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dslab2018.github.io

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Website for the EPFL Lab in Data Science 2018

dslab2018.github.io 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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## Description

This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up prototypes to Spark clusters. It exposes the students to the entire data science pipeline, from data acquisition to extracting valuable insights applied to real-world problems.

## Questions

Questions and discussions about the course are gathered on mattermost: https://mattermost-dslab.epfl.ch

## Virtual Machine
- Link: <https://os.unil.cloud.switch.ch/swift/v1/DataScienceLab/vm-lab-data-science.ova>

## Lab Sessions

### Week 1 - 21.02.2018 - Module 1 - Python for data scientists 1/4
- Slides: [week 1](/slides/DSLab_week1.pptx)
- Python Quick Reference: [notebook](http://nbviewer.jupyter.org/github/justmarkham/python-reference/blob/master/reference.ipynb)
- Exercises: [download](/notebooks/DSLab_week1_CCPython.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week1_CCPython.ipynb)

### Week 2 - 28.02.2018 - Module 1 - Python for data scientists 2/4
- Slides: [week 2](/slides/DSLab_week2.pptx)
- Solutions to last week's exercises: [download](/notebooks/DSLab_week1_CCPython-Solution.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week1_CCPython-Solution.ipynb)
- Exercises - Set #1: [download](/notebooks/DSLab_week2_PythonDSLibs-NumPy_Matplotlib.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week2_PythonDSLibs-NumPy_Matplotlib.ipynb)
- Exercises - Set #2: [download](/notebooks/DSLab_week2_PythonDSLibs-Pandas_sklearn.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week2_PythonDSLibs-Pandas_sklearn.ipynb)

### Week 3 - 07.03.2018 - Module 1 - Python for data scientists 3/4

- Slides: [week 3](/slides/DSLab_week3.pptx)
- Solutions to last week's exercises #1: [download](/notebooks/DSLab_week2_PythonDSLibs-NumPy_Matplotlib-Solution.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week2_PythonDSLibs-NumPy_Matplotlib-Solution.ipynb)
- Solutions to last week's exercises #2: [download](/notebooks/DSLab_week2_PythonDSLibs-Pandas_sklearn-Solution.ipynb) - [view on github](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week2_PythonDSLibs-Pandas_sklearn-Solution.ipynb)
- Exercises: [Instructions](/labs/week3/)
- Docker cheat sheet to get started and as reference: <https://github.com/wsargent/docker-cheat-sheet>

### Week 4 - 14.03.2018 - Module 1 - Python for data scientists 4/4

- Slides presenting the graded homework: [week 4](/slides/DSLab_week4.pptx)
- Solutions to last week's exercises: [solutions](/labs/week3/Solutions)
- Setup instructions: [Instructions](/labs/week4/)
- Feedback on graded homework: [Feedback](./slides/DSLab_Module1_Assignment_Feedback.pptx)
- Solutions for the graded homework: [Notebook](https://git-dslab.epfl.ch/dslab2018/homework1/blob/solutions/SOLUTION_Assignment_DataScience_Lab_week4.ipynb)

### Week 5 - 21.03.2018 - Module 2 - Distributed computing with Hadoop 1/2

- Slides: [week 5](/slides/DSLab_week5.pptx)
- Exercises: [Instructions](labs/week5/)

### Week 6 - 28.03.2018 - Module 2 - Distributed computing with Hadoop 2/2

- Slides: [week 6](/slides/DSLab_week6.pptx)
-  Solutions to last week's exercises: [solutions](https://raw.githubusercontent.com/dslab2018/dslab2018.github.io/master/notebooks/DSLab-Week5-answers.json) (Right click and copy the url to import it into Zeppelin)
- Setup instructions: [Instructions](/labs/week6/)

### Week 7 - 11.04.2018 - Module 3 - Distributed processing with Apache Spark 1/3

- Slides: [week7](/slides/DSLab_week7)
- Exercises: [Instructions](/labs/week7/)

### Week 8 - 18.04.2018 - Module 3 - Distributed processing with Apache Spark 2/3

- Solutions to last week's exercises: [solutions](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week7_gutenberg_solutions.ipynb)
- Slides: [week8](/slides/DSLab_week8)
- Exercises: [Instructions](/labs/week8/)

### Week 9 - 25.04.2018 - Module 3 - Distributed processing with Apache Spark 3/3

- Solutions to last week's exercises: [solutions](https://github.com/dslab2018/dslab2018.github.io/blob/master/notebooks/DSLab_week8_language_classification_solutions.ipynb)
- Homework 3: [repository](https://git-dslab.epfl.ch/dslab2018/homework3-spark) - start by reading the README

### Week 10 - 02.05.2018 - Module 4 - Real-time data acquisition and processing 1/2

- Slides: [week10](/slides/DSLab_week10.pptx)
- Exercises: [Instructions](/labs/week10/)

### Week 11 - 09.05.2018 - Module 4 - Real-time data acquisition and processing 2/2

- Solutions to last week's exercises: [solutions](https://git-dslab.epfl.ch/dslab2018/week10-stream-processing/blob/master/Week10-stream-processing-solutions.ipynb)
- Homework 4: [repository](https://git-dslab.epfl.ch/dslab2018/homework4) - start by reading the README

### Week 12 - 16.05.2018 - Module 5 - Final Project 1/3

- Slides: [Final assignment](/slides/DSLab_week12.pptx)
- Final assignment: [repository](https://git-dslab.epfl.ch/dslab2018/final_project) - start by reading the README

### Week 13 - 23.05.2018 - Module 5 - Final Project 2/3

### Week 14 - 30.05.2018 - Module 5 - Final Project 3/3

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