chore: sync content to repo (#10065)

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src/data/roadmaps/data-analyst/content/apis@4DFcXSSHxg5wv0uXLIRij.md

@@ -4,5 +4,6 @@ Application Programming Interfaces, better known as APIs, play a fundamental rol
 
 Visit the following resources to learn more:
 
+- [@roadmap@Visit the Dedicated API Design Roadmap](https://roadmap.sh/api-design)
 - [@article@What is an API?](https://aws.amazon.com/what-is/api/)
 - [@article@A Beginner's Guide to APIs](https://www.postman.com/what-is-an-api/)

src/data/roadmaps/data-analyst/content/bar-charts@EVk1H-QLtTlpG7lVEenDt.md

@@ -1,6 +1,6 @@
-# Bar Charts in Data Visualization
-
-As a vital tool in the data analyst's arsenal, bar charts are essential for analyzing and interpreting complex data. Bar charts, otherwise known as bar graphs, are frequently used graphical displays for dealing with categorical data groups or discrete variables. With their stark visual contrast and definitive measurements, they provide a simple yet effective means of identifying trends, understanding data distribution, and making data-driven decisions. By analyzing the lengths or heights of different bars, data analysts can effectively compare categories or variables against each other and derive meaningful insights effectively. Simplicity, readability, and easy interpretation are key features that make bar charts a favorite in the world of data analytics.
+# Bar Charts
+ 
+Bar charts display categorical data with rectangular bars whose lengths represent values. They are used to compare quantities across different categories. Horizontal bar charts work well for long category names; vertical bar charts (column charts) are standard for time-based comparisons.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/big-data-concepts@m1IfG2sEedUxMXrv_B8GW.md

@@ -1,6 +1,6 @@
 # Big Data Concepts
-
-Big data refers to extremely large and complex data sets that traditional data processing systems are unable to manage effectively. For data analysts, understanding the big data concepts is crucial as it helps them gain insights, make decisions, and create meaningful presentations using these data sets. The key concepts include volume, velocity, and variety - collectively known as the 3Vs. Volume refers to the amount of data, velocity is the speed at which data is processed, and variety indicates the different types of data being dealt with. Other advanced concepts include variability and veracity. These concepts provide a framework for understanding and working with big data for data analysts. With the growing importance of big data in various industries and sectors, a comprehensive grasp of these concepts equips a data analyst to more effectively and efficiently analyze and interpret complex data sets.
+ 
+Big data concepts describe the properties and challenges of working with very large datasets. The three Vs (volume, velocity, variety) capture the main dimensions: how much data there is, how fast it arrives, and how many formats it comes in. Understanding these concepts helps in choosing appropriate storage and processing technologies.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/big-data-technologies@_aUQZWUhFRvNu0MZ8CPit.md

@@ -1,6 +1,6 @@
-# Big Data and Data Analyst
-
-In the modern digitized world, Big Data refers to extremely large datasets that are challenging to manage and analyze using traditional data processing applications. These datasets often come from numerous different sources and are not only voluminous but also diverse in nature, including structured and unstructured data. The role of a data analyst in the context of big data is crucial. Data analysts are responsible for inspecting, cleaning, transforming, and modeling big data to discover useful information, conclude and support decision-making. They leverage their analytical skills and various big data tools and technologies to extract insights that can benefit the organization and drive strategic business initiatives.
+# Big Data Technologies
+ 
+Big data technologies handle datasets that are too large or complex for traditional tools to process. The defining characteristics of big data are often described as volume, velocity, and variety. Distributed processing frameworks like Hadoop and Spark allow computation to be spread across clusters of machines.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/central-tendency@BJTVa4ur_bJB7mMtD2-hQ.md

@@ -1,6 +1,6 @@
 # Central Tendency
-
-Descriptive analysis is a significant branch in the field of data analytics, and under this, the concept of Central Tendency plays a vital role. As data analysts, understanding central tendency is of paramount importance as it offers a quick summary of the data. It provides information about the center point around which the numerical data is distributed. The three major types of the central tendency include the Mean, Median, and Mode. These measures are used by data analysts to identify trends, make comparisons, or draw conclusions. Therefore, an understanding of central tendency equips data analysts with essential tools for interpreting and making sense of statistical data.
+ 
+Measures of central tendency describe the center or typical value of a dataset. The three main measures are mean (arithmetic average), median (middle value), and mode (most frequent value). The choice between them depends on the data's distribution and whether outliers are present.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/charting@Vk3JErqxpnPY44iyfkLMl.md

@@ -1,6 +1,6 @@
 # Charting
-
-Excel serves as a powerful tool for data analysts when it comes to data organization, manipulation, recovery, and visualization. One of the incredible features it offers is 'Charting'. Charting essentially means creating visual representations of data, which aids data analysts to easily understand complex data and showcase compelling stories of data trends, correlations, and statistical analysis. These charts vary from simple bar graphs to more complex 3D surface and stock charts. As a data analyst, mastering charting under Excel substantially enhances data interpretation, making it easier to extract meaningful insights from substantial data sets.
+ 
+Excel charts turn tabular data into visual representations like bar charts, line charts, pie charts, and scatter plots. Charts are created directly from selected data and can be formatted and embedded in reports and dashboards. Knowing which chart type suits different data and messages is an important analytical skill.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/cleanup@nC7tViln4UyQFYP_-fyjB.md

@@ -1,6 +1,6 @@
 # Cleanup
-
-The Cleanup of Data is a critical component of a Data Analyst's role. It involves the process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision making. This process is crucial for Data Analysts to generate accurate and significant insights from data, ultimately resulting in better and more informed business decisions. A solid understanding of data cleanup procedures and techniques is a fundamental skill for any Data Analyst. Hence, it is necessary to hold a high emphasis on maintaining data quality by managing data integrity, accuracy, and consistency during the data cleanup process.
+ 
+Data cleanup (or data cleaning) is the process of identifying and correcting errors, inconsistencies, and missing values in a dataset. Common tasks include handling null values, removing duplicates, fixing formatting issues, and standardizing categories. Clean data is a prerequisite for accurate analysis.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/cnns@XlCv2OirEfuBFa4ranyyE.md

@@ -1,6 +1,6 @@
 # CNNs
-
-Convolutional Neural Networks (CNNs) form an integral part of deep learning frameworks, particularly within the realm of image processing. Data analysts with a focus on deep learning applications often turn to CNNs for their capacity to efficiently process high-dimensional data, such as images, and extract critical features relevant to the problem at hand. As a powerful tool for modeling patterns in data, CNNs are frequently employed in applications ranging from image recognition to natural language processing (NLP). Understanding CNNs, therefore, provides a robust foundation for data analysts aspiring to harness the potential of deep learning techniques.
+ 
+Convolutional Neural Networks (CNNs) are a type of deep neural network designed for processing grid-structured data like images. They use convolutional layers to automatically learn spatial features such as edges, textures, and shapes. CNNs are the standard architecture for image classification, object detection, and computer vision tasks.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/collection@mDUSpexdq3ITV6qokwSJl.md

@@ -1,6 +1,6 @@
-# Data Collection
-
-In the realm of data analysis, the concept of collection holds immense importance. As the term suggests, collection refers to the process of gathering and measuring information on targeted variables in an established systematic fashion that enables a data analyst to answer relevant questions and evaluate outcomes. This step is foundational to any data analysis scheme, as it is the first line of interaction with the raw data that later transforms into viable insights. The effectiveness of data analysis is heavily reliant on the quality and quantity of data collected. Different methodologies and tools are employed for data collection depending on the nature of the data needed, such as surveys, observations, experiments, or scraping online data stores. This process should be carried out with clear objectives and careful consideration to ensure accuracy and relevance in the later stages of analysis and decision-making.
+# Collection
+ 
+Data collection is the process of gathering raw data from various sources for analysis. Sources can include databases, spreadsheets, APIs, web scraping, surveys, and sensor data. The quality and completeness of collected data directly affects the quality of any analysis downstream.
 
 Visit the following resources to learn more:
 

src/data/roadmaps/data-analyst/content/concat@SIiIfcy-jeiRoDlxw385V.md

@@ -1,6 +1,6 @@
-# Concatenation
-
-The term 'Concat' or ‘Concatenation’ refers to the operation of combining two or more data structures, be it strings, arrays, or datasets, end-to-end in a sequence. In the context of data analysis, a Data Analyst uses concatenation as a basic function to merge or bind data sets along an axis - either vertically or horizontally. This function is commonly used in data wrangling or preprocessing to combine data from multiple sources, handle missing values, and shape data into a form that fits better with analysis tools. An understanding of 'Concat' plays a crucial role in managing the complex, large data sets that data analysts often work with.
+# CONCAT
+ 
+`CONCAT` joins two or more text strings into one. It is the modern replacement for the `&` operator and the older `CONCATENATE` function. `CONCAT` is used to combine name fields, build labels, and merge data from multiple columns.
 
 Visit the following resources to learn more: