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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:
- 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 #1 Python Installation and Introduction Essential Unit Questions
- How can I use mathematics in data science to make sense of the world?
- How can computing and the use of computational tools foster creative expression? Guiding Content Questions
- What is the single most important skill for a computer scientist?
- What is a program?
- What is debugging and what different types of errors can occur when writing and executing a program?
- What is the core philosophy behind Python?
- What is Anaconda and what is the main advantage of using Anaconda?
- What is Jupyter Notebook/IPython Notebook? Learning Targets: ● I can install the Anaconda Distribution of Python. ● I understand the key features of the Anaconda Distribution of Python. ● I can launch Jupyter Notebook from within the Anaconda Distribution of Python. ● I can interact with Python using both the command prompt and Python shell. ● I can perform basic print commands and debugging techniques. ● I can describe the overall structure of Python and its benefits. ● I can explain the difference between a high-level programming language and a low-level programming language and describe the advantages of a high level language? ● I understand how to write comments and I know what they are used for. ● I can describe the key differences between Python 3 and Python 2. ● I can explain what debugging is. ● I can identify the different types of errors that can occur when writing and executing 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 #2 Python Fundamentals and Basics Essential Unit Questions
- 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 can I apply those strategies/tools in unique settings?
- How can mathematics, computational models, and simulations help make predictions, generate new understandings, and solve problems? Guiding Content Questions
- What is some of the basic vocabulary of programming?
- What are some of the fundamental building blocks of Python?
- 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 #4 Python Lists Essential Unit Questions
- What strategies and tools transcend all mathematical and data science problems, and how can I apply those strategies/tools in unique settings?
- 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? Guiding Content Questions
- What is a Python list?
- How can Python lists be created and manipulated?
- 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,
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 #5 EDA Using Python Essential Unit Questions
- 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? Guiding Content Questions
- What are the major steps in tackling a data science problem?
- Why do data scientists need a defined, systematic methodology and approach?
- What does it mean to understand data?
- What does it mean to clean, prepare, and wrangle data?
- What is an EDA (Exploratory Data Analysis) and why is it important?
- How do you form a concrete business/research question upon which you will perform the data science analysis?
- What are the most widely used Python libraries for data analysis? Learning Targets: ● I can use EDA techniques for cleaning and wrangling data. ● I understand what the major steps are in tackling a data science problem? ● I understand why data scientists need a defined, systematic methodology and approach. ● I understand what it means to understand data. ● I understand what EDA (Exploratory Data Analysis) is and why it is important. ● I can form a concrete business/research question upon which I will perform a data science analysis? ● I understand how NumPy uses vectorized operations. ● I can create NumPy arrays from lists and from built-in NumPy functions, ● I can discuss the attributes of a NumPy array. ● I can perform basic math with NumPy arrays. ● I can index, slice, and reshape NumPy arrays. ● I understand the data types associated with Numpy arrays. ● I can transpose a Numpy array. ● I can create a pandas series and a pandas DataFrame. ● I understand the anatomy of a DataFrame. ● I can manipulate data to remove null, missing, duplicate, and irrelevant values. ● I can combine and merge data sets. ● I can select a column or subset of columns. ● I can analyze data to locate outliers and other anomalies. ● I can perform methods on data to determine its shape and structure. Assessment Evidence: Performance Assessment Options May include, but are not limited to the following: ● Problem Sets 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 #7 Python Strings and Dictionaries/Web-Scraping in Python Essential Unit Questions
- 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? Guiding Content Questions
- How do you work with strings in Python?
- What are Python dictionaries and why are they useful?
- Why is Python good for scraping data from the web?
- 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
- 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? Guiding Content Questions
- What is an exception?
- What are some fundamental principles for using exceptions?
- How do you manipulate data from files?
- What is the purpose of data structures?
- 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
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
