Mohammedali Saiyed’s Post

The "Date Stored as Text" Nightmare. 📅 We have all seen it. You try to sort by date, and Excel sorts it alphabetically because the date is "240115". Text-to-Columns works... until you get a new file next week. Python's to_datetime function is the ultimate fix. You teach it the format once, and it never gets confused again. Swipe to see how to force dates to behave. 👉 #codingtips #analytics #data #dataanalysis #pythonforexcel #excel #datacleaning 

One of the most important steps in Data Analysis is Exploratory Data Analysis (EDA). Before building dashboards or models, I always spend time understanding the dataset. Here’s what I usually focus on: 🔍 Checking missing values 📊 Understanding distributions 🔗 Finding relationships between variables Using Python libraries like Pandas and Matplotlib makes this process much easier and more insightful. Sometimes, a simple visualization can reveal patterns that are not obvious in raw data. 💡 In my experience, strong EDA leads to better decisions and more accurate insights. 👉 What’s your favorite library for data analysis and why? #Python #EDA #DataScience #Analytics #Learning

Most beginners use Pandas the wrong way. They try to analyze the entire dataset. That’s why they struggle. Real data analysts do one thing first: They FILTER. Example: Your manager says “Give me all customers from New York who spent more than 1000 Sort them from highest to lowest You have 5 minutes” In Excel? You panic. In Pandas? Done in seconds. This is exactly what I cover in Day 9 of my Data Analysis series. If you can master filtering and sorting you can solve most real business problems. Link in Comment #dataanalysis #python #pandas #excel #pythonfordataanlysis

As part of building out my data science portfolio, I’ve put together a small project exploring how SQL-style analysis translates into pandas workflows. The goal was to take a typical analytical task — working with transactional data — and implement it end-to-end in Python, while keeping the structure and clarity of SQL queries. The notebook covers: - filtering and aggregating data - ranking and comparing groups - analysing how revenue is distributed across customers This sits alongside my other projects in machine learning, A/B testing, and time series, and helps round out the core toolkit: moving from data extraction → analysis → insight. https://lnkd.in/eN77xEw2

🚀 From Raw Data to Real Insights – My Data Cleaning Journey Yesterday, I worked on a dataset that looked clean at first glance… but as always, the truth was hidden beneath the surface. I asked myself a simple question: 👉 “Where is my data incomplete?” So, I started digging deeper… Using Python, I analyzed missing values across all columns and visualized them with a clean bar chart. And that’s when the real story appeared: 📊 Key Findings: Rating, Size_in_bytes, and Size_in_Mb had the highest missing values (~14–16%) Most other columns were nearly complete A clear direction for data cleaning and preprocessing emerged 💡 This small step made a big difference. Because in Data Analytics, better data = better decisions 🔥 What I learned again: Don’t trust raw data. Explore it. Question it. Visualize it. Every dataset has a story… Your job is to uncover it. 💬 What’s your first step when you get a new dataset? #DataAnalytics #Python #DataCleaning #DataScience #LearningJourney #Visualization #Pandas #Matplotlib

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

So there’s this exciting concept in data called “imputation.” Okay it’s not that exciting, I just like the name, but it’s actually pretty important. It’s basically when you deal with missing values by filling them in using the rest of the dataset. Not in a vague “surrounding data” way, but using actual methods like mean, median, or mode, sometimes forward or backward fill, and in more serious cases even models to estimate what should be there. The other option is to just delete the missing data. Either drop the rows or even the whole column. This is common with large datasets, especially when the missing values are small enough that removing them won’t mess with the overall analysis. But it’s not something you just do blindly, because depending on why the data is missing, you can end up biasing your results without realizing it. So yeah, it sounds like a small step, but it actually matters. #LearningInPublic #Python #DataCleaning #DataAnalysis #Data

Just explored a powerful Pandas Cheat Sheet for data analysis, and it’s a game-changer! From importing data (read_csv, read_excel) to cleaning (dropna, fillna) and advanced operations like groupby, pivot_table, and time series analysis — everything is covered in one place. What stood out to me: • Efficient data selection using loc, iloc, and query • Strong data manipulation with merge, apply, and melt • Handy tips like using .pipe() and avoiding inplace=True A must-have quick reference for anyone working with data in Python. #Python #Pandas #DataAnalysis #DataScience #Learning

80% of analysis time is data cleaning. Here's the playbook. Nobody posts about this part. It's not glamorous. But it's where the real work happens. This free notebook covers: → Identifying missing values (isnull, info, patterns) → Visualizing missingness — is it random or systematic? → Imputation strategies: mean, median, mode, forward fill → When to drop vs when to impute (decision framework) → Finding duplicates (exact and fuzzy) → Deduplication: keep first, keep last, custom logic → Validating your cleaned dataset Real messy data. Not textbook-clean CSVs. The kind of data you'll actually encounter at work. Free: https://lnkd.in/gBG_CBqH Day 2/7. Yesterday was SQL. Tomorrow: Advanced Pandas. #DataCleaning #Python #Pandas #DataAnalyst #DataScience #DataQuality #FreeResources #DataAnalytics

📊 Day 12 | Choosing the Right Test & Practical Tips 🧠📊 Today, I learned how to choose the right statistical test based on the data and problem. After exploring multiple statistical tests, I realized that the most important skill is not just knowing tests, but knowing when to use which test. The selection depends on: 🔹 Type of data (Numerical or Categorical) 🔹 Number of groups (1, 2, or more) 🔹 Relationship between data (independent or dependent) Some simple rules I learned: ✔ One group vs value → One-sample t-test ✔ Two independent groups → Two-sample t-test ✔ Same group (before/after) → Paired t-test ✔ More than two groups → ANOVA ✔ Categorical data → Chi-Square test I also learned some common mistakes: ❌ Relying only on p-value without understanding data ❌ Not checking assumptions like normality ❌ Misinterpreting results To understand this better, I applied multiple tests on a dataset using Python 💻 This helped me see how different tests are used in different scenarios. Instead of guessing, we can now select the right test and make data-driven decisions 📊🚀 #Statistics #HypothesisTesting #DataScience #DataAnalytics #LearningInPublic #Python