Data Analysis Beyond Visualization: Cleaning & Understanding

Strong analysts follow strong practices. This 30-point blueprint highlights the habits that actually improve your analysis, communication, and delivery. Save it. Apply one at a time. Follow for more practical insights from my data journey. #DataAnalytics #DataAnalyst #AnalyticsCareer #DataSkills #BusinessIntelligence #CareerGrowth #SQL #Python

A small but powerful data lesson I’ve been revisiting lately: SQL helps you ask the right questions. Python helps you explore the answers. SQL is incredible for: filtering large datasets aggregating data efficiently understanding what is happening Python shines when you want to: clean and transform messy data explore patterns and outliers visualise trends and test assumptions What I’m learning is that the real strength isn’t choosing one over the other — it’s knowing when to use each and how they work together in a data workflow. Strong data analysis isn’t about tools alone; it’s about clarity of thinking. #Python #SQL #DataAnalytics #OpenData #LearningInPublic #DataSkills #MScJourney

𝐎𝐧𝐞 𝐭𝐡𝐢𝐧𝐠 𝐈’𝐦 𝐧𝐨𝐭𝐢𝐜𝐢𝐧𝐠 𝐰𝐡𝐢𝐥𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 While practicing Python and SQL lately, one thing is becoming very clear, data analysis is not just about tools. Most of the time actually goes into understanding the data itself. Looking at patterns, asking the right questions, and figuring out what the numbers really represent. Even small exercises start getting interesting when you try to interpret the results instead of just writing code. Still early in the journey, but slowly getting more comfortable working with data and thinking more analytically. #DataAnalytics #Python #SQL #LearningJourney

Data analysts’ struggles in 2026: 😺 sped up with hotkeys in Jupyter Notebook 🤖 Outdated. JupyterLab. 😺 mastered pandas 🤖 Cute. Try Polars. 😺 installing packages with pip 🤖 Please. Stop it. uv. 🤓 rooting for true knowledge and skill 🌐 AI #python ❤️

Today I practiced Aggregate Functions in Pandas while working with datasets in Python 📊🐍 Aggregate functions help summarize large amounts of data and quickly generate useful insights. Some commonly used functions: • sum() – Total value • mean() – Average value • min() – Minimum value • max() – Maximum value • count() – Number of records Example: df["Sales"].sum() df["Sales"].mean() df["Sales"].max() These functions are extremely useful for data analysis, reporting, and business insights. Step by step building stronger skills in Python and Pandas for Data Analytics. #Python #Pandas #DataAnalytics #LearningJourney

🧹 Data preprocessing matters more than we think. Before any model or insight, data needs work—a lot of it. Up to 80% of a data scientist’s time goes into cleaning messy data: missing values, duplicates, wrong formats, and inconsistencies . Tools like Python & Pandas make this easier with functions to detect, remove, and intelligently fill missing values—but the real skill is knowing what to fix and how. Better data = better decisions. Always. #DataScience #DataCleaning #Python #Pandas #MachineLearning #Analytics

Learning every tool will not make you better. Clarity will. Start with the type and scale of your data, then align tools to your goal. Excel and SQL for summaries and dashboards. Python for deeper analysis and predictions. Focus beats overload. 📕 https://lnkd.in/d42rindX #DataAnalytics #DataScience #SQL #Python #Excel

📊 Fun data thought of the day Every day we generate around 2.5 quintillion bytes of data worldwide. But the interesting part is this: Most companies still use less than 30% of the data they collect. Which means the biggest opportunity in business today is not collecting more data — it’s understanding the data we already have. This is one of the reasons I enjoy learning data analytics. Behind every dataset there is a story waiting to be discovered. Still learning Python, SQL and data analysis — and enjoying the journey. #dataanalytics #datascience #python #sql #learning #datadriven

📊 Day 22 — 60 Days Data Analytics Challenge | Pandas Data Transformation Today I practiced transforming and analyzing categorical data using some useful Pandas functions. 🔎 What I practiced: • Counting category frequency using value_counts() • Creating new columns using map() • Replacing values in datasets using replace() 💡 Key Learning: These functions are very helpful for transforming and organizing categorical data before performing deeper analysis. #60DaysDataAnalyticsChallenge #Python #Pandas #DataAnalytics #LearningInPublic

📊 Day 19 — 60 Days Data Analytics Challenge Today I learned about Crosstab in Pandas, which helps summarize data by showing the relationship between two categorical variables. 🔍 What I practiced today: • Creating cross-tabulations using pd.crosstab() • Understanding category-wise data distribution • Using margins=True to include total values • Improving table readability with row and column labels This feature is very helpful during Exploratory Data Analysis (EDA) because it allows us to quickly compare categories and identify patterns in the dataset. #DataAnalytics #Python #Pandas #60DaysChallenge #LearningJourney