- Name of sponsoring organization: Thinkful
- Details of the activity (e.g., where, when): Nov 5th from 8pm -9:30pm. Link: https://app.livestorm.co/thinkful/started-data-science/live?s=d03f66cd-3631-485d-a036-6a86ecedbb34#/chat
- 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.
- Frame the Questions
- Collect the data
- Process the data
- Explore the data
- Make predictions
- Communicate results
Data Science use case
- 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.
- 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.
- How the activity relates to coursework or your career goals: The notes I took above help me to understand data science more.