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Day 48 at Luminar Technolab Handled missing data in the Framingham dataset: • Used mode for categorical data like education, cigsPerDay, and BPMeds. • Filled numeric data like BMI with mean imputation. Mastering how to clean and preprocess real-world data. #Python #Pandas #DataCleaning #MissingData #DataScience #LearningJourney

Some problems are not about matching values, but about matching them correctly. Day 23/100 — Data Structures & Algorithms Journey Today’s Problem: Bulls and Cows This problem was a great exercise in handling comparisons carefully and avoiding double counting. Approach: I divided the problem into two parts: First, I counted the "bulls" — digits that match in both value and position. Then, for the remaining digits, I used frequency counting to find "cows" — digits that exist but are in the wrong position. By using two arrays to track digit frequencies, I ensured that duplicate values were handled correctly. At each step: Identify exact matches (bulls) Store unmatched digits Count common digits using frequency (cows) Key Takeaways: Separating logic into steps simplifies complex problems Avoiding double counting is crucial in matching problems Frequency arrays are very useful for digit-based problems Clear thinking leads to accurate results This problem improved my understanding of string comparison and counting techniques. #DSA #LeetCode #Strings #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

NumPy for Data Analysis 📊 NumPy is one of the libraries in Python used for data analysis and numerical computing. It provides a fast and efficient way to work with large datasets using arrays instead of regular Python lists. In data analysis, this makes it easier to organize, clean, and transform data for deeper insights. From applying NumPy, I’ve seen how it helps with: • storing process variables in arrays • fast calculations using vectorization • filtering abnormal values • and comparing performance across operating conditions NumPy is useful when working with large datasets where speed and efficiency matter. ✨ My takeaway: NumPy makes data analysis more efficient especially in simplifying how we structure and compute numerical data. #ProcessEngineering #DataAnalysis #Energy #Sustainability #NumPy

🚀 Stop iterating through your DataFrames like it's 2010. I recently refactored a pipeline processing 50M rows. We were using a standard loop to calculate a rolling average, which was choking our CPU and stalling the entire cluster. Before optimisation: for i in range(len(df)): df.loc[i, 'avg'] = df['val'].iloc[i-5:i].mean() After optimisation: df['avg'] = df['val'].rolling(window=5).mean() Performance gain: 45x faster execution time. By moving from a row-based loop to a vectorised rolling window function, we cut the execution time from 12 minutes down to 16 seconds. The underlying implementation of Polars and Pandas handles these operations in highly optimised C/Rust, which no Python loop can match. Stop writing row-wise operations and start leveraging vectorisation. It’s the single biggest win for data processing throughput. What is the most expensive loop you have ever managed to replace with a vectorised operation? #DataEngineering #Python #PerformanceTuning #DataScience #CodingTips

Combining data from multiple sources is one of the most common tasks in data analysis and data engineering and in pandas, pd.concat() is the primary tool for getting it done. But there is more to it than just passing two DataFrames and getting one back. Understanding when to use axis=0 vs axis=1, how join handles mismatched columns, why concatenating inside a loop is a performance trap, and when to use concat vs merge. These are the details that separate clean, efficient data pipelines from slow, buggy ones. Get comfortable with pd.concat() and combining data from multiple sources becomes one of the fastest steps in your workflow. Read the full post here: https://lnkd.in/es7KJ7Y9 #Python #Pandas #DataScience #DataEngineering #Analytics #ETL

Continuing from my previous post https://lnkd.in/gtyziw-6 here is the actual implementation part of the same project. In this video, I’ve shown my full Jupyter Notebook workflow where I performed the analysis step by step. What this includes: • Data preprocessing and filtering • Handling missing and incorrect values • Feature-level analysis • Applying statistical logic to derive insights This is where the real learning happened — not in theory, but in execution. Debugging errors, fixing logic, and making sure the output actually makes sense. Still improving, but this is a solid step toward building practical data skills. #jupyter #python #dataanalytics #statisticsproject #handsonlearning #careerbuilding #datasciencejourney

Some problems are not about moving elements, but about understanding structure. Day 22/100 — Data Structures & Algorithms Journey Today’s Problem: Rotate List This problem helped me understand how linked lists behave when we manipulate their structure efficiently. Approach: Instead of rotating the list step by step (which is inefficient), I first calculated the length of the list. Then, I connected the tail to the head to form a circular linked list. By finding the correct breaking point using k % length, I was able to determine the new head and tail of the rotated list. At each step: Convert list into a circular structure Find the new tail position Break the circle to form the rotated list Key Takeaways: Understanding structure is more important than brute force Modulo operation helps avoid unnecessary rotations Linked list problems often become easier when visualized as cycles Efficient thinking leads to cleaner and faster solutions This problem strengthened my understanding of linked list manipulation and optimization techniques. #DSA #LeetCode #LinkedList #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

📢⚡ 𝐋𝐚𝐬𝐭 𝐦𝐨𝐧𝐭𝐡, 𝐚 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐬𝐡𝐨𝐰𝐞𝐝 𝐚 𝐬𝐮𝐝𝐝𝐞𝐧 𝐬𝐩𝐢𝐤𝐞 𝐢𝐧 𝐫𝐞𝐯𝐞𝐧𝐮𝐞. 👉 Everyone assumed it was a great business day. 🤕 But something felt off. 👉 We checked the pipeline… no failures. 🙂 Everything ran successfully. 👉 Digging deeper, we found duplicate records were ingested. 📍 No validation. No alerts. 👉 The pipeline didn’t break — it silently passed bad data. 👉 That’s when we realized: 🔑 Data quality issues don’t crash systems… they corrupt decisions. #DataEngineering #DataQuality #BigData #DataPipelines #DataArchitecture #ETL #AnalyticsEngineering #DataPlatform #DataGovernance #ScalableSystems #EngineeringExcellence #spark #optimization #python

🔍 What’s coming up at Bristol Data Week 2026? This week, we'll be sharing details of the training on offer from 1 - 5 June. Spotlight on Day 1 of Bristol Data Week 2026: Monday 1 June Introduction to Python (9:30 - 12:30) You will be introduced to the fundamentals of Python, a popular, versatile language known for its simple syntax. Additionally, you will get a first look at "pandas," a powerful tool used to organize and query data. Data Privacy and Synthetic Data in the Energy Sector (13:00 - 17:00) The workshop will demonstrate how sharing detailed datasets can lead to issues around privacy and how generating synthetic data can help to reduce or remove these sensitivities whilst maintain utility. Find out more and book your free ticket here: https://lnkd.in/eRf-tFT4 #BristolDataWeek2026 #Python #Energy #Data

Days 68-69 of the #three90challenge 📊 Today I explored NumPy operations — specifically indexing and slicing arrays. After understanding NumPy basics, this step made it easier to access and manipulate data efficiently. What I practiced today: • Accessing elements using indexing • Extracting subsets of data using slicing • Working with multi-dimensional arrays • Performing operations on selected data Example thinking: Instead of looping through data manually, I can directly select and operate on specific parts of an array. Example: import numpy as np arr = np.array([10, 20, 30, 40, 50]) print(arr[1:4]) # Output: [20 30 40] This makes data manipulation faster and more intuitive. From handling data → to controlling it efficiently 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #NumPy #DataAnalytics #LearningInPublic #Consistency #Upskilling