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    Intro to Python for Non-Programmers

    Intro to Python for Non-Programmers

    A five-day, fast-paced, code-intensive lecture and hands-on lab course for people with programming experience

    Overview

    Intended for people with at least some programming experience and based on the innovative textbook Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud (https://deitel.com/IntroToPythonContentsDiagram) by Paul Deitel and Harvey Deitel, this course provides a code-intensive introduction to Python—one of the world’s most popular and fastest-growing programming languages.

    In the context of scores of real-world code examples ranging from code snippets to large scripts, attendees will write Python code using the interactive IPython interpreter, Jupyter Notebooks, Google Colab and the latest Python version. Via extensive hands-on labs, attendees quickly will become familiar with Python programming idioms, key Python Standard Library modules (collections, csv, decimal, doctest, json, math, random, sys, statistics) additional open-source libraries (matplotlib, seaborn, numpy, pandas, scipy) and an intro to data-science concepts, including simulations with static and dynamic visualizations using, pandas Series and DataFrames, data wrangling and CSV/Excel files. You’ll see how Python helps maximize your productivity via its standard libraries and abundant free open-source libraries. The course offers extensive hands-on lab coding practice. Deitel LabAssist notes provide hints for each hands-on lab, enabling you to work through many labs efficiently.

    About Your Instructor

    Paul Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 40 years in computing. He is one of the world’s most experienced programming-languages trainers, having taught hundreds of instructor-present and virtual courses worldwide (based on his Pearson Education programming-language college textbooks and professional books) to software developers of all skill levels since 1992. He is a top author on O’Reilly Online Learning where his Python Fundamentals LiveLessons, Java Fundamentals LiveLessons and C# Fundamentals LiveLessons 50+ hour asynchronous video courses have ranked #1 eight of the last 12 years among 6,000 video products from 200+ publishers. Thousands of users have viewed his asynchronous streaming Python Fundamentals LiveLessons videos for over 10.5 million contact minutes. 10,000+ students have attended his live (synchronous), one-day, code-intensive Python Full Throttle and Python Data Science Full Throttle courses for over 2.7 million live contact minutes.

    Together, he and his co-author, Dr. Harvey M. Deitel, are among the world’s best-selling programming-language textbook, professional book, video and interactive multimedia e-learning authors, having written 100+ Pearson Education/Prentice Hall college textbooks and professional books on Python, Java, C++, C, C#, Internet/Web, Android, iOS, Swift, Visual Basic and more. Their books have 100+ translations into Italian, Japanese, German, Russian, Spanish, French, Polish, Simplified Chinese, Traditional Chinese, Korean, Portuguese, Greek, Urdu and Turkish.

    Deitel clients include some of the world’s largest companies, government agencies, branches of the military, and academic institutions—Schlumberger, UCLA Anderson School of Management, Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, White Sands Missile Range, the National Oceanic and Atmospheric Administration (NOAA), the National Severe Storm Laboratory, Rogue Wave Software, Boeing, Puma, iRobot and many more.


    • Test Drives—Interactive Python coding in IPython and Jupyter Notebooks
    • Introduction to Python Programming—Variables; operators; function print; single-, double and triple-quoted strings; function input; objects and dynamic typing; Intro to Data Science: basic descriptive statistics.
    • Control Statements—ififelseifelifelsewhilefor; function range; augmented assignments; formatted strings; type decimal for monetary amounts; breakcontinue; Boolean operators; Intro to Data Science: Measures of central tendency—mean, median and mode.

    • Functions—Function definitions; random-number generation; Python Standard Library; math module; IPython tab completion for discovery; default parameter values; keyword arguments; arbitrary argument lists; methods; scope rules; import statements; Intro to Data Science: Measures of dispersion.
    • Sequences: Lists and Tuples (Part 1)—Lists; tuples; unpacking sequences; sequence slicing; del statement; sorting lists; searching sequences; list methods.

    • Sequences: Lists and Tuples (Part 2)—Functional-style programming with list comprehensions, generator expressions, filter, map and reduce; two-dimensional lists; Intro to Data Science: Simulation and static visualization with the Matplotlib and Seaborn open-source libraries.
    • Dictionaries and Sets—Creating a dictionary; iterating through a dictionary; basic dictionary operations; dictionary methods; dictionary comparisons; dictionary comprehensions; creating sets; iterating through sets; basic set operations; set methods; set comparisons; set comprehensions; Intro to Data Science: Dynamic visualization with Matplotlib FuncAnimation.

    • Array-Oriented Programming with NumPy (one of Python’s most important open-source libraries)—Creating arrays; array attributes; list vs. array performance: introducing %timeitarray operators; numpy calculation methods; NumPy universal functions; indexing and slicing; shallow vs. deep copy; reshaping and transposing; Intro to Data Science: Pandas Series and DataFrames.
    • Strings: A Deeper Look—Formatting strings; concatenating and repeating strings; stripping whitespace; changing character case; comparing strings; searching for substrings; replacing substrings; splitting and joining strings; characters and character-testing methods; raw strings; introduction to regular expressions; Intro to Data Science: Pandas, regular expressions and data munging.

    • Files and Exceptions—Text-file processing; serialization with JSON; handling exceptions; finally clause; explicitly raising an exception; stack unwinding and tracebacks; Intro to Data Science: Working with CSV files; Python standard library module csv; Reading CSV files into Pandas DataFrames and analyzing them; Intro to Pandas visualization.
    • Object-Oriented Programming—Custom class definitions; controlling access to attributes; properties for data access; simulating “private” attributes; case study: card shuffling and dealing simulation; inheritance: base classes and subclasses; building an inheritance hierarchy; polymorphism; duck typing; operator overloading; named tuples; data classes; unit testing with docstrings and doctest; namespaces and scopes; Intro to Data Science: Time series and simple linear regression.

    Intended Audience

      • People with at least some programming experience who want a fast-paced, code-intensive, hands-on introduction to Python.
      • Programmers using other languages who would like to learn Python in one five-day course.
      • People who took a college-level or professional Python course or programmed in Python a while back and want to refresh and update their Python skills.
      • Programmers using a limited range of Python features who would like to see what other features are available.
      • Managers who are considering moving their teams to Python.
      • R programmers whose organizations are considering adding or switching to Python and who want a code-intensive introduction to Python.
      • People who are tight on time and would prefer to get up-to-speed in Python in one week.
      • Note: People who would feel more comfortable with a slower-paced course for non-programmers should consider the two-week course sequence Intro to Python for Non-Programmers: Parts 1 and 2.


    Key Topics

      • Interactive Python coding in IPython, Jupyter Notebooks and Google Colab
      • Python scripts
      • Types, statements, strings, input/output, built-in functions
      • Control statements
      • Functions
      • Importing libraries
      • Built-In data structures: Lists, tuples, dictionaries and sets
      • Intro to static and dynamic visualization with matplotlib and seaborn
      • Functional-style programming: List comprehensions, generators, filter/map/reduce
      • High-performance array-oriented programming with numpy (one of Python’s most important open-source libraries)
      • Strings and regular expressions
      • Text-file processing, JSON serialization, CSV-file processing
      • Exception handling
      • Object-oriented programming
      • Intro to Data Science sections: Basic descriptive statistics. Measures of central tendency. Measures of dispersion. Simulation. Static and dynamic visualization. pandas Series and DataFrames. Regular expressions and data munging. Analyzing data from a CSV file. Time series and simple linear regression.

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