Best Linkedin posts about Data Scientist

Data Scientist's biggest dilemma, Accuracy vs. Interpretability? This is one of the common problems we face while talking to the business. All the latest deep learning models may be good for accuracy and faster execution but logistic regression still remains the best in Interpretability for classification problems. Does a business want to know why a customer will churn? Further, having interpretable models will justify the value proposition of data science techniques. The business users will start appreciating the value of using these techniques to solve real business use cases. Have you ever faced this challenge, when you were asked to implement a complex algorithm and explain the factors driving results to business becomes a challenge? How will you balance this? Let's talk in the comments 👇 Celebrating 25k followers. 25 % off, link to course is available on my profile and comment 👇 Follow Mohammad Arshad #datascientists #analytics #innovation #data #artificialintelligence #dubai #polls

What's the worst thing that can happen to a data scientist? It's when your stakeholders don't trust you. This is exactly what happened to me 👇🏻 It was my first data science job after graduation. I still remember after spending days on analysis, I couldn't wait to share my findings to my boss and manager. I was very excited and confident at the same time, because I thought it was the time to prove my worth. I presented my findings and some graphs to them. In particular, I showed them the probablity density plots to compare A and B. After the presentation was over, the room was... silent. I was confused. My boss looked even more confused. My manager? He looked a bit skeptical. My boss asked, "Admond, from the probability density plot, you said that A is higher than B, but it looks like B is higher than A, what makes you think you're right?" I was speechless. I was not prepared for the question. Ok I said, "I think... My conclusion was based on the overall distribution of the data as shown in the probability density plot." My boss and manager said something like this, "I still don't trust the results and conclusion that you gave as I still think B is higher than A." It was a complete disaster. I didn't have any numerical evidence to support my claim. Worst thing? My stakeholders didn't trust my work. My credibility instantly fell apart. At that point of time, My self-confidence plummeted to the lowest. Not gonna lie, it was a painful lesson to me. But an important lesson that helps me become a better data scientist. Ever since that day, I often challenge my own assumption to make sure every analysis and conclusion are sound and backed by data, not what "I think...". 👇🏻 #datascience

I once met a data scientist who can... Think twice as fast. Analyse data effortlessly. Build more accurate ML models. Present "better" insights and convince everyone. Than I could and I felt like an imposter. We joined the same company at the same time. He was 10x better than me. He progressed 10x faster than me. He was who I wanted to become. I was jealous. I couldn't deal with what I was lacking. But today, I know that it's okay to be... Taught. Humbled. And shown the way. Your growth does not make you an imposter. Don’t judge yourself to be the data scientist, because you’re growing to be. Trust the process. 🙏🏻 #datascience #careers

You don't need a job to do what you love. If you want to be a data analyst then analyze data. If you want to be a data scientist then build ML models. If you want to be a data engineer then build data pipelines. The only thing you need for a side project is curiosity and google. Do it for yourself before you do it for an employer.

What data scientists think to success: • Complex Math Knowledge • Great Programming • Deep Learning What they SHOULD be to succeed: • Good communication • Bussiness Understanding • A deep knowledge of the data #machinelearning #datascience #data #deeplearning #statistics #analytics #python #ai #bigdata #artificialintelligence #ml #datascientist #career #dataanalytics #cornelly Rapid iteration + solid theory + solid coding = success"

What most data scientists do: - conflate the problem - explore the data indefinitely - agonise over the technical solution - keep tweaking the model in isolation - only present findings after exhausting the budget What you should do: - deeply listen to the stakeholders - get SME feedback consistently - build the most basic solution - present progress and plan - iterate to success #DataScience #DataScientist #Leadership

There’s a lot of great data jobs out there that aren’t called data scientist. If you’re trying to get some experience working with data, look at some of these: Process Improvement Analyst Human Resources Analyst Business Intelligence Analyst Quantitative Analyst Product Analyst Financial Analyst What are some others that I missed? #jobs #analytics #experience #datascientist #data

Wanted to make career as Data Engineer or Data Scientist? SQL is one of the most important skill to have  𝙄 𝙖𝙢 𝙜𝙤𝙞𝙣𝙜 𝙩𝙤 𝙩𝙖𝙠𝙚 𝟭𝟬 𝙁𝙧𝙚𝙚 𝙡𝙞𝙫𝙚 𝙘𝙡𝙖𝙨𝙨𝙚𝙨 𝙛𝙤𝙧 𝙎𝙌𝙇 What are the details of sessions - It covers Database concepts - Learn SQL with practical - Covers SQL from DE & DS perspective - Makes you SQL practitioner level - Real World SQL Issues like performance and scaling  No prior knowledge of any sort needed. Do support this initiative by Like/Love/Share this post. Press a bell 🔔 on my profile, to get notification for session details # #sql #data #career #jobs #database #cloud #layoffs

Here are the top 🔟 Data Experts on LinkedIn: 1️⃣ Kate Strachnyi ⭐ Founder & Community Manager at the DATAcated Circle  Focused on helping data companies reach their audience. 2️⃣Lex Fridman ⭐ Research Scientist at MIT Focused on research in AI, human-robot interaction, autonomous vehicles, and machine learning at MIT. 3️⃣Shashank Kalanithi ⭐ Senior Data Engineer at Fanatics Betting & Gaming with a focus on deriving value from the manipulation, cleansing, modelling, and visualization of data through the use of Alteryx, Python, SQL, and Tableau. 4️⃣ Alex Freberg ⭐ Manager of Operational Analytics and Intelligence at AmerisourceBergen Specialize in SQL, Python, and Cloud Applications. 5️⃣ Jérémy Ravenel ⭐ Chief executive officer at naas.ai  Democratizing data science with notebooks, low-code formulas & templates. 6️⃣ Rob Mulla ⭐ Senior data scientist at Biocore LLC  Expert at Data Science, Predictive Modeling, Machine Learning 7️⃣ Brian Femiano ⭐ Senior Data Engineer at Apple Hoarder of computing knowledge, especially distributed systems and data processing at scale.  8️⃣ Francesco Gadaleta, Ph.D. ⭐ Senior software engineer and chief data scientist Founder of Amethix Technologies  Host of Data Science at Home Podcast 9️⃣Kristen Kehrer Kehrer ⭐ Developer Advocate at Comet Passion for diversity in data and bridging the communication gap between data scientists and the business. 🔟 Kenneth Leung ⭐ Data Scientist at Boston Consulting Group Proficiency and experience in data science & analytics, digital innovation, technical writing, and web development. I did some research and found these top 🔟 amazing people. I would love to hear from you all.  If you are a data expert and I have not mentioned you in this list, feel free to tag yourselves in the comment section; I would love to know about you.  #dataexpert #datascience #AI #mlsense

4 Mistakes you should avoid as Data Scientist! 1. Not Optimizing the model for your data! 2. Accuracy of the model is not the most important part! 3. While analyzing data, keep business at top of your head 4. Not giving ample amount of time to just explore and visualize the data! #datascientist #datascience #ml #dataanalytics #dataanalysis