Technical Documentation Page - Build a Technical Documentation Page

**I am working on the tech doc and keep having the same error.not sure what to do?
Each .main-section should have an id that matches the text of its first child, having any spaces in the child’s text replaced with underscores (_ ) for the id’s.
You should have the same number of .nav-link and .main-section elements.

  • Each .nav-link should have text that corresponds to the header text of its related section (e.g. if you have a “Hello world” section/header, your #navbar should have a .nav-link which has the text “Hello world”).

  • Failed:Each .nav-link should have an href attribute that links to its corresponding .main-section (e.g. If you click on a .nav-link element that contains the text “Hello world”, the page navigates to a section element with that id).**


<!DOCTYPE html>
<html lang="en">
    <meta charset="UTF-8" />
    <link rel="stylesheet" href="styles.css" />
    <title>Technical Documentation Page</title>
    <nav id="navbar">
      <header><br />What Are Technical Skills?</header>
      <hr />
      <a href="#introduction" class="nav-link">Introduction</a><br />
      <hr />
      <a href="#definitions" class="nav-link">Definitions</a><br />
      <hr />
      <a href="#technical skills examples" class="nav-link">Technical skills examples</a><br />
      <hr />
      <a href="#soft skills" class="nav-link">soft skills</a><br />
   <hr />
      <a href="#technical skills" class="nav-link">Technical skills</a<br>   
    <hr />
      <a href="#cybersecurity" class="nav-link">Cybersecurity</a
      ><br />
      <hr />
      <a href="#data science " class="nav-link">Data science</a><br />
      <hr />
      <a href="#finance " class="nav-link">Finance </a><br />
    <main id="main-doc">
      <section class="main-section" id="introduction">
          Welcome to a basic introduction of technical skills.We will help you grasp what technical skills are, which technical skills employers are looking for, how to improve your technical skills, and how to list them on a resume.
      <section class="main-section" id="definitions">
         What are technical skills?
            <b>Technical skills:</b> They are also known as hard skills, are qualities acquired by using and gaining expertise in performing physical or digital tasks. 
            <b>Programming languages:</b> A system of notation for writing computer programs
            <b>Common operating systems:</b> An operating system (OS) is the program that, after being initially loaded into the computer by a boot program, manages all of the other application programs in a computer. The application programs make use of the operating system by making requests for services through a defined application program interface (API).
      <section class="main-section" id="technical skills examples">
        <header>Technical skills examples</header>
         Technical skills, sometimes referred to as hard skills, involve the practical knowledge you use in order to complete tasks. Some examples of technical skills are:<br /><br />
          <code>Data analysis </code><br /><br />
          Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.
          <li><b>Web development:</b> Web programming, also known as web development, is the creation of dynamic web applications. Examples of web applications are social networking sites like Facebook or e-commerce sites like Amazon.</li>
            <b>Expression:</b> There are two expressions in this example. They
            are "x+5" and "12".
            <b>Computer programming languages:</b> A programming language is a type of written language that tells computers what to do. Examples are: Python, Ruby, Java, JavaScript, C, C++, and C#. Programming languages are used to write all computer programs and computer software.
      <section class="main-section" id="soft skills">
        <header>Soft Skills</header>
          Soft skills are non-technical skills that relate to how you work. They include how you interact with colleagues, how you solve problems, and how you manage your work.<br /><br />
          <code>Key Takeaways</code><br /><br />
          Soft skills are non-technical skills that impact your performance in the workplace.
          You likely already have soft skills from your school and work experience.
          You can also develop soft skills at work, school, volunteer activities, and by participating in training programs and classes.
         Include your soft skills in your resume and cover letter.
            >Demonstrate your soft skills during job interviews.</b
          ><br />How Soft Skills Work<em>Soft skills relate to how you work. </em>, Soft skills include interpersonal (people) skills, communication skills, listening skills, time management, problem-solving, leadership, and empathy, among others.
          They are among the top skills employers seek in the candidates they hire because soft skills are important for just about every job.<br /><br />
          <code> Example of Soft Skills</code><br /><br />
          Soft skills are particularly important in customer-based jobs, for example. These employees are in direct contact with customers. It takes several soft skills to be able to listen to a customer and provide that customer with helpful and polite service. <b>Note</b> Even if you're not in a client-facing role, you need to be able to get along with co-workers, managers, vendors, and other people you interact with at work.<br /><br />
          <code>Types of Soft Skills/code><br></code><br />
          Soft skills include the personal attributes, personality traits, and communication abilities needed for success on the job. Soft skills characterize how a person interacts in his or her relationships with others.
          <h1>Soft skills include:</h1>
           <li>Adaptability </li>
<li> Communication </li>
<li> Compromise</li>
<li> Creative thinking</li>
<li> Dependability</li>
<li> Leadership</li>
<li> Listening</li>
<li> Work ethic </li>
<li> Teamwork</li>
<li> Positivity </li>
<li> Time management</li> 
<li> Motivation </li>
<li> Problem-solving </li>
<li> Critical thinking</li>
<li> Conflict resolution</li>
<li> Negotiation</li>

      <section class="main-section" id="#technical skills"<a href class="nav-link"><header>Technical Skills</header><hr />
        <header>What Technical skills are employers looking for?</header>
          A technical skill is the ability to carry out a task associated with technical roles such as IT, engineering, mechanics, science or finance. A typical technical skill set might include programming, the analysis of complex figures or the use of specific tools.<br /><br />
          <code>Some technical skills employers are looking for include:</code><br /><br />
          <li>General computer skills</li>
<li>Operating systems</li>
<li>Programming languages</li>
<li>Project management</li>
<li>Productivity software</li>
<li>Artificial intelligence</li>
<li>Social media</li>
<li>Cloud computing</li>
<li>Accounting software</li>
<li>Design software</li>
<li>Spreadsheet proficiency</li>
<li>Content management systems</li>
<li>Video software</li>
<li>SEO (search engine optimization)</li>
<li>Google analytics</li>
<li>Marketing skills and software</li>
<li>Certified management skills</li>
<li>Research skills</li><br /><br />
          <code>15 Examples of Technical Skills to Put on Your Resume</code><br /><br />
          When considering the technical skills to add to your resume during your job search, think about what the job requirements are.

There is no point in using your technical skills in a particular programming language if you are looking for a role that is primarily about video editing.

Technical skills can be categorised in several different ways, with specific knowledge in each group.
          1. Programming Languages
          <b>Programming skills are not just reserved for developers.</b>.<br /><br />
          <code>Other IT staff, such as customer service teams or project managers, need to have a basic understanding of programming to support customers or coordinate projects.</code><br /><br />
         <ul>Examples include:</ul>

      <section class="main-section" id="cybersecurity">
          Cybersecurity is the practice of deploying people, policies, processes and technologies to protect organizations, their critical systems and sensitive information from digital attacks.<br /><br />
          <code>What does cybersecurity mean for your business?</code><br /><br />
          Cybersecurity is a business problem that has been presented as such in boardrooms for years, and yet accountability still lies primarily with IT leaders.  <b>In the 2022 Gartner Board of Directors Survey,</b> 88% of board members classified cybersecurity as a business risk; just 12% called it a technology risk. Still, a 2021 survey showed that the CIO, the chief information security officer (CISO) or their equivalent were held accountable for cybersecurity at 85% of organizations.<br /><br />
          <code>The IT Roadmap for Cybersecurity</code><br /><br />
          By 2023, 75% of organizations will restructure risk and security governance to address the widespread adoption of advanced technologies, an increase from fewer than 15% today. 
          A resilient cybersecurity strategy is essential to running the business while protecting against security threats and preventing data breaches and other enterprise cybersecurity threats. <b>The roadmap provides security and risk leaders with:

An overview of the initiative’s key stages and milestones

</b> Key resources to save time and ensure successful execution<sup>Perspectives on the cross-functional team to support the initiative</sup>.
          What is the cybersecurity impact of Russia’s invasion of Ukraine?<br />
          The Russian invasion of Ukraine is marked by both military and destructive malware attacks. As the invasion expands, the threat of attacks to critical infrastructure — and the potential for fatal outages — grows. No business is immune. Many organizations already face a range of lurking security failures, but now, it’s especially important to rely on threat intelligence tailored for your organization and to watch for guidance from your government contacts around how to prepare for attacks you may not be ready to handle. 
          As the C-suite strategizes its response to the Russian invasion of Ukraine, prioritize cybersecurity planning. Focus on what you can control. Make sure your incident response plans are current. Increase awareness and vigilance to detect and prevent potential increased threats, but be mindful of the added stress and pressure your organization is feeling. A human error due to these forces may have a greater impact on your organization than an actual cyber attack.
          <b>3 Must-Haves in Your Cybersecurity Incident Response Plan</b>.The 3 Must-Haves in Your Cybersecurity Incident Response eBook allows you to be prepared before it’s needed. CISOs will:
         Build a plan.
          Develop a response.
          tDefine incident severity.
          Assign roles.
          <br /><br />
          <code>What are the cybersecurity concerns for critical infrastructure?</code><br /><br />
          Critical infrastructure sectors include energy production and transmission, water and wastewater, healthcare, and food and agriculture. In many countries, critical infrastructure is state-owned, while in others, like the U.S., private industry owns and operates a much larger portion of it.Not only are each of these sectors critical to the appropriate functioning of modern societies, but they are also interdependent, and a cyberattack on one can have a direct impact on others. Attackers are increasingly choosing to deploy attacks on cyber-physical systems (CPS).
          The risks were very real even before Russia invaded Ukraine. Attacks on organizations in critical infrastructure sectors rose from less than 10 in 2013 to almost 400 in 2020, a 3,900% increase. It’s not surprising, then, that governments worldwide are mandating more security controls for mission-critical CPS. 
          <a href=""
            >The Russian invasion of Ukraine increases the threat of cyberattacks for all organizations. You need to develop a holistic, coordinated CPS security strategy while also incorporating into governance emerging security directives for critical infrastructure. The U.S. “National Security Memorandum on Improving Cybersecurity for Critical Infrastructure Control Systems,” for example, is prioritizing the electricity and natural gas pipeline sectors, followed by the water/wastewater and chemical sectors.</a
          >The crux of the problem is that traditional network-centric, point-solution security tools are no longer sufficient to combat the speed and complexity of today’s cyberattacks. This is particularly the case as operational technology (OT), which connects, monitors and secures industrial operations (machines), continues to converge with the technology backbone that processes organization’s information technology (IT).<br /><br />
          <code>Conduct a complete inventory of OT/Internet of Things (IoT) security solutions in use within your organization. Also perform an evaluation of standalone or multifunction platform-based security options to further accelerate CPS security stack convergence.</code>
      <section class="main-section" id="data science">
        <header>Data science?</header>
          Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.<br /><br />
          <code>Data ingestion: </code><br /><br />
          The lifecycle begins with the data collection--both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more. <b>Data storage and data processing: </b>.
          Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
          Data analysis: <br />
          Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.<b>Communicate:</b> of
          Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.<br /><br />
          <code>Data science versus data scientist</code>
         Data science is considered a discipline, while data scientists are the practitioners within that field. Data scientists are not necessarily directly responsible for all the processes involved in the data science lifecycle. For example, data pipelines are typically handled by data engineers—but the data scientist may make recommendations about what sort of data is useful or required. While data scientists can build machine learning models, scaling these efforts at a larger level requires more software engineering skills to optimize a program to run more quickly. As a result, it’s common for a data scientist to partner with machine learning engineers to scale machine learning models.<br /><br />
          <code>Data science versus business intelligence</code><br /><br />
          It may be easy to confuse the terms “data science” and “business intelligence” (BI) because they both relate to an organization’s data and analysis of that data, but they do differ in focus.<br /><br />
          <code>Data science tools</code><br /><br />
          Data scientists rely on popular programming languages to conduct exploratory data analysis and statistical regression. These open source tools support pre-built statistical modeling, machine learning, and graphics capabilities. These languages include the following (read more at "Python vs. R: What's the Difference?"):<br /><br />
          <code>Data science and cloud computing</code><br /><br />
          Cloud computing scales data science by providing access to additional processing power, storage, and other tools required for data science projects.
          Since data science frequently leverages large data sets, tools that can scale with the size of the data is incredibly important, particularly for time-sensitive projects. Cloud storage solutions, such as data lakes, provide access to storage infrastructure, which are capable of ingesting and processing large volumes of data with ease. These storage systems provide flexibility to end users, allowing them to spin up large clusters as needed. They can also add incremental compute nodes to expedite data processing jobs, allowing the business to make short-term tradeoffs for a larger long-term outcome. Cloud platforms typically have different pricing models, such a per-use or subscriptions, to meet the needs of their end user—whether they are a large enterprise or a small startup.
          Open source technologies are widely used in data science tool sets. When they’re hosted in the cloud, teams don’t need to install, configure, maintain, or update them locally. Several cloud providers, including IBM Cloud®, also offer prepackaged tool kits that enable data scientists to build models without coding, further democratizing access to technology innovations and data insights.<br /><br />
          <code>Data science use cases</code><br /><br />
          Enterprises can unlock numerous benefits from data science. Common use cases include process optimization through intelligent automation and enhanced targeting and personalization to improve the customer experience (CX). However, more specific examples include:<br /><br />
          <code>Here are a few representative use cases for data science and artificial intelligence:</code><br /><br />
          An international bank delivers faster loan services with a mobile app using machine learning-powered credit risk models and a hybrid cloud computing architecture that is both powerful and secure.<br /><br />
          <code>An electronics firm is developing ultra-powerful 3D-printed sensors to guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.</code><br /><br />
         A robotic process automation (RPA) solution provider developed a cognitive business process mining solution that reduces incident handling times between 15% and 95% for its client companies. The solution is trained to understand the content and sentiment of customer emails, directing service teams to prioritize those that are most relevant and urgent.<br /><br />
          <code>A digital media technology company created an audience analytics platform that enables its clients to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.</code><br /><br />
      <section class="main-section" id="finance">
        <p>Coming Soon!</p>
        <p>Keep an eye out for new additions!</p>
      <section class="main-section" id="more_information">
        <header>More Information</header>
        <p>Check out the following links for more information!</p>
            <a href=""
            is a great source for multiple tech fields.
            <a href="{sourceid}&g_merchantid=&g_placement=&g_partition=&g_campaignid=12144922272&g_ifproduct=&utm_id=t_aud-1122464209802:dsa-1147291854894:ag_114763098417:cp_12144922272:n_g:d_c&utm_source=google&utm_medium=paid-search&utm_term=&utm_campaign=&utm_content=494035656896&g_adtype=search&g_acctid=243-039-7011&gclid=CjwKCAjw5P2aBhAlEiwAAdY7dKgWvCfYz15JzJXiEqIZ-_N6UqHqBAxA7pGtCSheXEgCVaiIfLGf4BoCKZ0QAvD_BwE"
              >CodeAcademy's Web Development page</a
            for more general information.
    <a href="../">Check out Appacademy</a> |
    <a href="">Thanks for your time</a>

and my css :slight_smile:

* {
  background-color: #3a3240;
a {
  color: #92869c;
a:hover {
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  color: #3a3240;
#navbar {
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  border-width: 5px;
  border-color: #92869c;
  height: 100%;
  top: -5px;
  left: -5px;
  padding: 5px;
  text-align: center;
  color: #92869c
@media (min-width: 480px) {
  #navbar {
    position: fixed;
main {
  margin-left: 220px;
  color: #92869c
header {
  font-size: 20pt;
code {
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  border-style: dashed;
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  border-color: #92869c;
  padding: 5px;
  color: black;
footer {
  text-align: center;

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