The Ultimate Python Certification Course
Learn the world's most in-demand programming language by enrolling in the same Python certification program used by the worlds top financial institutions.
Shareable on LinkedIn and in resumes
Finish in 1 month
Recommended pace 12 hours/week
The same Python certification program used at top financial institutions
Major Skills Covered
- Python Data Types
- Functional Programming
- Object Oriented Programming
- JSON & Rest API
- Python Libraries
Learn the world's fastest-growing programming language
- No Coding Experience Needed With 40+ hours of step-by-step video and access to real-world examples with fully-annotated Jupyter code notebooks, this course has all the tools you need to gain Python fluency and Python problem solving skills.
- Understand Python from the Ground Up Learn how Python actually works at a fundamental level — allowing you to write better, cleaner and more effective programs. By the end of your journey, you'll have mastered advanced functional programming topics such as closures and decorators, crucial to the use of Python by finance professionals.
Your Path to Certification
This certification course is designed to prepare you for real-world applications of the Python programming language. We’ll help you understand how to use Python to explore, analyze and visualize data, and to manipulate data using automated and repeatable processes. We begin with the Python programming environment and basic Python data types. We move ahead into programming with loops and conditional statements and how to write and execute key functions. We'll cover advanced Python programming concepts using the standard Python library and dive into commonly used 3rd party libraries you can use for data analysis and real world problem solving.
Complete in 1 Month
- 1 Introduction to Python Weeks 1-2
- 2 Intermediate Python Weeks 2-3
- 3 Python 3rd Party Libraries Weeks 3-4
The Wall Street Prep Python Certification is a blockchain verified certificate you can share on LinkedIn and resumes.
Third-Party Python Libraries
What You'll Learn
- Basic and advanced Python data types.
- Functional and Object Oriented Programming.
- Closures and Python decorators.
- Exception handling.
- Datetime and timezone handling.
- Reading and writing CSV files.
- JSON and making REST API requests.
- Using NumPy for highly efficient calculations.
- Using Pandas for loading and analyzing data sets.
- Using Matplotlib for generating charts.
In this introduction to Python you will learn:
- How to install Python.
- How to create and use virtual environments (and why you should use them).
- How to program in Python using Jupyter Notebooks.
- Boolean (True/False) data type and algebra (and/or/not).
- Numeric types, including integers and floats.
- Sequence types (lists, tuples, strings).
- What Unicode is
- Dictionaries and sets.
- Looping (for, while).
- Conditional execution (if...elif...else).
- Writing and using functions, including lambda functions.
- Built-in functions such as round, sorted, min, max and zip.
Expand on the basics covered in Course 1 and learn more advanced Python programming concepts. You'll cover:
- Higher-order functions.
- Python Decorators.
- Importing modules.
- Reading/writing text files.
- Reading/writing CSV files.
- The 'Decimal' type as a more precise alternative to floats.
- Epochs, dates, times and timezones using native Python.
- The 'math', 'statistics' and 'random' standard library modules.
- Creating custom classes.
Continue on to focus on a number of commonly-used third-party libraries that are very useful in data analysis. These include:
- The 'pytz' and 'dateutil' libraries for dealing with dates, times and timezones.
- The 'requests' library to query Web APIs and ingest JSON data.
- The 'Numpy' library for highly efficient manipulation of arrays and matrices.
- The 'Pandas' library for easy and powerful data table manipulations.
- The 'Matplotlib' library for charting data.
Used on the Street
This is the same comprehensive course our corporate clients use to prepare their analysts and associates.
Real Coding Examples
You'll learn-by-doing by using fully-annotated Jupyter code notebooks as you work through the course; no coding experience necessary.
Taught by experienced software developers
Our instructor is a software engineer with a math PhD and over 20 years of real-world development experience.
Have a question on course content? Communicate directly with instructors by asking questions throughout the course.
Frequently Asked Questions
- Do I need any programming experience to take this course?
Prior programming experience is not required. Knowing how to solve problems "algorithmically" will be helpful to you in grasping the course material faster, but the course is designed to get you started from the ground up.
- Do I need to know Python in order to start this course?
No prior Python knowledge is needed; we'll start with fundamental Python concepts and work our way up.
- What data types will I learn about?
You will learn about integers, floats (and the difference between them and Decimal types), lists, tuples, dictionaries, sets and more.
- Dealing with dates, times and timezones is notoriously difficult. Does Python make it easier?
We'll cover some third-party libraries that help make these problems much simpler to deal with.
- My data comes from a variety of sources. Will this course show me how to ingest data from these sources?
This course will cover how to load data from CSV files (and variants such as tab-delimited), Excel spreadsheets, and JSON data retrieved from Web APIs.
- I often analyze data using a spreadsheet - and that can take a long time. Will Python help?
Yes, absolutely. And if native Python is not fast enough, this course will show you how to use a library called NumPy that is used for high-speed computations.
- When I use a spreadsheet, I'm used to having rows and columns for my data. Does this work the same in Python?
Not natively, no. But we'll study a library called Pandas that will allow you to deal with and manipulate data in a row/column paradigm, including sorting, filtering and indexing.
- I currently use Excel to generate charts from my data. Can I do that in Python?
We'll study a library called Matplotlib that is used for charting. You'll gain enough knowledge about this library to continue learning it and use the huge variety of chart types and styles available in Matplotlib.
- Are there downloadable course notes?
This course uses Jupyter notebooks which provide a way to combine Python code and formatted text in a single notebook. Every coding lecture in this course has a corresponding downloadable Jupyter notebook that contains both the code we work on together as well as full contextual explanations.
- How can I get better at Python programming?
The key is practice. Use the code videos to code along (pause the video, rewind). Each section also contains exercises that build in difficulty as the course progresses. Don't skip those — it's good practice. But additional practice is required as well — you should explore topics on your own, try code out, break things, and learn as you do so. The more you practice, the better you become, and the easier it gets.
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