There’s a lot of information on how to become a data scientist or a data analyst but there’s little on how stressful these positions can be. As someone first starting out as a data scientist or a data analyst you may unknowingly put yourself into a stressful situation. Today I’d like to share my experiences when I was a data scientist versus a data analyst and my advice to prevent stressful situations from forming.

So much work, so little time

Stress caused by a looming a due date is very common. Reduce stress by working with your stakeholders to prioritize requests and agree on a project timeline and deliverables. …

It’s 2021 and time for annual performance reviews and setting new goals for the year. If you’re looking to make the jump from junior to senior data analyst this is my advice based on experience working with junior analysts that have made it to the next level.

Learn to be independent

Junior analysts tend to ask more questions due to lack of experience. A senior analyst asks fewer questions because they’re able to make decisions from past experience. This doesn’t mean you shouldn’t ask questions as a junior analyst. You’ll never gain experience without asking questions and making mistakes along the way. However, avoid your first instinct to ask a question right away and think about how you might approach the problem and why. Then ask your manager or a senior analyst for advice and explain why you came up that approach. …

As a data scientist it was difficult to explain machine learning results to non-technical stakeholders. After some trial and error I came up with a way to transform model results into a format my stakeholders were able to understand. Today I’d like to share my methodology that I’ve used with great success.

Methodology 1

This methodology can apply to any model that generates probability score values between 0 and 1.

1. First sort your model scores from high to low and decile them. Decile 1 will contain the highest scores and decile 10 will have the lowest scores.
2. Next calculate the minimum, median, and maximum score value for each decile. …

One skill needed to become a successful data analyst is knowing how to develop and manage stakeholder relationships. When I first became a data analyst, I had no clue how to deal with stakeholder requests or manage expectations. To prevent you from having to learn from the beginning, today I’d like to share what I learned over the years to effectively manage my stakeholders.

Understand stakeholder goals

Goal setting is common in organizations to measure performance at the end of the year. Goals can be set by stakeholders or cascaded down from company goals. Knowing your stakeholder’s goals helps you understand what defines their success. For example, if you’re supporting a product manager whose individual goal is to drive user engagement and the company goal is to increase revenue then your analysis should try to address the impact to those two goals. …

I’ve written about the importance of soft skills to become a successful data analyst. One main soft skill is to learn how to tell stories with data. When I first became a data analyst I didn’t know that meant. Years later I can finally answer the question “how to tell a story with data” and how you can develop this key skill to present data results effectively.

Description

I describe “telling a story with data” as the ability to answer a business question and explain why it matters to the audience. …

Whether you’re a data scientist or data analyst, at one point in your career you’ll have to present your results to an audience. Knowing what to say and include in your presentation will impact your success. After giving many data presentations over the years, I’d like to share my tips on how to increase your chances for a successful presentation.

Know your audience

The audience for your presentation will dictate the level of detail and information you’ll present. There are three main types of audiences:

1. Peers — These are data analysts, data scientists, and anyone in analytics that understand what you’re explaining if you drill down to methodology, analytic approaches, or code. Detailed information is preferable for this audience to share your work and for them to understand your approach and possibly leverage for their projects. …

I graduated with a finance degree and naturally gravitated to my first job working as a financial analyst. Since then I’ve changed careers multiple times and have had roles as a data engineer, data scientist, and now as a data analyst. My career changes weren’t smooth transitions and it was tough to adapt to my new roles. Today I want to share the challenges I faced along the way and my advice if you decide to switch careers to become a data analyst.

Overcome your imposter syndrome

Imposter syndrome is “the idea that you’ve only succeeded due to luck, and not because of your talent or qualifications”. As a data analyst, stakeholders looked to me as the analytics expert but often times I felt like I was an imposter trying to demonstrate expertise in a subject I didn’t fully understand. I learned to overcame my fears by identifying areas where I felt my knowledge was weak and I took courses and read up as much as possible to improve my understanding and become the expert. Don’t feel you have to know everything in the beginning. …

Office Hours

There’s a wealth of information on how to prepare for data analyst interviews but there’s little on what convinces an employer to hire you. Having been on both sides of the table as the interviewer for a data analyst job and as the interviewee trying to get a job I want to share what to expect in a data analyst interview and what makes a great candidate to increase your chances of getting the job.

HR interview

The first step after your resume passes the initial requirements screen is a call with HR. This call is typically with the recruiter for the position to get a sense of your job experience, provide you details about the position, gauge your interest, and ask about your salary expectations. …