DOC: Emphasize NumPy in Ecosystem openers

layouts/partials/array-libraries.html

@@ -1,17 +1,16 @@
 <!-- Array libraries Tab Content -->
 <li class="array-libraries">
     <p>
-        Numpy array forms the core of the organically growing numeric
-        Python <b>array library</b> ecosystem that now supports GPUs, sparse,
-        distributed arrays and more.
-    </p>
-    <p>
-        Several of these newer libraries such as CuPy, Sparse and Dask,
-        implement the NumPy API adding support for modern user cases,
-        newer hardware and higher scalability of array computing. Other
-        array libraries such as Xarray, TensorLy consume NumPy API and
-        build newer functionality on top of it, thus enhancing array
-        computing in Python beyond Numpy capabilities.
+        When libraries emerge to exploit new
+        hardware technologies and architectures, they take
+        NumPy as their starting point.
+        <a href="https://cupy.chainer.org">CuPy</a>,
+        <a href="https://sparse.pydata.org/en/latest/">Sparse</a>, and
+        <a href="https://dask.org/">Dask</a>
+        implement the NumPy API with support for modern user cases and
+        scalable hardware;
+        <a href="https://xarray.pydata.org/en/stable/index.html">Xarray</a> and
+        <a href="http://tensorly.org/stable/home.html">Tensor.ly</a> add newer functionality.
     </p>
     <table>
         <tr class="highlight-th">
@@ -56,7 +55,7 @@
                 astronomy, satellite imagery and mobile network modeling.</td>
         </tr>
         <tr>
-            <td><img class="first-column-layout" src="images/content_images/arlib/CuPy.png" alt="CuPy"></td>
+            <td><img class="first-column-layout" src="images/content_images/arlib/cupy.png" alt="CuPy"></td>
             <td class="full-center-text"><a href="https://cupy.chainer.org">CuPy</a></td>
             <td class="left-text">NumPy-compatible matrix library accelerated by CUDA used to implement Neural Networks
                 for Deep Learning.</td>
@@ -82,7 +81,7 @@
         </tr>
         <tr>
             <td><img class="first-column-layout" src="images/content_images/arlib/xtensor.png" alt="xtensor"></td>
-            <td class="full-center-text"><a href="" https://github.com/xtensor-stack/xtensor-python>xtensor </a> </td>
+            <td class="full-center-text"><a href="https://github.com/xtensor-stack/xtensor-python">xtensor</a> </td>
                     <td class="left-text">Multi-dimensional arrays with broadcasting and lazy computing for numerical
                     analysis.</td>
         </tr>

layouts/partials/data-science.html

@@ -8,27 +8,44 @@
         </div>
         <div>
             <p>
-                Data Science makes it possible to analyze massive amounts of data
-                and gain meaningful insights. A typical data science workflow involves
-                various techniques and tools such as:
+                NumPy lies at the core of a rich ecosystem of data science libraries.
+            </p>
+            <p>
+                Data science is the analysis of massive amounts of data
+                to gain insight. A typical workflow might be:
+
                 <ul class="content-tab">
-                    <li><b>Extract, Transform, Load (ETL):</b> Pandas, Beautiful Soup, Intake</li>
-                    <li><b>Explore:</b> Seaborn, Matplotlib</li>
-                    <li><b>Model:</b> Scikit-learn, SciPy, statsmodels</li>
-                    <li><b>Evaluate:</b> NumPy, TensorFlow </li>
+                    <li><b>Extract, Transform, Load (ETL):</b>
+                        <a href="https://pandas.pydata.org">Pandas</a>,
+                        <a href="https://www.crummy.com/software/BeautifulSoup/">Beautiful Soup</a>,
+                        <a href="https://intake.readthedocs.io/en/latest/"> Intake</a>
+                    </li>
+
+                    <li><b>Explore:</b>
+                        <a href="https://seaborn.pydata.org"> Seaborn</a>,
+                        <a href="https://matplotlib.org">Matplotlib</a>,
+
+                    </li>
+
+                    <li><b>Model:</b>
+                        <a href="https://scikit-learn.org">scikit-learn</a>,
+                        <a href="https://www.scipy.org">SciPy</a>,
+                        <a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>.
+                    </li>
+
+                    <li><b>Evaluate:</b>
+                        NumPy,
+                        <a href="https://www.tensorflow.org">TensorFlow</a>
+                    </li>
+
                     <li>
-                        <b>Presentation:</b>
+                        <b>Display:</b>
                         <a href="./index.html/#tab-visual"> Data Visualization Tools</a>
                     </li>
                 </ul>
             </p>
         </div>
     </div>
-    <p>
-        Python has a rich ecosystem of libraries that enable Data Science 
-        workflows. <b> NumPy</b> is the foundation of almost all of these tools
-        such as Pandas, Seaborn, Beautiful Soup and several others.
-    </p>
     <div class="grid-container">
         <div>
             <p>
@@ -37,13 +54,13 @@
                 data access and distribution, while
                 <a href="https://www.crummy.com/software/BeautifulSoup/">Beautiful Soup</a>
                 is widely used for web-scraping and gathering data sets.
-                <a href="https://seaborn.pydata.org"> Seaborn</a> is well known for its
-                <a href="https://towardsdatascience.com/how-to-perform-exploratory-data-analysis-with-seaborn-97e3413e841d">exploratory data analysis (EDA)</a>
-                capabilities, <a href="https://scikit-learn.org">Scikit-learn</a> and
-                <a href="https://www.scipy.org">Scipy</a> (statistical computing) serve some
+                <a href="https://seaborn.pydata.org"> Seaborn</a> is well known for
+                <a href="https://towardsdatascience.com/how-to-perform-exploratory-data-analysis-with-seaborn-97e3413e841d">exploratory data analysis (EDA)</a>;
+                <a href="https://scikit-learn.org">scikit-learn</a> and
+                <a href="https://www.scipy.org">SciPy</a> (statistical computing) serve some
                 of the backbone processes required for machine learning (regression methods,
                 classification, clustering, model validation and selection).
-                Statistical data exploration, estimation of various statistical models
+                Statistical data exploration, estimation of various statistical models,
                 and conducting statistical tests are some of the functions offered by
                 <a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>.
             </p>
@@ -53,11 +70,11 @@
         </div>
     </div>
     <p>
-        Effective data analytics require deep knowledge of the data domain (e.g.,
-        Retail, Healthcare, Marketing, Finance, Social Media, Automation, Sales, Travel,
-        etc.) as well as other core disciplines of Data Science, Data Engineering and
-        Data Visualization. Tools such as <a href="https://mlflow.org">MLFlow</a> address
-        experiment hyper-parameter and result tracking needs, while
+        Effective data analytics requires deep knowledge of the data domain (e.g.,
+        retail, healthcare, marketing, finance, social media, automation, sales, travel,
+        etc.) as well as other core disciplines of data science, data engineering, and
+        data visualization. Tools such as <a href="https://mlflow.org">MLFlow</a> address
+        experiment hyperparameter and result tracking needs, while
         <a href="https://dvc.org"> DVC</a> provides data version control for data science
         and machine learning workflows.
     </p>

layouts/partials/machine-learning.html

@@ -13,29 +13,21 @@
         </div>
         <div>
             <p>
-                <b>Machine learning</b> (ML) enables computers to learn using
-                data, without having to be explicitly programmed.
-                <b>NumPy</b> is the foundation of all data pre-processing
-                that happens in the implementation of several ML Algorithms.
-            </p>
-            <p>
-                Python’s rich machine language and deep learning ecosystem
-                provides powerful tools such as
-                <a href="https://scikit-learn.org/stable/">Scikit-learn</a>
-                that is built on top of NumPy and
-                <a href="https://www.scipy.org">SciPy</a> and offers data
-                mining and analytics using classical ML algorithms.
-            </p>
-            <p>
-                <a href="https://www.tensorflow.org">Tensorflow’s</a>
-                deep learning capabilities help to define and run
-                computations involving tensors that have broad
-                applications in Speech and image recognition, Text-based
-                applications, Time-Series analysis and Video Detection.
-                <a href="https://pytorch.org">PyTorch </a> is another deep
-                learning library that is very popular among researchers for
-                computer vision and NLP applications. <a href="https://github.com/apache/incubator-mxnet">MXNet</a>
-                is another AI package that provides blueprints and
+                NumPy forms the basis of powerful machine learning libraries
+                like
+                <a href="https://scikit-learn.org">scikit-learn</a> and
+                <a href="https://www.scipy.org">SciPy</a>.
+                As machine learning grows, so does the
+                list of libraries built on NumPy.
+                <a href="https://www.tensorflow.org">TensorFlow’s</a>
+                deep learning capabilities have broad
+                applications &mdash; among them speech and image recognition, text-based
+                applications, time-series analysis, and video detection.
+                <a href="https://pytorch.org">PyTorch</a>, another deep
+                learning library, is popular among researchers in
+                computer vision and natural language processing.
+                <a href="https://github.com/apache/incubator-mxnet">MXNet</a>
+                is another AI package, providing blueprints and
                 templates for deep learning.
             </p>
         </div>
@@ -44,15 +36,15 @@
         <div>
             <p>
                 Statistical techniques called
-                <a href="https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205">Ensemble</a>
+                <a href="https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205">ensemble</a>
                 methods such as binning,
-                bagging, stacking and boosting are widely used in various ML
+                bagging, stacking, and boosting are among the ML
                 algorithms implemented by tools such as
                 <a href="https://github.com/dmlc/xgboost">XGBoost</a>,
-                <a href="https://lightgbm.readthedocs.io/en/latest/">LightGBM</a>,
-                <a href="https://catboost.ai">CatBoost</a> - one of the
+                <a href="https://lightgbm.readthedocs.io/en/latest/">LightGBM</a>, and
+                <a href="https://catboost.ai">CatBoost</a> &mdash; one of the
                 fastest inference engines.
-                <a href="https://www.scikit-yb.org/en/latest/">Yellowbrick</a>,
+                <a href="https://www.scikit-yb.org/en/latest/">Yellowbrick</a> and
                 <a href="https://eli5.readthedocs.io/en/latest/">Eli5</a>
                 offer machine learning visualizations.
             </p>

layouts/partials/scientific-domains.html

@@ -1,14 +1,12 @@
 <!-- Scientific Domains Tab Content -->
 <li class="scientific-domains">
     <p>
-        Data acquisition (experimental, simulation), processing and
-        visualization are the core data related tasks in almost all the
-        scientific domains. Visualization of results through high quality
-        figures make scientific reports and publications easy to
-        understand. Python is easier to learn and computationally
-        efficient for scientific computing. NumPy, SciPy and Matplotlib
-        form the core Python packages that are used across various
-        scientific domains.
+        Nearly every scientist working in Python draws on the power of NumPy.
+    </p>
+    <p>
+        NumPy brings the computational power of languages like C and Fortran
+        to Python, a language much easier to learn and use. With this power
+        comes simplicity: a solution in NumPy is often clear and elegant.
     </p>
     <!-- First Row -->
     <table>
@@ -99,7 +97,7 @@
             <td class="center-text"></td>
             <td class="center-text"></td>
             <td class="center-text"><a
-                    href="https://towardsdatascience.com/easy-steps-to-plot-geographic-data-on-a-map-python-11217859a2db">NumPy</a>
+                href="https://towardsdatascience.com/easy-steps-to-plot-geographic-data-on-a-map-python-11217859a2db">NumPy</a>
             </td>
             <td class="center-text"></td>
         </tr>
@@ -122,9 +120,4 @@
             <td class="lastrow-center-text"></td>
         </tr>
     </table>
-    <p>
-        NumPy’s powerful array processing capabilities and elegant syntax
-        helps to clearly and efficiently express computational algorithms
-        in various scientific computing domains.
-    </p>
 </li>

layouts/partials/visualization.html

@@ -54,47 +54,24 @@
         </div>
         <div>
             <p>
-                <a href="https://www.slideshare.net/Visage/data-visualization-101-how-to-design-chartsandgraphs">Data
-                    Visualization</a>
-                exposes patterns, trends and correlations in textual-data, making it easier for humans to
-                analyse and interpret large volumes of data.
-            </p>
-            <p>
-                <a href="https://python-graph-gallery.com">Visualization elements</a>
-                such as bar graphs, pie charts, line charts, maps, infographics, dashboards,
-                geographic maps, heatmaps, and interactive images offer valuable
-                insights for making data-driven decisions.
-            </p>
-        </div>
-        <div>
-            <p>
-                NumPy is the key data transformation building block for the burgeoning
-                <a href="https://pyviz.org/overviews/index.html">Python visualization landscape</a> comprising of
+                NumPy is an essential component in the burgeoning
+                <a href="https://pyviz.org/overviews/index.html">Python visualization landscape</a>, which includes
                 <a href="https://matplotlib.org">Matplotlib</a>,
                 <a href="https://seaborn.pydata.org">Seaborn</a>,
                 <a href="https://plot.ly">Plotly</a>,
                 <a href="https://altair-viz.github.io">Altair</a>,
                 <a href="https://docs.bokeh.org/en/latest/">Bokeh</a>,
                 <a href="http://holoviz.org">Holoviz</a>,
-                <a href="http://vispy.org">Vispy</a> and
+                <a href="http://vispy.org">Vispy</a>, and
                 <a href="https://github.com/napari/napari">Napari</a>,
                 to name a few.
             </p>
             <p>
-                By performing parallel operations on large arrays, all at once, NumPy accelerates data-processing and
-                visualization of large quantities of data, beyond Python's native performance levels, for data
-                visualization at
-                scale.
+                NumPy's accelerated processing of large arrays allows researchers to visualize
+                datasets far larger than native Python could handle.
             </p>
         </div>
         <div>
-            <p>
-                <a href="https://rougier.github.io/python-visualization-landscape/landscape-colors.png">
-                    <img src="images/content_images/vis-landscape.png"
-                        alt="Mindmap linking several concepts, such as Javascript, Matplotlib, d3js and OpenGL."
-                        align="left">
-                </a>
-            </p>
         </div>
     </div>
-</li>
+</li>