Add datacamp resources

src/components/TopicDetail/TopicDetail.tsx

@@ -399,6 +399,10 @@ export function TopicDetail(props: TopicDetailProps) {
 
   const shouldShowAiTab = !isCustomResource && resourceType === 'roadmap';
 
+  const hasDataCampResources = paidResources.some((resource) =>
+    resource.title.toLowerCase().includes('datacamp'),
+  );
+
   return (
     <div className={cn('relative z-92', wrapperClassName)}>
       <div
@@ -548,6 +552,29 @@ export function TopicDetail(props: TopicDetailProps) {
                     </>
                   )}
 
+                  {resourceId === 'ai-data-scientist' &&
+                    hasDataCampResources && (
+                      <div className="mt-5 rounded-md bg-yellow-100 px-4 py-3 text-sm text-gray-600">
+                        <p className="text-balance">
+                          Follow the resources listed on the roadmap or check
+                          out the premium courses by DataCamp listed below.
+                        </p>
+
+                        <p className="mt-3 text-balance">
+                          They also have an{' '}
+                          <a
+                            href="https://datacamp.pxf.io/POk5PY"
+                            className="font-medium text-blue-600 underline hover:text-blue-800"
+                            target="_blank"
+                          >
+                            Associate Data Scientist in Python
+                          </a>{' '}
+                          track that covers all the key data scientist skills in
+                          one place.
+                        </p>
+                      </div>
+                    )}
+
                   {links.length > 0 && (
                     <>
                       <ResourceListSeparator

src/data/roadmaps/ai-data-scientist/content/machine-learning@kBdt_t2SvVsY3blfubWIz.md

@@ -1,9 +1,3 @@
 # Machine Learning
 
-Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.
-
-Learn more from the following resources:
-
-- [@article@Advantages and Disadvantages of AI](https://medium.com/@laners.org/advantages-and-disadvantages-of-artificial-intelligence-cd6e42819b20)
-- [@article@Reinforcement Learning 101](https://medium.com/towards-data-science/reinforcement-learning-101-e24b50e1d292)
-- [@article@Understanding AUC-ROC Curve](https://medium.com/towards-data-science/understanding-auc-roc-curve-68b2303cc9c5)
+Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

src/data/roadmaps/ai-data-scientist/content/mathematics@aStaDENn5PhEa-cFvNzXa.md

@@ -1,6 +1,3 @@
 # Mathematics
 
 Mathematics is the foundation of AI and Data Science. It is essential to have a good understanding of mathematics to excel in these fields.
-
-- [Mathematics for Machine Learning](https://imp.i384100.net/baqMYv)
-- [Algebra and Differential Calculus](https://imp.i384100.net/LX5M7M)

src/data/roadmaps/ai-data-scientist/content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md

@@ -1,5 +1,3 @@
 # Statistics
 
-Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples.
-
-Learn more from the resources given on the roadmap.
+Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples.