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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="icon" href="images/favicon.svg" type="image/svg+xml">
<title>Data Visualisation with Python | Sarath Prabhavu</title>
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</head>
<body>
<div id="navbar-placeholder"></div>
<!-- HERO -->
<header class="py-hero">
<div class="container" style="position:relative;">
<a href="https://shasthrasnehi.com/" target="_blank" style="position:absolute;top:0;right:0;display:flex;align-items:center;text-decoration:none;">
<img src="assets/shasthrasnehi_logo.png" alt="Shasthrasnehi" style="height:46px;width:auto;filter:brightness(1) drop-shadow(0 1px 4px rgba(0,0,0,0.3));">
</a>
<div class="py-badge">📊 Python Course Series · Data Visualisation</div>
<h1 class="hero-name" style="font-size:clamp(1.9rem,5vw,3rem);">Data Visualisation <span class="hero-accent">with Python</span></h1>
<p class="hero-tagline" style="max-width:620px;">
From raw data to compelling charts — learn NumPy, Pandas, Matplotlib, Seaborn, and interactive
visualisation tools in <strong style="color:#fff;">Malayalam</strong>, for students, researchers, and teachers.
</p>
<div class="py-lang-badge">🗣️ Conducted in Malayalam</div>
<div class="d-flex flex-wrap gap-3 mt-4">
<a href="https://forms.gle/DwkRW7iUvzZxNq2V9" target="_blank" class="btn-hero-primary btn" style="background:var(--py-green-mid);border:none;">
Register Now <i class="bi bi-arrow-right ms-1"></i>
</a>
<a href="courses.html" class="btn-hero-outline btn">← Back to Courses</a>
</div>
</div>
</header>
<!-- MAIN CONTENT -->
<div style="background:var(--bg-alt);">
<div class="container py-5">
<div class="row g-4">
<!-- LEFT COLUMN -->
<div class="col-lg-7">
<!-- ABOUT -->
<div class="info-card">
<div class="py-section-title">About This Course</div>
<p class="section-body mb-2">
This course takes you from data wrangling to visual storytelling using Python's most powerful scientific libraries.
You will master <strong>NumPy</strong> for numerical computing, <strong>Pandas</strong> for structured data manipulation,
and <strong>Matplotlib</strong> for creating publication-quality charts — including geographic maps with Cartopy and
statistical plots with Seaborn.
</p>
<p class="section-body mb-0">
Each session is <strong>1 hour of instruction + 30 minutes of hands-on coding</strong>. Live recordings
are available after every session. Batch size is kept small (15–20 students) for a personalised experience.
</p>
</div>
<!-- CURRICULUM -->
<div class="info-card">
<div class="py-section-title">Week-by-Week Curriculum</div>
<div class="week-card">
<div class="week-card-header">
<span class="week-num">MODULE 1</span>
<span class="week-title">Introduction to NumPy</span>
</div>
<div class="week-card-body">
<p>NumPy forms the numerical foundation of Python's scientific computing ecosystem. This module covers array operations, memory layout, and the tools that make large-scale numerical computation efficient.</p>
<ul class="week-topics" style="padding-left:1.2rem;margin-bottom:0;">
<li>How Python manages data types internally, and how NumPy improves on built-in structures for numerical work</li>
<li>Core properties of NumPy arrays — shape, dtype, indexing, slicing, and reshaping</li>
<li>Universal functions (ufuncs) for fast element-wise operations across arrays</li>
<li>Aggregation methods including sum, minimum, maximum, and statistical summaries</li>
<li>Broadcasting rules that allow operations between arrays of different but compatible shapes</li>
<li>Boolean masking and logical comparisons for conditional selection and filtering</li>
<li>Advanced indexing with integer arrays for non-contiguous data access</li>
<li>Techniques for sorting arrays along different axes</li>
<li>Structured arrays for working with heterogeneous, record-style data</li>
</ul>
</div>
</div>
<div class="week-card">
<div class="week-card-header">
<span class="week-num">MODULE 2</span>
<span class="week-title">Data Processing with Pandas</span>
</div>
<div class="week-card-body">
<p>Pandas provides labelled, table-oriented data structures that make real-world data cleaning and analysis practical. This module moves from raw arrays to structured datasets.</p>
<ul class="week-topics" style="padding-left:1.2rem;margin-bottom:0;">
<li>The Series and DataFrame objects — their design, construction, and relationship to NumPy</li>
<li>Label-based and position-based selection using .loc, .iloc, and related indexers</li>
<li>Arithmetic operations in Pandas and how index alignment works automatically</li>
<li>Strategies for detecting, removing, and imputing missing values</li>
<li>Multi-level (hierarchical) indexing for working with higher-dimensional data in a flat structure</li>
<li>Stacking datasets vertically using concatenation</li>
<li>Merging and joining tables on shared keys, similar to relational database operations</li>
<li>Grouping data and applying aggregation functions with groupby</li>
<li>Reshaping data into pivot table format for summary and cross-tabulation</li>
<li>String method vectorization for efficient text processing on Series</li>
<li>Time series functionality including resampling, rolling windows, and date-based indexing</li>
<li>Performance-oriented querying with eval() and query() for large DataFrames</li>
</ul>
</div>
</div>
<div class="week-card" style="margin-bottom:0;">
<div class="week-card-header">
<span class="week-num">MODULE 3</span>
<span class="week-title">Visualization with Matplotlib</span>
</div>
<div class="week-card-body">
<p>Matplotlib is Python's foundational plotting library. This module covers everything from basic charts to publication-quality figures and geospatial maps.</p>
<ul class="week-topics" style="padding-left:1.2rem;margin-bottom:0;">
<li>Creating and formatting line plots for continuous data</li>
<li>Building scatter plots and encoding additional dimensions through colour and size</li>
<li>Adding error bars and uncertainty bands to represent measurement variability</li>
<li>Drawing filled contour and density plots for two-dimensional distributions</li>
<li>Plotting histograms and kernel density estimates for univariate distributions</li>
<li>Controlling legend placement, content, and styling</li>
<li>Selecting and configuring colormaps for effective data encoding</li>
<li>Arranging multiple panels using subplots, GridSpec, and figure-level layout tools</li>
<li>Adding annotations, labels, and arrows to highlight features in a figure</li>
<li>Fine-grained control over axis tick locations and formatters</li>
<li>Applying style sheets and rcParams for consistent visual themes</li>
<li>Creating 3D line, scatter, and surface plots with mpl_toolkits.mplot3d</li>
<li>Plotting geographic data using Cartopy — map projections, coastlines, and gridlines</li>
<li>Statistical visualisation with Seaborn — distribution plots, categorical plots, pair plots, and heatmaps</li>
</ul>
</div>
</div>
</div>
<!-- LEARNING GOALS -->
<div class="info-card">
<div class="py-section-title">Learning Goals</div>
<div class="goal-item">
<div class="goal-icon">🔢</div>
<div style="font-size:0.9rem;color:var(--text);">Understand how <strong>NumPy arrays</strong> work under the hood — shape, dtype, broadcasting, and advanced indexing.</div>
</div>
<div class="goal-item">
<div class="goal-icon">🗂️</div>
<div style="font-size:0.9rem;color:var(--text);">Use <strong>Pandas</strong> to load, clean, merge, group, and summarise real-world datasets confidently.</div>
</div>
<div class="goal-item">
<div class="goal-icon">📈</div>
<div style="font-size:0.9rem;color:var(--text);">Create publication-quality charts with <strong>Matplotlib</strong> — from line plots and scatter plots to 3D surfaces.</div>
</div>
<div class="goal-item">
<div class="goal-icon">🌍</div>
<div style="font-size:0.9rem;color:var(--text);">Plot geographic data with <strong>Cartopy</strong> — map projections, coastlines, and gridlines.</div>
</div>
<div class="goal-item" style="margin-bottom:0;">
<div class="goal-icon">📊</div>
<div style="font-size:0.9rem;color:var(--text);">Build statistical visualisations with <strong>Seaborn</strong> — distributions, pair plots, heatmaps, and categorical plots.</div>
</div>
</div>
<!-- FEEDBACK -->
<div class="info-card">
<div class="py-section-title">What Learners Say</div>
<p style="font-size:0.82rem;color:var(--muted);margin-bottom:1.25rem;">Feedback from previous Python course participants</p>
<div class="feedback-card">
<div class="fb-text">"The course is excellent and it gave me the idea about how Python works. As a beginner your way of teaching and classes are excellent."</div>
<div class="fb-meta">
<div class="fb-avatar">GP</div>
<div class="fb-name">Girikrishnan P R</div>
<div class="fb-stars">★★★★★</div>
</div>
</div>
<div class="feedback-card">
<div class="fb-text">"It encouraged me to think deeply, analyse ideas from different perspectives, and develop greater confidence in my learning journey. Deep knowledge, better structure of modules, simple language usage, clarity, and patience."</div>
<div class="fb-meta">
<div class="fb-avatar">SK</div>
<div class="fb-name">Sajna K</div>
<div class="fb-stars">★★★★★</div>
</div>
</div>
<div class="feedback-card">
<div class="fb-text">"Sessions were very nice and discussion of the concepts was very useful. The clarity in explaining concepts stood out."</div>
<div class="fb-meta">
<div class="fb-avatar">EP</div>
<div class="fb-name">Elsa Paulson</div>
<div class="fb-stars">★★★★★</div>
</div>
</div>
<div class="feedback-card" style="margin-bottom:0;">
<div class="fb-text">"The best aspect of the course was the clear explanation of concepts and the practical approach to learning. Sir made complex topics easy to understand."</div>
<div class="fb-meta">
<div class="fb-avatar">KV</div>
<div class="fb-name">K S Vaishnavi</div>
<div class="fb-stars">★★★★☆</div>
</div>
</div>
</div>
</div>
<!-- RIGHT COLUMN -->
<div class="col-lg-5">
<div style="position:sticky;top:80px;">
<!-- DETAILS + REGISTER -->
<div class="info-card" style="border-color:var(--py-green);border-width:2px;">
<div class="py-section-title">Course Details</div>
<table class="details-table w-100">
<tr><td>📅 Date</td><td><strong>July 13 - August 7, 2026</strong></td></tr>
<tr><td>🕐 Timing</td><td>Evening · Weekdays</td></tr>
<tr><td>⏱ Per Session</td><td>1.5 hrs (1 hr + 0.5 hr coding)</td></tr>
<tr><td>💰 Price</td><td><strong>₹1,800</strong></td></tr>
<tr><td>💻 Mode</td><td>Online — Live + Recorded</td></tr>
<tr><td>📼 Recordings</td><td>After each session</td></tr>
<tr><td>👥 Batch Size</td><td>15 – 20 students</td></tr>
<tr><td>🗣️ Language</td><td>Malayalam</td></tr>
<tr><td>🎯 For</td><td>Students, researchers, teachers</td></tr>
</table>
<a href="https://forms.gle/DwkRW7iUvzZxNq2V9" target="_blank" class="btn-register mt-4">
Register Now <i class="bi bi-arrow-right ms-2"></i>
</a>
<p style="font-size:0.78rem;color:var(--muted);text-align:center;margin-top:0.6rem;margin-bottom:0;">Limited seats — 15 to 20 per batch</p>
</div>
<!-- TOOLS & LIBRARIES -->
<div class="info-card">
<div class="py-section-title">Tools & Libraries</div>
<div>
<span class="tool-pill">NumPy</span>
<span class="tool-pill">Pandas</span>
<span class="tool-pill">Matplotlib</span>
<span class="tool-pill">Seaborn</span>
<span class="tool-pill">Cartopy</span>
</div>
<p style="font-size:0.82rem;color:var(--muted);margin-top:0.85rem;margin-bottom:0;">All tools are free and open-source. A working Python installation (Jupyter Notebook) is all you need.</p>
</div>
<!-- PREREQUISITE -->
<div class="info-card">
<div class="py-section-title">Prerequisite</div>
<p style="font-size:0.88rem;color:var(--text);margin-bottom:0.85rem;">Basic familiarity with Python is helpful but not strictly required. If you are new to programming, consider starting with:</p>
<a href="python_course.html" style="display:flex;align-items:center;gap:0.75rem;padding:0.85rem 1rem;background:var(--py-green-light);border:1px solid var(--py-green-accent);border-radius:10px;text-decoration:none;">
<div style="width:36px;height:36px;background:var(--py-green);color:#fff;border-radius:8px;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0;">1</div>
<div>
<div style="font-weight:600;font-size:0.9rem;color:var(--py-green);">Python for Absolute Beginners</div>
<div style="font-size:0.78rem;color:var(--muted);">Completed · Available on request</div>
</div>
<i class="bi bi-arrow-right ms-auto" style="color:var(--py-green);"></i>
</a>
</div>
</div><!-- /sticky wrapper -->
</div>
</div>
</div>
</div>
<footer class="site-footer">
<div class="container text-center">
<p class="footer-name">Sarath Prabhavu J.</p>
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