LAUNCH -- Python for Data Science, Study notes of Mathematics

Download LAUNCH -- Python for Data Science and more Study notes Mathematics in PDF only on Docsity! LAUNCH -- Python for Data Science        Desired Results    Course Description and Purpose​: ​This course introduces core features of the Python programming  language, while demonstrating and utilizing fundamental concepts in computer science. It  provides an in-depth discussion of data representation strategies, showing how data structures  are implemented in Python along with demonstrating tools for data science and software  engineering. While working on data analysis problems and data manipulation tasks, students will  employ various programming paradigms, including functional programming, object-oriented  programming, and data stream processing. Special attention is paid to the standard Python  library and packages for analytics and modeling (Pandas, Numpy, Matplotlib, etc.).    Curriculum/Content Area: ​Mathematics/Data  Science (LAUNCH)  Course Length: ​2 Terms  Course Title: ​Python for Data Science  Date last reviewed:   Prerequisites: ​No formal prerequisite.  Board approval date: ​TBD (February 2021)  Secondary Resources and Teacher Reference Materials:      ● DataQuest … DataQuest Free Classroom Plan  ● DataCamp … DataCamp Free Classroom Plan  ● How to Think Like a Computer Scientist: Learning with Python 3  ● The Python Tutorial … Documentation available from www.python.org.   ● LinkedIn Learning -- Python Essential Training  ● Think Python  ● TowardsDataScience (blog)  ● Sharp Sight  ● w3schools.com Python Tutorials  ● Python for Data Analysis [W. McKinney]  ● Python Data Science Handbook [J. Vanderplas]  ● Introduction to Computation and Programming Using Python [J. Guttag]  ● Become a Python Data Analyst [A. Fuentes]   ● Python Programming Language [D. Beazley]  ● Memorable Python [J. Hale]  ● The Quick Python Book [Cedar, Naomi]  ● A Better Way to Learn Python [M. Myers]  Enduring Understandings​:     Essential Questions​:     ❖ Mathematicians and Data Scientists make  sense of problems and persevere in  solving them.    ❖ Mathematicians and Data Scientists  How can I use mathematics in data science to  make sense of the world?    What strategies and tools transcend all  mathematical and data science problems, and how      reason abstractly and quantitatively.    ❖ Mathematicians and Data Scientists  embrace creative development as an  essential process for creating  computational artifacts    ❖ Mathematicians and Data Scientists  construct viable arguments and critique  the reasoning of others.    ❖ Mathematicians and Data Scientists model  with mathematics.      ❖ Mathematicians and Data Scientists use  appropriate tools strategically.    ❖ Mathematicians and Data Scientists  attend to precision.    ❖ Mathematicians and Data Scientists look  for and express regularity in repeated  reasoning.  can I apply those strategies/tools in unique  settings?    How can we as mathematicians and data  scientists evaluate and question whether an  argument is accurate?     How can mathematics, computational models, and  simulations help make predictions, generate new  understandings, and solve problems?    How can computing and the use of computational  tools foster creative expression?           PRIORITY STANDARDS  MEANING-​ ​(The Priority Standards help students  construct understanding of…)  The Python for Data Science Course Skills Priority Standards are distinct skills that are integrated  throughout the course and derived from Elmbrook Mathematical Priority Standards & Progressions,  Advanced Placement Calculus (APC) and Advanced Placement Computer Science Principles (APCS).  These standards ensure our Elmbrook Scholars learn to think and act like data science modelers and  problem solvers, and are authentically integrated in each unit through the instructional approach of  problem-based, experiential learning.  ➔ APCS 1B- COMPUTATIONAL SOLUTION  DESIGN: Determine and design an  appropriate method or approach to achieve  a purpose.    ➔ APCS2.B- ALGORITHMS AND PROGRAM  DEVELOPMENT: Implement and apply an  algorithm    ➔ APC1.D-IMPLEMENTING MATHEMATICAL  PROCESSES: Identify an appropriate  mathematical rule or procedure based on  the relationship between concepts or  processes to solve problems.  ➔ APCS5.A- COMPUTING INNOVATIONS:  Explain how computing systems work.    ➔ APCS5.B- COMPUTING INNOVATIONS:  1. Developing a structured and conceptual  understanding of the Python   programming language along with  incorporating best practice computer  science methods.  Build a solid technology/coding skill base  and programming foundation that will  position students to:  ● readily learn both new technologies  and more advanced programming  concepts  ● effectively use coding as a  complementary skill that can be  applied to other disciplines and to  a variety of scenarios  ● efficiently earn and stack  Module #2 Python Fundamentals and Basics  Essential Unit Questions  1. How can I use mathematics in data science to make sense of the world?  2. What strategies and tools transcend all mathematical and data science problems, and how can I  apply those strategies/tools in unique settings?  3. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?    Guiding Content Questions  1. What is some of the basic vocabulary of programming?  2. What are some of the fundamental building blocks of Python?  3. What are some best practice debugging techniques?  Learning Targets:  ● I understand what a value is in Python.  ● I can determine and recognize the data type (class) of a value.  ● I can convert values from one type to another.  ● I understand what a Python variable is and I can determine if a variable name is legal or not.  ● I am aware of the different types of statements that the Python interpreter can handle (e.g.  assignment, while, for, if, import, etc.).  ● I understand what a Python expression is and what it means to evaluate a Python expression.  ● I can recognize the different Python operators and I understand the rules of precedence that  determine the order of evaluation.  ● I can perform mathematical calculations using operators.  ● I understand variable assignment, variable reassignment, and how to update a variable.  ● I understand what Boolean values and Boolean expressions are and I understand how they can  be used.  ● I can recognize and understand the purpose, structure, and syntax of the different types of  conditional statements/selection statements that there are.  ● I can write and execute conditional statements.  ● I can discuss the algorithmic process of programs.  ● I am aware of tips that will help me avoid debugging and tips that will help me debug.  ● I know how to read and interpret the different types of error messages.  Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  ● Coding Tasks  ● Feedback on Success/Professional  Skills   Other assessment options  May include, but are not limited to the following:    ● Project reflection    Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation  and Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python  Programming Language [D. Beazley], Memorable Python [J. Hale], The Quick Python Book [Cedar, Naomi],    A Better Way to Learn Python [M. Myers], TowardsDataScience (blog), Sharp Sight, w3schools.com  Python Tutorials  Module #3 Python Functions and Modules  Essential Unit Questions  1. What strategies and tools transcend all mathematical and data science problems, and how can I  apply those strategies/tools in unique settings?  2. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?  3. How can computing and the use of computational tools foster creative expression?  Guiding Content Questions  1. What is the best way to get help about the Python programming environment?  2. What is a Python module?  3. What is a Python function?  4. What is functional decomposition and why is it important?  5. What is iteration and what are the different types of Python loops?  Learning Targets:  ● I can use the Python Documentation site to get help about the Python programming environment.  ● I understand what a Python module is and I know how to access a list of all the modules that are  available as part of the Python standard library.   ● I can create Python modules.  ● I understand what a Python function is and I can explain the main purpose of a function.  ● I can define/create new Python functions using proper function syntax and structure.  ● I understand how functions can be used and called from other functions.  ● I can perform calculations using built-in functions.  ● I understand how to use functional decomposition to break a problem into smaller subproblems.   ● I understand what the flow of execution of a function means and how it works.  ● I can use composition to build functions from other functions.  ● I can create and execute for, while, and nested loops.  ● I understand the importance of comments within a program.  Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  ● Coding Tasks  ● Feedback on Success/Professional  Skills   Other assessment options  May include, but are not limited to the following:    ● Project reflection    Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation  and Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python  Programming Language [D. Beazley], Memorable Python [J. Hale], The Quick Python Book [Cedar, Naomi],    A Better Way to Learn Python [M. Myers], TowardsDataScience (blog), Sharp Sight, w3schools.com  Python Tutorials  Module #4 Python Lists  Essential Unit Questions  1. What strategies and tools transcend all mathematical and data science problems, and how can I  apply those strategies/tools in unique settings?  2. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?  3. How can computing and the use of computational tools foster creative expression?  Guiding Content Questions  1. What is a Python list?  2. How can Python lists be created and manipulated?  3. How can I read and write a file in Python?  Learning Targets:  ● I can create lists in a variety of ways.  ● I can determine the length of a list.  ● I can access the elements of a list.  ● I can append a list.  ● I can concatenate lists and repeat items in a list a given number of times.  ● I can delete elements from a list.  ● I can clone a list.  ● I can take slices from a list.  ● I understand what a nested list is and I know how to extract elements from a nested list.  ● I can use the split method to break a string into a list of words.  ● I can use the dot operator to access built-in methods of list objects.  ● I can perform list traversal using both iteration by item and iteration by index.  ● I understand the similarities and differences between tuples and lists and I know how to  perform tuple assignment.  ● I can read data from various file types.  ● I can find files on a disk.  ● I can open and close files.  ● I can use a variety of methods to read data from an input file.    ● I can discuss the flexibility of reading files with Python.  ● I can write data to an output file.    Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  ● Coding Tasks  ● Feedback on Success/Professional  Skills   Other assessment options  May include, but are not limited to the following:    ● Project reflection    Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,    Skills   Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation  and Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python  Programming Language [D. Beazley], Memorable Python [J. Hale], The Quick Python Book [Cedar, Naomi],  A Better Way to Learn Python [M. Myers], TowardsDataScience (blog), Sharp Sight, w3schools.com  Python Tutorials  Module #7 Python Strings and Dictionaries/Web-Scraping in Python  Essential Unit Questions  1. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?  2. How can computing and the use of computational tools foster creative expression?  Guiding Content Questions  1. How do you work with strings in Python?  2. What are Python dictionaries and why are they useful?  3. Why is Python good for scraping data from the web?  4. How do you use Python to scrape data from the web?  Learning Targets:  ● Execute statements related to variables, expressions, and control flow   ● Create formatted output and write to a file   ● Manipulate text strings, convert input data, and perform calculations   ● Investigate uses for extracting internet data  ● I can perform concatenation with strings.  ● I can use the index operator to work with characters of a string.  ● I can determine the number of characters in a string.  ● I can select a slice from a string.  ● I can use string comparison to see if two strings are equal.  ● I can use both the for loop and the while loop to perform a traversal on a string.  ● I understand that a Python dictionary is Python’s built-in mapping type which allows  associations to be made from a key to a value.   ● I can access, change, add, and remove items from a dictionary.  ● I can loop through a dictionary and copy a dictionary.  ● I understand what nested dictionaries are.  ● I am aware of a number of powerful dictionary methods and the purpose for each of these  methods.  ● I can use the Beautiful Soup Python package to scrape data from the web.  Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  Other assessment options  May include, but are not limited to the following:    ● Project reflection      ● Coding Tasks  ● Feedback on Success/Professional  Skills   Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation  and Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python  Programming Language [D. Beazley], Memorable Python [J. Hale], The Quick Python Book [Cedar, Naomi],  A Better Way to Learn Python [M. Myers], TowardsDataScience (blog), Sharp Sight, w3schools.com  Python Tutorials  Module #8 Python Exceptions/Reading and Writing Files  Essential Unit Questions  1. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?  2. How can computing and the use of computational tools foster creative expression?  Guiding Content Questions  1. What is an exception?  2. What are some fundamental principles for using exceptions?  3. How do you manipulate data from files?  4. What is the purpose of data structures?  5. How do you build data structures from files?  Learning Targets:  ● I can handle different types of exceptions.  ● I understand the exceptions syntax.  ● I can define new exceptions.  ● I understand paths and pathnames.  ● I know how to get information about files.  ● I can process all files in a directory subtree.  ● I can open files and file objects.  ● I can open files in write and other modes.  ● I know how to close a file.  ● I know which abstract data types and data structures are built into the Python standard library.  ● I know the different types of data structures -- both primitive and non-primitive.  ● I know how the most common abstract data types map to Python’s naming scheme.  ● I know how to put abstract data types to practical use in various algorithms.  Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  ● Coding Tasks  ● Feedback on Success/Professional  Other assessment options  May include, but are not limited to the following:    ● Project reflection      Skills   Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation and  Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python Programming  Language [D. Beazley], Memorable Python [J. Hale], The Quick Python Book [Cedar, Naomi], A Better Way  to Learn Python [M. Myers], TowardsDataScience (blog), Sharp Sight, w3schools.com Python Tutorials  Module #9 Classes and Object-Oriented Programming  Essential Unit Questions  1. How can mathematics, computational models, and simulations help make predictions, generate  new understandings, and solve problems?  2. How can computing and the use of computational tools foster creative expression?  Guiding Content Questions  1. What is object-oriented programming?  2. What are the pillars of object-oriented programming?  3. What is a class?  4. What is Python inheritance?  5. What is an object?  6. What is a package?  Learning Targets:  ● I can describe object-oriented programming.  ● I can create classes and attach methods.  ● I can define and manipulate objects.  ● I can create a parent class and a child class to utilize Python inheritance.  ● I can create an instance.  ● I can add properties and methods to the child class.  ● I can organize code bases into packages.  Assessment Evidence:    Performance Assessment Options  May include, but are not limited to the following:    ● Problem Sets  ● Project-based/Problem-based activities  ● Unit Assessment  ● Coding Tasks  ● Feedback on Success/Professional  Skills   Other assessment options  May include, but are not limited to the following:    ● Project reflection    Digital Tools & Supplementary Resources:  Python software, Dataquest, DataCamp, How to Think Like a Computer Scientist: Learning with Python 3,  The Python Tutorial, LinkedIn Learning -- Python Essential Training, Think Python, Python for Data  Analysis [W. McKinney], Python Data Science Handbook [J. Vanderplas], Introduction to Computation  and Programming Using Python [J. Guttag], Become a Python Data Analyst [A. Fuentes], Python