A step by step comparison

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When I first became a data scientist I realized machine learning was a vague concept that people heard about but didn’t quite understand. I struggled to explain machine learning in a way that non-technical people could relate to.

Fast forward to today when it hit me that building a machine learning model is like cooking — a universal activity everyone can relate to unless your idea of cooking is throwing a frozen dinner in the microwave. Without further ado, let me walk you through the ways building a machine learning model is like cooking.

The first step in building a…


Combining data science with data analytics

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Analytic functions are divided into data science vs. data analytics. Rarely is there mention of combining both together. Today I’d like to share how I used a machine learning model for a data analysis and my approach to translate model results into actionable insights.

A couple years into my first data analyst role, I felt I had worked on all the possible projects I could supporting marketing and was no longer learning anything new. …


Secrets to success I wished I had known from the beginning

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There are many articles about the skills needed to be a data scientist vs. a data analyst but there are few that tell you the skills needed to be successful — whether it is getting an exceptional performance review, praise from management, a raise, a promotion, or all of the above. Today I’d like to share my firsthand experience as a data scientist vs. a data analyst and what I learned to become successful.

I was fortunate enough to be offered a data scientist position without any experience in data science. How I managed this is a story for another…


Tips to add value faster

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Have you ever started at a new company and the onboarding process was less than optimal? Your manager didn’t have a clear idea where you should begin and onboarding meant learning as you went along. After working as a data scientist and then a data analyst this is what I’ve found helped me onboard faster and add value even when I didn’t have clear guidance.

My first onboarding item was to understand the company business model, primary sources of revenue, and KPIs used to measure success. This knowledge helped me during A/B test discussions on experiment design and decide on…


and how you can avoid being one

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First impressions are hard to change and if you’ve just started at a company as a data analyst you don’t want to start off with a bad one. I’ve worked with my fair share of bad data analysts over the years and want to share their bad habits and how to avoid them.

The worst bad habit I’ve seen is data analysts that don’t do any exploratory data analysis ( EDA ) on new tables and jump immediately to writing SQL. When I was a consultant I had a new colleague join two tables together without first looking at the…


Tips to maximize output without working more

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As a data analyst, I’m often faced with a long list of requests and limited time to work on them because I’m either in a meeting, fielding questions from stakeholders, or troubleshooting an ETL problem. However, I’ve still managed to complete my projects on time without working more. Today I’d like to discuss the tips I’ve used to be more productive and how you can be too.

Stakeholders may not put in all the necessary details into a request and analysts will need to clarify the requirements. This wastes time for both parties to discuss requests that could’ve been saved…


A case study investigating a drop in DAU

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If you’re in a data analytics role, at some point you’ll get asked to investigate an abnormal change in a company KPI. There can be many possibilities for the root cause but how you go about troubleshooting can either take up an enormous amount of your day or no time at all. Having had many questions like this as a data analyst I’d like to walk through the steps I take to research the problem and the most common causes to help you find the answer faster.

Let’s assume a common KPI such as DAU in your company dashboard showed…


How supporting product made me a better data analyst

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Before working as a product data analyst the majority of my data roles supported marketing. You may think supporting product is the same as any other division but I can tell you from experience that’s not the case. Today I’d like to discuss my experience transitioning to a product data analyst role and how working with product made me a better data analyst.

Unlike universal marketing concepts such as SEO and SEM, each product is different and has a learning curve for you to understand what the product is for and how users interact with it. Allocate extra time you’ll…


Pros and cons from my experience in marketing analytics

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It’s common for a data analyst to support marketing but there’s not much information telling you what it’s actually like being a marketing data analyst. Having supported marketing as a data analyst in multiple companies I want to discuss my experience and what’s in store if you decide to take on a marketing analytics role.

Marketers have a lot of ideas and they need to have this ability to be successful in their job. Unfortunately, this also means the data requests come off the top of their head as new ideas form. …


A career strategy to ensure success

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You’re probably wondering why someone would advocate for a data scientist to become a data analyst. As someone that’s been a data scientist and a data analyst I want to discuss why you should consider becoming a data analyst even if you’ve trained to be a data scientist.

A study released by Interview Query reports that “data science interviews plateaued in 2020. Data science interviews only grew by 10% after previously growing by 80% year over year. The second fastest position growth within data science roles went to business and data analysts which increased by 20%.”

Data science roles are…

Vicky Yu

Musings of a data scientist turned data analyst. Sharing my data experiences one story at a time.

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