Tech Careers Part 1

Published on May 21, 2022

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Tech Careers: Part 1

By Hana Gabrielle Bidon

Whether you are learning your first programming language or have been coding since you were six years old, tech provides a wide range of career paths to pursue. This article, which is the first one in an eight part series, can cover a small percentage of tech careers and tech-adjacent careers, provides an introduction to some tech careers that combine other interests.

To kickstart your career in tech, discover the right role for you. This can come in the form of messaging people on LinkedIn who are in a career that you aspire to be in like software engineering. Another way is to read articles like this and search on Google different types of tech careers you can potentially look into.

Furthermore, I highly recommend you self-reflect about what you want in a career regardless if a tech career or a tech-adjacent career. Some questions you may ask yourself can be as follows:

  1. How can I potentially combine my strengths and interests in a career?
  2. What do I want to do day-to-day in my future job? Do I want to primarily code or have lots of face-to-face interaction with my clients?
  3. How would I take the steps to get to a tech career I would enjoy? Would I be fine with a bachelor’s degree or need to get an advanced degree?

In this article, I am discussing some of the most popular tech careers:

  1. Data Science
  2. Product Management
  3. Software Engineering

Data Science

Data science is a new field compared to other tech careers mentioned in this article, namely software engineering. Additionally, it emerged out of fields like data mining and statistical analysis. Data mining is “the process of finding [outliers] and patterns within large data sets to predict outcomes using a variety of methods” (SAS Institute 2022) and statistical analysis, “the science of collecting, analyzing, and presenting large” data sets to provide insights on trends and patterns (SAS Institute 2022). According to Oracle, data science took off by 2008 with the increasing volume of data and companies needing to gather lots of data on its customers to provide better services and gain more profit.

As a data scientist, they are responsible for developing strategies to analyze data. When analyzing data, they first explore data and then develop data visualizations and/or build machine learning models trained to recognize certain patterns.

Here are some questions data scientists consider when analyzing data:

  1. What do I do with all this data?
  2. What are its real world applications?
  3. What programming language(s) would I use to analyze this data? Python and R are the most common ones for data scientists in 2022 according to DataCamp.
  4. Would I need to store my data set in a database (e.g. organized collection of data in an electronic base)?
  5. How can I best visualize the insights that I gathered from analyzing the data in ways that people can easily understand?
  6. Which machine learning models would best fit based on the parameters of the data?

Digital data is vital in this field, and benefits businesses and our everyday lives. For example, geospatial data (e.g. geographical information in real-time in Google Maps or weather maps, for instance) can be used to calculate the fastest route to work by different modes of transportation (e.g. walking, driving, etc.). After commuting to work, data scientists can use data to provide insights to benefit their company. For example, data scientists can analyze data to present how their company can gain more profit by adding a new feature to their website. Other team members can benefit from the insights provided by data scientists like software engineers and product managers.

To succeed in data science, here are some technical skills you need:

  1. Expert in programming languages, namely in Python, R, and SQL
  2. Proficient in data visualization tools like Tableau and libraries in Python and R
  3. Proficient in artificial intelligence and machine learning
  4. Familiarity with deep learning and natural language processing depending on your role

Product Management

Product managers implement their expertise in business, tech, and their focus on user needs to create strong product outcomes. In other words, they strategize and direct every stage of the product lifecycle from research and development to building technically feasible products fulfilling both the users needs and business goals. To achieve this, product managers analyze the user, tech, and business. Then, they define solutions and guide their product team to meet these objectives.

Some of the responsibilities include meeting user needs, and defining the vision of the product and creating a roadmap. Also, they follow seven product management processes. For the sake of this article, I will list out three of these processes.

Product Management Processes:

  1. Find a problem to solve by figuring out the user pain points your product could address.
  2. Think about business goals by conducting user interviews to understand how solving the problem identified in the first step would help meet user goals, and organizational goals.
  3. Test possible solutions.

To thrive in product management, aspirants would possess these soft skills like storytelling, empathy for their users, research, and leadership.

Software Engineering

Software engineers combine their knowledge in Computer Science (CS) and engineering to create software. Depending on the company, the responsibilities of a software engineer may vary widely. Types of software that a software engineer may develop are computer games, mobile applications, and websites. In addition, software engineers analyze the needs of their users and provide consultation service when discussing design elements, which requires professionals to consider using mathematical models to meet project outcomes.

Software engineers should be well-versed in their technical skills, also known as hard skills, and soft skills that show how they fit in their team. For software engineers, some hard skills include learning how to code in programming languages like Python, Java, and Javascript. Additionally, software engineers should be proficient in algorithms and data structures, testing software, and debugging software. As for soft skills, software engineers should develop their communication skills, time management, collaboration skills with other technical members (e.g. data scientist) and non-technical members (e.g. program manager).

References

What is Data Science? | Oracle

What is Data Science? A Complete Guide. | Built In

What is data mining? | SAS

Statistical Analysis — What is it? | SAS

Top programming languages for data scientists in 2022 | DataCamp

What is product management? A definitive guide

What does a software engineer do? — CareerExplorer

Software Engineer Careers | ComputerScience.org

12 Types of Software Engineers To Help You Find Your Place in Technology