Master Plotly for Interactive Research Stories

📌 You can turn static data into interactive stories by mastering Plotly, version control, and reproducible deployment in just three steps. These practices will accelerate your research timeline, boost publication impact, and streamline collaboration, delivering measurable gains across your scientific career. ✓ 🎓 Complete the free Plotly Python tutorial on plotly.com and create your first interactive scatter plot. ✓ 📁 Store Plotly scripts in a GitHub repository, document each graph in README, and update monthly. ✓ 🚀 Deploy an interactive Plotly Dash app via GitHub Pages, mint DOI on Zenodo, and mentor lab. 🟢 Which of these steps will you implement first in your own research workflow? #DataVisualization #Plotly #OpenScience #ResearchTools #CareerGrowth

Shipping a discussion guide is just the beginning. Running the sessions can sometimes reveal thin findings, often due to overlooked details. Introducing qual-sniffer, a Python CLI designed to review your discussion guides before you run them. This tool: 1. Flags leading questions, embedded assumptions, and double-barreled questions. 2. Catches yes/no questions that may cut participant responses short. 3. Checks for a warm-up, adequate follow-up probes, and ensures no sensitive topics are present in the first three questions. 4. Returns a score from 0 to 100, along with a letter grade and fix suggestions for any identified issues. Qual-sniffer works without requiring an API key and offers an optional LLM summary for a written critique. This is tool #3 of 40 in my CPR Orbital series, providing at least one open-source research tool every week throughout 2026. #UXResearch #ComputationalProductResearch #OpenSource #QualitativeResearch #AIForResearch (GitHub link in first comment)

Some problems are not about removing elements, but about removing them wisely. Day 25/100 — Data Structures & Algorithms Journey Today’s Problem: Remove K Digits This problem helped me understand how greedy decisions can lead to an optimal solution. Approach: Instead of trying all possible combinations, I used a stack-based greedy approach. While traversing the number, I removed digits that were larger than the current digit to make the number as small as possible. If there were still digits left to remove, I removed them from the end. Finally, I handled leading zeros to get the correct result. At each step: - Remove larger digits from stack - Add current digit - Handle remaining removals - Remove leading zeros Key Takeaways: Greedy algorithms help make optimal local decisions Stack is useful for maintaining order Small changes can significantly impact the final result Thinking step-by-step avoids unnecessary complexity This problem strengthened my understanding of greedy strategies and stack usage. #DSA #LeetCode #Greedy #Stack #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

If anyone is interested in developing their skills in NumPy, here are a few quick tips based on my experience that might help: 💬 Start with the basics — understand arrays, shapes, and indexing thoroughly. A strong foundation makes everything easier. 💬 Practice vectorization — replacing loops with NumPy operations can significantly improve performance. 💬 Get comfortable with broadcasting — it may feel tricky at first, but it’s a powerful feature once you grasp it. 💬 Work on real datasets — applying NumPy to actual problems helps solidify concepts much faster than theory alone. 💬 Explore documentation regularly — NumPy’s official docs are incredibly useful and often overlooked. 💬 Combine with other libraries — using NumPy alongside pandas or matplotlib can expand your practical understanding. Consistency is key. Even small daily practice can lead to big improvements over time 🚀 #Python #NumPy #DataScience #MachineLearning #CodingTips

Weekly Challenge 14: Nearest Neighbor For Week 14 of my Python challenge, I decided to practice some real-life optimization by combining my Master's degree homework with my weekly coding goal. I implemented the Nearest Neighbor Heuristic to solve the Traveling Salesperson Problem (TSP). This is a classic "Greedy" algorithm. The logic is simple but powerful: 1.- Start at a location. 2.- Look around and go to the closest unvisited point. 3.- Repeat until all points are visited. 4.- Return home. It might not always find the perfect global minimum, but it finds an incredibly efficient route in $O(n^2)$ time, making it practical for real-world logistics and routing problems. I used NumPy for the vector math and Matplotlib to visualize the exact path the algorithm took. Full source code on my GitHub: https://lnkd.in/g6RqgJDp #Python #OperationsResearch #Optimization #Algorithms #DataScience #TSP #GreedyAlgorithms #CodingChallenge #MastersDegree

🚀 **From Idea to Execution in One Shot!** We at **Study Trigger Institute** are excited to share our latest YouTube video: **“Student Result Analysis Project”** — completed in a single 45-minute session, covering everything from setup to final output. In this video, we’ve focused on: ✔️ Real-world project approach ✔️ Step-by-step execution ✔️ Clear and practical explanation (no cuts, no shortcuts) This session is specially designed for students who want to move beyond theory and actually *build something meaningful*. If you're preparing for **Data Analytics roles** or looking to strengthen your practical skills, this video will definitely add value to your learning journey. 🎥 Watch the full video here: https://lnkd.in/g4bk8gM5 We’d love to hear your feedback and thoughts! #StudyTrigger #DataAnalytics #SQL #StudentProjects #LearningByDoing #YouTubeEducation #python

Day 98 New topic unlocked: Backtracking 🔁 #Day98 📚 Learning Backtracking Fundamentals • Understood the core idea: → choose → explore → backtrack • Covered concepts like: → Permutations → Rat in a Maze (theory + dry runs) → Recursive decision making 🧩 78. Subsets • Tried solving using recursion • Started understanding how choices build the solution • Realized why backtracking (undo step) is important • Carefully tracking state at each step Big takeaway: Backtracking is not about speed — it’s about exploring all possibilities in a controlled way. Think → choose → explore → undo. New pattern, new mindset. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

🚀 Day 13 of #100DaysOfDSA Today’s problem was simple but powerful: 👉 Calculate x raised to the power y (x^y) using a while loop. 💡 Key Learning: How to use a loop for repeated multiplication Understanding how exponentiation works internally Strengthening logic-building with basic iteration 🧠 Approach: Initialize power = 1 Multiply x repeatedly y times Decrease y in each iteration ✅ Code (Python): x = int(input()) y = int(input()) power = 1 while y > 0: power *= x y -= 1 print(power) ⚡ Time Complexity: O(y) ⚡ Space Complexity: O(1) 📌 Small problems like this build strong fundamentals for advanced topics like recursion and fast exponentiation. Consistency is the real game 🔥 See you on Day 14! #DSA #Python #CodingJourney #WhileLoop #100DaysChallenge

🚀 Day 50 of My Coding Journey Today, I explored powerful NumPy operations — sum() and prod() — and learned how combining them can solve interesting problems efficiently. 🔍 What I learned: sum(axis=0) → adds elements column-wise prod() → multiplies elements together By combining both, we can transform and reduce arrays in a single flow 💡 Problem Insight: Given a 2D array, 👉 First, compute the column-wise sum 👉 Then, find the product of that result 📌 Example: Input: 2 2 1 2 3 4 Step 1: Sum along axis 0 → [4, 6] Step 2: Product → 4 × 6 = 24 ✨ Key Takeaway: Understanding how axes work in NumPy makes complex operations simple and efficient. 💻 Code Snippet: import numpy as np n, m = map(int, input().split()) arr = [list(map(int, input().split())) for _ in range(n)] my_array = np.array(arr) result = np.prod(np.sum(my_array, axis=0)) print(result) 🔥 Day 50 done! Staying consistent and learning something new every day. #Day50 #Python #NumPy #CodingJourney #LearningEveryday

Learn with Lantana is back! 🎉 We’re starting with Python for applied Public Health, a series built for analysts working across surveillance, quality measurement, and beyond. If you’ve ever wondered how Python fits into your public health toolkit… this one’s for you. Launching in April for National Public Health Week 🚀 #PublicHealth #Python #HealthData #DigitalHealth #DataScience