mbds21

Midwest Big Data Summer School 2021 files Adisak Sukul

Tutorial 1: Introduction to Data Science with Python

Topics:

1. Introduction on Data Science
2. Basic data exploration and visualization techniques
3. Foundational Statistics concepts for data analysis
4. Python programming for Data Science: data types, Selections, Iterations, Functions, and Working with Lists
5. Working with Datasets and Data Frames: Working with Pandas Dataframes
   • Task 1: Intro to pandas - practice on colab
   • Task 2: Lemonade Sale with pandas
6. Data Manipulation and Visualization
   • Task 3: Youtube Data Analysis
7. Data Science Process: How to do the Data Project
8. Introduction to Interactive Data Visualization with Bokeh
   • Task 4: Bokeh Quickstart
9. Intro to Machine Learning with scikit-learn: Classification and Regression (this will covered in Wednesday’s session)

Slides: MBDS21-T1-01 - Introduction to Data Science https://docs.google.com/presentation/d/1aME45GBR1rxftMW1R4dMhjP2YOHgMUyAtjNcYGelPEI

MBDS21-T1-02 - Foundational statistics that can be used to analyze data https://docs.google.com/presentation/d/1ibVD7JqrmjvvkWusk2VZ5LaYdP5kmIXOyb28FrtiRcg

MBDS21-T1-03–Python for Data Science https://docs.google.com/presentation/d/1d9NT9Lv5-dpmp7slwcNEyuuHv4_EN_6W0eh38eKiEEU

MBDS21-T01-04 – Pandas Dataframe https://docs.google.com/presentation/d/1FXN4I3zsVOVu2twOx5U9W7L0LG8v01Ok0M2aEzAnnqY

MBDS21-T1-05 – Cleaning and Manipulating Data with pandas https://docs.google.com/presentation/d/1dgSWRXTYpEdzFvysJs9PREWI_QqT4orS1NmU05ZZlXE

MBDS21-T1-06 - Data Science Process https://docs.google.com/presentation/d/1tn0gKWNdOe-SqR-RfbLFi72sjY9MjDg2Ny6t0MOChWs

MBDS21-T1-07– Introduction to Visualization with Bokeh https://docs.google.com/presentation/d/1NNUocs7hfpm2rkgIaXLhriv2wyDeV2llQY_JWH3dqFE

=================================================================================== Tutorial 2:

Practical Data Science and Machine Learning on the Cloud Topics:

1. Introduction to machine learning in the cloud. Task 1: Classification sample: https://github.com/asukul/mbds21/blob/main/Intro_to_Machine_learning_Classifier_Iris_KNN.ipynb Task 2: Regression sample

2. Building machine learning models with AI Platform: TensorFlow, AI Platform, and the ML workflow AutoML. Task 2: Try Quiklabs quest "Baseline: Data, ML, AI" https://google.qwiklabs.com/quests/34

3. Google's pre-trained machine learning APIs: Vision API, Text-to-Speech API, Speech-to-Text API, Cloud Translation API, Cloud Natural Language API, and Video Intelligence API Perform Foundational Data, ML, and AI Tasks Task 3: Qwiklabs lab "Video Intelligence: Qwik Start" https://google.qwiklabs.com/focuses/603?parent=catalog

Software: We are going to use Google Cloud Platform in this session.

Software requirement: If you are affiliate with academic, and having .edu email:

Join the GoogleCloudReady program: Link to the form - https://goo.gle/us-gcrf-site enroll in the program with your edu email address. Choose Iowa State University as a "facilitator's institution". Signup for Qwiklabs with your edu email. Then go through this first lab to get a free 1 month subscription

• Open an incognito window. Note: Prior instructions incorrectly asked students to log into Qwiklabs before clicking the link below. This is incorrect and they should click the link below before logging into Qwiklabs to ensure they get their subscription.
• Go to https://goo.gle/us-gcrfp-stu-credits-unique and sign into Qwiklabs.
• Complete the "A Tour of Qwiklabs and Google Cloud" lab. (Spend at least 5 minutes, though taking the full tour is very helpful!)
• Be sure to hit the "end lab" button.
• Note: you can verify that you've received a "monthly subscription" under your profile at the top right corner.

If you do not have the .edu email address: Create a free trial on Google Cloud with your Gmail account. https://cloud.google.com/

Slides:

MBDS21-T2-01 - Machine Learning: Classification and Regression https://docs.google.com/presentation/d/10AA38Pe5y6PHa6QWTJjFLuSjbevkm3EV96QLFydv7s8

MBDS21 - Google Cloud: Let Machines Do the Work https://docs.google.com/presentation/d/1tONRX6gR_t-Cs2_UgOE7llXhr1FA6qYjNWBG5sfAbmI