Project Development: Machine Learning, Data Analytics, and Data Science

Topic outline

• General

  Collapse all Expand all
  Not available unless: Your Email address is info@811tech.ca

• Workshop Description
  • 

    Data Science Project Development: A high level and step by step demonstration. Page

    Mark as done

• Contact and Teacher/Instructor/Professor/Faculty Information
  • Mark as done

    ...

• Machine Learning Review
  • Mark as done

    Download folder

    • Overview Of AI and ML Algorithm
      • AI Algorithms - 1.pdf
      • AI Algorithms - 2.pdf
      • AI Algorithms - 3 .pdf
      • AI Algorithms - 4.pdf
      • AI Algorithms - 5.pdf

• Content on Project Development Steps
  • 

    Statistics for Data Analytics and Machine Learning Projects Page

    Mark as done
  • 

    Statistics: McNemar’s Test Page

    Mark as done
  • 

    Data Analytics Project: Problem Framing and Project Lifecycle Page

    Mark as done
  • 

    Inductive and Deductive Methods for Data Analytics Projects Page

    Mark as done
  • 

    Data Requirements for Data Analytics Projects Page

    Mark as done
  • 

    Make Sense of your Data: For Data Analytics Project Page

    Mark as done
  • 

    Initial and Exploratory Analysis for Data Analytics Projects File

    Mark as done
  • 

    Initial Analysis of Text and Image Data (Data Analytics and ML Projects) Page

    Mark as done
  • 

    Dimensionality Reduction Page

    Mark as done
  • 

    Factors/Variables to Consider For Experimental Design for Data Analytics Projects Page

    Mark as done
  • 

    Experimental Design Examples (Data Analytics Projects) Page

    Mark as done
  • 

    Model Selection for your Project Page

    Mark as done
  • 

    Model Selection Page

    Mark as done
  • 

    Possible Data Analytics Project Goals Page

    Mark as done
  • 

    Threat To Validity for Your Data Analytics Projects - 2 Page

    Mark as done
  • 

    Threat To Validity for Your Data Analytics Projects Page

    Mark as done
  • 

    Reporting your Project Outcome Lesson

    Mark as done

• Misc. Concepts on Data Science and Machine Learning
  • 

    Important Basic Concepts: Statistics for Big Data Page

    Mark as done
  • 

    Part 1: Some Math/Stat Background that (true) Data Scientists will know/use: from the internet Page

    Mark as done

• Session Resources such as Presentation Slides
  • 

    Methodology Image File

    Mark as done

    Methodologies Step by Step. If you just have to see one single thing. In the image, from the Greyed Boxes at the bottom, go back to top. That way the image will look pretty simple.

  • 

    Link to the In-Progress Presentation Slides URL

    Mark as done

    Presentation Slides: https://docs.google.com/presentation/d/13RELxQhucZH716WIfPDSKhuBJfJ07Cel5Cw5IS1aeUk/edit?usp=sharing
    

  • 

    Can you answer these questions on Data Science Project Development Page

    Mark as done
  • 

    Questions to Answer for Data Science Project Development URL

    Mark as done

    In-Progress: Questions to Answer for Data Science Project Development: http://bangla.salearningschool.com/recent-posts/can-you-answer-these-random-questions-on-data-science-project-development/
    

• Useful Resources: From the Project
  • 

    A Github repo that contains related resources URL

    Mark as done

    https://github.com/sayedum/acr-prediction-from-dietary-patterns-for-ckd-patients
    

  • 

    Poster Presentation for the Project URL

    Mark as done

    You can just check the methodology diagram. The diagram though might look complex at  first glance, there are actually only three different paths. I can add one more diagram to show that. https://github.com/sayedum/acr-prediction-from-dietary-patterns-for-ckd-patients/blob/master/final_MRP_poster_sayed_ahmed_2019.pdf

    
    

  • 

    Python code for one of the small components of the project URL

    Mark as done

    You can ignore this code for this session.https://github.com/sayedum/acr-prediction-from-dietary-patterns-for-ckd-patients/blob/master/Python-or-R-Code/food-subgroup-regression-prediction.ipynb

    The most important part in the code to look is: When you will be described on 5 or 10 fold cross validations - that might seem complicated conceptually i.e. if you want to write all the code to divide data and keep track what to merge, what not to merge, what is the train dataset and what is the test dataset. In reality, in Python code this is very basic; you just have to use the right library function and right parameters.

• Books
  • Mark as done

    Statistics for Big Data for Dummies: This book will help you a lot to develop your research methodologies for Data Science projects. Kind of must read.

  • 

    Alpaydin: Introduction to Machine Learning URL

    Mark as done

    Chapter 19: Design and Analysis of Machine Learning Experiments: Gives details on Research Methodologies for Data Science Projects.  Not everyone will find the book to be an easy read. You do not have to read though this.https://www.academia.edu/28701585/Introduction_to_Machine_Learning_2e_Ethem_Alpaydin 

• Add your questions if any to this Google Sheet
  • 

    Keep adding your questions here to answer at the end of the session URL

    Mark as done

    The URL that will open: https://docs.google.com/spreadsheets/d/1m6yoKA-_BPxntKOKOHevnGPcpxoPecvP-4d3p3MFUiE/edit?usp=sharing

    
    

• Misc Resources
  • 

    NHANES: Survey Data Files: CSV Format URL

    Mark as done

    NHANES: Survey Data Files: CSV Format: https://drive.google.com/drive/folders/1fi_eQ9zWbi-RSegBuLSrpYLfaDML67pW?usp=sharing
    

  • 

    Exploratory Analysis: Regression with Excel URL

    Mark as done

    Using Excel Data Analysis Plugin: https://docs.google.com/document/d/1vfkqgUZlc_TfbTg23TEKhIEriBDnoT7b7PRj5mL0fyU/edit?usp=sharing
    

  • 

    Factor Analysis as Part of Exploratory Analysis URL

    Mark as done

    For my project this was not important. Try to know what Factor Analysis is and How does it differ from PCA (Principal Component Analysis). https://drive.google.com/file/d/1fLJMCKVXCJUS1DXY-eDvg7ntJ1Wl26LZ/view?usp=sharing

  • 

    Factor Analysis with Transposed Data URL

    Mark as done

    Used XLSTAT excel Package. https://docs.google.com/spreadsheets/d/1aVDjcz_5sjFxb4M0sNfafMXrsn-lMeB4/edit?dls=true

  • 

    Experimental Design: An Example URL

    Mark as done

    The URL: https://drive.google.com/file/d/1-rwMcCsy2oKWWULEX9BFDf_gLhoyD-9l/view?usp=sharing
    

  • 

    Images from the Project: Dataset, Exploration, Methodology, Experiment, Result URL

    Mark as done

    The URL: https://github.com/sayedum/acr-prediction-from-dietary-patterns-for-ckd-patients/tree/master/random-images-from-the-project
    

• Future Research by extending the project
  • 

    ACR Value Prediction from Dietary Data Page

    Mark as done

    On Github: https://github.com/sayedum/acr-prediction-from-dietary-patterns-for-ckd-patients

    On Researchgate: https://www.researchgate.net/project/Albumin-to-Creatinine-prediction-from-Dietary-Data.

  • 

    Association of Mortality/Survival of CKD patients with Dietary Patterns Page

    Mark as done

    Execute the same work and steps as my current project did. However, combine data from 1996-2016 first and then apply on this big dataset. My current work was applied only on the data from 2015 - 2016. 

    Will share Github and Researchgate projects. 

  • 

    Association of Blood Pressure measures with Dietary Pattern Page

    Mark as done

    Association of Blood Pressure measures with Dietary Pattern. The NHANES survey also provided Blood Pressure measures. Hence, using the similar approach you will be able to associate the measures with dietary intake. Also, if enough correlation whether you can predict or not. 
    

  • 

    How the assets inside an Investment Portfolio affecting the investment return. Page

    Mark as done

    Using the similar approach as my project utilized, you might be able to associate how the assets inside an Investment Portfolio affecting the investment return. This eventually can help to create a great portfolio (sure that will need to integrate other components of Investing)

• Misc Python Code as Used in the Project
  • 

    Sample Python Code for Exploratory Analysis File

    Mark as done

    Quantitative, Graphical, Univariate, BiVariate, Regression

  • 

    Experiment, PCA, and Regression on PCA outcome to find the final association File

    Mark as done

    Sample Python code and plots

• Misc Resources
  • 

    On Medium, I did put couple of notes URL

    Mark as done

    The content on medium might not be free. Hence, read on bangla.salearningschool.con or read details from the book Statistics for Big Data. http://bangla.salearningschool.com/recent-posts/category/statistics-for-big-data/
    

    https://medium.com/@SayedAhmedCanada/important-basic-concepts-statistics-for-big-data-46b4e3f2177d

    
    

    https://medium.com/@SayedAhmedCanada/questions-answered-by-exploratory-data-analysis-eda-99353ef9c1f7
    

    https://medium.com/@SayedAhmedCanada/best-practices-in-data-preparation-407ecefd58d1
    

• DBMS Tables and Stored Procedures as Used
  • 

    Database (tables, stored procedures) used for the exploration phase File

    Mark as done
  • 

    Database (Tables, Stored Procedures) as used for the Experiment and Result Phase File

    Mark as done

• Chat with Classmates
  • 

    Chat with the Participants

    Mark as done

    Chat with the Participants
    

• Discussion Forum
  • 

    Short Workshop on Project Development: Machine Learning, Data Analytics, and Data Science Forum
  • 

    Discuss the Workshop Topics Forum

    Mark as done

    Discuss the Workshop Topics