Appendix: DSDJ Links

• https://www.analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science/

• http://fmwww.bc.edu/repec/bocode/t/transint.html

• https://python-graph-gallery.com/

• https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/unlocking-the-power-of-data-in-sales

• https://cntk.ai/pythondocs/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.html

• http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf

• http://benalexkeen.com/bg-nbd-model-for-customer-base-analysis-in-python/

• http://danielweitzenfeld.github.io/passtheroc/blog/2015/01/19/s-b-g/

• https://courses.analyticsvidhya.com/courses/a-comprehensive-learning-path-for-deep-learning-in-2019/?utm_source=facebook.com&utm_medium=social

• https://courses.analyticsvidhya.com/courses/a-comprehensive-learning-path-to-become-a-data-scientist-in-2019/?utm_source=facebook.com&utm_medium=social

• https://courses.analyticsvidhya.com/courses/Intro-to-NLP?utm_source=facebook.com&utm_medium=social

• https://courses.analyticsvidhya.com/courses/Introduction-to-Neural-Networks?utm_source=facebook.com&utm_medium=social

• https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b

• https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-arima-in-python-3

• https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-prophet-in-python-3

• https://www.pecan.ai/resources/data-scientist-and-the-ceo-a-new-important-relationship-pecan

• https://machinelearningmastery.com/sarima-for-time-series-forecasting-in-python/

• https://www.datascience.com/blog/production-level-code-for-data-science

• https://www.datasciencedreamjob.com/products/data-science-dream-job-full-course/categories/879629/posts/2888388

• Hemanshu:speech_balloon: Jul 19th at 10:04 AM Hi @Kyle @Harpreet @Chris @Randy Lao I have created a house price prediction system and hosted in AWS cloud EC2 Instance. From past couple of months i created many ML models but i was always curious on how they will be deployed in real world. Keeping this in mind i started a project to design a complete end to machine learning business case with main focus on UI design. After learning basic of php and javascript i designed a website for house price prediction system. For simplicity i used Linear Regression and Boston housing dataset. Here is the link for the website: http://18.222.217.177/house_price_project/ Let me know your thoughts on this and where i can improve further.

• https://datascienceplus.com/building-a-book-recommender-system-the-basics-knn-and-matrix-factorization/

• https://aws.amazon.com/machine-learning/learn/?&trk=psm_a131L000005vAEcQAM&trkCampaign=pac-MLAI-2019-MLU-Go_Deep_With_ML-Webpage&sc_channel=psm&sc_campaign=pac-mlai-2019-mlu-psm-li-skill_up_for_free_with_aws_go_deep&sc_publisher=LI&sc_category=Machine%20Learning&sc_country=NAMER&sc_geo=NAMER&sc_outcome=AIML_Digital_Marketing&sc_detail=1200x628&sc_content=Go_Deep_With_ML&sc_segment=Skill-up&sc_medium=PAC-AIML-Multi%7CLI%7CSocial-Multi%7CAll%7CPA%7CMachine%20Learning%7CAIML%7CNAMER%7CEN%7CSponsored%20Content%7Cxx%7CGo-Deep-With-ML

• https://francescopochetti.com/how-to-build-an-expense-tracker-with-aws-textract/

• https://towardsdatascience.com/introduction-to-decision-intelligence-5d147ddab767

• https://realpython.com/pyspark-intro/

• https://markmanson.net/do-something

• http://18.222.217.177/house_price_project/

• https://case.edu/datascience/sites/case.edu.datascience/files/2018-06/Leek-ADataAnalysisStructureAndOrganizing.pdf

• https://towardsdatascience.com/my-secret-sauce-to-be-in-top-2-of-a-kaggle-competition-57cff0677d3c

• https://tech.instacart.com/predicting-real-time-availability-of-200-million-grocery-items-in-us-canada-stores-61f43a16eafe

• https://pypi.org/project/pyforest/0.1.1/

• https://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/

• https://university.custora.com/for-data-scientists

• https://thenewstack.io/3-new-techniques-for-data-dimensionality-reduction-in-machine-learning/

• https://realpython.com/inheritance-composition-python/

• http://drivendata.github.io/cookiecutter-data-science/

• https://arxiv.org/ftp/arxiv/papers/1810/1810.08055.pdf

• https://www.notion.so/The-Best-Artificial-Intelligence-Machine-Learning-and-Data-Science-Resources-b3b97fa097b747698e87fd3badc657cf

• https://mrdbourke.com/blog/aimastersdegree

• https://towardsdatascience.com/6-techniques-which-help-me-study-machine-learning-five-days-per-week-fb3e889fad80

• https://towardsdatascience.com/deploy-a-machine-learning-model-as-an-api-on-aws-43e92d08d05b

Sri Harsha Ravi 12:31 AM @channel I have been provided with the take home challenge which contains the 8 million rows Data in the attached format. They have asked me to build the below 4 models: Churn/Customer Retention: Predict gain in Revenue with improved Customer Retention. Predictive Customer Lifetime Modelling: Predict future activities of customer segments and provide recommendations for customer targeting Inventory Management: Assist the business in making better Inventory decisions through patterns observed in the data Market Basket Analysis: Provide cross-sell recommendations based on basket / shopping cart analysis I am wondering how to approach it. Can you please help me with your valuable suggestions. Thank you in advance (edited) Data.PNG Data.PNG

• https://cntk.ai/pythondocs/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.html

• http://danielweitzenfeld.github.io/passtheroc/blog/2015/01/19/s-b-g/

• https://medium.com/googleplaydev/predicting-your-apps-monetization-future-27180e82ae34

• http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf

• http://benalexkeen.com/bg-nbd-model-for-customer-base-analysis-in-python/

• https://www.datascienceatthecommandline.com/

• https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6

• https://www.boozallen.com/content/dam/boozallen_site/sig/pdf/publications/2015-field-guide-to-data-science.pdf