Best Python Data Science Classes Online

The process of gathering, cleaning, and organizing data has become a massive industry in and of itself. Virtually every professional industry is currently leveraging big data in some capacity in order to help make more informed decisions about the direction that it takes on projects, investments, and other future plans. This is only possible because data collection tools have become significantly more robust and accessible, in part because of the Python programming language. Python is, per the TIOBE index, the most popular programming language in the world, and it is among the most versatile languages that you can learn. 

Python is also one of the easiest programming languages for new coders to learn, making it a good place to start your professional coding journey. Many classes and online seminars offer students the chance to learn the basics of working with Python, even if they have no prior skills with the language, before they start to work on more advanced data science tasks, since Python’s major selling point is its versatility. Learning Python in this way is a good introduction to the world of computer programming and an ideal way to kick off a new career in a high-paying and in-demand career field.

Common Job Roles and Responsibilities for Python Data Science Professionals

Per the Bureau of Labor Statistics, data science-related jobs are some of the fastest-growing positions in the nation, far outstripping the growth rate of the national average. According to the BLS, data scientists are anticipated to see a 36% growth in employment from 2023 to 2033, a nine-fold increase over the national average of 4%. While some individual job titles and positions won’t be growing this fast, it can be safely assumed that if a position involves a relationship to data, then the numbers are going to be fairly good over the next decade.

• Data Scientist: Data Scientists are responsible for creating and maintaining the tools and infrastructure that allow other data professionals to make use of specific functional data sets. For the most part, Data Scientists won’t be working on projects that involve practical data sets (i.e., specific, real-world data sets that are being used by a company or institution). Rather, they will be working on theoretical data questions that they will answer to help make the work of managing those practical data sets easier. In essence, a Data Scientist will be responsible for creating the tools that Data Analysts use to manage and leverage data.
• Data Analyst: Data Analysts, by contrast, handle the real-world data-related tasks that make data such an important part of managing a business or institution. While Data Scientists work on data projects in the abstract, Data Analysts will be working on specific projects for a company, institution, or other relevant stakeholders. This means that they will need to better understand how to interpret and analyze the datasets and how to communicate their findings to other decision makers in order to get the most out of the data that has been collected.
• Machine Learning Engineer: Machine Learning Engineers are specialized data science experts who use languages like Python to build and train AI models, LLMs, and other machine learning algorithms for practical and theoretical purposes. Some Machine Learning Engineers will work for companies like OpenAI or Google to help develop general-purpose machine learning algorithms and AI tools, while others will work for specific companies looking to build and integrate their own proprietary machine learning applications (like Amazon or Netflix developing automated recommendation tools). Machine Learning Engineers are in high demand as more and more companies are trying to leverage their ability to use AI tools in their regular workflow.
• Data Engineer: Data Engineers are professionals who are tasked with building and maintaining the specific data infrastructure systems that large businesses, institutions, and organizations use to access and analyze their data. Data Engineers will tend to be hired in industries that need to maintain and update huge amounts of data in a relatively consistent and continuous manner, such as the financial industry, banking, online retail, or healthcare. These professionals won’t be focused on analyzing the data but will instead be focused on understanding how the data is accessed and best utilized by analyst professionals.
• Business Intelligence Analyst: Business Data Analysts are responsible for taking a larger and more holistic approach to the question of how a business should address the concerns and issues arising from their various data-related observations. These professionals will be focused on how a business can leverage its data collection tools and processes to make better decisions and strategic moves. The major difference between a BI Analyst and a Data Analyst is the granularity of their focus, since a data analyst may wear many hats, while a BI Analyst will solely focus on high-level business analysis projects.

An Overview of Data Science Industries and Demand

Since data collection tools have become so easy to use and accessible for even novice programmers, more industries than ever are looking for skilled professionals to help them make use of their data, and these professionals can find work in virtually any industry they want to pursue. A few of the most common industries hiring data experts include:

• Finance: The financial industry requires constant data analysis in order to keep up to date on the movement of markets and the prices and values of assets. This is true for everyone from the investment banker working with clients’ 401(k)s to hedge fund managers moving millions of dollars in investments. Without access to the proper amount of data, investors are flying blind, and that is a very risky thing to do when you know that other financial professionals are making use of data to have a clearer picture of the market.
• Healthcare: The healthcare industry relies heavily on data science tools and practices to handle the massive amount of user health data that is generated by even a simple doctor’s visit. Not only are data analytics tools important for doctors, providers, and insurers, but machine learning techniques are quickly changing the way that diagnostic medicine is handled. Now, with AI tools, we can compare a person’s medical information against billions of other data points in an instant, leading to a much higher rate of detection and prevention of serious ailments and diseases.
• Retail: The retail industry, particularly as it is connected to ecommerce, makes heavy use of data as a tool for tracking consumer interest and engagement with different elements of an online shopping platform. Data will track how long customers spend on any given page, how they reach those pages in the first place, and how they move throughout a website before they stop and make a purchase or leave the site.
• Marketing: Marketing experts also rely heavily on data collection and analysis tools in order to complete their professional goals. In addition to working with the kinds of data that retail and ecommerce companies use, markets will also engage with more traditional forms of data collection, like surveys, focus groups, and direct customer engagement, to determine whether or not an ad campaign is working and how to make it more effective at its goals.