DOC: Tighten ecosystem->data science per numpy#242

Proposed edit per @rgommers comment in numpy#242:
"Still would like to make this tab a little more compact."

Author: bjnath

layouts/partials/data-science.html

@@ -8,74 +8,20 @@
         </div>
         <div>
             <p>
-                NumPy lies at the core of a rich ecosystem of data science libraries.
+                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>
-                        <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>Display:</b>
-                        <a href="./index.html/#tab-visual"> Data Visualization Tools</a>
-                    </li>
-                </ul>
-            </p>
-        </div>
-    </div>
-    <div class="grid-container">
-        <div>
-            <p>
-                <a href="https://pandas.pydata.org">Pandas </a>helps in data discovery and handling,
-                <a href="https://intake.readthedocs.io/en/latest/"> Intake</a> helps with
-                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
-                <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,
-                and conducting statistical tests are some of the functions offered by
-                <a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>.
-            </p>
-        </div>
-        <div>
-            <img src="images/content_images/data-science.png" alt="Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'." align="centre" width="75%">
+            <ul>
+                <li><a href="https://pandas.pydata.org">Pandas</a> for data discovery and handling</li>
+                <li><a href="https://intake.readthedocs.io/en/latest/"> Intake</a> for
+                data access and distribution</li>
+                <li><a href="https://www.crummy.com/software/BeautifulSoup/">Beautiful Soup</a>
+                for web-scraping and gathering data sets</li>
+                <li><a href="https://seaborn.pydata.org"> Seaborn</a> for
+                    <a href="https://towardsdatascience.com/how-to-perform-exploratory-data-analysis-with-seaborn-97e3413e841d">exploratory data analysis (EDA)</a></li>
+                <li><a href="https://scikit-learn.org">scikit-learn</a> and
+                    <a href="https://www.scipy.org">SciPy</a> for
+                    backbone processes of machine learning</li>
+                <li><a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>
+                    for statistical tests.</li>
+            </ul>
         </div>
-    </div>
-    <p>
-        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>
-</li>