7 Days to Data Analytics with Python Essentials

I Tracked My Expenses Using Python & NumPy — Here's What ₹38,940 Taught Me About My Spending Habits I built a Personal Finance Tracker using just Python and NumPy — no Pandas, no fancy libraries. Here's what I discovered about my own spending 👇 The project started simple: a CSV file with 50 transactions across 3 months. But when I ran the numbers through NumPy, the insights hit different. What the data revealed: • Shopping eats 40% of my budget — with just 6 transactions • My Top 5 purchases alone = 36% of total spending • Average spend (₹779) vs Median (₹465) — proof that a few big buys skew everything • 56% of money goes to just 11 "high-tier" transactions What I actually built: → Read raw CSV data using Python's csv module → Converted everything to NumPy arrays for fast computation → Used np.sum(), np.mean(), np.max(), np.median(), np.std() → Boolean masking to filter by category & month → np.argsort() to rank top expenses → np.percentile() for distribution analysis → A formatted summary report printed right to the console. Key takeaway: You don't need complex tools to get powerful insights. NumPy + a CSV file + curiosity = real, actionable data about your life. Watch the screen recording below to see the full report output! This is Week 1 of my Python data journey. Next stop: Pandas & Matplotlib. #NumPy #DataAnalysis #PersonalFinance #LearningInPublic #PythonProjects #BuildInPublic #Python #DataScience #CodeNewbie #Programming #TechTwitter #DataDriven #100DaysOfCode #FinanceTracker

Python is great for data science. But using it to clean data is overkill. A popular YouTube tutorial shows how to clean SurveyMonkey data using Python and Pandas, it took the developer 1 hour. The same transformation in Power Query? 5 minutes. Most data analysts don't realize Excel can do this. They assume Python is the only serious option for data cleaning. But Power Query has been built into Excel since 2010, and it handles transformations like unpivoting, merging, grouping, and calculated columns without writing a single line of code. In this video, I walk through the exact same dataset and show you how to clean it 12x faster using Power Query. If you've been putting off learning Python just to clean data, you don't need to. Watch the video and download the practice file: https://lnkd.in/d7E3TiDU ❓Do you use Python or Power Query for data cleaning? #Excel #Python #DataCleaning

Check out this Very Useful Post & #Tutorial from My Online Training Hub ⬇️ to see how messy #Data can be cleaned in a short amount of time, using #PowerQuery in #Microsoft #Excel. #MicrosoftExcel Rulezzzz Forever 🤩😍💪💪🙌🙌. #ExcelTutorials #DataCleaning #ExcelTips #ExcelTricks

Episode 9: What I Can Do With Python One common challenge in data cleaning is this: How do you quickly see all the unique values across every column in a dataset? Not just one column at a time…but the entire dataset in one view. If you’ve worked with real data, you know how important this is. It helps you spot inconsistencies, compare entries with a data dictionary, and decide what needs to be cleaned or standardised. This week, I attempted to do exactly that. My first instinct was Excel. I tried combining functions, nesting formulas, and exploring different approaches to get all unique entries across columns at once. It sounded like something that should be possible, but after spending quite some time on it, I couldn’t get exactly what I wanted. And VBA wasn’t something I wanted to rely on (you would know about this from my previous post 😩). So I switched to Python. I wrote a simple function (maybe not so simple) and brought it back into Excel using Python. I called it 'uniq_row_per_col()' . The function takes a dataset (as an Excel range) and returns the unique values for each column. It assumes the first row contains headers, handles duplicate column names automatically (similar to pandas), and keeps case sensitivity so inconsistencies can be clearly identified. In practice, this makes data cleaning much easier. Instead of checking columns one by one, I can now: — View all unique entries across the dataset at once — Compare them directly with a data dictionary Identify inconsistencies quickly (typos, casing differences, variations) — Decide what needs standardisation or removal Behind the scenes, pandas handles the data manipulation, while xlwings manages the interaction between Excel and Python. This is something I’m beginning to appreciate more, not just using tools as they are, but extending them to fit the workflow I need. I’ve attached a short demo video showing how it works. Would this be useful in your data cleaning process? See you in Episode 10 🚀 #WhatICanDoWithPython #Python #DataCleaning #Excel #DataAnalysis #Automation #BuildInPublic #xlwings

Excel or Python? Which one is better? 👇 Lately, I’ve been navigating the "Great Divide" between Excel and Python while handling large-scale datasets (90,000+ rows). Here’s what my recent experience has taught me: 📉 The Excel Reality Check: Excel remains the undisputed king for quick analysis, ad-hoc reporting, and day-to-day business tasks. It’s intuitive, fast, and accessible. However, once complex operations meet massive row counts, the "spinning wheel" starts to appear or even crash. 🐍 The Python Advantage: This is where Python truly shines. For scalability, automation, and handling heavy data lifting smoothly, Python is a game-changer. It transforms a potential crash into a seamless, repeatable workflow. The Verdict? They aren't rivals; they’re complementary. I’ve found the most success using: 1️⃣Excel for speed, simplicity, and stakeholder-ready reporting. 2️⃣Python for deep analysis, data cleaning, and long-term scalability. The most important thing is to choose the right tool for the job! 🛠️ #DataAnalytics #Python #Excel #Learning #Data #TechTips

🎓 Just completed "Hypothesis Testing in Python" on DataCamp! Solid course for anyone who wants to go beyond knowing the theory and actually implement statistical tests in Python. Here's a quick breakdown: 📚 What you'll learn across 4 chapters: 1️⃣ Hypothesis Testing Fundamentals The core workflow — one-sample proportion tests, z-scores, p-values, and false positive/negative errors. The essential foundation. 2️⃣ Two-Sample & ANOVA Tests T-tests for two groups, then ANOVA for 3+ groups — crucial for avoiding Type I error inflation from running too many t-tests. 3️⃣ Proportion Tests & Chi-Square Testing categorical data with chi-square independence and goodness-of-fit tests. Super practical for real-world survey and behavioral data. 4️⃣ Non-Parametric Tests ← my personal highlight 💡 When your data violates normality assumptions — Mann-Whitney U, Wilcoxon, Kruskal-Wallis. Often overlooked, but incredibly useful in practice. 🐍 What makes it stand out: Hands-on Python with real-world datasets. Theory meets code — which is exactly how it should be taught. 📎 I put together a free PDF revision sheet with all key code examples + a cheat sheet for choosing the right test — drop a comment if you like it! 📌 Recommended for: Data Analysts, Economists, Finance professionals, anyone making data-driven decisions. #DataScience #Python #Statistics #HypothesisTesting #DataCamp #ContinuousLearning

Most analysts know SQL. Most analysts know Python. Very few know how to combine them efficiently. That’s why many stay average. Here are a few things I wish I learned earlier: In SQL: → WHERE cannot filter aggregated results If you're filtering grouped data, use HAVING. → Window functions save messy subqueries Use RANK(), ROW_NUMBER(), SUM() OVER() for ranking and running totals. → LAG() and LEAD() beat self-joins Comparing current vs previous period? One line does what multiple joins often can’t. In Python: → Do not load unnecessary data Filter in SQL before bringing it into pandas. → Avoid for loops in pandas Vectorized operations and apply functions are significantly faster. → Stop hardcoding dates Use datetime so your scripts stay dynamic and reusable. The real power comes when you combine both: → Pull data with SQL → Transform it in Python → Push results back with to_sql() That workflow alone will make you more efficient than most analysts around you. Knowing SQL or Python is useful. Knowing how to use both together is what separates strong analysts from average ones. #DataAnalytics #SQL #Python #AnalyticsEngineering #CareerGrowth

Day 9/120 – Today I learned something most beginners ignore… but pros don’t 😳🔥 Yesterday → Lists Today → CONTROL over data 👇 👉 Tuples & Sets in Python Here’s the problem 🤯 Lists can be changed anytime… But what if your data SHOULD NOT change? ❌ Example: Coordinates 📍 Dates 📅 Configurations ⚙️ That’s where TUPLES come in 👇 data = (10, 20, 30) ✔ Cannot be modified ✔ Safe & reliable Now comes something even more powerful 👇 👉 SETS nums = {1, 2, 2, 3, 3} Output? 😳 {1, 2, 3} ✔ No duplicates ✔ Clean data This is HUGE in Data Analytics 📊 Now I can: ✔ Protect data (Tuples) ✔ Clean data (Sets) This is getting serious now 🔥 Comment “DATA” if you're learning with me 💪 #Day9 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency

🔢 Why NumPy Matters in Data Science (More Than I Thought) Hi everyone! 👋 While learning Python for data work, I came across NumPy — and initially, it just looked like another library. But after spending some time with it, I realized why it’s so widely used. At its core, NumPy is about working efficiently with numbers and arrays. A few things that stood out to me: ✔️ Faster computations compared to regular Python lists ✔️ Ability to perform operations on entire datasets at once (no loops needed) ✔️ Foundation for libraries like Pandas, Scikit-learn For example, instead of looping through values one by one, NumPy lets you do operations in a single line — which is both cleaner and faster. This made me think about real-world scenarios: When dealing with large datasets, performance really matters. Even small optimizations can save a lot of time. Coming from SQL and ETL, this feels similar to optimizing queries — but now at a programming level. Still exploring more, but it’s clear that understanding NumPy well can make a big difference in data processing and model performance. Have you used NumPy in your work? Or do you rely more on Pandas/SQL? #DataScience #Python #NumPy #MachineLearning #LearningInPublic

I’ve been learning data analysis with Python, and recently built a small end-to-end project that made me think beyond just “cleaning data.” GitHub 👉🏻: https://lnkd.in/gwxrkzys The dataset contained customer information Instead of stopping at cleaning, I tried to approach it from a real-world perspective: • Cleaned and standardized messy customer data • Handled inconsistencies and formatting issues • Applied business rules to filter out “Do Not Contact” customers • Created a final “call-ready” dataset for outreach teams What stood out to me was this — clean data alone isn’t enough. It needs to be usable and aligned with business goals. This project helped me understand how data analysts actually support decision-making, not just analysis. Still early in my journey, but projects like this are making things click. Would love any feedback or suggestions 🙌 #DataAnalysis #Python #Pandas #DataProjects #LearningInPublic