Python is a general-purpose, object-oriented programming language that is popular in data science thanks to its rich libraries and frameworks offering deep learning capabilities, structured machine learning and its ability to deal with large volumes of data. Python’s simple syntax and ease of integration into other software makes it a quick and nimble solution for both data scientists and developers.
What Python Skills Are Required for Data Science?
Basic data manipulation, algorithm use and data visualization skills are all required, plus an understanding of Python’s fundamentals.
Data science requires that analysts and scientists be able to gather, store, clean, analyze and categorize data from many sources, then present the most valuable information in clear visualizations to stakeholders. All of these things are possible with Python, but require a variety of skills.
To begin learning Python for data science, it’s important to understand fundamental Python components such as data types, including integers, strings and floating point numbers, as well as loops and conditional statements. These data types are used to execute blocks of code through the loop.
Additionally, knowing how to manipulate data with Python, how to clean data to produce a CSV file, how to create data visualizations, how to communicate data with others and which algorithms to use when building different types of models are all vital skills.
Finally, an understanding of the many machine learning and deep learning libraries and frameworks, such as Scikit-Learn, TensorFlow and Keras, will be important when using Python for data science.
How Can I Learn Python for Data Science?
Though mastery takes years of hands-on experience working with the language in a data context, several options exist for learning the fundamentals of Python for data science.
A top option for those interested in learning at their own pace is to take online courses and complete certifications on various Python for data science topics and concepts. Additionally, earning a bachelor’s degree in data science or a similar data field will provide you with the foundational skills and experience needed to understand exactly what you want to do with Python or any other language.
Finally, one of the best ways to learn Python for data science is through hands-on experience with data manipulation. Learning to utilize tools and libraries such as Anaconda, Random, Math, Collections, NumPpy, Python, Matplotlib and Scikit-Learn, along with basic statistical skills, will provide you with enough Python programming experience to handle actual data science projects.
What Is a Data Scientist’s Salary?
According to Built In, the median salary of a data scientist in the U.S. is $122,271.
The average base salary of a data scientist in the United States is $122,271, with $13,075 in additional cash compensation, making the average total salary of U.S.-based data scientists $135,346.
The range for data scientist salaries can vary greatly, with the maximum reported salary being $345,000 and the minimum reported salary landing at $50,000. Salaries for senior data scientists skew even higher. Built In’s salary data is submitted directly by industry professionals and is updated daily.
