Industrial Data Analytics • ML • Backend (Python/FastAPI/Django) | Sharing tech facts, projects & lessons from chemistry → code | Open to collabs
Nigeria
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Sometimes we spend most of our time on the computer having it performed all the heavy maths but understand that is also a heavy part of machine Learning.
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Statistics don't matter if you can't translate them into ROI. 📈 Today, I used R to run a Welch’s Two-Sample t-test on 10,000 chemical batches. The data mathematically proved that a new catalyst increases production yield by an average of 5.85% (p < 2.2e-16).
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Exploring data structures with SQL!
Writing queries to inspect customers, count rows, and find distinct values for Project Sentinel. Clean data exploration is key before any modeling begins.
#SQL #DataAnalysis #DataScience #BuildInPublic
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Learning how data imputation affects variance. I used R and tidyverse to clean missing data by mean imputation and used ggplot2 to visualise the artificial spike it creates. Always verify your data transformations.
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Just finished my project on building a pipeline that cleans raw shipment logs with SQL and visualises performance in a live dashboard. From backend data transformations to real-time insights, the full workflow is now automated. 🏗️ #DataEngineering #SQL #BuildInPublic
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📊 10 Essential Excel Functions Every Data Professional Should Know
From XLOOKUP and SUMIFS to Pivot Tables and FILTER, these functions can save hours of work.
Which Excel function do you use the most?
#Excel #DataAnalysis #DataScience #Analytics #LearningInPublic #Productivity
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Currently working on a full-stack machine learning application that can predict wherethere a reaction will be a succes or a failure base on the reaction parameters.
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Humidity vs. Chemical Purity! 🧪
Analysed how ambient humidity affects Emulsion Thickness and Active Purity for different suppliers. Used pandas pivots and seaborn scatterplots to visualise covariance effects. Data-driven R&D!
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Data pipelines from scratch! 🧪
Engineered a cosmetics quality dataset with Python to practice advanced data prep. Wrote scripts to parse text anomalies, strip unit strings, mask outliers, and run mean imputations, all before evaluating a correlation matrix.
Process over noise!
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Bridging Industrial Chemistry & Data Science! 🧪📊 Optimised a cosmetic formulation dataset using Python & Excel. Grouped by formula & temp to analyse Viscosity, pH, and Stability, then validated the impact with a MANOVA test (p < 0.001).Data-driven manufacturing! 👇
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🔬 Chemistry meets Data Science 📊
Exploring career paths that combine chemistry, analytics, and technology, from research analytics to cheminformatics and life science data science 🚀
Still learning. Still building.
#DataScience #Chemistry #Python #STEM
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I’ve realized chemistry and data science follow a very similar process:
Experiment → Analyze → Improve
Made this infographic to show how chemical workflows relate to data science workflows using simple visual steps.
#Python #DataScience #Chemistry #DataAnalytics #WomenInTech
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🔬 Chemistry meets Data Science 📊
🧪 Mole Concept ↔️ 📈 Data Points
Both help us measure, analyze, and understand systems.
Created this infographic while exploring the connection between chemistry and data analytics 🚀
#DataScience #Chemistry #Python #STEM
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Turned a chemistry concept into a machine learning problem.
Analyzed reaction rates vs temperature using Python and linear regression to estimate activation energy.
This is the kind of work I want to keep doing bridging science and data.
kaggle.com/code/deeeja/predi…
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I’ve been working on a project normalizing lab machine data & building a usage dashboard.
125 total lab hours tracked
HPLC_03 is our MVP machine
Data cleaned & joined using SQL
The intersection of Chemistry & Data is where the magic happens. 🚀
#SQL #DataAnalytics #Chemistry
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Built a Python project to detect peaks in a noisy UV-Vis spectrum
Used:
• Savitzky–Golay smoothing
• SciPy peak detection
Big lesson: small parameter changes = completely different results.
Still learning, still building.
#Python #DataScience #Chemistry
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Bridging the gap between the lab and the notebook! 🧪🐍
Used Python to analyze titration data:
Handled missing values with Pandas interpolation.
Plotted the titration curve & first derivative with Matplotlib.
Pinpointed the equivalence point at 10.1 mL.
#Chemistry #Python #Data
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Overfitting vs. Underfitting: The ultimate balancing act in Data Science! ⚖️
In the lab or the industry, finding the "Sweet Spot" is key to making models that actually work in the real world. 🧪💻
Check out the breakdown!
Which are you currently battling?
#DataScience #Python
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Linear vs. Logistic Regression: Which one do you need? 📈📉
I'm currently diving deep into predictive modeling in my Advanced Google Data Analytics course! 🚀
Check out this quick breakdown of how they work. Which one are you using today? 👇
#DataScience #Python #Learning
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📊 P-values, simplified.
H₀: “This could be random.”
H₁: “There’s a real effect.”
• p < 0.05 → Reject H₀
• p ≥ 0.05 → Could be chance
Statistics doesn’t explain why it tests if results are likely random.
👉 How do you explain p-values?
#DataAnalytics #Statistics
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EDA is how we truly understand datasets before modeling. From discovery & cleaning to visualizing trends, it's crucial for unlocking insights.
Check out this infographic for the breakdown! 👇
#DataAnalytics #EDA #DataScience
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Why I like Tableau:
✔️ Explore data interactively
✔️ Spot patterns faster
✔️ Communicate insights clearly
Visualization is part of the analysis.
#DataAnalytics #Tableau
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Data work isn’t linear.
The PACE strategy shows how it really works:
Plan → Analyze → Construct → Extract → repeat ♾️
Insights lead to better questions, and better questions lead to better data work.
Data is iterative, not linear.
#DataAnalytics #chemistry #PACE
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Python 🐍 basics:
Attributes are the what (data/characteristics) an object has.
Methods are the how (actions/functions) an object does.
Think of a car: color is an attribute, drive() is a method! Simple yet crucial.
What's your favorite Python concept? 👇
#Python #Programming
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Thread alert: Comparing a chemical process flow (like distillation) to a frontend user flow in React. Step 1: Input stage raw materials vs user login. More in thread! #ProcessFlow #FrontendDev #ReactJS #Chemistry #SciTech #UserFlow
Follow the thread and let me know your thoughts
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In chemistry, a reaction mechanism explains how reactants become products. In coding, functions explain how inputs become outputs. Same thinking. Different tools. 🧪➡️💻
#SciTech #Chemistry #Coding #STEM
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