi C Urdan
    • Head First Statistics: A Brain-Friendly Guide by Dawn Griffiths
    • ISLR
    • ESLR
    • blog - https://lnkd.in/gpYNgFBn