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Course overview
Overview
Databases and other collections of data are everywhere. Retailers use data about customers and their purchases to make decisions that increase profits. Researchers analyze data about the human genome to find treatments for diseases. Policymakers analyze socioeconomic data to gain insights that guide their decisions. Online music and video services perform data mining to deliver customized recommendations. How does all this work? CS 105 explores the ways in which collections of data are organized, stored, and analyzed. Topics include relational databases and the SQL query language, the writing of simple programs to process data, data visualization and the graphical display of data, and data-mining techniques for discovering patterns in data. Applications from a variety of domains (including business, the arts, the life sciences, and the social sciences) are used to illustrate the course's key concepts.
Curriculum for this course
32 Lessons
03:46:04 Hours
Lecture
32 Lessons
03:46:04 Hours
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Fundamental Facts About Data
Preview 00:06:19
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The Relational Model
00:03:25
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Keys, Candidate Keys, and Primary Keys
00:05:42
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Capturing Relationships Using Foreign Keys
00:06:00
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Constraints and Null Values
00:07:11
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SQL: Simple SELECT Commands
00:07:43
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SQL: Pattern Matching; Comparisons Involving NULL
00:06:07
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SQL: Removing Duplicates; Aggregate Functions
00:05:33
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Subqueries in SQL
00:06:52
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SQL: Queries Involving Subgroups
00:05:55
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SQL: Data Types; Creating Tables and Inserting Rows
00:08:54
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SQL: Cartesian Product; Joins
00:06:10
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SQL: Joins Revisited
00:09:24
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SQL: Outer Joins
00:07:05
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SQL: Other Commands
00:07:02
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Getting Started with Python
00:05:51
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Python Building Blocks
00:09:54
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Python: Built-In Functions and User Input
00:07:04
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Python: A First Look at Lists; the range() Function
00:02:37
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for Loops in Python
00:04:29
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Writing Your Own Functions
00:08:29
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Cumulative Computations
00:04:09
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Making Decisions: Conditional Execution
00:08:43
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Working with Strings and Lists
00:08:26
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Using Objects; Splitting and Joining Strings
00:09:00
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Accessing a Database from Python
00:09:25
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Working with Text Files; File Writing
00:06:40
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Reading from a Text File
00:08:09
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Data Mining Fundamentals
00:07:48
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Evaluating a Model Learned in Data Mining
00:07:44
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Classification Learning Using 1R
00:08:39
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Classification Learning: Learning a Decision Tree
00:09:35
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