Data Literacy Notes PDF

## UNIT-2 DATA LITERACY ### Learning Outcomes - Defining Data Literacy - Why is Data Literacy Essential? - How to Become Data Literate? - Data Security and Privacy - Acquiring Data, Processing, and Interpreting Data - Usability, Features and Preprocessing of Data - Methods of Data interpretation -...

## UNIT-2 DATA LITERACY ### Learning Outcomes - Defining Data Literacy - Why is Data Literacy Essential? - How to Become Data Literate? - Data Security and Privacy - Acquiring Data, Processing, and Interpreting Data - Usability, Features and Preprocessing of Data - Methods of Data interpretation - Importance of Data Interpretation - Data Pyramid and Its Different Stages - Impact of Data Literacy - Data Literacy Process Framework - How are Data Security and Data Privacy related to AI? - Data Acquisition/Acquiring Data - Data Terminology - Types of Data Interpretation - Using Tableau for Data Presentation Data refers to any collection of raw facts, figures, or statistics that can be stored and processed by a computer. It can be in different forms like numbers, text, images, audio, and video etc. ### Defining Data Literacy Literacy refers to the ability to read, comprehend and use information effectively. Data + Literacy = Data Literacy Data literacy means knowing how to understand, work with, and talks about data. It's about being able to collect, analyse, and show data in ways that make sense. Data literacy is essential because it enables individuals to make informed decisions, think critically, solve problems, and innovate. **Data** - Raw facts or figures **Literacy** - Ability to read, comprehend, and communicate. Data literacy is the ability to understand, interpret and communicate with data. ### Data Pyramid and Its Different Stages The Data Pyramid is a conceptual model that illustrates the hierarchical structure of data processing, depicting the progressive transformation of raw data into actionable wisdom. It starts with raw data, which initially has no use. Through processing and analysis, this data evolves into meaningful information, then knowledge, and ultimately wisdom. This transformation enables informed decision-making and a deeper understanding of the world around us. - **Wisdom** - Why? - **Knowledge** - How? - **Information** - - **Data** - Raw Data and information ### Different Stages of the Data Pyramid When moving up from the bottom in the Data Pyramid we understand that the pyramid is made up of: - **Data(Base Level):** - In this stage data is in its most basic form, unprocessed and unstructured. - It has no meaning and is not very useful in this form. - **Information:** - It is a processed data that collectively carries a logical meaning. - It is obtained by analysing raw data to make it useful for decision making. - **Knowledge:** - It is useful information that leads to a deeper understanding. - It represents a more profound comprehension of how things happen. - It is the ability to use information to achieve desired output. - **Wisdom (Top Level)** - It is the highest level of understanding. - It is the ability to understand why things are happening in a particular way. - It involves critical thinking to interpret data and make good consistent decisions. Let's understand Data Pyramid with a simple “Traffic Light” example: - **Wisdom** - I need to stop the car - **Knowledge** - Traffic light in my direction has turned red - **Information** - South facing traffic light on ABC street has turned red - **Data** - Red, Traffic Light, 1 Let us do another example of Creating a Data Pyramid for planning a picnic as a student after receiving the official letter from school: - **Wisdom** - Decision-making based on a holistic understanding of the picnic experience and its broader implications. - **Knowledge** - Significance of the picnic and its potential impact on social interaction, relaxation, and personal well-being. - **Information** - Researching the picnic location to learn about its amenities, facilities, and nearby attractions. Checking the weather forecast for the picnic day.. - **Data** - What to bring, dress code, and expected behavior. ### Why is Data Literacy Essential? Data literacy can equip individuals with skills and knowledge to improvise in a data-driven world. The following are some of the factors: - Data literacy enhances decision making ability in individuals based on evidence. Based on sources of data, emerging trends and interpretations, individuals can make decisions that are data-driven. - To think critically Data literacy is able to cultivate critical thinking skills to understand and explore data's implications by questioning assumptions, to reach logical conclusions, identifying patterns, and evaluating evidence and data accuracy. ### Impact of Data Literacy Data literacy has an immense impact on various aspects of society like business, education, healthcare, and public policy as given below: - **Business:** It improves the decision-making skills of a person. Data-literate employees can effectively analyse data to gain insights into market trends, customer behaviors, and operational performance. - **Education:** It empowers the teaching-learning process. Students can engage more deeply with course material, particularly in STEM fields. - **Healthcare:** Healthcare professionals can use data to improve diagnostics, treatment plans, and patient monitoring. Hospitals and clinics can use data to optimize resource allocation, reduce waste, and improve operational efficiency. - **Public policy:** Policymakers can use data to design, implement, and evaluate policies more effectively. Data literacy promotes transparency, allowing the public to hold policymakers accountable through data-driven evidence. - **Social equity:** Data literacy can highlight disparities in areas such as education, healthcare, and employment and can promote social equity. It helps ensure that resources are distributed effectively to areas of greatest need. ### Data Literacy Process Framework The data literacy framework provides a comprehensive and structured approach to developing the necessary skills for using data efficiently and with all levels of awareness. Each level builds upon the previous one, fostering a deeper and more understanding ability to work with data. Here are the typical levels of awareness in a Data Literacy Process Framework: - **Plan:** Any program starts with a discussion on defining the goal, understanding the participants, execution strategy and timeframe. - Design a communication plan explaining the purpose of the goal and requesting for commitment towards it. - **Communicate:** Ensuring clear and consistent communication about data literacy will ensure an efficient data literacy framework within an organisation. - Sharing stories and case studies that demonstrate the positive impact of data literacy. It will help in motivating and encouraging the team. - Monitoring continuously the effectiveness of communication efforts and make adjustments as needed. It will help in minimising the risk of any associated costs. - **Assess:** Introducing participants to data literacy assessment tools and finding out how comfortable they are with data is crucial in a data literacy program. - Identifying what they need to learn, and create customized training plans for them. - **Develop Culture:** To integrate data literacy skills into the organisational culture it is important to make data-driven decision-making a fundamental part of everyday work. This will help to understand that: - Leaders play a crucial role in fostering this data-driven culture in an organisation. - Training programs will help to build data literacy skills through learning across all levels. - Encouraging a collaborative environment will lead to imbibing this new culture into the existing culture with the time. - **Prescribe Learning:** By implementing a prescriptive learning approach, organisations can provide a set of diverse resources that align with individual learning styles. This approach ensures that there is: - Customised learning journeys tailored according to different people (for example different educational background) based on individual needs and preferences. - A variety of learning materials that caters to different learning styles and help in easier grasping of concepts. - Enough leverage or advantage to the learners to progress at their own pace, accommodating their schedules and learning speeds. - Creating an environment that supports continuous learning and encourages self-directed exploration. - Each participant can choose the materials and methods that work best for them, leading to more effective learning and greater improvement in data literacy skills over time. - **Evaluate:** Designing an evaluation metric for the data literacy program involves creating a structured framework to assess participants' progress and the effectiveness of the program overall. - Establish a schedule for assessing participant progress to monitor their development over time. ### Data Security and Privacy The terms data security and data privacy are often used interchangeably, but they mean different things. Data privacy determines who can access the data, while data security involves tools and policies to restrict access to the data. - **Data Privacy** - It is governing how data is collected, shared, and used. - **Data Security** - It is protecting data from attackers who might want to misuse it. ### What is Data Privacy? Data privacy, referred to as information privacy, is concerned with the proper handling of sensitive data, including personal data and other confidential data, such as certain financial data and intellectual property data, to meet regulatory requirements as well as protecting the confidentiality and immutability of the data. So, when we talk of Data Privacy, it is expected that any platform or individuals who have access to this information ensures that data is used in a way that respects and fulfills legal requirements and compliance of handling privacy rights. These include how the data is collected and shared for usage and who all have access to data. Here are examples of two things which may compromise our data privacy. - Downloaded an unverified mobile application - Accepted the Terms of Service without reading ### Why is Data Privacy Important? It is important because: - Data breach at a government agency can put top secret information in the hands of an enemy state. - Data breach at a hospital can put personal health information in the hands of those who might misuse it. - Data breach at a corporation can put proprietary data in the hands of a competitor. - Data breach at a school can inconvenience to the parents, continuous calls from tuition and coaching centers cause annoyance and stress. The following best practices can help you ensure data privacy: - Understanding what data you have collected, how it is handled, and where it is stored. - Necessary data required for a project should only be collected. - User consent while data collection must be of utmost importance. ### What is Data Security? Data security involves safeguarding digital information from unauthorised access, corruption, or theft throughout its entire lifecycle. Essentially, security means protecting anything from theft and misuse. It aims to prevent unauthorised access, theft, or corruption of data, regardless of whether the data is personal or not. Systems and networks must be established to prevent malicious and fraudulent activities from harming, destroying, misusing, or stealing crucial digital data. ### Why is Data Security Important? Cyber attacks are becoming more frequent as a result of the growing volume of data stored in the cloud. The best course of action given the volume of traffic being produced is to regulate and secure the transmission of private or sensitive data everywhere that it is known to exist. Avoid entering sensitive information on unrecognised and unsafe websites, such as your address, PAN, or Aadhar number. The most possible reasons why data security is more important now are: - A constant fear – Cyber-attacks affect all the people. - The fast-technological changes will boom cyber attacks. - A persistent fear everyone is impacted by cyberattacks. - Rapid technical advancements will increase the frequency of cyberattacks. ### Types of Data Security Controls Different types of data security controls are as follows: - **Strong Passwords:** Strong passwords are a combination of upper and lower-case letters, numbers, and special characters that is difficult for unauthorised individuals or automated programs to guess or crack. It is a very basic step that one should take and never share the same with even the most trusted. Avoid using birth dates, anniversary dates, common combinations of numbers. Some examples of strong passwords are: m#P52s@ap$V, “N4&vQ2! p". - **Authentication:** It also refers to multi-factor authentication (MFA) and is an additional security layer in online data systems. After a user enters their password to log in, MFA requires them to provide one or more additional forms of authentication to verify their identity. This could include one-time generated code as a security token in smartphones or emails or a fingerprint or facial recognition. - **Access Controls:** Access controls refer to the security measures and protocols to restrict access to sensitive data, ensuring that only authorised individuals or entities can view, modify, or interact with it. This reduces the risk of unauthorised access by limiting the number of users who can interact with sensitive data. - **Data Backup:** Data backup refers to the process of creating copies of data to ensure that it can be restored in the event of data loss due to natural disasters, accidents, cyber-attacks, or other unexpected events. Sometimes physical backup media is used to secure in access-controlled environments. Another method to secure data can be on the cloud backup which is considered more reliable. - **Encryption:** Encryption is a security technique that transforms readable data (plaintext) into an unreadable format (ciphertext) using an algorithm and an encryption key. This process ensures that only authorised individuals with the correct decryption key can access the original data. - **Data Disposal:** Data disposal refers to the process of securely destroying or deleting data that is no longer needed to prevent unauthorised access, recovery, and misuse. Proper data disposal practices are essential to ensure that sensitive and confidential information does not fall into the wrong hands. Paper documents, CDs, DVDs, and other physical media can be shredded to render them unreadable. - **Firewall and Antivirus Software:** Using firewall and antivirus software can stop and alert users of any suspicious activity happening on their device. With the timely updated versions of the same, can go a long way in ensuring data security. - **Training:** Corporates must take up regular Data Security sessions of their staff to sensitise them about following the data protection processes being implemented and the importance of doing so. Making them conscious of suspicious emails, links that they might receive, not leaving their devices unlocked when unattended, keeping software's up to date and not sharing passwords, are some of the things that can be taken up. - **Audits and Testing of Security System:** Regular audits and testing of security policies, integrated malware protection, firewalls, Wi-Fi connections security, checking applications security, email security and compliance also play very important role in maintaining data privacy and providing data security. - **Other Basic Preventions:** Being aware of surroundings and threats from insiders, complying with security regulations which might be shared by entrusted agencies or bodies which track online cyber activities all across the world are few other ways to provide cyber security. ### Differences between Data Security and Data Privacy - **Data Privacy** - Data privacy ensures the ethical and lawful use of data. - It focuses on how data is collected, used, shared, and stored so that the rights of individuals over their data is protected. - **Data Security** - Data security ensures the protection of data from unauthorised access and breaches. - It focuses on safeguarding personal data, business data, intellectual property, and many more from various threats. ### How are Data Security and Data Privacy related to AI? Data Security and Data Privacy are crucial components in Artificial Intelligence (AI). - **Data Security in AI:** AI systems often rely on vast amounts of data for training and operation. Unauthorised access and tampering could lead to inaccurate AI models and compromised outcomes. Many AI applications process sensitive data, such as personal, financial, or health-related information. Strong data security measures can stop data breaches and unauthorised access. - **Data Privacy in AI:** Data privacy brings the ethical use of AI. This ensures that AI systems comply with data privacy laws and regulations (such as GDPR, CCPA) to help protect individuals' rights and maintain public trust. AI systems must ensure that data is collected and used in ways that users have explicitly consented to, maintaining transparency and trust. ### Best Practices for Cyber Security Cyber security involves protecting computers, servers, mobile devices, electronic systems, networks, and data from harmful attacks. The best practices for cyber security are constantly evolving to keep up with the cyber threats. ### Acquiring Data, Processing, and Interpreting Data Working with data involves three key steps: acquiring, processing, and interpreting. First, gather data from sources like surveys and databases. Next, process it by cleaning and organising it for accuracy. Finally, analyse the data to find patterns and insights that help make informed decisions. ### Types of Data In statistics, various types of data are gathered, analysed, interpreted, and presented. These data consist of individual factual pieces recorded for analysis. Data analysis involves interpretation and presentation, producing statistics. Data classification and handling are crucial processes that use multiple tags and labels to define data, ensuring its integrity and confidentiality. Artificial Intelligence is crucial, with data serving as its foundation. We come across different types of data and information every day. - **Textual Data (Qualitative Data):** Textual data is the information that is written or expressed using words and language. It includes things like articles, books, emails, messages, and any other written content. Instead of numbers, it's made up of letters, words, and sentences that convey meaning and information. - Example: "Learning AI is fun" - **Numeric Data (Quantitative Data):** Numerical data means information that's in numbers, not words or descriptions. It's often called quantitative data because it's collected as numbers and can be used for math and stats. For instance, if you know the total number of workers and how many are men, you can figure out how many are women by subtracting. This ability to do math with numerical data makes it great for doing statistics and analysing data. - For example, Marks, Temperature, etc. Numeric data can be further classified as Continuous Data and Discrete Data: - **Continuous Data:** - Continuous data can take as a numeric value given within a range. - This type of data can be infinitely subdivided and often includes decimal points. - Often used to analyse using statistical techniques such as mean, median, standard deviation, and correlation. - Example: Dimensions of classroom, Height, Weight, Temperature, Time, etc. - **Discrete Data:** - Discrete data refers to countable, distinct values. It consists of whole numbers without decimal parts that represent distinct categories or values. - Discrete data cannot be subdivided meaningfully. - It is used to analyse using frequency distributions, bar charts, and probability distributions. - Examples: Number of girls and boys in class, Number of subjects in class 9th, Count of anything. ### AI Domains and Type of Data Various types of data are utilised across different domains to train models, make predictions, and generate insights. Here are the types of data commonly used in three key domains of AI. - **Natural Language Processing (NLP):** Natural Language Processing (NLP) is all about teaching computers to understand and work with human language. Types of data used in NLP is: - Textual Data: This includes a wide range of written text, such as articles, books, emails, social media posts, web content, PDF files, etc. - Audio Data: Audio recordings of spoken language, which are transcribed into textual data. - **Computer Vision:** Computer Vision is like giving eyes to computers. It helps them look at pictures and videos from the real world and understand what they're seeing. With Computer Vision, computers can figure out what’s in a picture or video, just like we do. They can recognize objects, people, and even actions happening in videos. - Types of data used in Computer Vision include: - Image Data: Digital images captured by cameras or satellite imagery, medical scans, and surveillance footage. - Video Data: Video data captured using a camera - **Machine Learning:** Machine Learning is like teaching computers to learn from examples and make decisions on their own. Imagine if you showed a computer lots of pictures of dogs and cats, and you told it which ones were dogs and which ones were cats. After seeing many examples, the computer learns to tell dogs and cats apart on its own. Types of data used in Machine Learning include: - Numeric Data: Data taken from tables, Excel sheets, etc. ### Data Acquisition/Acquiring Data Data acquisition, also known as acquiring data, refers to the procedure of gathering data like raw facts, figures or statistics from relevant sources either for reference or for analysis needed in AI projects. This involves searching for datasets suitable for training AI models. The process typically comprises three key steps and plays a crucial role in obtaining and preparing data for analysis: - **Data Discovery:** Searching for new datasets. - **Data Augmentation:** Adding more data to the existing data. - **Data Generation:** Generating data if data is not available. ### Usability, Features and Preprocessing of Data Data is indeed a collection of information gathered through various means such as observations, measurements, research, or analysis. This information can include a wide range of elements like facts, numbers, names, figures, or descriptions of things. To make data easier to understand and analyse, it is often organised into formats such as graphs, charts, or tables. ### Usability of Data Imagine completing a school project. You need clear instructions, a neat workspace, and accurate information. Similarly, using data effectively relies on its clarity, organisation, and accuracy. There are three primary factors determining the usability of data: - **Structure of Data:** Defines how data is stored. Data needs to have a clear structure. It should be organised in a way that makes sense so that it can be used effectively. For example: - **Marks of a students arranged in a spreadsheet:** Spreadsheet - Good structure. Data is stored in a sheet with the details of each individual stored according to a set of rules. - **Text document:** Text document - Poor structure. Data is stored in a text document with no set of organising rules. - **Cleanliness:** Clean data should not have duplicates, missing values, outliers, and other anomalies so that its reliability and usefulness for analysis is not affected. - **Accuracy:** Accuracy is same as reliability so it indicates how well the data matches real-world values. Accurate data closely reflects actual values without errors, enhancing the quality and trustworthiness of the dataset. ### Features of Data Data features are also called the characteristics or properties of the data. They describe each piece of information in a dataset. They define what each data point represents and help us make sense of the data. - In a table of student records, features could include things like the student's name, age, or grade. - In a photo dataset, features might include properties like the colour present in each image, the resolution, brightness, or the presence of certain objects. ### Independent and Dependent - **Independent:** Independent variables (sometimes called predictor variables) are those that are used to generate predictions about or to account for the variation in the dependent variable (the goal). These features are the input to the model – they're the information we provide to make predictions. - **Dependent:** The dependent variable is the variable about which predictions or explanations are being sought. These features are the outputs or results of the model—they're what we're trying to predict. ### Data Preprocessing Data preprocessing is an essential phase in the machine learning process that prepares datasets for effective machine learning applications. It includes multiple processes to clean, transform, reduce, integrate, and normalise data: - **Data Cleaning** - **Data Transformation** - **Data Reduction** - **Data Integration & Normalisation** - **Feature Selection** ### Data Processing and Data Interpretation Data processing means preparing and analysing raw information to train models or predict outcomes, including tasks like cleaning and training. Data interpretation in AI involves analysing model outputs to understand patterns, refine models, and make informed decisions. ### Methods of Data Interpretation Data Interpretation is the process of making sense out of a collection of data that has been processed. This collection may be present in various forms like bar graphs, line charts and tabular forms and other similar forms. - **Quantitative Data Interpretation:** It is the process of analysing and understanding numeric data. This type of data often comes from surveys, experiments, and numerical measurements. Quantitative data provides statistical insights and helps in identifying patterns and trends. - The interpretation of quantitative data focuses on measurable outcomes and numerical relationships. - It helps us answer questions like “when,” “how many," and "how often". - For example: (how many) numbers of likes on the Instagram post. Data collection methods in quantitative data interpretation involve systematic techniques like surveys and experiments to gather numerical data. These approaches ensure data accuracy, facilitating reliable analysis and inference across various fields such as social sciences and healthcare. - **Qualitative Data Interpretation:** It is the process of analysing and understanding non-numeric data. This type of data often comes from interviews, surveys, observations, or textual content. Qualitative data tells us about the emotions and feelings of people. Qualitative data interpretation is focused on insights and motivations of people. ### Data Acquisition Data acquisition, also known as acquiring data, refers to the procedure of gathering data like raw facts, figures or statistics from relevant sources either for reference or for analysis needed in AI projects. This involves searching for datasets suitable for training AI models. The process typically comprises three key steps and plays a crucial role in obtaining and preparing data for analysis: - **Data Discovery:** Searching for new datasets. - **Data Augmentation:** Adding more data to the existing data. - **Data Generation:** Generating data if data is not available. ### Data Processing Data processing involves tasks to refine raw data for analysis or application, including cleaning, organising, transforming, and summarising information. - It ensures data accuracy, relevance, and accessibility for effective decision-making and analysis. - It is crucial across various sectors like business, science, and technology, facilitating better utilisation of data assets. - Data processing helps computers understand raw data. ### Data Interpretation Data interpretation is the process of making sense of data by analysing it to uncover patterns, trends, and insights. It involves examining the data to understand its meaning, implications, and significance, helping to inform decision-making and draw conclusions: - It is the process of making sense out of data that has been processed. The interpretation of data helps us answer critical questions using data. #### Process of Data Interpretation - **Acquire:** This initial step involves gathering raw data from diverse sources such as surveys, databases, or sensors. It ensures that all relevant information is collected to provide a comprehensive dataset for analysis. - **Process:** Once the data is collected, it undergoes cleaning and organisation to remove errors, inconsistencies, or irrelevant information. This step ensures that the data is in a standardised format and ready for further analysis. - **Analyse:** In this phase, the cleaned and organised data is scrutinised to identify patterns, correlations, or trends. Statistical methods, algorithms, or data visualisation techniques may be employed to extract meaningful insights from the data. - **Interpret:** After analysing the data, the results are interpreted to derive actionable insights or conclusions. This involves understanding the implications of the analysis findings in the context of the problem or question at hand. - **Present:** The final step involves presenting the interpreted findings in a clear and engaging manner. This could include visualisations such as graphs or charts, along with concise summaries, to effectively communicate the insights derived from the data analysis. These steps make sure that working with data is organised, complete, and useful, so that organisations can make smart choices based on the data. ### Importance of Data Interpretation Data interpretation is crucial as it transforms raw data into actionable insights, guiding informed decision-making. By analysing and understanding data, organisations can uncover trends, patterns, and relationships, enabling them to optimize strategies, mitigate risks, and drive growth. ### Using Tableau for Data Presentation Using Tableau for data presentation involves connecting to various data sources, creating diverse visualisations, and enabling interactive features. It supports sharing and collaboration, offers advanced analytics capabilities, and promotes best practices for clear and effective data communication. ### What is Tableau? Tableau is a powerful data visualisation and business intelligence tool for visualising and analysing data in order to aid in business choices. It takes in data and produces various charts, graphs, maps, dashboards, and stories. ### Steps to Download Tableau 1. Visit the link https://public.tableau.com/en-us/s/download 2. Click on DOWNLOAD TABLEAU PUBLIC. It will display the given screen where you enter your details. 3. Click on the DOWNLOAD THE APP button to begin with the download process as shown below: 4. After finishing with the downloading of the files, double-click the installer of the Tableau Public Desktop. The Tableau Public 2024.1 Setup wizard opens. 5. Select the I have read and accept the terms of the license agreement check box to accept the terms of the license agreement. 6. Click on the Install button. Process of installation begins as shown below: 7. As soon as the installation process is over the application is ready for use. 8. You can also open the Tableau application by double-clicking the shortcut icon of the Tableau application on the Desktop. ### Creating a Bar Graph Using Tableau The steps to draw a Bar Chart in Tableau are as follows: 1. Create an Excel file and save it as student.xlsx with the following data: 2. Double-click on the Tableau app shortcut icon on the Desktop. The Tableau app opens. 3. Select the Microsoft Excel option from the Connect pane to access the Excel data that is used for visualising the representation in Tableau. The Opens dialog box appears. 4. Navigate the location where the Excel file is stored. 5. Select the student.xlsx file 6. Click on the Open button. 7. The data of the Excel file is displayed in the Data Source window. 8. Click on Sheet 1 in the Sheet tab. 9. Drag the Student field to the Columns shelf. 10. Drag the Score field to the Rows shelf. 11. Drag the Subject field to the Columns shelf. Tableau generates a bar graph, as shown below: You can sort the bars in graph in ascending or descending order by clicking the Ascending or Descending option in the toolbar. ### Changing Colour of the Graph To make a colourful bar graph, drag the Subject field from the Data pane and place over the Color option in the Marks card. ### Changing Label of the Graph The steps to change the label of the graph are as follows: 1. Click on the Label option in the Marks card. 2. Select the Show mark labels check box to show the labels on the graph. 3. Change the font type and size according to your requirement. In this case, we have changed the font size to 12 and the font to Comic Sans MS. The labels are displayed in the graph with the specified font type and size. ### Change the Graph Type Tableau often auto-selects the graph type based on the data. If the default graph type does not suit your data, you can change it accordingly. Perform the following steps to change the graph type: 1. Click on the Show Me button. The Show Me panel appears. 2. Select the desired graph type from the Show Me panel. In this case, we have selected the Packed Bubbles chart type. The type of the graph will be changed to Packed Bubbles chart type. ### Duplicating a Chart The steps to duplicate a chart are as follows: 1. Right-click the sheet in the Sheet tab whose chart you want to duplicate. 2. Select the Duplicate option from the context menu. A duplicate sheet is added in the Sheet tab. ### Save and Share a Workbook Once you are creating a graph, you can save your workbook. The steps save your Tableau Public workbook are as follows: 1. Click on the File → Save as option from the menu bar. The Save as dialog box opens. 2. Navigate the location where you want to save you workbook. 3. Type the name of the workbook in the File name text box. 4. Click on the Save button. The tableau public workbook is saved with the specified name. You can export the tableau workbook as a package by selecting the Export Packaged Workbook options in the File menu. You can also share your tableau workbook by saving it to Tableau Public or Tableau Server if you have access to those services. For this, you need to select Save to Tableau Public option in the File menu. ## Data Acquisition Data acquisition, also known as acquiring data, refers to the procedure of gathering data like raw facts, figures, or statistics from relevant sources either for reference or for analysis needed in AI projects. This involves searching for datasets suitable for training AI models. The process typically comprises three key steps and plays a crucial role in obtaining and preparing data for analysis. The three key steps involved in Data Acquisition are given below: - **Step 1: Data Discovery:** Data discovery is about hunting for valuable information in different places, checking if it’s good quality, and making sense of what we find. - **Step 2: Data Augmentation:** Data augmentation is the process of increasing the amount and diversity of data. We do not collect new data, rather we transform the already present data. Data augmentation means increasing the amount of data by adding copies of existing data with small changes. - **Step 3: Data Generation:** Data generation refers to generating or recording data using sensors. ### Types of Data Interpretation There are three ways in which data can be presented: - **Textual DI:** Data is put into words, like in a paragraph, which works well for small amounts of data that can be easily understood. But for larger amounts, this type of presentation may not be the best because it can get too complicated. - **Tabular DI:** Data is organised systematically in rows and columns within a table, facilitating structured representation. - **Graphical DI:** Some of the graphs include bar graphs, line graphs, pie charts, and scatter plots, which help in visualising trends, relationships, and distributions within the data. ### Data Processing Data processing involves tasks to refine raw data for analysis or application, including cleaning, organising, transforming, and summarising information. - It ensures data accuracy, relevance, and accessibility for effective decision-making and analysis. - It is crucial across various sectors like business, science, and technology, facilitating better utilisation of data assets. - Data processing helps computers understand raw data. #### Using Computers to Perform Different Operations on Data Use of computers to perform different operations on data is included under data processing. ### Data Interpretation Data interpretation is the process of making sense of data by analysing it to uncover patterns, trends, and insights. It involves examining the data to understand its meaning, implications, and significance, helping to inform decision-making and draw conclusions: - It is the process of making sense out of data that has been processed. - The interpretation of data helps us answer critical questions using data. #### Process of Data Interpretation - **Acquire:** This initial step involves gathering raw data from diverse sources such as surveys, databases, or sensors. It ensures that all relevant information is collected to provide a comprehensive dataset for analysis. - **Process:** Once the data is collected, it undergoes cleaning and organisation to remove errors, inconsistencies, or irrelevant information. This step ensures that the data is in a standardised format and ready for further analysis. - **Analyse:** In this phase, the cleaned and organised data is scrutinised to identify patterns, correlations, or trends. Statistical methods, algorithms, or data visualisation techniques may be employed to extract meaningful insights from the data. - **Interpret:** After analysing the data, the results are interpreted to derive actionable insights or conclusions. This involves understanding the implications of the analysis findings in the context of the problem or question at hand. - **Present:** The final step involves presenting the interpreted findings in a clear and engaging manner. This could include visualisations such as graphs or charts, along with concise summaries, to effectively communicate the insights derived from the data analysis. ### Importance of Data Interpretation Data interpretation is crucial as it transforms raw data into actionable insights, guiding informed decision-making. By analysing and understanding data, organisations can uncover trends, patterns, and relationships, enabling them to optimize strategies, mitigate risks, and drive growth. ### Informed Decision Making A decision is only as good as the knowledge it is based on. It means When we analyse data, we get a clearer picture of what's going on. This helps us make decisions that are more likely to lead to success. For example, if the average height of students is known, school can custom design the chairs and tables according to the requirement of the class. ### Reduced Cost Identifying needs can lead to reduction in cost. It means by knowing what’s necessary, we can cut down on waste. We can use resources more efficiently and not spend money on things that aren't important. For example, restaurant owner could decide to drop/modify some dishes of the menu which aren’t popular or have got bad reviews. ### Identifying Needs We can identify the needs of people by data interpretation. It means understanding what people want or require by looking at the information we have. For example, in a Pizza Shop there are possibilities that Veg Farmhouse Pizza is a popular choice among age group 8-10. ### Data Interpretation Methods Data interpretation is the process of making sense out of a collection of data that has been processed. This collection may be present in various forms like bar graphs, line charts and tabular forms and other similar forms. - **Quantitative Data Interpretation:** It is the process of analysing and understanding numeric data. This type of data often comes from surveys, experiments, and numerical measurements. Quantitative data provides statistical insights and helps in identifying patterns and trends. - The interpretation of quantitative data focuses on measurable outcomes and numerical relationships. - It helps us answer questions like “when,” “how many,” and “how often”. - For example: (how many) numbers of likes on the Instagram post. Data collection methods in quantitative data interpretation involve systematic techniques like surveys and experiments to gather numerical data. These approaches ensure data accuracy, facilitating reliable analysis and inference across various fields such as social sciences and healthcare. - **Qualitative Data Interpretation:** It is the process of analysing and understanding non-numeric data. This type of data often comes from interviews, surveys, observations, or textual content. Qualitative data tells us about the emotions and feelings of people. Qualitative data interpretation is focused on insights and motivations of people. #### How to collect qualitative data: - **Interviews:** Quantitative interviews play a key role in collecting information. - **Polls:** A poll is a type of survey that asks simple questions to respondents. Polls are usually limited to one