Crunchbase’s Post

As a bit of a follow up to my “job descriptions are bad and most organizations don’t know what they really need out of data” post yesterday, the flip side of that coin is that it is a hard market and data aspirants really need to prepare for that. Jobs aren’t just snatching up every “I know Python and scikit learn” professional anymore. You need to keep learning and growing. There’s no off-ramp. This is an ever evolving field, and only accelerating with AI, you need to grow with it. Most importantly, you need to lean into value creation. What is your work building? What’s it improving? What’s the payoff for all of that flashy stuff on your portfolio (you DO have a portfolio, right)? If an organization doesn’t really know what they need and you really want the job, it’s going to be on you to make the sale on why YOU are what they need. Yes, it’s exhausting and emotionally draining, but it’s what’s necessary right now. There is likely a market realignment coming as the dust settles from the end of ZIRP, big tech layoffs, etc. But in the meantime, it sure seems like we are tethered to “we want a PhD with 10 pubs, and 15 years of experience for this junior analyst position that pays $60k/yr” (…yes, slightly exaggerated for effect).

🎯 Data Cleaning in Action: Unlocking Insights from the World Layoffs Dataset I'm thrilled to showcase my recent project on cleaning and analyzing the World Layoffs dataset with 5000 records. Here’s what I achieved: 🔹 Key Steps: Addressed null values using isnull() Standardized data by trimming and resolving inconsistencies with concat() Removed duplicates using row_number() Eliminated irrelevant rows and columns to refine focus 🔧 Tools Used: SQL Server: For robust data cleaning and processing Canva: For clear and impactful data representation 🔍 Insight Unveiled: The analysis highlighted that Cue, a player in the healthcare industry, raised the highest funding of $999M, making it a clear standout in the dataset. 💡 Takeaway: Clean and well-structured data unlocks powerful insights. This project reaffirmed the importance of data preparation in driving impactful results. 📂 Code & Process: Check out the complete code and workflow on my GitHub:https://lnkd.in/geRH3M3z 📢 Let’s connect and discuss your favorite data cleaning tools and strategies! #DataCleaning #DataAnalytics #DataScience #Insights #Healthcare #Python #SQL #GitHub #Visualization

🏈 One last big push in #hiring before we begin our busy season! There's no better time to join Swish Analytics! ⚽ Product Scientist: Expand utilization and adoption of existing models and accelerate adoption of commonly used proprietary frameworks; write clean and efficient production code ⚾ Frontend Engineer: Design & develop the next-generation data analytics platform using #React, #Node, and JS; requires a background in data analytics 🎾 Staff Software Engineer: Develop microservices and API's, data stream processing via Kafka, Kubernetes development and optimization, devtools development like SDKs and CLIs Apply below 👇 https://lnkd.in/eHXSQRtw #Startup #Technology #Innovation

Switching back to Analyst Builder today to try a medium problem (https://lnkd.in/g86uSWW3) Solved it with a nested round in #sql ```sql SELECT  company,  Round((Round((employees_fired::DECIMAL / company_size::DECIMAL), 4) * 100), 2) layoff_prc FROM tech_layoffs ORDER BY company ASC; ``` #Python with #Pandas using series creation and fillna() ```python # access datasets as pandas dataframes import pandas as pd; # create new column for percent tech_layoffs['Percentage_Laid_Off'] = round((tech_layoffs['employees_fired'] / tech_layoffs['company_size'] * 100), 2) tech_layoffs[['Percentage_Laid_Off']].fillna(0, inplace=True) # assume printout similar to jupyternotebook output tech_layoffs[['company', 'Percentage_Laid_Off']].sort_values(by="company") ```

🎯 Day 38: Tackling Data Cleaning & EDA in SQL 🎯 Today, I dove deep into a real-world SQL project focusing on data cleaning and EDA, uncovering some surprising insights! Working with a layoffs dataset from Kaggle, I started with a staging table to safely clean and transform raw data. 🚀 🔄 Data Cleaning Process: 🧹 Handling Duplicates: Used SQL’s ROW_NUMBER function with PARTITION BY to pinpoint and eliminate duplicate records. 🗃️ Standardizing Fields: Tidied up inconsistent values, like aligning Crypto categories and removing extra spaces in country names. 📅 Date Formatting: Reformatted dates for consistency using STR_TO_DATE. 🕳️ Nulls & Blanks: Filled missing industry fields and removed rows where essential data was unavailable. 📊 Exploratory Data Analysis: 📈 Industry Trends: Analyzed which industries faced the highest layoffs, with a focus on startups and sectors like tech and crypto. 📆 Yearly Impact: Found patterns in layoffs by year, shedding light on economic shifts and specific company challenges. 🏢 Company-Specific Insights: Sorted companies by total layoffs, identifying those that struggled most and seeing how funding influenced layoffs. This project deepened my SQL skills, from data manipulation to insightful EDA. Keen to share more as I progress! 💡 #DataCleaning #SQLProject #DataAnalysis #DataJourney

📊 Explore the world of Exploratory Data Analysis using #SQL! 🚀 Delve into insights on layoffs, funding, and industry trends. Uncover which companies and countries are most affected. Let's navigate through the data together to uncover valuable insights! #DataAnalysis #SQL #Insights #DataCleanup #ClearInsights #ReadyToAnalyze #CleanData #ClearInsights #SimplifiedTech #DataAnalytics #DataAnalysis #DataCleaning #BusinessAnalyst #ResearchAnalyst #FinanacialAnalyst

🚀 World Layoffs Analysis Dashboard 📊 I just completed a comprehensive World Layoffs Analysis Dashboard using Power BI. This project dives into key metrics and trends, including: ✅ Top Companies with Layoffs ✅ Industries Impacted Most ✅ Yearly and Monthly Layoff Trends ✅ Insights on Maximum Layoffs by Companies 📈 With SQL and Power BI, I transformed raw data into valuable insights, creating an interactive dashboard that visualizes: 🔹 Total Layoffs: 383.7K 🔹 Companies like Amazon, Google, Meta, and industries like Consumer Retail were significantly affected. This project helped me strengthen my analytical skills and further explore real-world data patterns! A big thank you to Alex Freberg for his guidance and mentorship throughout the project. 🙏 🔗 GitHub Repository: https://lnkd.in/dpycYrJ3 Let me know what you think of the insights! 🌟 Feedback is always welcome. #DataAnalytics #PowerBI #SQL #DataVisualization #LearningByDoing #LayoffsAnalysis #ThanksToMyMentor

For anyone thinking about transitioning into or entering analytics as a career, the barrier to entry in terms of skills is not exactly low, but it may also not be as high as you think, and it's definitely accessible. Note I'm not talking about data science even though alot of my work blurs the lines between the two fields. First of all, why would you want to move into analytics as a field? Aside from all the sex, money and fame, my analysis on tech layoffs earlier this year showed that whilst no job is ever truly safe, a role in data certainly offers a higher level of job security (but comes with a lower level of job satisfaction because you end up having to deal with a bunch of know-it-alls). Over the coming weeks and months I'm going to be writing short but frequent posts and articles on developing core analytics skills with practical exercises. Don't worry, I don't want money or anything, a picture of me in your living room is more than enough. You might have to follow me or my newsletter if you want to receive updates but that's up to you. The posts will also be for anyone just looking to develop their analytical skills.

🚀 Exploratory Data Analysis (EDA) on Layoffs Data 🚀 I’m excited to share a recent project where I performed Exploratory Data Analysis (EDA) on a layoffs dataset. The goal was to uncover valuable insights related to layoffs across different companies, industries, countries, and stages. 🔍 Key Findings: Max & Min Insights: Analyzed maximum layoffs and their percentages across the dataset. Top Companies & Industries: Aggregated layoffs by company and industry to understand which sectors were hit hardest. Geographical Insights: Explored layoffs by country, revealing trends in different regions. Time Trends: Analyzed layoffs over time, with monthly rolling totals to track the overall layoff trends. Company Rankings: Ranked companies by total layoffs and identified the top companies over time. 🧑💻 Tools Used: SQL for data extraction and aggregation. Data Cleaning and Transformation to standardize and filter records. 🎯 Check out the full project here: 🔗 https://lnkd.in/g_pCSkPt I’m excited about the insights gathered and looking forward to using this knowledge for future data-driven decisions! 💡 #DataAnalysis #ExploratoryDataAnalysis #SQL #DataScience #LayoffsData #BusinessIntelligence #DataVisualization #MachineLearning #Python #GitHub #Analytics