Technology / Programming

Do I Need to be Good at Programming to be a Data Scientist?

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Published on October 2, 2023

In the business world, data science has emerged as a driving force behind innovation and strategic decision making. Organizations are investing in data science at a rapid pace to gain competitive advantage by analyzing data for insights about their own businesses and their competitors. It is not surprising there is strong demand across all segments for qualified data scientists and data analytics professionals. 

In recent posts, we discussed the roles of data analysts and data scientists and the value in obtaining the CompTIA Data+ data analytics accreditation. We also looked at how that certification can boost your career, and the various job opportunities available to CompTIA Data+ certified professionals.

In this article, we’ll dig deeper into the skills required to be a data scientist and whether data scientists need to be proficient programmers. But first, let’s look at the role of data scientists and why they might (or might not!) need to know programming. 

The Data Scientist Role: What Do Data Scientists Do? 

Data scientists analyze and interpret complex data sets, then present management with insights and actionable information. Much of the data they analyze may be in databases created as a result of day-to-day enterprise operations. This data is structured and well-defined and can be readily extracted for analysis. 

The data scientist is also expected to identify other sources of information that may be combined with current data for additional analysis. Additional data sets might include lists purchased from third-parties, web traffic files, data scraped from third party web sites, call center audio recordings, video and image files from security and process control systems – and the list goes on!

So what is entailed in this clean-up and manipulation process? Frequently, the data from these sources will require substantial work to make it usable. The files need to be structured and cleaned to remove duplicates or inconsistencies, to back-fill incomplete data and correct inaccuracies, and to standardize formats (e.g., zip codes, phone numbers, and state abbreviations). Data scientists may also restructure and realign fields so they match the format required for analysis — a process known as data wrangling.

Common Programming Languages Used in Data Science 

Data cleaning and restructuring may be one-time work, but more often it needs to be repeatable to support regular processing and analysis to measure changes in data over time. In either case, the messy data is cleaned and structured for analysis and visualization as appropriate for reporting.

Some of the data cleaning tasks can be managed by data cleaning tools, but most can only be handled by using programs written to handle specific cleaning tasks. In the case of capturing — or scraping — data from third party web sites, this can only really be done programmatically! 

So how does this programming get done? Well, some data scientists might call upon their friendly programming department to write the programs for them, but it's vastly more efficient for them to be able to specify and write the programs themselves. A 2017 Forbes magazine article cited programming as “perhaps the most fundamental of a data scientist’s skill set”. And, the programming language of choice for data scientists is Python!

Python: The Data Science Programming Language of Choice 

Data scientists love Python because it's simple to learn and has a large selection of data science libraries. These libraries help data scientists prototype, build, and test cleaning and manipulation routines quickly and efficiently – without requiring extensive custom coding.

Here are some of the most popular Python libraries: 

  • NumPy (Numerical Python) is a library of high-level mathematical functions and methods that allow data scientists to undertake operations on advanced arrays and matrices.

  • Pandas (Python Data Analysis Library) is a data manipulation and analysis library that can, for example, be used to convert data lists into column-format data frames, add/delete columns, impute missing data, and create histograms from the data.

  • Matplotlib is a data visualization library used to create two-dimensional graphs and charts, such as histograms and scatterplots, as well as graphs using cylindrical or polar coordinates.

  • Seaborn is a data visualization library that creates attractive, easily-understood statistical graphics.

Other languages such as Ruby and the R statistical language are popular for data scientists, but they have drawbacks. R is more complex to learn and, while Ruby is good for data cleaning and data wrangling, it has fewer data science libraries than Python.

Alternative Paths to a Data Science Career 

Do you need to be able to program to become a data scientist? While it can be one of the most important assets for the job, some say programming is not essential! 

However, there are other skills that data scientists absolutely need to have! For example, in the previously-cited Forbes magazine article on the top skills needed to be a data scientist, programming was followed by quantitative analysis, product intuition, communication, and teamwork. And you won't go far in your data scientist career unless you're also a skilled problem solver with a good grasp of trends in your industry. 

But programming? As we mentioned earlier, it’s likely there are expert programmers in the IT department and alternatives are emerging. For example, off-the-shelf products such as the Alteryx Designer Cloud for data wrangling and the Tableau visual analytics platform have drag-and-drop interfaces that allow non-programmers to clean and organize data. Beyond that, automated data science and machine learning solutions such as AutoML and DataRobot, are designed to help non-programmers choose and locate the correct data cleaning methods and algorithms.

Elevate Your Data Scientist Career: Learn Programming 

If you are not a programmer, you can still aspire to become a data scientist! However, you will be at a disadvantage when competing against job candidates who are proficient coders. Hiring managers know you’ll be less effective compared to coding candidates, who don’t have to rely on the IT department’s programmers or on less flexible or scalable off-the-shelf tools. 

Are you a new or aspiring data analyst or data scientist – but you’re a coding newbie or not fluent in Python? Are you already in a data analyst or data scientist role, but you’re a non-coder? In both cases, you can't go wrong with getting some Python coding expertise under your belt! 

Check out CBT Nuggets’ new Programming for Data Science online training course. In this intermediate level training, you’ll learn how to write Python code using object-oriented programming (OOP), create reusable Python functions for data science, wrangle data with NumPy and Pandas, and visualize data with Matplotlib and Seaborn. 

If you’re nervous about jumping in the coding deep end, then get your toes wet first with the entry-level Introductory Python for Data Analysts training course. 

Not a CBT Nuggets subscriber? Sign up today for 7-day free trial access to our Programming for Data Science training.


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