Keze Lin

Major: BBA MIS
Graduation: May 2020


MIS Badge

Official Professional Achievement badge awarded by the Department of Management Information Systems

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  1. Name of sponsoring organization: Thinkful
  2. Details of the activity (e.g., where, when): Nov 5th  from 8pm -9:30pm. Link:
  3. What you expect to learn:What is data science
    • Collecting all the info in the company and make something valuable
    • Analytics and predictions

    Big Data (Better GPUs)

    • Capture, store, analyze, manage
    • Web 2.0, mobile, wearables, IoT.

    Data science: Venn diagram of Statistics, Computer Science, and Domain Expertise.

    Project pipeline:

    1. Frame the Questions
    2. Collect the data
    3. Process the data
    4. Explore the data
    5. Make predictions
    6. Communicate results

    Data Science use case

    1. Tala, Microfinance
    • The booming middle class in Kenya
    • Little or no access to financial services
    • Task: How to create a score for people who have a little history
    • Asking questions to determine the variables we need.
    • Such as GSM data, spending habits, social networks, price of airtimes, text
    • Use patterns to make a prediction. Classification problem. Decision tree…
    • Communicate to the product, marketing, finance, C-Level.
    1. Industry: Entertainment recommending, Healthcare predicting illness, Agriculture when seed should be planted.

    Data Science Toolkit:

    Foundation: Stat, Linear algebra

    Applications: Python, SQL


    Supervised: Classification with discrete outcomes. Regression with a continuous outcome

    Unsupervised: Clustering, grouping data in meaningful ways.

    Environment: Jupyter Notebooks

    We then run a logistic regression model on Jupyter Notebook with python to predict the type of flower by using a pre-loaded Iris data set from Sci-kit learn.

  4. How the activity relates to coursework or your career goals: The notes I took above help me to understand data science more.

Professional Achievements

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