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How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

Before this week, I thought Excel was just a fancy calculator with boxes. But after three days of my Data Science & Analytics course, I realise I was wrong. Really wrong. Excel is a spreadsheet tool used by millions of people from small business owners to data analysts at giant companies. And the best part? You don’t need to be a programmer to use it. You just need to know a few tricks. Here’s how Excel helps solve real-world problems using exactly what I learned in Week 1. 3 Real-World Ways Excel Is Used Business decisions with logic Managers use IF() statements to answer yes/no questions. Example: =IF(Sales>1000, "Bonus", "Needs Improvement"). One cell can decide who gets paid more. Cleaning messy data Real data is never clean. Marketing teams use Remove Duplicates, Find & Replace, and Text to Columns to fix hundreds of messy rows in seconds. No manual typing. Tracking deadlines and ages HR teams use DATEDIF() to calculate employee ages or years of service. TODAY() and NOW() keep reports automatically updated. No more “oh, I forgot to update the date.” 3 Excel Features I Learned This Week Remove Duplicates – One click, and Excel deletes repeated rows. Saved me from sending the same customer email twice. IFERROR() – Hides ugly errors like #DIV/0! and shows something friendly instead (e.g., “Check data”). Your boss will thank you. Sort & Filter – With AutoFilter, I can find all sales above $500 in one second. Then Custom Sort lets me sort by date and region together. My personal reflection Honestly? Learning Excel has changed how I see data. I used to look at a messy spreadsheet and feel lost. Now I see Remove Duplicates, Text to Columns, and TRIM() as tiny tools that bring order to chaos. Data isn’t scary anymore. It’s just a puzzle and Excel gives me the pieces. I’m only one week in. But I already feel like a junior data analyst in training.

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

Plug an oscilloscope probe into almost any real circuit and the trace will be fuzzy. Riding on top of the signal you actually want is a haze of higher-frequency noise — switching hash, radio pickup, digital crosstalk. The signal and the noise occupy different parts of the frequency spectrum, and that separation is an opportunity. If you can build something that passes the low frequencies and quietly turns down the high ones, the fuzz disappears and the signal stays. That something is a low-pass filter, and in its simplest form it is just a resistor and a capacitor. This article explains where the cut-off frequency comes from, works a concrete RC example, and clears up the misunderstandings that most often trip up a first filter design. Why this calculation matters Low-pass filters are everywhere a clean signal is needed. They sit in front of analog-to-digital converters as anti-aliasing filters, smooth the ripple out of power supplies, condition sensor outputs, and recover audio from a noisy line. Even an averaging operation in software is a low-pass filter wearing different clothes. The calculation matters because the cut-off frequency is a design decision with real consequences in both directions. Set it too low and you blur the signal you were trying to protect — its fast edges and genuine high-frequency content vanish along with the noise. Set it too high and the noise sails straight through. The cut-off is a deliberate line drawn through the frequency spectrum, and a passive RC filter places it with just two component values. The core formula A first-order RC low-pass filter is a resistor in series with the signal and a capacitor from the output node to ground. At low frequencies the capacitor is effectively an open circuit, so the output simply follows the input. At high frequencies the capacitor's impedance becomes small, shorting the high-frequency content to ground. The crossover between those two regimes is the cut-off frequency: f_c = 1 / ( 2 * pi * R * C

Those ousted included ADA journal editor-in-chief Steven Kahn and former ADA president Desmond Schatz

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

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Introduction Excel is a spreadsheet application developed by Microsoft that helps users organize, analyze and visualize data. It is used by businesses, organizations, researchers and students worldwide because it makes working with data easier and more efficient. Business Decision Making One of the ways Excel is used in real-world data analysis is in supporting business decision-making. Companies collect data such as customer information, financial transactions and sales records. Excel helps in organizing and analyzing this data using tools such as formulas and PivotTables. This makes it easier to identify trends and patterns in business performance, such as which products to stock and when to restock them. For example, a supermarket can analyze the monthly sales in Excel to identify the best-selling products and ensure that they remain in stock. Marketing Performance Excel is also used to analyze marketing performance. Businesses use it to track data from marketing campaigns such as website visits, social media engagement and sales conversions. This information is organized using charts and reports, which help evaluate which strategies are producing the best results. This allows companies to allocate their resources more effectively and improve future campaigns based on data rather than assumptions. As a result, Excel plays an important role in helping businesses understand their customers and improve the effectiveness of their marketing efforts. Financial Reporting Excel is widely used in financial reporting. It helps businesses to organize and analyze financial statements such as income statements, cash flow reports and balance sheets. It is also used to record transactions, calculate totals, and generate summaries that show the financial health of the business. By using built-in formulas and functions, accountants can quickly compute profits, expenses, taxes and forecasts with a high level of accuracy. Excel also allows the creation of financial charts and dashb

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(

If you're a networking instructor — at a university, technical college, boot camp, or corporate training program — you know the frustration of teaching the OSI Model. Static PowerPoint slides can only do so much. Students nod along in class, but when exam time arrives, the layers blur together. The PDU names become a confusing jumble. The OSI Model Simulator by Roboticela was built with educators in mind. It transforms a passive lecture into an interactive demonstration that students engage with, remember, and take home to explore on their own. Classroom Use Cases Live Demonstration Project the simulator on a classroom screen. Have students suggest messages to send and protocols to use. Step through each layer together as a class, stopping to ask questions: "What's happening here? What header was added? What device would operate at this layer?" The interactive format maintains attention far better than any lecture. Lab Assignments Assign students to run specific simulations and document their findings: "Run HTTP and HTTPS simulations. Screenshot the Presentation Layer for each. Explain in writing what differs and why." This assignment tests both tool usage and conceptual understanding. Flipped Classroom Send students to app.osi-model-simulator.roboticela.com before class. Ask them to run three simulations and come prepared to discuss what they observed. Class time becomes richer discussion rather than basic concept delivery. Protocol Comparison Exercise Have students run simulations for all five protocols — HTTP, HTTPS, SMTP, DNS, FTP — and create a comparison chart noting the differences at each OSI layer. This develops deep protocol literacy that traditional instruction rarely achieves. Why It Works: The Science of Active Learning Research in educational psychology consistently shows that active learning produces dramatically better retention than passive instruction. The "Learning Pyramid" (Edgar Dale's Cone of Experience) suggests: Lecture: ~5% retention after 2

Networking certifications like CompTIA Network+ and Cisco's CCNA are career-defining credentials. They validate your understanding of networking fundamentals — and both exams test OSI Model knowledge extensively. In fact, the OSI Model is arguably the single most tested conceptual framework in entry-level and intermediate networking certifications. Why OSI Is So Critical for Certification Exams Exam questions on OSI take many forms: "At which layer of the OSI model does a router operate?" (Layer 3) "What PDU is used at the Transport Layer?" (Segment) "Which protocol operates at the Application Layer?" (HTTP, DNS, SMTP...) "A user cannot connect to a website. Troubleshooting should begin at which OSI layer?" (Layer 1, then up) "Which device operates at Layer 2?" (Switch) "What is the function of the Presentation Layer?" (Translation, encryption, compression) These questions seem straightforward on paper but are notoriously confusing under exam pressure without deep conceptual understanding. How the OSI Simulator Accelerates Your Studies Visual Memory Formation Research in cognitive science consistently shows that visual and kinesthetic learning creates stronger memories than text-only reading. When you watch the OSI Simulator animate your message through all seven layers, you're forming episodic memories — vivid, experience-based memories that are far more durable than rote memorization. Protocol-to-Layer Association One of the most commonly missed exam categories is protocol-to-layer mapping. The OSI Simulator makes this automatic: when you select HTTP, the Application Layer is highlighted. When you watch TCP headers form, you associate TCP with Layer 4 viscerally, not just verbally. PDU Name Mastery Data, Segment, Packet, Frame, Bits — the five PDU names are shown explicitly at each layer in the simulator. After running 10 simulations, these names become second nature. No flashcard can match this experiential learning. Troubleshooting Framework Practice Network+ an

Getting started with the OSI Model Simulator takes less than 60 seconds. The interface is thoughtfully designed to be intuitive for beginners while offering enough depth to satisfy advanced learners. Here's your complete step-by-step guide. Step 1: Open the Simulator Navigate to app.osi-model-simulator.roboticela.com in any modern web browser. No account required, no download necessary, and no cost. The app loads instantly and is ready to use immediately. Alternatively, visit the landing page to learn more about features and download the desktop app for offline use. Step 2: Enter Your Message In the message input field, type any text you like. This is the "data" your simulation will encapsulate. Examples: Hello, World! GET /index.html HTTP/1.1 {"user": "alice", "action": "login"} Your own name or a phrase you'll remember Using a personally meaningful message makes the encapsulation feel real rather than abstract. Step 3: Choose Your Protocol Select from five real protocols: HTTP, HTTPS, SMTP, DNS, or FTP. Each choice changes the Application Layer headers added to your data. For beginners, start with HTTP. Then re-run with HTTPS to see the Presentation Layer encryption difference. Step 4: Choose Your Transmission Medium Select your Physical Layer medium: Ethernet, Wi-Fi, Fiber Optic, Coaxial, or Radio. This affects how the Physical Layer is visualized at the end of the simulation. Step 5 (Optional): Set Custom IP Addresses For a more realistic Network Layer demonstration, enter a source IP address (simulating your device) and a destination IP address (simulating the server). This makes the Layer 3 packet header concrete and personally relevant. Step 6: Run the Simulati on Click the Run or Start button. Watch as your message travels through all seven layers: Application Layer adds protocol headers Presentation Layer adds encryption (if HTTPS) Session Layer adds session management Transport Layer segments and adds TCP/UDP header Network Layer wraps in IP packet Data Li

The Physical Layer's choice of transmission medium profoundly affects the performance, cost, security, and reliability of a network. The OSI Model Simulator supports all five major media types — making it a powerful tool for understanding how physical choices ripple up through all seven OSI layers. Medium Speed Max Distance Security Cost Ethernet Up to 10 Gbps+ 100m (Cat6a) High (physical access) Low Wi-Fi Up to ~9.6 Gbps (Wi-Fi 6) ~100m indoor Medium (WPA3) Low Fiber Optic Terabits/s 100s of km Very High High Coaxial Up to 1 Gbps 500m (RG-8) Medium Medium Radio Variable (5G: Gbps) km to global (satellite) Low–Medium Variable Ethernet: The Reliable Standard Ethernet is the dominant wired networking standard in homes, offices, and data centers. Using twisted-pair copper cables (Cat5e, Cat6, Cat6a), it provides reliable, high-speed connectivity with predictable latency. The IEEE 802.3 standard governs Ethernet, and modern variants include 1GbE, 10GbE, 25GbE, 40GbE, and 100GbE. Wi-Fi: Wireless Freedom Wi-Fi (IEEE 802.11) eliminated the need for physical cables in most consumer settings. Wi-Fi 6 (802.11ax) and Wi-Fi 6E deliver impressive speeds, but shared medium access, interference, and radio propagation challenges mean it will never fully replace wired Ethernet for critical applications. Fiber Optic: The Internet's Backbone Fiber optic cables carry data as pulses of light through glass or plastic strands. They're immune to electromagnetic interference, support enormous bandwidth, and can span continents — literally. Every major internet exchange, submarine cable, and data center interconnect uses fiber. Coaxial Cable: The Cable TV Legacy Coaxial cable — familiar from cable TV connections — consists of a central conductor surrounded by insulating layers and a braided metal shield. DOCSIS-based cable internet connections (common from ISPs like Comcast) use coaxial as the last-mile medium. Radio: Wireless at Scale From the cellular 5G network in your pocket to satellite

What’s the difference between a person, an artifact, and an ecosystem?

New simulations reveal that the moons of Uranus may retain traces of giant planets.

Introduction A traditional database. That is what many who have not really interacted with Excel to a great extent would define it as in its most basic form. Not that they are wrong, only that is the scope their utilization of Excel covers. Mostly record keeping, basic operations, and data representation. But for those whose utilization scope of Excel is broader, we definitely know better. This underestimation of Excel is a grave mistake for anyone considering themselves as tech-oriented, especially for anyone dealing with data operations, be it simple record keeping or complex concepts involving data. What is Excel A spreadsheet program or tool that facilitates data organization, analysis, and visualization through mathematical operations, chart creation, and building financial models. Real-world application of Excel in Data Analytics Reporting and visualisation Excel facilitates data representation in the form of charts(bar charts, pie charts, line graphs) and dashboards. Businesses and organisations utilize this to get an organised, more insightful, and simplified view and report of their raw data. Financial Accounting Excel's provision for mathematical operations, functions, and formulas in analysis facilitates financial accounting. Balance sheets and income statements preparation, budgeting, and expense tracking are just some of the ways Excel can be used in accounting. Decision-Making Businesses and organisations heavily rely on analysis to support their decision-making. Excel helps in the analysis through different data metrics comparisons, e.g., sales across seasons and locations, forecasting, and tracking key performance indicators. This helps businesses make the best decisions based on the insights gathered from the analysis. Beginner Excel Features and Formulas for Data Analysis Learnt so far Sort and Filter By applying the Filter feature for each column, data in specific columns can not only be sorted from newest to oldest, but also be filtered based on

Key Use Cases Power BI Visual Monitoring can be used for: power bi visual monitoring power bi report visual monitoring visual regression testing for Power BI power bi screenshot monitoring monitoring Power BI visuals visual monitoring for Power BI Report Server automated Power BI dashboard validation visual correctness control for BI reports Power BI Visual Monitoring: Automatically Detecting Broken Visuals in Power BI Reports In large Power BI environments, analytics teams often face the problem of silent regressions : even minor changes in data or models can break individual visuals without any obvious errors. Report owners frequently don’t notice that a visual has stopped rendering or is showing incorrect data — this can happen due to changes in data source structure, access rights, deleted fields, broken measures, or refresh failures. Manually checking hundreds of report pages across multiple dashboards in such conditions is extremely inefficient and nearly impossible. We, a team of BI developers and analysts, encountered this pain point during a large analytics implementation project and decided to create a solution for automated Power BI visual monitoring . Project Source Code: GitHub: https://github.com/svergio/Power-bi-report-visual-monitoring Documentation: https://svergio.github.io/Power-bi-report-visual-monitoring/ Wiki: https://github.com/svergio/Power-bi-report-visual-monitoring/wiki Why Standard Power BI Tools Don’t Solve the Problem Standard Power BI tools such as Usage Metrics and Performance Analyzer help analyze report usage and performance but do not detect visual issues. For example, built-in usage metrics show “how those dashboards and reports are being used” — number of views, popular reports, and who is viewing them. These metrics are important for assessing analytics adoption, but they say nothing about whether the visuals themselves are displaying correctly. Similarly, Performance Analyzer shows load times for each visual, helping identify s

The reactor, from a startup called Antares, isn't ready to generate power yet.

Releasing sterilized flies can crash a local population of flesh-eating screwworms. But the US currently has limited capacity to produce them.