What Data Science Career is For You?

What data science career is for you? Read our blog to learn about roles available throughout the data science pipeline.

If you’re struggling to see yourself as a data science professional, it may be because the field of data science can be rather broad. While many positions in data science work across the entire data science pipeline, some are more active in a particular area. Codeup prepares you to take any number of career paths throughout this pipeline. To help explain the differences, we’ll discuss where some of our alumni have ended up. Keep reading to see what appeals to you!


Data: Plan and Acquire

First up is the data portion of the data science pipeline, where we plan and acquire the data. 

Business Analysts like Jesse Ruiz and Jeff Hutchins, as well as People Analytics Specialists like Tim Sotirhos help their companies or departments identify what questions need to be answered, and what data they need to answer those questions. This often involves researching or reviewing existing processes and strategizing how they can be improved. They may work closely with project managers to create an action plan.


Information: Process and Prepare


Then, in the information portion, we process and prepare the data for future exploration.

Data Analysts like Matthew Zapata aggregate and manage, that is, combine, process, prepare, and store, the data for easy access. They research and improve on data quality and safeguard against data loss or errors. They also explore the data for potential relationships between variables, usefulness, and outliers. Data Analysts may also acquire the data and/or analyze the data for potential insights, but typically do not build and deploy machine learning models.


Knowledge: Explore and Analyze


In the knowledge portion, data is analyzed and turned into meaningful statistics

This stage is typically combined with Information or Intelligence rather than existing on its own. In a given company, their Data Analysts may both prepare and explore data, or the Machine Learning Engineers will both explore and model the data. Data Scientists, the broadest career in data science, typically will do all three, like Ednalyn De Dios. Unlike other data professionals (unless otherwise trained), Data Scientists can work anywhere along this continuum. 


Intelligence: Model


Once we have that knowledge, we use the intelligence stage to turn it into future predictions with machine learning models.

When the data is ready, Machine Learning Engineers like Michael Moran and Eric Escalante write the algorithms necessary to build, deploy, and continue to improve upon machine learning models. A machine learning model is a file that uses an algorithm to identify patterns and trends from data.

Decision Science Analysts, like Jeff Roeder, use math, stats, and machine learning models to convert intelligence-driven insights to actionable recommendations. They answer the questions asked by Business Analysts earlier in the timeline to draw conclusions for their company.


Action: Delivery


Finally, the action stage lets us integrate these insights into tests, then continue to monitor and evaluate new data.

Security Data Analysts like Sara Pena need to be able to identify trends from automated models that they or someone else created. They meet with stakeholders to discuss their analysis and help develop tests and prototypes of their recommendations. They then own and execute projects related to continuous improvement of their company’s processes.


Where Do You Fit In?

It’s totally possible for YOU to take on any of these job titles! Maybe it’s time to explore these careers even further, especially if one excited you. While Codeup does prepare and train Data Scientists, you may find your niche and prefer one stage to another. Whatever you choose, Codeup will help you land the job you want not only with our expert, employer-driven curriculum but also with professional help and placement services. Click here to learn how we help you land a job you love.

What is Python?

Another installment of our What is it Wednesday - this week answering the question what is Python?

If you’ve been digging around our website or researching tech tools, you may have heard of Python. Python is a programming language that can be used by software developers, accountants, mathematicians, and especially, data scientists. Python is actually the dominant programming language in data science, often used for data wrangling, analysis, visualization, and machine learning.

What is Python Used For?

Python is an object-oriented language, meaning that the focus is on data and procedures rather than functions and logic. By writing out procedures, you can actually modify the data you have. It can help you automate mundane, repetitive tasks, such as downloading websites, changing the format of those websites, renaming those files, and uploading them to servers. With Python, you can program all of these tasks to be done automatically every time you download a website. This automation saves data scientists a lot of time. 

Is it Good for Beginners?

Yes! One of the key features of Python is the short, easy to understand syntax in which you write your code. Its minimalistic nature allows you to focus on solving your problem rather than solving the code itself. It reads and feels more like English as compared to other common programming languages. Sometimes, what would take you 4 lines of code in Java can be done with 1 line of code in Python. Because it is a high-level language, meaning it is largely independent of your computer specifications, you can get started using Python on whatever device you have – even phones, tablets, or PlayStation!

How is it Used in Data Science?

Python can be used for data wrangling, data analysis, data visualizations, and applying machine learning algorithms. Data wrangling involves preparing raw data for use by parsing it, meaning to convert each data point to a standard format, and by cleaning it, which means to separate useful data from erroneous or missing data points. This is relatively easy if you have only a few data points, but parsing Big Data by hand, like a thousands-of-terabytes large list of every transaction a retail store has ever had, is out of the question. So, Python is used to automate these tasks. It’s also used for data analysis – helping you find trends, correlations, variations, and outliers; data visualizations – helping you chart or graph your findings to show to others; and machine learning, as the complex algorithms and workflows that it’s capable of make it great for Artificial Intelligence.

While this all sounds quite complex, remember, every data scientist started out knowing nothing about Python. Want to try it out? Check out our events page to see when our next free, beginner-friendly, Python Workshop is! If you don’t see one listed, check again soon – we usually have one every other month.