Debunking myths about switching from SBC to PBC

“DSA isn’t required for Data Science roles.” Wrong ❌ Product companies do test DSA to check if you can think in algorithms, reason about complexity, and write clean, efficient code. So here’s the smarter way to prepare: 1) Master only the relevant core concepts Arrays, hashmaps, heaps, trees, graphs, sliding window, recursion, DP. No need to grind 800 LeetCode questions - depth beats volume. 2) Practice DSA in ML/Data context Use algorithms inside data tasks: → KNN from scratch → distance + sorting → Decision tree manually → recursion + entropy → Priority queue for jobs → heaps This builds intuition for real-world ML systems. 3) Write clean, efficient, explainable code Know time and space complexity. Practice outside notebooks - real interviews need Python scripts and clarity in thought. You don’t need 5 hours a day. You need focus, discipline, and the right roadmap. And to help you prepare in a structured way for top Data Science roles, Bosscoder Academy has a program for you. Check it here → bcalinks.com/2Yx6A3x They’ve helped 2200+ professionals switch into Data & ML roles at top tech companies by offering: ✅ Structured curriculum covering ML, Deep Learning & GenAI ✅ Industry projects with real world datasets that build your portfolio ✅ 1:1 mentorship from Data Scientist at top product companies ✅ Placement support to help you land your dream data role #dsa #datascience #sponsoredpost

“I don’t have time after my 9-5 to learn DSA for the Data Science role” But I have a solution for you, If you follow these ways to improve your DSA for at least 1-2 hours/day after your working hours. 1) Nail core patterns, not random problems Like Sliding window, two-pointer, binary search, recursion, DP basics. These are asked in interviews a lot. So solve 2-3 questions daily on these patterns instead of jumping around. 2) Master DS you’ll actually use Like Hashmaps, heaps, trees, graphs. These show up in ML pipelines, feature engineering, and data processing tasks. 3) Blend DSA with real data tasks Pick a dataset & try building solutions where you manually apply search, traversal, or map-reduce style logic before using pandas/Numpy shortcuts. This builds genuine problem-solving instincts. Make sure to keep it practical & you’ll feel the improvement in 2-3 weeks. But in reality this is not enough, you need more than just DSA to prepare for Data Science roles. So, to help you prepare from scratch & cover all necessary topics, I’d recommend Bosscoder Academy’s Data Science program. Check it here → bcalinks.com/yTf6onA They’ve helped 2200+ professionals switch into Data & ML roles at top tech companies by offering: ✅ Structured curriculum covering ML, Deep Learning & GenAI ✅ Industry projects with real world datasets that build your portfolio ✅ 1:1 mentorship from Data Scientist at top product companies ✅ Placement support to help you land your dream data role #DSA #datascience #sponsoredpost

The #1 problem people have while upskilling to Data Science (& I’ve shared the solution as well) → A Structured Roadmap As a Senior Data Scientist at Fractal, I can confirm this roadmap will make you job-ready. 1) Math + Stats (foundation first) Mean, variance, probability, hypothesis testing. 2) Python Learn data types, loops, functions Then Pandas, NumPy 3) SQL Joins, filtering, aggregations, then window functions and query optimization 4) Data Wrangling Handling missing values, feature engineering & cleaning messy business data 5) Machine Learning Linear/logistic regression → decision trees → clustering 6) Business Thinking + Communication Explain insights simply. Tie results to impact Stick to this and ignore random noise. So, now you have the plan, all you need to do is take action & for that I’d recommend enrolling in Bosscoder Academy’s Data Science program. Check it here → bcalinks.com/HBfsNwS They’ve helped 2200+ professionals switch into Data & ML roles at top tech companies by offering: ✅ Structured curriculum covering ML, Deep Learning & GenAI ✅ Industry projects with real world datasets that build your portfolio ✅ 1:1 mentorship from Data Scientist at top product companies ✅ Placement support to help you land your dream data role #datascience #roadmap #sponsoredpost

Only 2 months left in 2025 & still waiting to switch to Data Engineering? This Gold Level Roadmap is all you need to make it happen. If you’ve been stuck in analytics, testing, or support and dream of joining data-driven giants like Amazon, Flipkart, or EY… Then random YouTube tutorials or tool-hopping won’t cut it. You need a 𝗰𝗹𝗲𝗮𝗿 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 that builds both depth and direction from fundamentals to production-grade systems. This curated guide from Bosscoder Academy is your structured Data Engineering Roadmap, designed by mentors working in FAANG & top MNCs. So, if you’re serious about making the switch to Data Engineering, don’t try to figure it out alone. Get guidance from experts in their comprehensive program: bcalinks.com/doRQaWq Learn what the Top Switchers Focus On: ✅ Mastering SQL & DBMS (the real foundation) ✅ Learning Python + Pandas + PySpark (for real data movement) ✅ Understanding OLTP vs OLAP & Data Modeling ✅ Building ETL Pipelines using Airflow, Kafka & Databricks ✅ Exploring Cloud (AWS | Azure | GCP) for end-to-end workflows P.S. Don’t wait for 2026 → start your Data Engineering journey today. Save this post for your weekend prep 🔁 Repost to help your fellow data professionals #DataEngineering #DataModeling #Sql #Pyspark #Roadmap #BrandPartnership

My fellowship at Developers Foundry has reinforced a crucial lesson for anyone in Data Science or ML: Garbage In, Garbage Out is real. The last two weeks have been laser-focused on the foundational building blocks: As an upskilling data analyst and growth fanatic, diving deep into these core concepts has been challenging yet incredibly rewarding. I've realized that the 'magic' of a prediction model is really just a reward for disciplined data preparation. Here's what dominated my learning log and reinforced the GIGO principle: The Power of Linear Regressors: Getting hands-on with this foundational algorithm and truly understanding how it models the relationship between variables. It's the simple elegance that begins the predictive journey. Minimizing the Cost (MSE): We learned to measure model performance using the Mean Squared Error (MSE). The journey to a better model is essentially a focused effort to minimize this error a great lesson in continuous improvement! The Data Prep Essential: My biggest takeaway is the crucial role of Data Cleaning, Manipulation, and EDA (Exploratory Data Analysis). Before you run a single line of model code, you must become a detective and truly understand your data's structure and quirks. Seeing the tangible impact of a Log Transformation on skewed data was a powerful lesson in ensuring data integrity for better model performance. We put all of this into practice with a taxi fare prediction project it was the perfect way to solidify these concepts in a real-world scenario. I've linked my project notebook below and would genuinely welcome any feedback or corrections on my approach. I'm excited about this learning journey and looking forward to what this new week brings! Let's Connect: Your Insights are Welcome! As I continue to build my foundation, I'd love to learn from your experience: For fellow data professionals: What is your absolute non-negotiable step during the initial Exploratory Data Analysis (EDA) phase? (Beyond simply handling missing values). For those who use Linear Regression: What's a common data transformation technique you rely on that beginners often overlook? Let me know in the comments! 👇 (Link to my GitHub project: [https://lnkd.in/gQAsHAFH]) #DataScience #MachineLearning #LinearRegression #DataAnalyst #Upskilling #DevelopersFoundry

Biggest lies people hear when switching to Data Science Let’s clear a few myths today. Especially if you're coming from a service company and trying to break into product roles. Myth 1: “You need IIT/NIT to get into a product company.” In reality, companies don’t hire your college name. They hire your skills, projects, and ability to solve problems. Myth 2: “PBCs only hire competitive programmers.” DS & ML interviews aren't just DSA gauntlets. You’ll face SQL, ML concepts, scenario-based questions, pipelines, and real-world data case studies. Myth 3: “Work-life balance sucks in product companies.” Depends on the company, team, and culture & not the label. Some product teams actually offer better balance and better growth than service firms. If you're serious about switching, here’s the truth: Skills + projects + consistent prep = offers. And to help you with all 3 things, Bosscoder Academy’s Data Science is perfect for working professionals. Check their program here → bcalinks.com/pPX3jEy They’ve helped 2200+ professionals switch into Data & ML roles at top tech companies by offering: ✅ Structured curriculum covering ML, Deep Learning & GenAI ✅ Industry projects with real world datasets that build your portfolio ✅ 1:1 mentorship from Data Scientist at top product companies ✅ Placement support to help you land your dream data role #myths #datascience #sponsoredpost

🚀 Ever wondered what a Data Scientist really does? From analyzing data to building AI tools — Data Scientists are the real game-changers behind every smart business decision! 💡 Here’s what they do: 🔹 Provide business solutions using data-driven techniques 🔹 Bridge the gap between IT and business 🔹 Analyze large datasets to solve real-world problems 🔹 Identify hidden patterns in unstructured data 🔹 Build AI tools to make data more actionable 📊 Want to master these skills and become a job-ready Data Scientist? Join Digicrome’s Post Graduate Program in Data Science & AI — in collaboration with IBM & Microsoft. 🎓 Learn | 💼 Get Certified | 🚀 Build a High-Paying Career 👉 Visit www.digicrome.com to get started today! #Digicrome #DataScience #ArtificialIntelligence #digicromeacademy #MachineLearning #CareerGrowth #DataScientist #UpskillWithDigicrome  #CareerInDataScience

🌐Unlock the Future of Data:Earn Your M.Sc. in Data Science Online from VIT University The world is being reshaped by data-every swipe, scroll and click leaves behind a trail of information. But only a select few have the skills to decode it, predict outcomes, and build smarter systems from it.Are you ready to be one of them ? Introducing the Online Master of Science (M.Sc.)in Data Science from VIT University - a two year program designed for the morden learner with big dreams in tech, innovation, and intelligence. 🎓Why Choose VIT's Online M.Sc. in Data Science? ✅UGC- entitled degree ✅ Completely online - learn anytime, anywhere ✅Industry- driven curriculum that evolves with technological shifts ✅ Top-tier faculty and real-time projects ✅Career support for entry into booming data fields Whether you're a beginner who has just tastes tech or a professional ready to level up, this program let's you master the art of data from the comfort of your home- no relocation, no hassle. 🔍What Will you learn? From predictive analytics to big data engineering, this course empowers you with: ▪️🔢Python, R, SQL, and Machine Learning ▪️📊Data visualization and statistical modeling ▪️☁️Cloud computing foundations ▪️🧠AI and deep learning concepts ▪️🗂️ Hands-on projects that prepares you for real-world challenges You walk out with not just a degree- but a portfolio of work that speaks for you. 🚀Career Prospects After MSc Data Science Data Science isn't the future- it's the now. Post- degree, you can launch your career in exciting roles like; ▪️Data Scientist ▪️ Business Analyst ▪️AI/ML Engineer ▪️Data Engineer ▪️ Analytics Consultant And the Oppertunities don't stop at India - this skill is demanded globally. 🏁Ready to Start Your Data Journey? VIT's Online MSc in Data Science isn't just another degree - it's your gateway into the high- impact world of numbers, patterns, and predictions. In two years, you don't just learn data science - you start living it. 🔗Apply today and shape the future from behind the screen, but ahead of the world; https://lnkd.in/ec2iYNKV

Headline: "Why Every Data Scientist Should Build an Education Project" Content: "After building a student performance prediction model, I realized education datasets are perfect for ML practice. Here's why: 🎯 Ideal Learning Ground: Clean, structured data Clear business problem Measurable outcomes Real-world impact potential 📊 Skills Demonstrated: Data preprocessing & feature engineering Multiple algorithm implementation Model evaluation & interpretation Business insight generation 🌟 Portfolio Value: Social impact narrative Technical depth showcase Business relevance Cross-domain applicability This project helped me master the full ML pipeline while working on something meaningful. What domain are you using to practice your data skills? #DataScience #MachineLearning #PortfolioProject #CareerDevelopment #ML"

🧩 From Data Analytics to Data Science (Part 1): Mindset Shifts I Didn’t Expect When I first started working with data, I thought the jump from data analytics to data science would be mostly technical: learn Python a little deeper, get better at statistics, and start training models. But through the Outamation Externship (facilitated by Satya and Kristhia), I learned that the real shift wasn’t about syntax or frameworks; it was about how I approach problems. In data analytics, my job was to summarize what happened. In data science, my job is to explore why it happened, and whether it’ll happen again. That mindset change sounds small, but it changes everything. 🔹 1. You start getting comfortable not knowing. ✅ At first, ambiguity felt uncomfortable, like I was doing something wrong if the answer wasn’t clear. ✅ Over time, I realized the best questions don’t have ready-made answers. Rather, they lead you to discover them. 🧠 You stop searching for certainty and start building reasoning. 🔹 2. You stop chasing clean outputs and start chasing better logic. ✅ In data analytics, you want the dashboard to look good. ✅ In data science, you care more about how the model thinks than how the chart looks. 🧠 Progress looks messy, but every debug, rerun, and tweak sharpens your intuition. 🔹 3. You realize “done” is a moving target. ✅ Models evolve. Pipelines break. Assumptions shift. ✅ You learn to adapt instead of aim for perfection. 🧠 “Done” becomes a checkpoint; not a finish line. 💡 What I’ve learned: Transitioning into data science isn’t about mastering every algorithm. It’s about learning to stay curious in uncertainty and trusting that every iteration teaches you something new. Next up, I’ll share the practical side of this transition: how projects, storytelling, and experimentation helped me connect everything I learned into something meaningful. #Outamation #ExternshipExperience #Extern #DataScience #DataAnalytics #AI #GenAI #LearningJourney #CareerGrowth #CuriosityDriven