Analytics Siksha

Highly Experienced Data Scientist. Prficiency in Data Analysis tools. Deliver client ready projects. Worked with MNCs and delivered industry ready projects.

Mastering Git, GitHub, GitHub Actions, and GitHub Codespaces with Python Examples

Git is a version control tool “Learn Git, GitHub, Actions & Codespaces with Python examples. Automate, collaborate, and code smarter with our guide.” In the world of modern software development, version control and collaboration tools have become indispensable. Among the most powerful tools available today are Git, GitHub, GitHub Actions, and GitHub Codespaces. Whether you’re […]

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GitHub Codespaces: Transform Cloud-Based Development

  In the ever evolving landscape of software development, setting up a consistent and efficient environment remains a challenge. GitHub Codespaces addresses this by offering instant, cloud based development environments, streamlining workflows, and enhancing collaboration.   What is GitHub Codespaces? GitHub Codespaces is a cloud hosted development environment that allows developers to code directly within

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Mastering MLflow: How to Track, Reproduce, and Deploy ML Models

MLflow Tutorial: Track, Reproduce, and Deploy ML Models with Python  Learn how to use MLflow to manage the full machine learning lifecycle-track experiments, log metrics, register models, and deploy seamlessly. Step-by-step Python example with clear explanations and visuals. Ideal for data scientists and MLOps teams. Building machine learning models is only half the battle. Managing

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What Is Data Version Control (DVC) and Why It Matters in Machine Learning

Data Version Control Learn what Data Version Control (DVC) is and how it helps manage datasets, models, and machine learning pipelines. A complete beginner-friendly guide to versioning data and building reproducible ML workflows.   What Is Data Version Control (DVC) and Why It Matters in Machine Learning? In software development, using Git for version control

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The Life Cycle of a Data Science Project: Complete Guide

Learn the complete life cycle of a data science project with easy steps, clear Python examples, and practical deployment tips. Learn the complete life cycle of a data science project with easy steps, clear Python examples, and practical deployment tips.   Data science is not magic ,  it is a process. Behind every smart prediction

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Data Science Projects for Beginners in 2025: Practical Ideas with Real KPIs

“Explore beginner-friendly Data Science projects for 2025 across Machine Learning, AI, NLP, Power BI, SQL, and Python. Build skills with real KPIs!” Starting your journey in data science can be overwhelming. With new tools, techniques, and AI trends emerging daily, beginners often get stuck choosing the “right” project. In 2025, companies demand real-world skills, not

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Why India Must Bet Big on Deep Technology: A Wake Up Call from Piyush Goyal

The Minister of India’s comments on India’s Startup ecosystem have sparked debate about Deep Technology innovation in India. Recently, India’s Commerce and Industry Minister, Piyush Goyal, urged Indian startup founders to shift gears and focus on deep technology development. His message is clear. it is time for Indian innovators to move beyond basic apps and

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10 Side Hustles for Working Professionals to Earn in Dollars

Discover 10 practical side hustles for working professionals to earn in dollars. Real examples, tips, and data to start making extra income today. In today’s economy, relying on just one source of income is risky. Rising living costs, unexpected expenses, and the desire for financial freedom have pushed more professionals to explore side hustles. The

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Data Analysis and Hypothesis Testing Using a Banking Dataset

Introduction In this analysis, we will explore a banking dataset, preprocess the data, build a classification machine learning model, and perform hypothesis testing to validate insights.   The goal is to classify customers based on their likelihood of subscribing to a term deposit and to use statistical tests to support our findings.   Step 1:

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