lecture 1 introduction to data structures and algorithms

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Lecture 1Introduction to Data Structures

and Algorithms

ECE 241 – Advanced Programming IFall 2021

Mike Zink

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ECE 241 – Data Structures Fall 2020 © 2020 Mike Zink

Overview

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• Introduction of data structures and algorithms• Information about course

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What is an algorithm?

• Well-defined computational procedure• Takes set of values as input and produces set

of values as output• Tool to solve well defined computational

problem

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Algorithm example: Sorting numbers

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• Input: A sequence of n numbers: <a1, a2, …, an>• Output: A permutation (reordering)

<a’1, a’2, …, a’n> of input sequence such that a’1 <= a’2 <= … <= a’n• Input: <31, 41, 59, 26, 41, 58>

Output: <26, 31, 41, 41, 58, 59>• Since sorting is essential, a large number of

sorting algorithms exist

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A Data-centric View of the World

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• Data are important in many disciplines• DNA sequences, images, network traffic, sensor readings,

maps, customer profiles, etc.• COVID-19 related data across all disciplines

• Past ECE focus: processing• Computation at core: processors, computers, etc.

• New ECE focus: data• Data at core: data centers, data security, data science, etc.

• Need structured approach to handling data

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What kind of problems are solved by algorithms?

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• Human Genome Project• Internet: Routing, searches, security• Electronic commerce• Commercial enterprises• …

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Human Genome Project

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• Complete mapping and understanding of all the genes of human beings• ~20,500 human genes • ~3 billion chemical base pairs that make up

human DNA• Requires algorithms to store and analyze

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Infectious Disease Modeling

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• Flu forecast• COVID-19: forecast of death and

hospitalization• Generate computer models (in form of

algorithms) to make predictions• Statistical modeling

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Internet

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• Routing algorithms that find shortest path and adapt to changes• Search engines that find pages with particular

information• Encrypted communication• Queue management

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Electronic Commerce

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• Ad placements• Google search• Recommendations: Netflix, Amazon, etc.• Hashing for authentication• Electronic trading

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Manufacturing and Commercial Enterprises

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• Allocation of scarce resources: • Assigning crews to flights• Routes for package deliveries• Storage and distribution of goods

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Why Data Structures?

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• Way to store and organize data• Facilitate access and modification of data• Different data structures have strengths and

weaknesses• Better suited for a specific algorithm than

others

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Data Structures and Algorithms

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• Data structures• Representation and organization of data

• Algorithms• Methods for implementing operations on data

structures• Data structures and algorithms are closely

related• Data structures are often tuned for certain

algorithms

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Big Data

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• Extremely large volumes of data• Can be computationally analyzed• Internet of Things• Social media content

• Requires algorithms and data structures to analyze

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Artificial Intelligence

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• Often ”trained” on big data sets• Image recognition• Voice recognition

• Relies on a large set of algorithm, varying in complexity• Linear regression• Statistics, probability

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Complexity Problems

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• Data sets can be very large• Operations may take a lot of time or memory• Efficient implementations can make a big difference

• Examples of large data sets and sizes?

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• Example: Million Song Dataset

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• http://labrosa.ee.columbia.edu/millionsong/• Data set of songs: title, artist, recording years, etc.

• How would you figure out• Which artist has recorded most songs?

• Which song has been covered the most times?• What are the most common words in a title?

• What are potential problems?

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What will you learn in the course?

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• How to think about data and operations on data

• How to design data structures for efficient use

• How to determine the efficiency of algorithms• A set of common data structures and algorithms for typical

operations on data (e.g., searching, sorting, etc.)

• Advanced programming techniques (e.g., recursion, etc.)

• How to implement a larger software project with a professional IDE

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http://labrosa.ee.columbia.edu/millionsong/

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Python

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• We will use Python as programming language• Data structures and algorithms are independent of programming

language

• This is not a programming course• We focus on higher level issues• You should already have experience with a programming

language (Python, Java, C/C++)• IDE used in this course: PyCharm• Professional environment

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PyCharm

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• IDE for Pyhton• https://www.jetbrains.com/pycharm/• Very important to become familiar with tool!!• See intro slides for

PyCharm (more during this week’sdiscussion)

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https://www.jetbrains.com/pycharm/

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Course Organization

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• Course web site:http://www.ecs.umass.edu/ece241/

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Course Events

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• Events to attend• Lectures• Discussions

• Assignments• Homework• Projects

• Exams• 2 exams during semester• 1 final exam

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http://www.ecs.umass.edu/ece241/

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Course Policy

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• Read the syllabus in its entirety!• Understand expectations for this course

• Deadlines are posted on web site• No extensions for homework or projects

• Assignments and grades managed through Moodle and Gradescope• No sharing of code• Automated checking of submissions

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Office Hours

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• TAs:• Tuesdays: 5 PM (Guoyi)• Wednesdays 7 PM (Zibin)

• Prof. Zink:• Fridays 8 – 9 AM

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Properties of Data Structures and Algorithms

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Quality Measures for Data Structures

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• What do you expect from a good data structure?

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Quality Metrics for Algorithms

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• What do you expect from a “good” algorithm?

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Correctness

• In some cases absolute• E.g., what is the sum of all values in array?

• Some algorithms have multiple solutions• Examples?

• Some algorithms may have solutions where correctness is hard to define• Examples?

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Multiple solutions

• Data set for number of wins by pitcher (2011):• {(Halladay, 21),

(Jiminez, 19),(Lester,19),(Price,19),(Sabathia,21),(Wainwright,20)}

• Which pitcher has most wins?28

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Unclear “correctness”

• Make image square:

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?

?

?

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Running time performance

• “The algorithm finishes in 12 seconds.”• How to express performance more accurately?

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Memory use performance

• “The data structure requires 727 bytes.”• How to express memory requirements in a more

meaningful way?

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It all depends…

• Quality of data structure depends on operation• Example: unsorted “pile” of papers• Very fast for adding item• Not very fast for finding item

• Example: papers sorted by last name• Slower for adding item• Faster for finding item (if search is by last name)• However: also slow if search is by first name

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Data types

• What are typical data types to store information?

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Data types

• Common types• Numeric types: integer, floating point• Boolean• Text types: character, string• Composite types• References (“pointer”)

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Algorithms and data types• Many algorithms can be used with different data types

• Sorting of integers• Sorting of strings• Sorting of composite types

• May require small adaptation in code• Different comparison functions• Different code for “moving data around”

• Why is this point important?• I do not want you to leave this course saying:

“Useless. We only learned how to sort numbers!”35

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Objects• For algorithm discussion, we can think of “objects”

in a very general way• Operations on objects• Creation (and destruction)• Comparison

• Operations on objects in data structures• Insertion• Deletion• Algorithm-specific operations: search, sort, etc. 36

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Next Steps

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• Next lecture “Asymptotic Notation and Merge Sort” on Tuesday

• First discussion TODAY!!• Will post first HW later today

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lecture 1 introduction to data structures and algorithms

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