Updated data visualization tab content with visualization libraries used along with NumPy ref numpy#43 numpy#64

Author: shaloo

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-                <img src="images/content_images/visualization.gif" alt="visualization figure" align="left" height="300" width="300" style="margin: 10px 25px; border-radius: 6px"> 
-                <p>Data Visualization refers to graphical representation of textual data that makes it human-readable, easier to understand and interpret by exposing patterns, trends and correlations within the data. Data visualization paves the way towards absorbing data and gaining actionable insights. It enables advanced data analytics for identifying areas that need attention and improvement. Users can make data-driven decisions and analyse huge volumes of data through visual elements such as graphs, bar, pie and line charts, maps, infographics, dashboards, geographic maps, heatmaps etc.</p>
-                <p>Data Scientists rely upon data visualization to manipulate, analyse and query large volumes of data using interactive images, visual analytics and gain useful insights into the data that cannot be otherwise detected in textual form.</p>
-                <p><mark>NumPy</mark> is the key building block of all heavyweight Python data processing and visualization libraries. Python’s native performance with large quantities of data is relatively slow, but NumPy, implemented by low-level libraries written in C and Fortran, performs parallel operations on large arrays all at once, making data-processing and visualization very fast.</p>
+                <a href="https://demo.bokeh.org/surface3d"><img src="images/content_images/bokeh_visualization.png" alt="visualization figure" align="left" height="300" width="300" style="margin: 10px 25px; border-radius: 6px"></a> 
+                <p><a href="https://www.slideshare.net/Visage/data-visualization-101-how-to-design-chartsandgraphs">Data Visualization</a> makes textual data human-readable, easier to understand and interpret through graphical representation. By exposing patterns, trends and correlations within large amounts of data, <a href="https://medium.com/swlh/effective-visualization-of-multi-dimensional-data-a-hands-on-approach-b48f36a56ee8">visualization</a> highlights areas that need attention and improvement. With these data insights, users can make data-driven decisions by analyzing huge volumes of data through visual elements such as graphs, bar, pie and line charts, maps, infographics, dashboards, geographic maps, heatmaps, interactive images, visual analytics etc. Data Scientists rely upon data visualization to gain useful insights into the data that cannot be otherwise detected in textual form.</p>
+                <p>NumPy is the <a href="https://www.analyticsvidhya.com/blog/2015/04/comprehensive-guide-data-exploration-sas-using-python-numpy-scipy-matplotlib-pandas/">key building block</a> of all heavyweight Python <a href="http://jalammar.github.io/visual-numpy/">data processing</a> and visualization libraries. Python’s native performance with large quantities of data is relatively slow, but NumPy, implemented by low-level libraries written in C and Fortran, performs parallel operations on large arrays all at once, making data-processing and visualization very fast.</p>
+                <p>Python has some of the most popular interactive <a href="https://towardsdatascience.com/data-visualization-with-mathplotlib-using-python-a7bfb4628ee3">data visualisation</a> tools such as <a href="https://matplotlib.org/">Matplotlib</a>. <a href="https://seaborn.pydata.org">Seaborn</a>,based on Matplotlib, is tightly integrated with PyData stack (NumPy, <a href="https://pandas.pydata.org">Pandas</a>) and offers high level interfaces for drawing attractive and informative statistical graphics. <a href="http://vispy.org">Vispy</a> and <a href="https://github.com/napari/napari">Napari</a> provide fast, scalable, interactive multi-dimensional image analytics and scientific visualization. <a href="https://plot.ly">Plotly's</a> Python graphing library makes interactive, publication-quality graphs with its unique contour plots, dendrograms, and 3D charts functionalities. <a href="https://altair-viz.github.io">Altair</a>, based on <a href="http://vega.github.io/vega">Vega</a> and <a href="http://vega.github.io/vega-lite">Vega-Lite</a>, provides declarative statistical visualization library for Python while <a href="https://docs.bokeh.org/en/latest/">Bokeh</a> is known for its dynamic, interactive visualization for web browser-based plotting.</p>
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-<p>Image Source: https://www.pinterest.com/pin/634866878694365512/</p>
+<p>Image Source: https://demo.bokeh.org/surface3d</p>
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