**Introduction to Computer Science and Programming using Python**

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This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

Content

Unit 1

We will start the semester by discussing the difference between imperative knowledge and definitional knowledge, as well as between fixed program and stored program computers, and finally the definitions of syntax, static semantics, and semantics. We cover straight line, branching, and looping programs. Other topics are binary representation of numbers, orders of growth, and debugging programs.

Python concepts covered in this unit include values, types, int, float, Boolean, strings (str), tuples, dictionaries (dict), and lists. We will also learn about expressions and statementsâ€”especially how to effectively use print statements in your programs. Other topics include assignment, conditionals, loops, assert, functions, scope, object models, mutation, and mutability.

By the end of Unit 1 you should be familiar with the following algorithmic techniques: guess and check, linear search, bisection search, successive approximation, and Newton-Raphson (Newton's method). You will also learn recursive definitions, problem solving techniques, and how to structure programs using decomposition and abstraction, including specifications and parameters.

Unit 1 ends with a quiz covering all material (lectures, recitations, and problem sets) through Efficiency and Order of Growth.

Session 1

This lecture covers course expectations, introduces computer programming and its uses, and begins to familiarize the student with concepts related to how programs work.

Lecture 1: Introduction to 6.00

Topics covered: Purposes of the course, declarative and imperative knowledge, flow of control, algorithms, fixed program and stored program computers, termination conditions, interpretation, compilation, syntax, static semantics, semantics, and types of errors.

Session 2

This lecture covers the building blocks of straight line and branching programs: Objects, types, operators, variables, execution, and conditional statements. It also discusses common errors related to the topics covered.

Lecture 2: Core Elements of a Program

Topics covered: IDLE, types of objects, operators, overloading, commands, variables, assignment, input, straight line and branching programs, looping constructs, turing completeness, conditionals, nesting.

Recitation 1: Introduction to Coding Concepts

Topics covered: Syntax, semantics, object types, comparison, loops, coding.

Session 3

This lecture covers the use of iteration to build programs whose execution time depends upon the size of inputs. It also introduces search problems and brute force and bisection for solving them.

Lecture 3: Problem Solving

Topics covered: Termination, decrementing functions, exhaustive enumeration, brute force, while loop, for loop, approximation, specifications, bisection search.

Session 4

This lecture introduces the notion of decomposition and abstraction by specification. It also covers Python modules, functions, parameters, and scoping. Finally, it uses the Python assert statement and type 'str'.

Lecture 4: Machine Interpretation of a Program

Topics covered: Decomposition, module, function, abstraction, formal parameter, actual parameter, argument, assert, scope, mapping, stack, last in first out, LIFO, strings, slicing.

Recitation 2: Loops, Tuples, Strings and Functions

Topics covered: Loops, tuples, concatenating tuples and strings, string operations, immutability, range function, slicing, types of data structures, decrementing function, global and local variables, global keepers.

Session 5

This lecture introduces Python tuples, lists, and dictionaries, as well as the concept of mutability and how to avoid problems relating to it.

Lecture 5: Objects in Python

Topics covered: Tuples, lists, dictionaries, methods, identifiers, modifying objects, aliasing, mutability.

Session 6

This lecture finishes the discussion of dictionaries, then introduces inductive reasoning and recursion. Examples include generating the Fibonacci sequence and solving the Towers of Hanoi problem.

Lecture 6: Recursion

Topics covered: Dictionaries, modular abstraction, divide and conquer, recursion, tower of Hanoi, base case, Fibonacci sequence.

Recitation 3: Lists and their Elements, Sorting, and Recursion

Topics covered: Tuples, lists, iteration, list elements, sorting lists, mutability, keys, dictionaries, chain method, recursion, base case, Tower of Hanoi.

Session 7

This lecture starts with a brief explanation of why floating point numbers are only an approximation of the real numbers. Most of the lecture is about a systematic approach to debugging.

Lecture 7: Debugging

Topics covered: Binary, float, floating point, approximations, debugging, runtime error.

Recitation 4: Recursion, Pseudo code and Debugging

Topics covered: Recursion, divide and conquer, base cases, iterative vs. recursive algorithms, Fibonacci numbers example, recursive bisection search, optional and default parameters, pseudo code, introduction to debugging, test cases and edge cases, and floating points.

Session 8

This lecture revolves around the topic of algorithmic efficiency. It introduces the random access model (RAM) of computation and "big O notation" as a way to talk about order of growth. It concludes with binary search.

Lecture 8: Efficiency and Order of Growth

Topics covered: Efficiency, problem reduction, RAM, best case, worst case, expected case, growth, exponential growth, polynomial growth, logarithmic growth, global variables.

Session 9

This lecture discusses how indirection is used to provide an efficient implementation of Python lists and other data structures. It also presents and analyzes the efficiency of selection and merge sort.

Lecture 9: Memory and Search Methods

Topics covered: Memory, storage, indirection, sorting.

Quiz I

Unit 2

Unit 2 begins with hash functions, which are useful for mapping large data sets. We will continue with a broad introduction to object-oriented programming languages (Python is an example), covering objects, classes, subclasses, abstract data types, exceptions, and inheritance. Other algorithmic concepts covered are "Big O notation," divide and conquer, merge sort, orders of growth, and amortized analysis.

The next several lectures introduce effective problem-solving methods which rely on probability, statistical thinking, and simulations to solve both random and non-random problems. A background in probability is not assumed, and we will briefly cover basic concepts such as probability distributions, standard deviation, coefficient of variation, confidence intervals, linear regression, standard error, and plotting techniques. This will include an introduction to curve fitting, and we introduce the Python libraries numpy and pylab to add tools to create simulations, graphs, and predictive models.

We will spend some time on random walks and Monte Carlo simulations, a very powerful class of algorithms which invoke random sampling to model and compute mathematical or physical systems. The Monty Hall problem is used as an example of how to use simulations, and the knapsack problem introduces our discussion of optimization. Finally, we will begin looking at supervised and unsupervised machine learning, and then turn to data clustering.

At the end of Unit 2 there will be an exam covering all material (lectures, recitations, and problem sets) from the beginning of the course through More Optimization and Clustering.

Session 10

This lecture starts by showing how hashing can be used to achieve near constant time lookups and the concept of classes as understood by a computer. It then introduces exceptions.

Lecture 10: Hashing and Classes

Topics covered: Hashing, bucket, collision, linear rehash, exceptions, classes, modules, built-in classes.

Session 11

In this lecture, we learn about object-oriented programming (OOP) and how classes are used to implement new types of objects in Python. As part of that discussion we introduce inheritance.

Lecture 11: OOP and Inheritance

Topics covered: Object-oriented programming (OOP), abstract data types, specifications, subclasses, inheritance.

Recitation 5: Quiz 1 Answers and Object-Oriented Programming

Topics covered: Double recursion, big O notation, binary function, run times, object-oriented programming, classes, encapsulation, methods, class hierarchy, subclasses, inheritance, polymorphism, accessor and mutator functions, Person example, underbar methods, self parameter.

Session 12

This lecture completes the introduction of classes by showing a way to implement user-defined iterators. It then starts a new unit with a discussion of simulation models, and illustrates some of the ideas underlying simulations modeling by simulating a random walk.

Lecture 12: Introduction to Simulation and Random Walks

Topics covered: Subclasses, inheritance, generator, analytic methods, simulation methods, simulations, models, random walk.

Optional Recitation: Algorithm Complexity and Class Review

Topics covered: Big O notation, algorithm complexity, algorithm comparison example, object-oriented programming, Person class example, defensive programming, private attributes, mutability, aliasing.

Session 13

This lecture returns briefly to random walks, and moves on to discuss different views of non-determinism and an introduction to probability. It concludes with examples of using pylab to plot data.

Lecture 13: Some Basic Probability and Plotting Data

This lecture begins a deeper look at simulations with an introduction to randomness, probability, and plotting through programming.

Session 14

This lecture starts with some examples of how to use pylab's plotting mechanisms. It then returns to the topic of using probability and statistics to derive information from samples.

Lecture 14: Sampling and Monte Carlo Simulation

Topics covered: Plotting, randomness, probability, Pascal's algorithm, Monte Carlo simulation, inferential statistics, gambler's fallacy, law of large numbers.

Session 15

This lecture presents ways of ascertaining how dependable information extracted from samples is likely to be. It covers standard deviation, coefficient of variation, and standard error. It also shows how to use pylab to produce histograms.

Lecture 15: Statistical Thinking

Topics covered: Variance, standard deviation, standard error.

Recitation 6: Probability and Statistics

Topics covered: Probability, statistics, Venn diagrams, distributions, standard deviation, Monte Carlo simulation, plotting graphs.

Session 16

This lecture starts by defining normal (Gaussian), uniform, and exponential distributions. It then shows how Monte Carlo simulations can be used to analyze the classic Monty Hall problem and to find an approximate value of pi.

Lecture 16: Using Randomness to Solve Non-random Problems

Topics covered: Gaussian distributions, analytical models, simulations, exponential growth, probability, distributions, Monty Hall problem.

Session 17

This lecture is about how to use computation to help understand experimental data. It talks about using linear regression to fit a curve to data, and introduces the coefficient of determination as a measure of the tightness of a fit.

Lecture 17: Curve Fitting

Topics covered: Arrays, curve fitting, numpy, pylab, least squares fit, prediction.

Recitation 7: Distributions, Monte Carlo, and Regressions

Topics covered: Data distributions, mean, standard deviation, Monte Carlo simulations, Monty Hall problem, Riemann sum method, data regressions, r^2 (r-squared), coefficient of termination, scientific applications of programming.

Session 18

This lecture returns to material covered in Lecture 17 Curve Fitting, emphasizing the interplay among theory, experimentation, and computation and addressing the problem of over-fitting. It then moves on to introduce the notion of an optimization problem, and illustrates it using the 0/1 knapsack problem.

Lecture 18: Optimization Problems and Algorithms

Topics covered: Modeling, optimization, greedy algorithms, 0-1 knapsack problem.

Session 19

This lecture continues to discuss optimization in the context of the knapsack problem, and talks about the difference between greedy approaches and optimal approaches. It then moves on to discuss supervised and unsupervised machine learning optimization problems. Most of the time is spent on clustering.

Lecture 19: More Optimization and Clustering

Topics covered: Knapsack problem, local and global optima, supervised and unsupervised machine learning, training error, clustering, linkage, feature vectors.

Quiz II

Unit 3

In Unit 3 we pick up our discussion of data clustering from the end of Unit 2. We introduce graphs as a set of nodes and edges, and learn how these can help solve degrees-of-separation problems and to find a shortest path. We will practice using pseudocode as preparation for writing code, and learn about dynamic programming as we attempt to write optimally efficient programs.

In order to become better statistical thinkers we will learn to spot and avoid several common logical and statistical fallacies like bias, data enhancement, causal fallacies, and the Texas sharpshooter fallacy. In the penultimate lecture we introduce queuing network simulations and important factors like the arrival process, service mechanism, and queue characteristics. We will compare the most common queue disciplines such as first-in-first-out, last-in-first-out, or shortest-remaining-processing-time. The last class presents different possible careers in computer science, and its application across diverse fields and industries.

Unit 3 concludes with a Final Exam covering all material (lectures, recitations, and problem sets) from the beginning of the course through Queuing Network Models.

Session 20

This lecture covers hierarchical clustering and introduces k-means clustering.

Lecture 20: More Clustering

Topics covered: Feature vectors, scaling, k-means clustering.

Recitation 8: Hierarchical and k-means Clustering

Topics covered: Unsupervised learning, k-means clustering, distance metric, cluster merging, centroid, k-mean error, hold out set, k value significance, features of k-means clustering, merits and disadvantages of types of clustering.

Session 21

This lecture begins by finishing up k-means clustering. It then moves on to introduce the notion of modeling things using graphs (sets of nodes and edges that link them).

Lecture 21: Using Graphs to Model Problems, Part 1

Topics covered: Pseudocode, graphs, nodes, edges, adjacency matrix, adjacency list.

Session 22

This lecture returns to graph theory. It defines and gives examples of some classic graph problems: shortest path, shortest weighted path, cliques, and min-cut. It then shows how memoization can be used to speed up some algorithms.

Lecture 22: Using Graphs to Model Problems, Part 2

Topics covered: Dynamic programming, optimal path, overlapping subproblems, weighted edges, specifications, restrictions, efficiency, pseudo-polynomials.

Recitation 9: Directed and Undirected Node Graphs

Topics covered: Node graphs, nodes, edges, directed and undirected graphs, weighted edges, depth-first search, breadth-first search, graph cycles, children of nodes, shortest path, lambda functions.

Session 23

This lecture introduces dynamic programming, and discusses the notions of optimal substructure and overlapping sub-problems.

Lecture 23: Dynamic Programming

Topics covered: Dynamic programming, optimal path, overlapping subproblems, weighted edges, specifications, restrictions, efficiency, pseudo-polynomials.

Session 24

This lecture discusses some common ways that people use statistics to draw invalid or misleading conclusions.

Lecture 24: Avoiding Statistical Fallacies

Topics covered: Statistics, plotting, correlation, causation, bias, logical fallacies, data enhancement, Texas sharpshooter fallacy.

Recitation 10: Introduction to Dynamic Programming

Topics covered: Dynamic programming, memoization, overlapping subproblems, optional substructure, Fibonacci memoization example.

Session 25

This lecture introduces queuing network models and simulations. The lecture is designed to prepare students to read the code they are asked to study in preparation for the final exam.

Lecture 25: Queuing Network Models

Topics covered: Queuing network simulations, Poisson distributions, wait time, queue length, server utilization, FIFO, LIFO, SRPT.

Session 26

This lecture provides some perspective on the material covered in the course. In addition to giving a high level view of the topics covered, it provides a glimpse of what one might do with an education in computer science.

Lecture 26: What Do Computer Scientists Do?

Topics covered: Careers in computer science, computational thinking, abstraction, automation.

Final Exam

*Genre: Video Tutorial / Computer Science, Development, Programming*This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

Content

Unit 1

We will start the semester by discussing the difference between imperative knowledge and definitional knowledge, as well as between fixed program and stored program computers, and finally the definitions of syntax, static semantics, and semantics. We cover straight line, branching, and looping programs. Other topics are binary representation of numbers, orders of growth, and debugging programs.

Python concepts covered in this unit include values, types, int, float, Boolean, strings (str), tuples, dictionaries (dict), and lists. We will also learn about expressions and statementsâ€”especially how to effectively use print statements in your programs. Other topics include assignment, conditionals, loops, assert, functions, scope, object models, mutation, and mutability.

By the end of Unit 1 you should be familiar with the following algorithmic techniques: guess and check, linear search, bisection search, successive approximation, and Newton-Raphson (Newton's method). You will also learn recursive definitions, problem solving techniques, and how to structure programs using decomposition and abstraction, including specifications and parameters.

Unit 1 ends with a quiz covering all material (lectures, recitations, and problem sets) through Efficiency and Order of Growth.

Session 1

This lecture covers course expectations, introduces computer programming and its uses, and begins to familiarize the student with concepts related to how programs work.

Lecture 1: Introduction to 6.00

Topics covered: Purposes of the course, declarative and imperative knowledge, flow of control, algorithms, fixed program and stored program computers, termination conditions, interpretation, compilation, syntax, static semantics, semantics, and types of errors.

Session 2

This lecture covers the building blocks of straight line and branching programs: Objects, types, operators, variables, execution, and conditional statements. It also discusses common errors related to the topics covered.

Lecture 2: Core Elements of a Program

Topics covered: IDLE, types of objects, operators, overloading, commands, variables, assignment, input, straight line and branching programs, looping constructs, turing completeness, conditionals, nesting.

Recitation 1: Introduction to Coding Concepts

Topics covered: Syntax, semantics, object types, comparison, loops, coding.

Session 3

This lecture covers the use of iteration to build programs whose execution time depends upon the size of inputs. It also introduces search problems and brute force and bisection for solving them.

Lecture 3: Problem Solving

Topics covered: Termination, decrementing functions, exhaustive enumeration, brute force, while loop, for loop, approximation, specifications, bisection search.

Session 4

This lecture introduces the notion of decomposition and abstraction by specification. It also covers Python modules, functions, parameters, and scoping. Finally, it uses the Python assert statement and type 'str'.

Lecture 4: Machine Interpretation of a Program

Topics covered: Decomposition, module, function, abstraction, formal parameter, actual parameter, argument, assert, scope, mapping, stack, last in first out, LIFO, strings, slicing.

Recitation 2: Loops, Tuples, Strings and Functions

Topics covered: Loops, tuples, concatenating tuples and strings, string operations, immutability, range function, slicing, types of data structures, decrementing function, global and local variables, global keepers.

Session 5

This lecture introduces Python tuples, lists, and dictionaries, as well as the concept of mutability and how to avoid problems relating to it.

Lecture 5: Objects in Python

Topics covered: Tuples, lists, dictionaries, methods, identifiers, modifying objects, aliasing, mutability.

Session 6

This lecture finishes the discussion of dictionaries, then introduces inductive reasoning and recursion. Examples include generating the Fibonacci sequence and solving the Towers of Hanoi problem.

Lecture 6: Recursion

Topics covered: Dictionaries, modular abstraction, divide and conquer, recursion, tower of Hanoi, base case, Fibonacci sequence.

Recitation 3: Lists and their Elements, Sorting, and Recursion

Topics covered: Tuples, lists, iteration, list elements, sorting lists, mutability, keys, dictionaries, chain method, recursion, base case, Tower of Hanoi.

Session 7

This lecture starts with a brief explanation of why floating point numbers are only an approximation of the real numbers. Most of the lecture is about a systematic approach to debugging.

Lecture 7: Debugging

Topics covered: Binary, float, floating point, approximations, debugging, runtime error.

Recitation 4: Recursion, Pseudo code and Debugging

Topics covered: Recursion, divide and conquer, base cases, iterative vs. recursive algorithms, Fibonacci numbers example, recursive bisection search, optional and default parameters, pseudo code, introduction to debugging, test cases and edge cases, and floating points.

Session 8

This lecture revolves around the topic of algorithmic efficiency. It introduces the random access model (RAM) of computation and "big O notation" as a way to talk about order of growth. It concludes with binary search.

Lecture 8: Efficiency and Order of Growth

Topics covered: Efficiency, problem reduction, RAM, best case, worst case, expected case, growth, exponential growth, polynomial growth, logarithmic growth, global variables.

Session 9

This lecture discusses how indirection is used to provide an efficient implementation of Python lists and other data structures. It also presents and analyzes the efficiency of selection and merge sort.

Lecture 9: Memory and Search Methods

Topics covered: Memory, storage, indirection, sorting.

Quiz I

Unit 2

Unit 2 begins with hash functions, which are useful for mapping large data sets. We will continue with a broad introduction to object-oriented programming languages (Python is an example), covering objects, classes, subclasses, abstract data types, exceptions, and inheritance. Other algorithmic concepts covered are "Big O notation," divide and conquer, merge sort, orders of growth, and amortized analysis.

The next several lectures introduce effective problem-solving methods which rely on probability, statistical thinking, and simulations to solve both random and non-random problems. A background in probability is not assumed, and we will briefly cover basic concepts such as probability distributions, standard deviation, coefficient of variation, confidence intervals, linear regression, standard error, and plotting techniques. This will include an introduction to curve fitting, and we introduce the Python libraries numpy and pylab to add tools to create simulations, graphs, and predictive models.

We will spend some time on random walks and Monte Carlo simulations, a very powerful class of algorithms which invoke random sampling to model and compute mathematical or physical systems. The Monty Hall problem is used as an example of how to use simulations, and the knapsack problem introduces our discussion of optimization. Finally, we will begin looking at supervised and unsupervised machine learning, and then turn to data clustering.

At the end of Unit 2 there will be an exam covering all material (lectures, recitations, and problem sets) from the beginning of the course through More Optimization and Clustering.

Session 10

This lecture starts by showing how hashing can be used to achieve near constant time lookups and the concept of classes as understood by a computer. It then introduces exceptions.

Lecture 10: Hashing and Classes

Topics covered: Hashing, bucket, collision, linear rehash, exceptions, classes, modules, built-in classes.

Session 11

In this lecture, we learn about object-oriented programming (OOP) and how classes are used to implement new types of objects in Python. As part of that discussion we introduce inheritance.

Lecture 11: OOP and Inheritance

Topics covered: Object-oriented programming (OOP), abstract data types, specifications, subclasses, inheritance.

Recitation 5: Quiz 1 Answers and Object-Oriented Programming

Topics covered: Double recursion, big O notation, binary function, run times, object-oriented programming, classes, encapsulation, methods, class hierarchy, subclasses, inheritance, polymorphism, accessor and mutator functions, Person example, underbar methods, self parameter.

Session 12

This lecture completes the introduction of classes by showing a way to implement user-defined iterators. It then starts a new unit with a discussion of simulation models, and illustrates some of the ideas underlying simulations modeling by simulating a random walk.

Lecture 12: Introduction to Simulation and Random Walks

Topics covered: Subclasses, inheritance, generator, analytic methods, simulation methods, simulations, models, random walk.

Optional Recitation: Algorithm Complexity and Class Review

Topics covered: Big O notation, algorithm complexity, algorithm comparison example, object-oriented programming, Person class example, defensive programming, private attributes, mutability, aliasing.

Session 13

This lecture returns briefly to random walks, and moves on to discuss different views of non-determinism and an introduction to probability. It concludes with examples of using pylab to plot data.

Lecture 13: Some Basic Probability and Plotting Data

This lecture begins a deeper look at simulations with an introduction to randomness, probability, and plotting through programming.

Session 14

This lecture starts with some examples of how to use pylab's plotting mechanisms. It then returns to the topic of using probability and statistics to derive information from samples.

Lecture 14: Sampling and Monte Carlo Simulation

Topics covered: Plotting, randomness, probability, Pascal's algorithm, Monte Carlo simulation, inferential statistics, gambler's fallacy, law of large numbers.

Session 15

This lecture presents ways of ascertaining how dependable information extracted from samples is likely to be. It covers standard deviation, coefficient of variation, and standard error. It also shows how to use pylab to produce histograms.

Lecture 15: Statistical Thinking

Topics covered: Variance, standard deviation, standard error.

Recitation 6: Probability and Statistics

Topics covered: Probability, statistics, Venn diagrams, distributions, standard deviation, Monte Carlo simulation, plotting graphs.

Session 16

This lecture starts by defining normal (Gaussian), uniform, and exponential distributions. It then shows how Monte Carlo simulations can be used to analyze the classic Monty Hall problem and to find an approximate value of pi.

Lecture 16: Using Randomness to Solve Non-random Problems

Topics covered: Gaussian distributions, analytical models, simulations, exponential growth, probability, distributions, Monty Hall problem.

Session 17

This lecture is about how to use computation to help understand experimental data. It talks about using linear regression to fit a curve to data, and introduces the coefficient of determination as a measure of the tightness of a fit.

Lecture 17: Curve Fitting

Topics covered: Arrays, curve fitting, numpy, pylab, least squares fit, prediction.

Recitation 7: Distributions, Monte Carlo, and Regressions

Topics covered: Data distributions, mean, standard deviation, Monte Carlo simulations, Monty Hall problem, Riemann sum method, data regressions, r^2 (r-squared), coefficient of termination, scientific applications of programming.

Session 18

This lecture returns to material covered in Lecture 17 Curve Fitting, emphasizing the interplay among theory, experimentation, and computation and addressing the problem of over-fitting. It then moves on to introduce the notion of an optimization problem, and illustrates it using the 0/1 knapsack problem.

Lecture 18: Optimization Problems and Algorithms

Topics covered: Modeling, optimization, greedy algorithms, 0-1 knapsack problem.

Session 19

This lecture continues to discuss optimization in the context of the knapsack problem, and talks about the difference between greedy approaches and optimal approaches. It then moves on to discuss supervised and unsupervised machine learning optimization problems. Most of the time is spent on clustering.

Lecture 19: More Optimization and Clustering

Topics covered: Knapsack problem, local and global optima, supervised and unsupervised machine learning, training error, clustering, linkage, feature vectors.

Quiz II

Unit 3

In Unit 3 we pick up our discussion of data clustering from the end of Unit 2. We introduce graphs as a set of nodes and edges, and learn how these can help solve degrees-of-separation problems and to find a shortest path. We will practice using pseudocode as preparation for writing code, and learn about dynamic programming as we attempt to write optimally efficient programs.

In order to become better statistical thinkers we will learn to spot and avoid several common logical and statistical fallacies like bias, data enhancement, causal fallacies, and the Texas sharpshooter fallacy. In the penultimate lecture we introduce queuing network simulations and important factors like the arrival process, service mechanism, and queue characteristics. We will compare the most common queue disciplines such as first-in-first-out, last-in-first-out, or shortest-remaining-processing-time. The last class presents different possible careers in computer science, and its application across diverse fields and industries.

Unit 3 concludes with a Final Exam covering all material (lectures, recitations, and problem sets) from the beginning of the course through Queuing Network Models.

Session 20

This lecture covers hierarchical clustering and introduces k-means clustering.

Lecture 20: More Clustering

Topics covered: Feature vectors, scaling, k-means clustering.

Recitation 8: Hierarchical and k-means Clustering

Topics covered: Unsupervised learning, k-means clustering, distance metric, cluster merging, centroid, k-mean error, hold out set, k value significance, features of k-means clustering, merits and disadvantages of types of clustering.

Session 21

This lecture begins by finishing up k-means clustering. It then moves on to introduce the notion of modeling things using graphs (sets of nodes and edges that link them).

Lecture 21: Using Graphs to Model Problems, Part 1

Topics covered: Pseudocode, graphs, nodes, edges, adjacency matrix, adjacency list.

Session 22

This lecture returns to graph theory. It defines and gives examples of some classic graph problems: shortest path, shortest weighted path, cliques, and min-cut. It then shows how memoization can be used to speed up some algorithms.

Lecture 22: Using Graphs to Model Problems, Part 2

Topics covered: Dynamic programming, optimal path, overlapping subproblems, weighted edges, specifications, restrictions, efficiency, pseudo-polynomials.

Recitation 9: Directed and Undirected Node Graphs

Topics covered: Node graphs, nodes, edges, directed and undirected graphs, weighted edges, depth-first search, breadth-first search, graph cycles, children of nodes, shortest path, lambda functions.

Session 23

This lecture introduces dynamic programming, and discusses the notions of optimal substructure and overlapping sub-problems.

Lecture 23: Dynamic Programming

Topics covered: Dynamic programming, optimal path, overlapping subproblems, weighted edges, specifications, restrictions, efficiency, pseudo-polynomials.

Session 24

This lecture discusses some common ways that people use statistics to draw invalid or misleading conclusions.

Lecture 24: Avoiding Statistical Fallacies

Topics covered: Statistics, plotting, correlation, causation, bias, logical fallacies, data enhancement, Texas sharpshooter fallacy.

Recitation 10: Introduction to Dynamic Programming

Topics covered: Dynamic programming, memoization, overlapping subproblems, optional substructure, Fibonacci memoization example.

Session 25

This lecture introduces queuing network models and simulations. The lecture is designed to prepare students to read the code they are asked to study in preparation for the final exam.

Lecture 25: Queuing Network Models

Topics covered: Queuing network simulations, Poisson distributions, wait time, queue length, server utilization, FIFO, LIFO, SRPT.

Session 26

This lecture provides some perspective on the material covered in the course. In addition to giving a high level view of the topics covered, it provides a glimpse of what one might do with an education in computer science.

Lecture 26: What Do Computer Scientists Do?

Topics covered: Careers in computer science, computational thinking, abstraction, automation.

Final Exam

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**All above links are interchangable. No password****Introduction to Computer Science and Programming using Python**

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