Lydia Iyamu

Data Science Bootcamps are bad. Here's why they get things ass backwards: They take this approach: 1) Learn skills via courses 2) Demonstrate skills via projects 3) Apply for jobs People have got jobs using this approach, but with the job market more competitive than ever a new approach is needed. I've talked to graduates from bootcamps (who use this approach) and are struggling to get jobs. The reason they struggle is they only think about what jobs to apply for AFTER they've done their projects. This means they have a portfolio of random projects unrelated to the job (think titanic, Boston housing, movie sentiment analysis...). With hundreds of aspiring Data Scientists applying for the same jobs, it's no wander they don't stand out. Here's what you should be doing instead: 1) Research and think about the industry you want to work in (e.g., healthcare, project management, nutrition) 2) Build Tailored Projects 3) Apply to jobs in that industry Employers will immediately see you've taken care with your application and care about the problems faced by the business, instead of being another random Linkedin easy apply. That's what my mate Lekan is currently doing - he wants to utilise his experience in project management o get a Data Science job in the same field, so we're building a Tailored Project to predict which major construction projects (e.g., HS2) are likely to fail and need support. So don't listen to bootcamps - focus on the industry you want to work in and backsolve from there. And that's before I get started on all the unnecessary stuff they teach you... If you want to learn more about how to do this, you can learn more via my newsletter: https://lnkd.in/eTAmRsDu (P.S you'll also get a free guide on Tailored Projects/the lowdown on my love life ;) )

4

2 Comments

I have created this #Streamlit app running on a #Docker container to help me manage my finances. It allows me to upload a csv bank statment and gives me some charts and summary data about income and expenses. Do you think something like this would be useful for you? What other features would you like to see in an app like this? #app #bank #finances #money #statistics #analysis

21

5 Comments

The 5 Best Data Science Blogs To Follow: There are countless excellent resources on data science, and it can be a little overwhelming to know where to start. Here are some options: 1. Data Science Central Run By: Vincent Granville  Website link: DataScienceCentral.com Data Science Central does exactly what its name suggests and acts as an online resource hub for just about everything related to data science and big data. 2. SmartData Collective Run By: Social Media Today  Websitelink: SmartDataCollective.com SmartData Collective is a community site focused on trends in business intelligence and data management. 3. What's The Big Data? Run By: Gil Press Website link: WhatsTheBigData.com What's The Big Data? takes a different approach to data science and focuses on the impact of big data’s growth into the digital behemoth it is today. T 4. No Free Hunch Run By: Kaggle  Website link: Blog.Kaggle.com This blog is slightly different than the others, offering a look directly into the minds of data scientists, as well as tutorials and news. This is the blog of the data science website Kaggle, which hosts data science projects and competitions that challenges data scientists to produce the best models for featured data sets. 5. InsideBIGDATA Run By: Rich Brueckner  Website link: InsideBIGDATA.com InsideBIGDATA focuses on the machine learning side of data science. It covers big data in IT and business, machine learning, deep learning, and artificial intelligence. Guest features offer insight into industry perspectives, while news and Editor’s Choice articles highlight important goings-on in the field. Happy reading Karl Ramsaran - Data AI Growth Talent Partner

5

👍 Uncommon Traits For Up and Coming Data Engineers Article by Tim Webster ➡ https://lnkd.in/emusm4QU - - - - - - - - - - - - - - - - - - - - - - - - - - - 💡 Follow for more great content! ✅ ✍️ Want to contribute? Connect with us. 🔗 Let's create together! ✅ - - - - - - - - - - - - - - - - - - - - - - - - - - - #dataengineering  #careergrowth #datascience #dataanalytics

4

I was researching tooling options for the "transform" part of an ELT or ETL data ingestion pipeline (with emphasis on python). Here is my summary:

2

Discover how eBay created their own language model using three billion item titles in this exciting video. We'll explore the innovative techniques eBay used, including training their BERT model from scratch on eBay's massive dataset, knowledge distillation to compress the model, and fine-tuning the compressed model to learn similarity better. With a 3.5% increased purchase order rate and enhanced customer engagement, this is a must-watch for anyone interested in the power of language models. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: youtu.be/h51nbWr7feo YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

9

💻 Where are the UK's Data Engineering Hubs?🌍 1) London (6,111) 2) Manchester (665) 3) Leeds (432) 4) Edinburgh (392) 5) Bristol (347) 6) Glasgow (297) 7) Birmingham (233) 8) Sheffield (185) 9) Liverpool (178) 10) Reading (177) (by number of professionals) #LinkedInTalentInsights #DataEngineeringInsights

17

2 Comments

Week 4 of DataTalksClub LLM-zoomcamp was rich in content. This week the focus was: 1) Setting up an LLM powered Streamlit app. The app provides users the capability to ask questions. We capture feedback from the user as part of the monitoring workflow. All data is stored and in a Postgres database. A visualisation dashboard is used for monitoring model performance (Grafana Labs). 2) We also explored how to use cosine similarity to deliver a verdict on how well an LLM is responding to user queries. A large language model was then used as a secondary evaluation method to guage quality of our user facing model. As always a big thanks to Alexey Grigorev and the team for making these available to many of us who enjoy collaborative learning and hands on projects. https://lnkd.in/drN-v7xv

12

1 Comment

I just helped a client land a final stage Data Science Interview. The best part? She only started her search 2 months ago. And she did this whilst balancing her current job, looking after her 2 children and managing a clothing business. Here's how we did it: 1) Positioning Instead of easy-applying for 100s of jobs on LinkedIn, we identified companies in industries we were interested in, and built relationships with employees/recruiters who worked there. 2) Communication Making sure your resume 'beats the ATS' is pointless if you don't have the underlying communication skills to show your value. And this didn't even require doing a long course on communication - we went over a few quick principles to put together and effective CV and cover letter. 3) Interview Prep Her Positioning, CV and cover letter helped her get an initial screening interview. Then we prepared accordingly, making sure the answers we prepared were suitable to the type of interview and showed her value properly. Now the preparation continues! If you're struggling to find interviews (even though you have good experience) and want help like this DM me 'Confident Data Scientist'

14

5 Comments

🚩 What Really Motivated Me to Pursue a Career in Data Science? To make more money 😂 Just joking. The truth is, what actually motivated me was my love for AI and machine learning. It’s always been my passion, my second love. 📍When I graduated from university, there weren’t really data-related jobs out there. So, I ventured into something I enjoyed, which was software development. It was fulfilling, but not completely. Deep down, I knew it wasn’t the end of my journey. That passion for AI and machine learning was still pulling at me. Eventually, I made the leap into data science, and the rest is history. What kept me moving wasn’t the trends. It wasn’t because data science suddenly became "cool" or "in-demand." It was because I genuinely loved it from the very beginning. Back then, the trending career in tech was Cybersecurity , but that didn’t sway me. For me, it was always AI and machine learning. It wasn’t about what was hot at the time, it was about what made me feel excited, curious, and fulfilled. 🚩What motivates you? It could be anything, and that’s perfectly okay. - Maybe it is the money (and that’s fine, too). - Maybe you’re motivated by the idea of using the knowledge to start your own business. - Maybe your motivation is personal growth or solving real-world problems. - Maybe you’re motivated by the desire to make an impact with your work. 📌Whatever it is, always keep it in mind. Because the truth is, this journey isn’t always easy. There will be challenges, setbacks, and days when you feel like giving up. In those moments, remember what motivated you in the first place. Use that to fuel you when things get tough. 📌At the end of the day, the key to longevity in this field, or any field, is to stay connected to why you started in the first place. ❓ What motivated you to pursue a career in Data? ♻️ Repost so others can learn #favouribude #dataliving

280

50 Comments

Here's a roadmap to be proficient in Statistics as a Data Scientist Start by getting the basics concepts down with Emma Ding's playlist - https://lnkd.in/gPh8TJ4k Focus should be on P-value, Confidence Interval, Power, Errors Build a solid understanding of A/B Testing with Udacity's Free Course - https://lnkd.in/eRCtUcnF Practice real-world problems on Interview Query - https://lnkd.in/gA_qhcM8 Do mock-interviews. Check out a few on Exponent here - https://lnkd.in/gZaGt5Kd

8

2 Comments

🚩 Did I Cram Python as a Data Scientist? Here’s the Honest Truth… You: “Favour, did you cram python”? Me: NO, because cramming doesn’t work when it comes to coding. 📍 If you’re aiming to become a data scientist, machine learning engineer, or software developer, one thing is for sure, you’ll be coding daily. Which means you can’t cram coding. 📍 Why Cramming Doesn’t Work How many methods, classes, or syntax rules can you memorize? Coding is not about memorization, it’s understanding why you’re using specific syntax or methods to solve problems. 📍 The ChatGPT Trap You cannot rely on ChatGPT to do your coding for you. I’ve experimented with it myself, and while it’s useful for quick snippets, about 85% of the time it gives errors. If you don’t know how to code, you’ll end up stuck in a cycle of copying, pasting, and fixing errors without even knowing what went wrong. 📍 How to Learn Coding Without Cramming Here’s what has worked for me, and it can work for you too: 1. Pick a Language: – Start with one programming language. Don’t try to learn multiple languages at once. 2. Start with the Basics: – Learn data types, variables, control structures, and functions. Don’t skip the foundations. 3. Break Problems Down: – When faced with a big coding challenge, break it into smaller tasks. Solve one piece at a time. 4. Understand the Why: – Don’t just memorize syntax; understand why you’re using certain methods or functions for specific problems. Once you grasp the “why,” it becomes easier to apply those concepts elsewhere. 5. Practice, Practice, Practice: – Build projects. Code regularly. The more you practice, the more you’ll improve. Each time you code, you’ll encounter new challenges, and that’s how you grow. 6. Use Resources, Don’t Rely on Them: – Tools like ChatGPT and Stack Overflow are great resources, but you need to understand what you’re copying and pasting. Otherwise, you’ll never truly learn how to code. 7. Pair Programming: – Pair programming is a fantastic way to learn. Working side by side with another developer allows you to see how they approach problems, and you’ll learn new techniques. If you’ve crammed, it’ll show. But if you understand the concepts, you’ll be able to keep up and contribute. 📌Note: AI should be a tool that supports you, not your main coding resource. The only way it can support you is if you already know what you’re doing. ♻️Repost so others can learn #favouribude #dataliving

770

146 Comments

🚀 New Article Alert! 🚀 Hi everyone! I'm excited to share my latest article on Medium: "Understanding Mutual Information (MI) in Machine Learning". In this piece, I delve into: 🔍 What Mutual Information (MI) is 🧩 Why MI is crucial in data analysis and feature selection 📈 How to calculate and interpret MI 💡 When to use MI over other techniques If you're keen on enhancing your data science and machine learning projects, this article is a must-read. Gain insights into making your models more accurate and interpretable by leveraging MI effectively. Check it out and let me know your thoughts! 👇 #DataScience #MachineLearning #FeatureSelection #MutualInformation #DataAnalysis #AI #TechBlog

8

Your First Year as a Data Scientist: A Survival Guide 5 tips I would’ve hugely benefitted from as a beginnerContinue reading on Towards Data Science »... https://lnkd.in/e-Ym6TRm #AI #ML #Automation

1

Curious to understand more about how a RAG (Retrieval-Augmented Generation) pipeline works using a large language model? I've prepared a tutorial to guide you, using Google open-weight model, Gemma. Just follow the instructions I've prepared as part of the “Google - AI Assistants for Data Tasks with Gemma” hackathon/competition. By mastering the basics, you will gain the essential foundations to make even the more sophisticated RAGs work for your own problem, also in complex domains such as finance or science. Medium article: https://lnkd.in/dyRnAv8X #Google #GDE #Gemma #RAG #LLM #AI #generativeAI

81

3 Comments