Python Programming

Data scientists should spend their time extracting actionable insights from data and not waiting for the code to finish running. This will allow you to reduce the time it takes to run your Python code, and also save computational resources. This will enable you to pursue your passions in Data Scientist. This course will show you how to make your code faster, cleaner, more efficient, and take advantage of Python's built-in data structures, functions, and modules. How to time and profile code to find bottlenecks. Then, you'll practice eliminating bottlenecks using Python's Standard Library or NumPy. After completing this course, you will be able write Python code efficiently.

Data scientists must be able work with large data sets efficiently and extract valuable information. When working with small amounts data, we often underestimate the speed at which code can be executed. This course will increase your Python knowledge as well as teach you how to make pandas faster by using its built-in functions. Pandas' functions are easy to use for simple tasks such as targeting entries and features. You can also apply functions to multiple entries faster than Python's standard methods. This course will teach you how to efficiently and quickly process large data sets, use functions based on feature values to apply them, and manipulate data from different groups. These techniques can be applied to real-world datasets like restaurant tips and poker hands.

You're now done with your analysis. What's next? If you want to make your model production-ready, your code must be stronger than the Jupyter notebook exploratory programs. Writing Functions in Python can help you create a strong foundation for creating complex and beautiful functions that can be used by your team to add engineering and research skills. You will learn useful tips such as how decorators and context managers can be created. You will also learn the best practices for creating reusable and easily maintained functions that are well-documented. Unicorns are people who can do high-quality research, write well-written code, and are able to make it happen. This course will teach you how to create magic.

Data scientists can benefit greatly from learning concepts from software engineering. This will enable them to reuse code more efficiently and share it easily with others. This course will cover modularity, documentation, as well as automated testing. These concepts will help you solve Data Science problems quicker and more efficiently. You'll also be able use your skills as a software engineer to create your Python package for performing text analytics.

Unit testing is a must for every data science project. Unit testing has many benefits. It can reduce development and maintenance times, improve documentation, and increase trust from end-users. It decreases downtime for productive systems. Nearly all companies use unit testing as a standard skill. This course will teach you how to use Python's most popular testing framework, pytest. This course will show you how to create a data science project testing suite. This course will show you how to create unit tests for data models, preprocessors and visualizations. It also teaches you how to interpret the results. Advanced concepts such as TDD, test organization and fixtures, mocking, and how to properly test data science projects will be covered.

Object-oriented programming (OOP) is a popular programming paradigm that reduces development time, makes it easier to understand, reuse and maintain your code, and allows you to modify it. OOP lets you see your code as more that a collection of actions. OOP allows you to see your code as more than a sequence of actions. Instead of seeing it as a series, OOP lets it be viewed as a collection of objects that interact with each other. This course will show you how to create classes which act as blueprints for all Python objects. You will learn how to reuse and optimize your code using principles such as inheritance and polymorphism. Learn how to create beautiful and efficient code.

This track will teach you how to program like a programmer. To optimize your code, you'll first learn how to use built-in functions and modules. Then, you will be able to practice writing functions using best practices. This includes how to use context managers and decorators and how to write documentation. You'll gain a better understanding of software engineering concepts such as modularity, documentation and automated testing. Next, you will dive in to unit-testing skills such as debugging code and Test Driven Development (TDD), using fixtures, and mocking. You'll also use packages such as NumPy and setuptools, Python, Pycodestyle, and NumPy. You'll use your object-oriented programming skills (OOP) to read, reuse and maintain your code by the end of this track. This track will take you on a Python programming journey.