Liudmyla Taranenko’s Post

Dear Data Analyst, Understanding the insights you want to drive with your data before deciding the best visual representation is KEY. Just as a story writer pens down his story before deciding on the best actor to tell it.

The unseen life of a data analyst: - Hours spent cleaning messy datasets - Countless Google searches for that one elusive function - Stack Overflow becoming your second home - Visualizations tweaked until your eyes cross - The existential crisis when your code won't run (it was a misplaced comma) That insightful analysis? It's not magic. It's meticulous effort and caffeine. Data doesn't speak for itself. It takes skill and a lot of unseen effort to make it work, and to make it useful. What's one data skill you're currently struggling to master?

Over 73% overanalyze, less than 30% address the real issue. The flex many data analysts enjoy in general life matters comes from repeated years of dealing with data. Here's what the process looks like- Before thinking of modularizing a code or even debugging it, you must recognize the exact issue. An error in recognizing the real issue will lead to a catastrophic end result. Regardless of how sound your process is, working on the wrong issues will drive you toward an erroneous conclusion. Just as in data analysis where you must—recognize the problem, modularize the code, or debug it, to arrive at a meaningful result, life’s procedure is roughly the same. Many times you have to - - Know your real struggles and turn the solutions into your goals. - Engage right procedure to achieve your goals. - Reach a definite and impactful end. I can help you save your company from analysis paralysis when it comes to data. Let's work ! #dataanalysis #dataanalyst #datascience #data

In numbers deep, a story lies, A data analyst sees through guise. Patterns hidden, secrets bare, Insights gleaned from data's glare. With queries sharp and charts so bright, They guide decisions, day and night. From chaos, clarity they find, A data analyst's keen mind. #DataAnalystLife

In problem-solving, data analysts often need to adapt and reconsider their approaches to find optimal solutions, much like the image portrays: "Sometimes you need to change direction!! Patience is not always the optimal way." As a data analyst, it’s crucial to recognize when a strategy isn’t yielding the desired results. Whether it's a specific data model or an approach to visualization, persistence with the wrong technique can waste valuable time. Don’t be afraid to pivot and explore new angles. Continuous evaluation and flexibility are key to unlocking valuable insights from data! #DataAnalysis #ProblemSolving #powerbi #dax #DataDriven #Efficiency #GrowthMindset

Data Analysis is not for the faint hearted... Lol For anyone who’s dived into the world of data analysis, you know it’s a mix of patience, persistence, and a touch of bravery! It’s not just about numbers on a screen—it’s about turning vast amounts of raw data into meaningful insights that can shape critical decisions. And yes, sometimes it means tackling complex algorithms, troubleshooting errors, and spending hours just to get that one perfect visualization! But here’s the thing: while it’s challenging, it’s also incredibly rewarding. Each analysis reveals hidden patterns, uncovers new insights, and brings clarity where there was once just data chaos. So, if you’ve ever felt the ups and downs of data analysis, know you’re not alone! Keep pushing through those tricky datasets and celebrating the small (and big) wins along the way. Anyone else find themselves laughing through the struggle? Share your experiences below! #emehglorychiamakawrites #DataAnalysis #Perseverance #DataDriven #EverythingData #InsightfulJourney #DataScienceLife #ProblemSolvingSkills #everythingdataandassistance

As data analysts, our job is like looking for a tiny needle in a big haystack of information. It might seem tough, but with the right tools and some smart tricks, you can become an awesome data detective. Here are a few tips to help you start: 1. Always begin with a clear question or idea in mind. 2. Really get to know your data—what it looks like, what's weird about it, and what its limits are. 3. Use exploratory analysis to notice any patterns or things that don't quite fit. 4. Don’t be shy to try new and fun ways to show your data. 5. Work with others to see things from different angles and get new ideas. Remember, the best data detectives are always curious, keep at it even when it's tough, and think creatively to solve problems.

As a Data Analyst, have you wondered that what is the difference between 𝗙𝗮𝗰𝘁 𝗧𝗮𝗯𝗹𝗲 and 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻 𝗧𝗮𝗯𝗹𝗲. If Yes, then don't worry! I am here to tell you: So, 𝗙𝗮𝗰𝘁 𝗧𝗮𝗯𝗹𝗲: It contain facts of a business process plus foreign keys which establish well-defined links to dimension tables. It contain detail level facts that have been aggregated. 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻 𝗧𝗮𝗯𝗹𝗲: It is a variable that categorizes facts. It is also called categorical variable in stats and machine learning. It is used to answer business questions but it's main use is grouping, filtering, grouping and labeling operations. That was it about the difference and I hope you guys liked it! #dataanalytics #fact #dimension #table #learningjourney

Excited to finally dig into “Weapons of Math Destruction” after hearing such great things about it from our guest speakers at the Data+Women NYC event earlier this month! Being a data analyst is so much larger than refining your technical skills. You must also know and acknowledge the implicit biases that come with data, its collection, and its management. How do certain systems, tools, and algorithms we use enforce biases both good and bad? I’m excited to explore this book and learn more on the topic! Have any favorite books about data? I’d love to have any recommendations, please share below! #dataanalytics #dataanalyst

The biggest mistake I see in aspiring data analysts: Starting with data. How should you do it? Start with a problem. Then bring in the data. Only by sticking to this order will you give yourself the best chance of: A) Creating valuable insights in a reasonable amount of time. B) Coming up with solutions that stakeholders will actually use. The best data analysis makes people's lives easier. From making decisions to understanding things quickly... Your colleagues and stakeholders won't be shy about telling you their problems. It's your job to figure out how to use data to solve them. Listen first, then act.