[Lecture Notes]Introduction to Data Science I, University of Maryland

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Introduction to Data Science I: Preparing, Storing, and Manipulating Data


Following is a tentative schedule of the topics we plan to cover and what the assignements will focus on. More details will be added as the course progresses.Note about assignments: One goal of this class is to get you to be comfortable with using a wide variety of tools (most of which are listed below). You are NOT expected to learn these tools on your own; we will provide step-by-step guidance on getting started with the tools and the actual assignments will be simple.
Note about readings: The links to the two textbooks can only be accessed when you are on the UMD network, because UMD has subscription for the Safari Online Books service.
[url=]If you are enrolled in the CMSC828, click here to see the assigned readings[/url]

[td]

Date	Lecture Topics and Materials	Assignments
Tue 9/2	Introduction: What is data science. Major tools used by data scientists. Class overview. Lecture Notes.Readings: Doing Data Science; Chapter 1 CACM Blog Post by Philip Guo References: Python for Data Analysis: Chapter 3: IPython Python for Data Analysis: Appendix on Python Language Essentials	Lab 0: Basic usage of github, VirtualBox, IPython Notebook (Due 9/12)
Thu 9/4	Basic Statistics: statistical tests, samples, fallacies. Lecture Notes.Readings: Doing Data Science; Chapter 2 Doing Data Science: Chapter 13: Causality References: Python for Data Analysis: Chapters 4, 5, 8
Tue 9/9	Basic Statistics: linear regression, classification, clustering. Lecture Notes.Readings: Doing Data Science: Chapter 3: Algorithms	Lab 1: Python basic stats and plotting (Due 9/19)
Thu 9/11	Data Models: Overview, Why modeling is essential, Commonly used models (Relational, JSON, Protocol Buffers) Lecture Notes.
Tue 9/16	Relational Databases, SQL Lecture Notes.	Lab 2: Basic SQL; Python Pandas and Dataframes; Avro (Due 10/3)
Thu 9/18	(cntd)
Tue 9/23	(cntd)
Thu 9/25	Data scraping and wrangling, Unix tools, GUIs Lecture Notes.	Lab 3: Advanced SQL and Pandas (Due 10/10)
Tue 9/30	(cntd)
Thu 10/2	Data Integration: Overview, Schema mapping, Entity Resolution (Lecture Notes Continued)	Lab 4: Data cleaning using unix tools, Data Wrangler (Due 10/17)
Tue 10/7	(cntd)
Thu 10/9	Information Extraction: Overview, Key Techniques (Lecture Notes Continued)	Lab 5: Entity Resolution and Information Extraction (Due 10/28)
Tue 10/14	Implementation of Relational Databases Lecture Notes.
Thu 10/16	(cntd)
Tue 10/21	Distributed programming frameworks: Parallel Databases, MapReduce, Apache Spark, Hadoop Ecosystem Lecture Notes.	Lab 6: Hadoop, Spark (Due: 11/7)
Thu 10/23	MIDTERM
Tue 10/28	(Cntd Distributed Programming Frameworks)
Thu 10/30	(cntd)
Tue 11/4	(cntd)	Lab 7: Cassandra and MongoDB (Due: 11/17)
Thu 11/6	(cntd)
Tue 11/11	Key-value stores: Basics, Differences from Relational Databases, Consistency/Replication issues Lecture Notes.	Lab 8: Spark Streaming, Storm (Due: 11/26)
Thu 11/13	(cntd)
Tue 11/18	Visualization: D3.js (see Lab 10 for notes)
Thu 11/20	Data streaming/Real-time analytics: Data streams in relational databases, Spark Streaming, StormLecture Notes.	Lab 9: Neo4j, GraphX (Due: 12/8)
Tue 11/25	(cntd)
Tue 12/2	Graph Databases and Graph Analytics Lecture Notes.	Lab 10: D3 (Due: 12/11)
Thu 12/4	(cntd)
Tue 12/9	Cloud computing: Overview, Virtualization, Data centers, Platform/Infrastrcture-as-a-Service Lecture Notes.
Thu 12/11	(cntd)

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