Santhosh Baskar

Building a Strong Data Team: My Insights from 10+ Years in the Data Field! 📣 For over a decade, I've been elbow-deep in the exciting world of data. And one thing I've learned for sure is that building a successful data team isn't about having everyone be the same kind of expert. The best teams win when everyone brings a different skill to the table, creating a well-rounded crew. Here's a breakdown of the key areas I've found to be crucial, based on my real-world experience. I have also provided course link in comments. 1️⃣ FOUNDATION SKILLS FOR EVERYONE: ✅ Python Programming: Think of Python as the common language many data folks use to work with data. It's a great skill to have, and there are fantastic resources to get you started, no matter your experience level. Even if you don't become a master coder, having a basic understanding is a huge plus. ✅ Data Warehousing Basics: This might sound fancy, but it's really just understanding how data is organized and stored. ✅ Product Analytics(Optional): If you want to understand how people interact with a product (website, app, etc.), this is your jam. It will teach you how to track user behavior and get valuable insights. ✅ Understanding Paid Ads (Optional): Data and marketing teams often work together, and knowing how paid advertising works can bridge the gap. It can help you understand the data coming from those campaigns. 2️⃣ BUILDING & MANAGING DATA SYSTEMS: ✅ Version Control: Imagine a "save point" for your data projects, like in a video game. Version control systems like Git and GitHub let you track changes and revert to earlier versions if needed. ✅ Automation: Let's face it, some data tasks can be repetitive. Automation tools like Jenkins/Airflow can help streamline those processes, freeing you up for more creative work. 3️⃣ DATA SCIENCE ESSENTIALS: ✅ Statistics for Data Science: Data is all about numbers, so understanding some basic statistics is key. ✅ Machine Learning 101: This is a powerful area of data science, but don't worry, you don't need a PhD to grasp the core concepts. The resources in comments will give you a solid foundation. 4️⃣ DEEP DIVES FOR DATA SCIENTISTS: If you're a data scientist who wants to specialize in a particular area, like machine learning or deep learning, there are tons of fantastic courses available. ✅ Supervised ML, Unsupervised Deep Learning ✅ Support Vector Machines, Hidden Markov Models ✅ Cluster Analysis ✅ Understanding Generative AI with LLMs The beauty of a strong data team is that everyone doesn't need to be an expert in everything. The true power comes from having a diverse group of people with complementary skills. Keep learning, stay curious, and be awesome!  For more insights follow me here - https://lnkd.in/gK2U8KtJ #LIPostingChallengeIndia #data #analytics #datajourney #datascience

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Because of You, I'm Launching My Data Analytics Website! I'm thrilled to launch 'Muvaamia' (meaning 'Because of You' in Mende), a website dedicated to data analysis! Whether you're a data whiz or just starting your journey, Muvaamia offers insights, experiences, and learnings to help you unlock the power of data. Stay tuned for data analysis techniques, case studies, industry trends, and more! Let's connect and grow together! ➡️ https://lnkd.in/gW47dQk4 #dataanalysis #dataanalytics #datascience #datascientist #dataanalysis #dataanalytics #datascience #datascientist #muvaamia #datamentoring #datadriven Vickie Remoe

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I appreciate this type of effort to consolidate market information. It reflects the intense level of work and development in this area, and its exciting and exhausting at the same time to witness it. A few thoughts that come to mind as I review the infographic below: • Huge overlap between data and AI governance tools, as the two should be approaches as an integrated endeavor • The high fragmentation of the market for such tools should be exciting as it is a sign of high demand for and recognition of the need for guidance in responsible development and use of AI • Recognizing that the market is yet to mature despite the high potential of AI. Immaturity here means that the entire business model needs to be primed and ready for proper development, implementation and use: data, people, systems, processes, tools align with tools and technology that make sense in each business context. The solution is a wholistic Data and AI Strategy that will design for each organization the journey that will align with the business goals and help it get there faster. Getting everyone in the organization ready and along for the journey will make the selection of tools such as in the infographic below more clear and part of a strategic vision.

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Way back when and I was working at Amazon, my line manager once asked me in a 1-2-1 what the last thing I read was. I don't think VBA Basics for dummies was what he had in mind though. He inspired me to carry on reading books to further myself and it's in part due to him I developed the insight to carry on buying books. The O'Reilly catalogue had plenty to offer, even though I've only got 2 of them myself (Sam Thomas gave me the BigQuery one) the knowledge inside catapulted my curiosity and pushed my desire to learn more. Here's a breakdown of each book and how it's helped me. Python For Data Analysis 🐍: Overall, Python for Data Analysis is an absolutely invaluable resource for anyone looking to enhance their data analysis skills using Python written by Wes McKinney Wes provides both the theoretical foundation and practical tools needed to get ahead in data analysis tasks. A few code led examples like the MovieLens 1M Dataset walkthrough and US baby names Analysis are shown in some pretty detailed ways among a plethora of other showcasings. Fun Fact, Wes is the creator of Pandas, the workhorse for data manipulation in Python Data Science From Scratch 🥼: Written by 🤠 Joel Grus, this book comes packed with examples and thought experiments laid out in code. I was pretty sceptical about buying this book as I thought I wouldn't have enough knowledge to put anything into practice and understand anything that goes on. He covers key areas of Data Science such as Data Cleaning, Linear Regression, Decision Trees and Neural Networks. Joel breaks them down into easily manageable chapters. Picking this book up again recently has led me to start thinking about implementing a recommender system at work. Google BigQuery, The Definitive Guide 😶‍🌫️: Another book that had plenty of new concepts and practical applications for me to learn and apply. Co-authored by Valliappa Lakshmanan and Jordan Tigani. Out of all of these books this one has had the most impact in my career. Being able to train a Decision Tree in Notebooks inside the BigQuery UI is new to me, being able to keep and save everything in the same tab is great. This book goes on to show how you can create Machine Learning Models with relative ease. Thanks for taking the time to get this far, book reviews are never an easy read, if you'd like to check out one of these books there will be links in the comments 👍 #Oreilly #StillLearning #Books #Data #DataScience #BigQuery

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Today, I want to address a trend I've been observing for a while. Many individuals are leaving their jobs to pursue expensive Data Analytics/Science courses, hoping to prepare without distractions. This decision raises two major concerns: 1. Why did you leave your current job? 2. Why did you invest in such expensive courses? My advice is twofold. First, don't quit your job; instead, balance your preparation with your work. It's a more secure and practical approach. Second, avoid expensive courses. They don't guarantee a job as a Data Analyst. Please don't fall for expensive courses. There's a wealth of knowledge available on YouTube, Udemy, and other affordable platforms. Invest wisely in your education and career. For career guidance in Data Analytics, job search strategies, resume review, interview preparation, etc., you can book a session with me here: https://topmate.io/itshb

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Huge kudos to Palantir for Foundry's innovative approach! The integration of decision-making workflows with data management and analytics is truly impressive. ❤️ One aspect I'm curious about is the data integration strategy. Foundry excels at ingesting data, but why not integrate with open data lakehouses like Apache Iceberg instead of having all data reside within the platform itself? Additionally, does Foundry offer compute pushdown capabilities for data already stored in warehouses or data lakes? Understanding these choices would further solidify my appreciation for Palantir's architecture. Palantir Technologies Sean S. #PalantirFoundry #DataManagement

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Curious about our latest success story? Discover how Flow Control Group (FCG) partnered with Redwood to achieve nearly 20% annual savings through a unified LTL execution platform across 150+ US locations. Dive into the case study for insights on driving efficiency and measurable results. #LTL #SupplyChain #BusinessSuccess https://hubs.li/Q02H7WhQ0

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