Data Science is used by businesses and researchers alike to find meaningful insights into the numbers. IT, e-commerce, healthcare, news organizations, political campaigns, and more are using data science to maximize their work. In 2018, 66% of data scientists reported using Python every day.
Python is a beginner-friendly programming language with an easy to read syntax and simple testing mechanisms. It’s loved by developers around the world who make up a strong support network for this open-source tool.
Python is essential for data science and it’s easy to see why it’s the most popular programming language for this field. It can be used to process, scrape data from the web, analyze, visualize, and forecast data that is useful to almost anyone in the digital age. Conveniently, developers have been compiling packages to make this work even more convenient.
Packages refer to Python libraries and frameworks which are compilations of reusable code and algorithms. Libraries like pandas and Numpy makes data manipulation and predictions easy for new and experienced data scientists alike. BeautifulSoup is a library that executes web scraping.
What are Python packages, libraries, and frameworks?
Packages refer to Python libraries and frameworks which are compilations of reusable code and algorithms. Libraries like pandas and Numpy makes data manipulation and predictions easy for new and experienced data scientists alike. BeautifulSoup is a library that executes web scraping.
What is Python used for in data science?
Python is one of the most flexible programming languages out there. It’s used in data science for manipulating data, harvesting data, algorithms, data visualization, data storage, statistics, and running embedded systems. Those small tasks turn into big functionality for companies large and small including social media platforms, video streaming sites, e-commerce, and even healthcare.
Python can be used to:
- Create recommendation engines on video streaming platforms and e-commerce sites
- Predict weather
- Forecast market changes
- Inform investment decisions
- Coordinate sharing commerce services
Who’s using Python for data science?
Organizations large and small are using Python to analyze data. From streaming platforms to social media, e-commerce to healthcare, you can find a job in almost any sector using Python. Big data is powering some of the world’s most successful businesses and small businesses are starting to utilize it too.
Here’s how leading U.S. companies are using Python and data science:
- Amazon uses Python to analyze customer’s buying habits to recommend products and deals
- Netflix uses Python to recommend movies, retain customers by making predictions, and even greenlight original productions.
- Uber uses Python to calculate ETAs, ride fares, as well as demand and supply
Why should I learn Python and data science?
Python is a valuable tool to learn right now because it’s in-demand, the popularity of Python is only growing, and because it’s the most efficient way to handle big data. The use of big data is evolving at a rapid rate and is projected to continue to do so through 2026. With 83% of companies are investing in big data projects in the US, now is the time to upskill or change careers!
Python is a dynamic computer programming language that’s primarily used for data science but can also be applied to machine learning, web development, software development, and even mobile development. It is the most versatile programming language in the world and also one of the most popular.
Learning Python for data science will boost your skills in data collection, manipulation, and analysis. Plus, you’ll learn how to take advantage of the most popular Python libraries and frameworks for data science so you’ll have both relevant and efficient skills that make you a marketable job candidate.
Whether you want to upskill in your current role or you want to learn this completely new skill to change careers, Python is the best place to start. It’s widely considered to be one of the best programming languages for beginners and one of the most useful languages for experts who are further into their careers as analysts.
Job Outlook for Data Scientists who know Python
There is a massive shortage of data scientists. Since 2012 there has been a 650% rate of growth for data scientists with an estimated 11 million new jobs by 2026. According to IBM, there is a need for 28% more data scientists within the next year. The growth in computer science and information careers are growing faster than average for all occupations.
Data scientists are in-demand and well-paid. The average salary for a data scientist in the U.S. is $122,338 per year. Entry-level salaries for this position begin at $69,373 per year. Even in states without large tech or corporate hubs like Hawaii and Kentucky, salaries for this position start at over $70,000 per year.
The demand for data scientists is huge and continuously growing because companies are ramping up their big data efforts to make well-informed business decisions using data. With the normalization of machine learning algorithms, data engineers and data scientists alike are seeing a spike in demand for their skills as well as an increase in the effectiveness of data analysis for business.
Social media, online shopping, the internet of things, and even in-person shopping, generate extensive amounts of data that businesses both large and small can use to their advantage. There are employment opportunities for data scientists in the retail, corporate, banking, tech, government, and healthcare sectors. Over 2 million open data science and analytics roles were posted in 2020.