Breaking Into Tech: Data Analytics Career Paths

Are you someone looking to break into the technology industry as a data analyst? Having recently graduated, I was that person—eager to learn what data analytics truly means, understand the variety of career paths, and identify the critical skills required for success. With the help of my Credera friends, I’ve developed a solid foundation necessary to explore a career in data analytics and am excited to share how you too can pursue a career in data analytics, no matter your background.

Let’s start with the basics. What does a data analyst do?

The typical definition you may find online is that data analysts gather data from various sources and transform the information into something meaningful. A data analyst’s work is impactful to any organization because data is the backbone of all business decisions. Among countless possibilities, businesses can capture key performance indicators to drive decisions, determine which acquisitions are worthwhile, evaluate the effectiveness of advertisements, or investigate customer sentiment toward a product or service.

There is no limit to the career possibilities of a data analyst and this is especially true in the field of consulting. Every client project brings a new challenge to break down, a new skill to learn (or familiar skill to master), and an opportunity to impact others.

At Credera, our data and analytics focus area is centered around three capabilities:

1. Data engineering

2. Data science

3. Technology management

These capability areas organize common skillsets, domain expertise, and interests to foster clear paths for professional growth. However, they’re not meant to limit what a consultant can explore, especially since data analytics projects typically require skills from some or all the capabilities.

For each of these capabilities, I’ll provide you with its job description, critical skills, and whether experienced Credera staff think a formal education in certain domains is necessary for the position. Then I’ll list general advice shared with me to become a data analyst and the resources I’ll be using to develop my personal skillset for each of these positions. From all this, you can evaluate your best next step in a data and analytics career.

Career Path 1: Data Engineering

Data engineers create the data architecture and pipelines necessary for data to flow within an organization from initial source to end user. The data engineering skillset is the most fundamental of all data careers because data always needs to be collected, cleaned, and transformed to be used for other analytics or data science tasks. Data practitioners of all kinds benefit from learning basic data engineering skills.

Data engineers also work on data logging, developing test cases, ensuring the data model is normalized, and data visualizations.

If you’d like to become a data engineering consultant, you’ll need to develop the following skills:

• Data modeling

• Scripting languages (Python, Java, C++)

• Relational databases and query languages

• Cloud development (AWS, GCP, Azure)

Is formal education in computer science necessary?

No, but it helps! If you have mathematical and problem-solving skills, you can develop the skills required to become a data engineer. Attending a bootcamp will teach you how to become a good programmer and develop skills even faster on the job. However, you do miss some core computer science principles, which may pose a challenge. Don’t let this discourage you, as there are many talented data engineers with non-technical backgrounds at Credera.

Career Path 2: Data Science

Data scientists are problem solvers who have an intellectual curiosity toward patterns and research in their field. Rather than building reports, data scientists focus on building data models to find unseen connections between different data entities. A strong background in statistics and machine learning (ML) is a requirement for this field.

If you’d like to become a data science consultant, you’ll need to develop the following skills:

• Fundamentals of statistics

• Familiarity with ML concepts, model types, and model building

• Scripting languages (Python, Java, C++, R)

• Relational databases and query languages

Is formal education in statistics, computer science, or data science necessary?

It depends on who you ask. In some cases, having a strong statistics background (regardless of how you learned it) and understanding information theory may be enough to get your foot in the door, but many believe a master’s degree or doctorate in data science (or related subjects) is necessary to work in this field. In either case, keeping up with data science related news and research articles is important since technology is always evolving. Be sure to learn about different models and not get discouraged by failure. Keep the curiosity alive!

Career Path 3: Technology Management

Technology managers serve as a blend between data analysts and management consultants and are often involved in developing strategy for a data-focused project. They have a high-level understanding of development work, as well as a passion for client-facing interactions and people management. A technology manager's role is always evolving. Since clients often have ever changing needs, a technology manager can be a versatile asset for any project.

If you would like to become a technology management consultant, you will need to develop the following skills:

• Effective communication and presentation skills

• Leadership and management skills

• Flexibility

• Scripting languages (Python, Java, C++)

• Relational databases and query languages

Is formal education in computer science/management information systems necessary?

No! Since technology management is cross-functional, you can leverage the business, leadership, or communication skills you already have and develop a stronger technical background during your career. Of course, high-level knowledge on data analytics, some programming experience, and the utilization of data visualization tools is required. These skills can be developed through online resources, such as those listed below.

Data and Analytics Career Resources

Like many of you, I’m just beginning my journey. Listed below are the resources I’ll be using to make myself desirable as a team member and client partner.

If you have no technical experience:

1. Learn Python through course providers like Khan Academy and Udemy or even YouTube tutorials. Once you have developed some Python skills, begin working on projects using open-source datasets that explore topics you find interesting (I enjoyed working with the Airbnb datasets).

2. Try to join a research project online, at work, or in an academic setting. Even if your subject matter is unrelated to data analytics, your newly developed skills and personal projects will indicate that you have a strong passion for learning.

3. Review agile principles and frameworks. This is standard practice in the tech industry and consulting. Work on group projects that utilize project management tools like Jira and learn Git to develop best coding practices.

4. Join LinkedIn if you haven’t already. Here you will find like-minded, career driven individuals who share a similar passion for data analytics.

5. If you still need more exposure, apply to bootcamps and programming schools. Many Credera team members have graduated from The Turing School of Software and Design.

What I am working on to build data and analytics skills:

1. Articles: Reading Medium articles is a great way to keep up with data science news and learn new skills. I like to comb through articles to see what interests me each week and I recommend you do the same!

2. Certifications: Getting certifications shows proficiency in a skill or topic. I plan to get the AWS Solutions Architect - Associate certification this year and the AWS Data Analytics - Specialty certification next year, but there are many other options. Other cloud certifications you could research are Microsoft Azure and Google Cloud. Of course, there are non-cloud certifications as well, including those offered through Tableau, Databricks, and Snowflake.

3. Competitions: Kaggle competitions and datasets are valuable resources to get exposure to data analytics and science. Another great resource is Analytics Vidhya. Once I have achieved the foundation needed, I’ll start newcomer competitions.

Your Future in Data Analytics

There are various data analyst career pathways. It is challenging work, but it is rewarding to know you make an impact on a field that is still being defined. Some non-technical advice shared with me that applies to all pathways is:

1. Be brutally honest. If you don’t understand a concept, ask someone to explain it and repeat the information to them in your own words.

2. You must be willing to take risks. Once you become comfortable with failing, you will see both personal and professional growth.

3. Find mentors who you admire and trust, they will be your biggest proponents for success.

If you’re interested in more information, contact us at findoutmore@credera.com, or me personally at sutrisna.roy@credera.com.

Think you’re ready? Find the position best suited for you here at Credera. Good luck!

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