Python 3 Crash Course for Newbies


Python is a dynamically typed, interpreted programming language adopted by many to use for Data Analytics today.

Keywords Definition

  • Dynamically typed: In many languages, when you declare a variable, you must specify the variable’s type (e.g., int, double, Boolean, string). Python does not require this
  • Interpreted: Programs in some languages (e.g., C/C++ and Java) are first compiled and then executed: In contrast, interpreted languages do not need the compile step and is directly executed using the interpreter. Each statement is executed immediately without waiting for the program to be finished. Although compiled programs are (generally) faster than interpreted programs, but at the cost of being more complicated.

Python syntax is considered simple and intuitive. Entry level learners find programming easy when they start with Python!

If you want to quickly learn Python with the aim of being an effective Data Analyst in the future, then this book is for you. This book also works as a starter (newbie) book for anyone who wants to take a debut into Python programming or just programming in general.

Python is an open source programming language developed in the 1990s by Guido van Rossum of the Netherlands as an easy to use general purpose programming language. Today Python is used for writing programs for web applications, desktop application and also for data analytics.

However, for data analytics, we predominantly use certain data structures of Python, which are all that are needed to produce great analytics. Learning a language in its entirety may be good from an academic stand point, but not for a budding data analyst who wants to be productive quickly. No point in learning certain concepts like object oriented programming, when you rarely get a chance to use it as a junior data analyst. Leave that learning for another day in the future. With this belief, this book presents the essential building blocks of Python 3 programming language without compromising on learning the essentials.

How is this book different from the umpteen number of resources available on the web on the same topic?

This book is written with three main goals which makes it unique:

  • The length of the book should be as short as possible: Reduce verbosity. Quite contrary to the umpteen number of resources which measure their success with the length and quantity of the material.
  • The examples should be as simple as possible: To keep any student engaged and motivated to finish the material.
  • Despite its simplicity, it should be the most effective book: To help you gain confidence to solve problems in Python.

You learn the seven most important concepts in Python. With easy to understand examples, students can quickly gain enough knowledge to write simple programs.

This book is the first of the eBook Series, being written to smoothly take anyone with basic algebra background, to be a confident junior level data analyst. If you are programming in another language like Java, C++ etc., you could even jump to Revision section to quickly understand Python. Then based on your need to understand certain topics better, you can dive into the specific sections to learn more.

The most effective way to learn any programming language is by solving problems hands-on. Practice makes you perfect! Students are encouraged to solve Quiz and Exercises given for enrichment in each chapter to achieve mastery. Once you finish this book you will be ready to start the next book in the series: A Crash Course on Python Libraries for Analytics; covering the most popular Python modules that are used in data analytics.

Further Reading

While this concise eBook gets you started, if you are hungry for more then you can refer to 'Python for Data Analysis' to take a deeper dive into Python, NumPy and Pandas. This book is written by Wes McKinney, the creator of the Python pandas project.

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