Prabhat Korshub Blogs

  Introduction Before diving into machine learning (ML), every data scientist must master data analysis and visualization . Think of it as preparing the soil before planting seeds—without clean, structured, and understood data, even the most powerful ML models will fail. In this guide, we’ll explore how NumPy, Pandas, Matplotlib, and Seaborn work together to make pre-machine learning analysis smooth, effective, and insightful. Why Pre-Machine Learning Analysis is Important Machine learning isn’t just about algorithms. Models only perform well if the data is accurate, structured, and meaningful . Pre-ML analysis helps to: Clean messy datasets Identify missing values Detect outliers Visualize patterns and relationships Transform raw data into model-ready formats The Python Data Analysis Ecosystem 1. NumPy: The Foundation of Numerical Computing NumPy is like the backbone of data science. It provides: ndarray (N-dimensional arrays) : Faster than Python lis...

  Microsoft Excel has been around for decades, but its relevance in data analytics and dashboards is stronger than ever. With powerful functions, data modeling features, and visualization capabilities, Excel remains one of the most versatile tools for professionals worldwide. Whether you’re an analyst, manager, or business owner, mastering Excel dashboards can transform raw data into actionable insights . Introduction Why Excel Still Dominates Data Analytics Despite the rise of tools like Power BI, Tableau, and Python, Excel continues to dominate because: It’s widely available and familiar. It balances simplicity and advanced features . It integrates seamlessly with other Microsoft tools. The Power of Dashboards in Decision-Making Dashboards turn raw data into visual stories . A well-designed Excel dashboard lets decision-makers see KPIs, trends, and forecasts at a glance—helping businesses move faster. Getting Started with Excel for Analytics Understanding Exc...

  In the world of Site Reliability Engineering (SRE), DevOps, and Cloud Operations , monitoring and observability are not optional—they are critical to ensuring uptime, performance, and customer satisfaction. Among the many tools available, Grafana stands out as one of the most powerful platforms for data visualization, querying, alerting, and automation. This guide will show how SREs, DevOps engineers, and cloud teams can leverage Grafana in 2025 to streamline operations, respond faster to incidents, and automate workflows. 🚀 Why Grafana? Open-source and flexible – integrates with 100+ data sources (Prometheus, Loki, InfluxDB, Elasticsearch, CloudWatch, Azure Monitor, etc.). Unified observability – one dashboard for metrics, logs, and traces. AI & automation support – Grafana Labs is investing heavily in AI-powered insights. Enterprise-ready – secure, scalable, and used by global organizations. 📊 1. Data Visualization in Grafana Visualization is at ...

  🚀 Introduction Data is everywhere—but making sense of it is a superpower . Whether you’re in business, health, education, or even journalism, knowing how to turn raw data into powerful stories can elevate your career. And that’s exactly where Tableau Public comes in. It’s free , intuitive, and designed for people who want to visualize data beautifully . Even if you're not a data analyst, Tableau makes it easy to build dashboards, segment your audience, and deliver insights that get people nodding. 📥 Getting Started with Tableau Public What Is Tableau Public? Tableau Public is the free version of Tableau that allows you to: Create dashboards and share them online Explore public data visualizations Build your data storytelling skills Downloading and Installing Head to public.tableau.com Download the installer, follow the prompts, and you’re ready to roll. Interface Overview When you open Tableau Public, you’ll see: Data pane (left): your dimensions an...

  Introduction Welcome to the ultimate 2025 Masterclass on Data Science using Python—your complete A to Z guide for entering and thriving in the world of Machine Learning. Whether you're just starting out or looking to sharpen your skills, this guide is crafted to take you from zero to job-ready hero in the exciting domain of data science. Python remains the leading language for data scientists—and with good reason. It’s readable, flexible, and packed with robust libraries that can turn raw data into powerful insights. Getting Started with Data Science Who Is This Course For? This course is for: Students aiming for a data-driven career Professionals switching into data science Analysts who want to level up with ML Tools You Need Python (3.10+) Jupyter Notebooks (via Anaconda) GitHub for version control Google Colab (optional for cloud use) Setting Up Your Environment Download and install Anaconda. Launch Jupyter Notebook. Test libraries l...

  Introduction Python is a popular programming language that has gained immense popularity in the field of data science. It is an open-source language that is easy to learn and has a vast community of developers. If you are a novice in Python and want to learn more about data science, then you are in the right place. In this blog, we will discuss how you can become an ML pro from a Python novice. Firstly, let’s understand what data science is. Data science is a field that involves the extraction of insights from data. It is a multidisciplinary field that includes statistics, machine learning, and computer science. Data science is used in various industries such as healthcare, finance, and e-commerce. Python is a popular language for data science because of its simplicity and ease of use. It has a vast collection of libraries that make it easy to perform data analysis and machine learning. Some of the popular libraries for data science in Python are NumPy, Pandas, Matplotlib, and S...

Introduction Welcome to the enchanting world of data science in 2023! The A-Z Masterclass for Machine Learning with Python is your ticket to unlocking the secrets of this mystical domain . In this comprehensive article, we'll explore the magic of data science, share insights based on personal experience, and provide you with a roadmap to join our masterclass . Unraveling the Magic The Power of Data In the age of information, data reigns supreme. Data is the lifeblood of data science, and in our masterclass, you'll learn how to harness its power. We'll cover data collection, cleansing, and storage, ensuring you have a solid foundation to perform magic . Python: The Wizard's Wand Python is the wand of choice for data scientists, and you'll become a wizard with it. Our masterclass delves deep into Python, teaching you to write spells (code) that can conjure insights from data . Machine Learning: The Sorcerer's Stone Machine learning is the sorcerer's stone of...