Hoping to start a career in tech in 2022? Don’t miss out on our last bootcamps of the year.Apply now!
Whatever your area of development, knowing how to use the most useful functions of the library you're working with is going to make your life a lot easier.
We’ve collated a collection of cheat sheets for you to get to grips with the main libraries used in data science.
They are grouped into the fields for which each library is designed: Basics, Databases, Data Manipulation, Data Visualization, Analysis, Machine Learning, Deep Learning and Natural Language Processing (NLP).
If you're just starting out in the world of data science, it's important to understand how at least two of the basic libraries work: Python and NumPy. These two libraries are used throughout the entire development process. The third library, Scipy, is a mathematical tool that can handle more complex calculations than NumPy.
Data can be stored in sets or, sometimes, in relational or non-relational databases that are imported into the working platform.
Are you enjoying this article? Keep learning about Data Analytics!
Take the first step into tech and find out more about our Data Analytics bootcamp
Before getting started with data analytics, it's essential to organise the data set's information so that it's easier to perform the necessary analytical operations. This process is known as data manipulation.
Data visualization is the graphic representation of data and is particularly important for conducting analyses or portraying analysis results, which can help us discover trends, outliers and patterns in the data.
Machine learning algorithms allow us to make predictions based on available data. These are known either as regression or classification algorithms, depending on the type of data in question. These processes can be supervised or non-supervised, depending on whether the machine learning model is trained using labelled data, or not, which is known as 'ground truth'.
Within the field of machine learning, there is a more specific field known as deep learning, which uses artificial neural networks to make predictions.
Within the field of data science, language analysis is an area that's increasingly gaining ground, with algorithms that have been developed to help us analyse text.
These cheat sheets contain each library's most useful functions and working methods to help you in your day-to-day development tasks. Happy Coding!
Ready to join?
+8,000 career changers and entrepreneurs launched their careers in the tech industry with Ironhack's bootcamps. Take a step forward and join the tech revolution!
What would you like to learn?
Where would you like to study?
Help data tell a story with Data Visualization and PythonRead more...
Learn the basics of data analytics: Intro to SQLRead more...
Intro to Pandas: how to manipulate Data in PythonRead more...
What is Python? Learn the top 3 best uses for Python programmingRead more...
From Sales into Data Analytics, interview with Vincent Laduc (Senior Business Analyst at Google)Read more...
What is the difference between a data engineer, a data scientist and a data analyst?Read more...