Hello everyone, I am Sir Terkiazensola and it is a pleasure to present this presentation with you today. When we gather like this, I am reminded of the power of connections and the opportunities they bring and I introduce myself and share a bit about my journey. I am an entrepreneur and visioner and lifelong learner. I am always seeking new ways to innovate and make a positive impact on the world around me. With a background in data engineering, I have had the privilege of working on various projects and collaborating with talented individuals from diverse backgrounds. Throughout my career, I have been driven by a passion for excellence and determination. Push the boundaries of what is possible. Whether it's building and managing successful startups, mentor inspiring entrepreneurs, or supporting causes close to my heart, I always strive to make a difference. You may have seen my LinkedIn profile also that I have been involved in projects related advanced visualization. These experiences have not only shaped my professional journey, but also reinforced my belief and transformative power of determination, collaboration and innovation. But beyond the arts and accomplishment, what a truly motivates me is the desire to make a meaningful difference in the life of others. I am committed to using my skills and souls for the greater good, whether it is creating jobs, supporting charitable cultures, or empowering individuals to pursue their passions. As I stand before you today, I am excited about the possibilities that chance to connect with like minded individuals who share my vision for a brighter future. Together, I believe we can overcome challenges and opportunities and create a world where the innovation thrives and everyone has a chance to succeed. Let's get started with the presentation advanced regulation techniques for complex data and then my first slide is my introduction. Firstly, today's topics are hypervolt data visualization interactive visualization interactive visualization with the visualization large data set in this combined and data visualization best practices. By the way, the introduction of healing, I want to give importance or benefits with lesion. For example, the first healing is and has to understand visual presentation of the data, make complex information more accessible and understandable. Charts, graphs, maps, provides and clear insights to trends, patterns and relationships between the data, allowing for better cooperation and analysis. But the second, I think is increased engagement. This means the visualizations are more engaging and memorable than the raw data and textual information. They capture attention and facilitate the communications leading to the greater engagement and retention of information among the organs. At the third hearing, I mentioned the efficiency of communication. So visualizations serve as a universal language that unscathed barriers such as language and expertise. They enabled efficiency communication of the couple of ideas. From the fluctuating collaboration at the knowledge sharing among the diverse audiences. This is the main item of the efficient of the communication. At 40 there is faster insight I think at the visual presentation of the data enable faster insight and understanding compared to traditional data analyzed methods. With the interactive visualization user can dynamically explore the data, drill down, drill out into details and gain the real time insights. Is accelerating the decision making process also and top five heading is storytelling is the most important one. I think all the storytelling is data visualization allows for the complete storyteller by presenting data in a narrative format. Visualization helps convey the story behind the data, evoke emotions per se, the audiences to take action based on the insight present. This healing is more important for the time because best practices becomes more important. I am going to the next slide for the types of data visualization. Pie charts, ant histograms, lines, area charts, gantt charts, network diagrams, read diagrams, scatter portals, all are data visualization types. If you are in process of trying to build a story, persuade a group of people or better understand your data, consider creating some sort of visual presentation to guide your truths and truths of the others. So these resolutions type are become more important to tell this story. What is the most popular one, do you think giving no, not I am giving Bangladesh and pie charts more important terms and the most popular ones elation types, the other forms line graphs at histograms, pivot tables, explodes, scatter plots, radar charts, bubble charts are the other one and so I'm going to my next slide. Interactive isolation refers to used to dynamic user driving feature in the data visualization to facilitate exploration. Analyzes the understanding of the complex data sets. Some benefits of the interactive visualization focusing on how it enables users to highlight the keys and explorer may filter the data. I will mention some important points of manages interactive visualization firstly enhance of the engagement. I think interactive isolations captive users attention and encourage active participation by allowing them to interact with the data in real time. It's important. I think the second one is the deeper understanding. Users can roll deeper into a data and gain a better understanding of the pattern's trends and relationships by the interactive exploring different aspects of the datasets. It's an important second important one. Third one is to customize the insights. Writing interactive visualization empower users to customize their analyzers. Selecting, filtering and manipulating the data or focus on specific area or interest or regional odds and efficient communication. It is easier to communicate complex insights by allowing users to interactively explore the data or discover the key files at their own. Also and maybe users alternatively refine their analyzers and hypothesis by dynamically adjusting visualization parameter, exploring alternative means and testing differing scenarios. Users also actionable decision making by the interactive isolation for the data driven decision is more important making by providing end users with actionable insights and enabling them to explore various options and outcomes interactively. Highlights about key insights I'm going to mention for example, interactive visualization allow users dynamically highlighting key insights by adjusting visual elements such as colors, size, shape or opacity and draw attention to important data points or trends. Users annotation is important also they can add interactive annotations and labels or tooltips having specific data points to the region of the interest to provide context and convey key insight efficiently. Focused and empathy is another feature that enables users to focus on a specific area of the data by zooming in or zooming out or panning or filtering out load information, making it easier to identify key findings and enabling user interaction. Enabling for user interaction is exploring and filtering data, filtering and filtering a section or the interactive isolation allow users to filter and select specific data point category or the time period of the interest, enabling them to focus on relevant subsets of data and also drill down and drill up. Users can drill up into detailed data on the higher level summary or lower level summary by interacting with the heiress or using interactive navigation controls for this item. Sorting and ranking is more important for the interactivity feature, cause the enabled users to dynamically sort and run data based on different criteria such as a value or alphabetic order or frequency or identify the top performer outlier or trends and another shuffling is the exploratory of the analyzers. Interactive visualization support exploratory analyzers by using users to dynamically change isolation time and adjust the parameter. Experiment with the different view to encourage hidden patterns or relationships in the data. So interactively, isolation empowers users to engage with the data in meaningful ways, enabling them to key insights and highlighting key insights. Explore and filter data efficiently and drive actionable insights and make informed decisions. This interaction islation is most important for the large data sets at the my next slide is main focus on presentation visualization technique for the complex data presentation in our increasing data driven world, the ability make sense of vast amounts of data is paramount for informed decision making and extracting valuable insights. We should devolve into various techniques that enable us to tackle the challenge of the visualizing latch datasets effectively. We should explore how these methods help us distill the complex data into meaningful presentations. For example, sampling and aggregations are foundational techniques that allow us to extract relevant insights from large datasets efficiently. By selecting a subset of data points and combining them into a summary of the statistics, we can capture the results of the entire datasets while reducing the computational overheads and so should think about the reduction of technique as methods enable us to decrease the volume of data while retaining essential features. Whether taught the principle of unlike or summarization statistics like medium, we can still fix data here to ocs representation. But what about handling high dimensional data? That is where the data clustering and dimensionality reduction come into play. That is where the clustering and the dimensional reduction clustering techniques clustering techniques help us group similarity data points together, while your dimensionality reduction method can enable us to visualization high dimension model into lower dimensional space. This approach empower crust explorer and understand complex data structure more efficiently. And lastly, I should explore hierarchical and nested with relations that provide a hierarchical view of data and a low form drilled out exploration. Three maps nested pie charts offer insights into relationships and structure with the data, enhancing our understanding of complex data sets. Conditioning large data sets requires a multi phase approach by employing techniques such as sampling, aggregation, data reduction, clustering, dimensional reduction and here are you can unlock valuable insights and drive informed decision making. For this, let's talk about the main subjects of this slide. Sampling the first one is the sample and aggregation technique. And this one this is the first sampling involves selecting a subset of the data points from a large population to present the whole. There are various sampling techniques suited to different type of data and analyze scores. Also random sampling for example involve selecting data points randomly from the population, ensuring that each data point has an equal chance of being selected. Approach is useful. The population is homogeneous and no specific pattern or structure to consider. Strophite sampling, the other hand involves wide population into distinct structures based on certain characters. For example, age, location and the sampling of each rattle separately approach ensure that each subgroup of population is adequately represented in the sample, making it useful when there significant variation within the population. Once we have our sample, we can use aggregation technique to summarize the data and extract meaningful esis. Aggregation involves combining multiple data points into summary statistics or aggregates. Yes. For example, we can calculate the average count of a certain variable in the sample to get an overall picture of its tuition or behavior. Aggregations help us to reduce the complexity of the data while absorbing portal trends and patterns, making it easier to analyze an inter patent. The second one is better reduction at the summarize reduction technique aimed to decrease the volume of that data while retaining its essential features a one common approach to data reduction is dimensionality reduction which involves reducing the number of the variables and features in the data set while preserving its essential structure. Table of two component analysis is a popular dimensional reduction table that identify the most important assets, variation in the data and x two and lower dimensional space. Another approach to data reduction is feature selection which involves selecting a subset of the original features that are most relevant to the analyzers and features. Selection methods can be supervised or pansparized depending on the weather can into account target variable in the data set. Once we have reduced the dimensionality of the data, we can further summarize using various techniques. Summary statistics such as median, standard deviation and percentiles provide insights into breadth and data at histograms box graphical presentation that visualize the distribution of the data and have identity outliers for the data reduction and summarization and the broadband is using data clustering and dimensional reduction. The clustering techniques group similar data poised together based on their features and attributes. Clustering helps us identify natural patterns and structures within the data chats cluster of the customer, a filler buying behavior for example or clusters of the genes are similar aspiration profiles. There are various clustering algorithms including enemies, clustering, error code clustering and dust based clustering each suited to different types of data. After analyzer calls. Dimensional introduction technique transforms the high dimensional data into a lower dimensional representations while preserving its essential structure. These techniques are particularly useful for vision high dimensional data into all three dimensions making it easier to explore and interpret place data sets and fourth one kubernetes nested isolation. This represents data in a hierarchical structure where elements are organized into and giant relationship provide a natural way to explore hierarchical data structure. Chance of organizational and maybe lasted categories or sonomist also remaps on board charts. Dandograms plots are examples of the hierarchical regulation that help us islamic the relationships between different level of the hierarchy and identity pattern and also structure within the data nested with display the data within multiple layers of the nested structures allowing users to explore relationships at the different level and the different regularity. These visualizations provide a hierarchical view for the data while enabling drill down and drill down explanation. Nested pie charts, nested bars charts and nested tree maps are the example of that help us with relating the complex data structure and uncover each size at the different level of the abstraction. This is the visualization highlights I mentioned. I'm going to go the next slide also and data visualization best practices first healing is your audience. This is the important one understand who will be viewing your visualization. Tailor them to their level of expertise. Important for practice because you must know your audience. And the second one is right visualization tool. Right visualization technique is much more important after the audience. It is a visualization that efficiently communicate your message and highlight the pattern and trends in your data. Consider factors such as the data type and the message you want to convey. Audiences familiarity with the different visualization types. It's the best choice. And the third one tell a story interactively and structure your visualization to this story that has a clear and panic story technical flow maybe a narrative art and to guide viewers the data at highlighting design is also important and trance of the way also and the fourth one is simply unclearfy. And please keep your visualization simple and focused avoiding a cluster and unnecessary elements. Use clearing labels annotations to help viewers interpret the data accurately and the conclusion of my presentation my presentation is about me. At the end of my presentation I have a key takeaways. This is so important clear data for good information. This one and second story storytelling for the best concept and the third one is goal and more functionality for the more function based and the third one is a visual form for metaphor. For the best metaphor. These items are the c none of the successful data visualization narratives aware of these items. As I read our presentation of advanced isolation techniques important to recognize that journey is ongoing knowledge and insights gained. Thus the boundaries of the utilizing isolation to unlock the full potential of complexity data sets. Thanks my sincere graduate for engagement and attention throughout this presentation and have a wonderful day. Goodbye. Thank you.