Did you know data science is a field with a significant labor shortage? Estimates suggest that around 250,000 more experienced data scientists are needed to fill the demands of the modern workforce. At overall salaries averaging over $120,000 per year, data science is a worthy career path.
But like any career decision, you should take a step back and consider all the factors before choosing a career path in data science. Various specializations within data science will define your responsibilities. Each one has its demands, training, and qualifications to keep in mind.
This article will serve as a brief guide into the big three data science career paths: data analyst, data engineer, and data scientist.
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The Path of a Data Analyst
For anyone looking to break into the world of data science, the role of a data analyst is a good place to start. As an analyst, it is your job to translate data into actionable business insights. This career path is great for analytical thinkers who love to problem solve and communicate solutions to a broader team.
While the data analyst path can be a good place for any data scientist to start, the position can scale to senior levels. Here’s what it entails.
What it Entails
You may think of all data scientists as having a role in the analysis of data to produce business insights. However, data analysts tend to fulfill this function more than data scientists. Data analysts are the “thinkers” of a data science operation, pouring over data to draw out every useful trend within a collected data set.
A data analyst scrubs data to understand where problems occurred or improvements could be made. They delve through vast collections of information to create narratives surrounding the performance of a business and how customers are interacting with it. As a result, analysts need specific skillsets and face particular challenges.
What to Consider
Before going down the path of a data analyst, consider what the scope of the job entails and if it will best suit your needs. At an average base pay of $61,166 per year, these data professionals have a lot of room to grow. Start with understanding the associated skills needed and the challenges you must overcome.
Skills needed:
- Programming
- Problem-solving
- Communications
- Database management
- Data visualization
Challenges to overcome:
- Creating data visualization tools that can aid communication.
- Analyzing data from multiple sources.
- Dealing with poor quality and unreliable data.
The Path of a Data Engineer
A data engineer, in contrast to an analyst, focuses on the software side of data science. Data collection and analysis requires comprehensive databases and pipelines before the data is even available for study. The data engineer uses their software and programming expertise in order to build architecture for data storage and communication.
As a data engineer, various duties will fall to you.
What it Entails
A data engineer is responsible for directing all data traffic into a usable form for the analysts that handle it next. This means devising useful pipelines that pull in the most recent and informative data across various sources. With modern innovation, this increasingly occurs within a cloud computing environment, complete with serverless architecture on the back end.
Because of the heavier reliance on cloud data systems in the wake of COVID-19, data engineers should always make data privacy and cybersecurity an aspect of their position. Data science has the potential to answer cybersecurity needs through enhanced methods of malware and spam detection. This is made possible through the accumulation and application of data in a secure infrastructure.
The job isn’t easy, but the payoffs can be great.
What to Consider
In considering a career path as a data engineer, it is helpful to consider the skills and challenges that come with the job. Data engineers keep all the data coming in smoothly, and this means a lot of programming knowledge and software design expertise. For a base average pay of $92,326, honing these skills can be highly lucrative.
Skills needed:
- Programming
- Fluent understanding of Python
- Experience building and working with large data sets
- Advanced Structured Query Language (SQL) skills
- Data pipeline experience
Challenges to overcome:
- Securing data pipelines.
- Complying with various data regulations.
- Comprehensively integrating data from multiple sources.
The Path of a Data Scientist
Finally, the path of a data scientist is one that encompasses a combination of skill sets, many of which are common to the other data science paths. For example, a data scientist will analyze and assess a data set much as an analyst would, only their position may also include building the machine learning algorithms that help structure this data into informative patterns.
As a data scientist, your duties can be a bit more varied than the other career paths.
What it Entails
The role of a data scientist entails the use of machine learning and other artificial intelligence functions to better structure and clean data for analysis. A data scientist often analyzes data much as a data analyst would, but their efforts are often less directed. Instead, data scientists freely explore data collections and infrastructures to derive out-of-the-box insights.
An understanding of machine learning processes—both supervised and unsupervised—is immensely valuable for any data scientist, with strong problem-solving and analytical skills needed as well.
What to Consider
Data scientists are typically more highly trained in complicated technical processes like machine learning. As a result, their average base pay is higher at $96,420. They play key roles in many parts of our evolving modern society, one example in particular being that of devising life-saving insights in the healthcare sector through the application of AI in pre-emptive care and diagnostic data tools, which encapsulates a growing industry and could potentially speak to job security in the future.
As the value of big data makes itself known in all kinds of industries, the opportunities of a data scientist are just about unlimited. Here are the skills and challenges they must first be aware of, however.
Skills needed:
- Programming
- Problem-solving
- Database management
- Machine learning
- Statistics and predictive modeling
Challenges to overcome:
- Company politics and lack of insight application.
- Navigating data privacy and security
- Finding the right queries and machine learning functions to analyze a data set.
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Final Considerations
Regardless of the career path you take, your training will likely involve programming certifications and an undergraduate degree at the very least. Master’s degrees are common for senior positions in any of these three fields, but you can also explore your options to learn data science fast.
The world increasingly needs experts in data science. These three career paths are all great, high-paying options for the right professional. Consider these items to help find the best fit for you.
The post Things to Consider While Choosing a Career Path in Data Science appeared first on JAXenter.
Source : JAXenter
