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My experiences:


And how to apply them as a data scientist

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When I first became a data engineer, I was lucky (or unlucky depending on how you look at it) to have a manager that wrote development guidelines outlining database table design and file naming conventions. It even went as far as setting standards for coding syntax. I didn’t appreciate the restrictions at the time, but years later I was grateful to have learned these best practices that helped me as a data scientist.

1. File names

As a data engineer, I had to troubleshoot ETL code written by other data engineers. …


And what you should do to prevent this from happening

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Harvard Business Review called data scientist the “sexiest job of the 21st century” but many of those jobs are not “sexy” and people often quit because expectations don’t match reality. As someone who’s worked as a data scientist and a data analyst, I want to share my tips on how to avoid wanting to quit your data scientist job within months of starting.

Expectation 1: I get to use cutting-edge machine learning algorithms to solve complex problems that will impact the business.

Sadly this isn’t the case with most data science jobs because you have to balance business needs with time. …


A beginner’s guide to presenting with data

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Telling stories with data is an important skill as a data analyst but there’s not much information to guide you on where to start. Having given many presentations as a data analyst I’d like to share my tips on how to become a successful data storyteller.

I describe “data storytelling” as answering a business question and explaining why it matters to the audience. The audience will determine what you’ll present to show why the data you’re presenting matters to them.

There are 3 main types of audiences:

  1. Peers — These are data analysts, data scientists, and anyone in analytics that…


Skills to advance your data analytics career

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If you’re one of the fortunate few that has a mentor to guide you on how to advance your data analytics career — congratulations, you can stop reading. For the rest that have to figure it out on their own as I did, I’d like to share skills I’ve added over the years as a data scientist and a data analyst that helped me move up to the next level and will hopefully do the same for you too.

1. Domain experience

Hiring managers want candidates with domain experience because they can connect business performance to the data and provide immediate value to…


Hands-on Tutorials

A product analyst primer from planning to evaluation

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Before becoming a product data analyst the majority of my data roles supported marketing. I had experience with marketing A/B tests but quickly realized not all A/B tests are equal, especially product ones. I found best practices that I wished I had known from the beginning through trial and error. Today I’ll discuss what I learned to give you a head start if you’re asked to work on product A/B tests.

The lifecycle of a product A/B test can be classified into 3 stages.

1. Planning

  • Experiment design and KPI(s) to measure success — In the planning stage, the product manager and…


A data analyst case study to drive subscriber growth

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As a data analyst supporting a mobile subscription business, Netflix’s Q1 ’21 subscriber growth miss is a classic example of when I would get called for recommendations to prevent a miss in the future. I thought this would make an interesting case study to discuss my approach to finding insights to drive subscriber growth. Sadly I’m not a Netflix employee and will be limited to publicly available data but the wealth of information on the Internet about Netflix is sufficient to generate insights for this case study.

Introduction


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.

Understand the company business

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.

1. Neglects Data

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.

Create a list of required information for requests

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…

Vicky Yu

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

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