Python Programmer By DataCamp

Start your journey to Data Science. Data Science is not a programming language. Bayes, the kidnapped Golden Retriever, will be solved by data. Additionally, you'll learn Python syntax as well as popular Data Science modules such Matplotlib (for charts & graphs) or Pandas (for table data).

Are you interested in learning more about Data Science? This course is for you. This course will consolidate and put into practice your knowledge of lists and dictionaries. These concepts can be applied to multi-step problems using lots of real data. A case study involving Chicago Metro Transit data will be shown. Additionally, you will learn how to use Python Collections for data storage and manipulation to support different Data Scientific purposes. You will be able to solve many Data Science problems Pythonically after this course.

Pandas is the most popular Python library and it's used for data manipulation. This course will show you how to manipulate DataFrames. This course will teach you how to extract, filter, and transform real data for analysis. Pandas will teach you the basics of data science. To learn how to clean up, calculate, import, and visualize statistics using pandas, you will be using real-world data like global temperature time series or Walmart sales figures. This will allow you to improve the power of Python.

It's time to learn Python. There are many great functions in Python. There are many more functions in the library ecosystem. Data scientists need to be able create their own functions in order to solve data-driven problems. This course will teach you how to create functions using Python Data Science Toolbox. This course will teach you how to create custom functions with multiple parameters, multiple return values, default arguments and variable-length arguments. This course will teach you how to use Python scoping and handle errors when writing functions. You will be able to use your new skills in each chapter to create functions that analyze Twitter DataFrames.

You will learn Python data science skills through the second Python Data Science Toolbox course. Your first lesson will be about iterators. These are objects that you may have seen within the context of loops. Next you'll learn about list comprehensions. These are very useful tools for Python data scientists. A case study will help you apply the techniques learned in this course.

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.

While you can't have a time machine, it would be great to have one that analyzes time. When time is added to any analysis, things can get quite crazy. It's easy to be confused by time zones and day-month boundaries, daylight saving time, and other factors. Python is the best tool to do any kind of time-related analysis. We will use data sets that include information about hurricanes and bicycle rides. We will be discussing how to count events and calculate the time between them. Also, how to plot the data over time. Both standard Python and Pandas will be used. We will also touch on dateutil, which is the only official Python documentation-endorsed timezone library. After this course, you will be able handle date/or time data in any format confidently.

Data scientists are often faced with situations where they have to extract key information, clear up text that contains strings or match patterns in order to find meaningful words. These are all part and parcel of text mining. They are necessary before you can use machine learning algorithms. This course will teach you the basics of string manipulation and regular expressions. This course will show you how to create regular expressions to allow you to find, extract, replace, and match strings. These skills can be used for streaming tweets or movie reviews.

Data science has always recognized the importance of tools that allow for the retrieval and analysis of information stored on the Internet. This course will show you how to navigate HTML code and create tools that automatically crawl websites. Although we will be using the Python library scrapy to scrape, many of these techniques can be used with other Python libraries like BeautifulSoup and Selenium. This course will give you a solid understanding of the html structure and the tools needed to access it. It is also possible to create simple scrapy spiders which can crawl the web on a large scale.

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.

The Unix command line is a versatile tool that allows users to perform complex tasks in a matter of keystrokes. It has been around almost 50 years. Because it allows users to run programs on clouds or clusters that might exist halfway around the world, and combine programs in new ways, the Unix command line is often called "universal glue" of programming. This course will cover its key elements and show you how to use them.

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.

This course offers a solution to the common problem of copying and pasting code into different files. It teaches you how to wrap your code into Python packages, making it easier to read and share. The course covers package structure and how to turn loose code into packages. It also introduces Flake8, a tool for maintaining import structure and code style. To save time, you'll learn how to use cookiecutter to create packages. Furthermore, the course guides you through the process of publishing your packages to PyPI, the global stage for Python packages, using tools like twine and setuptools. This comprehensive course is perfect for anyone looking to enhance their Python programming skills and become a proficient developer.

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.