Big data Ppt summary
Here are some key points about the big data powerpoint summary: ** 1. Data display chart selection ** 1. ** Reflects the trend of data change ** - Graphs were often used to show the changes in data over a period of time, such as annual data changes. In scenarios such as year-end summary, you can set the curve (such as curve setting, setting the base) to magnify the change effect, so that you can clearly show the data trend such as performance growth. 2. ** Prominent node data ** - The bar chart emphasized the node data. By changing the style and filling the graph, the data that you want to emphasize can be highlighted, which is suitable for displaying the indicator data. 3. ** Reflects the proportion of data ** - Pie charts were commonly used to show the proportion of various projects. Through the circle setting, pseudo-materialization design, and other techniques, the proportion of data could be well reflected, such as the share of various projects in the overall. 4. ** Comparing various indicators ** - The radar chart could be used to compare various indicators. For example, when displaying the comparison relationship between various indicators related to big data, a gradual design could be used to improve the presentation of the radar chart. 5. ** indicates the data conversion status ** - Funnel charts were very useful when reporting results, especially when it came to data conversion, such as income-output ratio, download conversion rate, page click rate, and customer purchase rate. 6. ** Multi-data comparison presentation ** - Nightingale diagrams were very advantageous in comparing and presenting multiple data items. For example, they could be used to compare multiple related data items in big data analysis. 7. ** Target dismantling and review ** - The circular bar chart was similar to the way the Apple Watch's sports data was presented. It could be used to disassemble and re-examine the target, making the data more attractive. 8. ** Prominent data change process ** - The dashboard chart took into account both dynamic expression and data presentation. It was suitable for situations where the process of data change needed to be highlighted, such as showing the dynamic change process of a certain indicator in big data over time or other factors. 9. ** Directly compare the two types of data ** - The left and right comparison chart was very effective for comparing two sets of data. It could disassemble and compare the nodes of the two types of data to give more details of the comparison. It could be used to compare two sets of related data in big data analysis. 10. ** Increase the attractiveness of the presentation ** - As a general web-based data expression, dynamic numbers had been introduced into PowerPoint in recent years. It was suitable for year-end summary and other scenes that required a presentation, making the PowerPoint more attractive. ** 2. PSP production ideas and techniques ** 1. ** In terms of logic and expression ** - It could be arranged according to logical relationships, such as the total score structure. For the content presentation, the key data had to be extracted and magnified separately. For example, the achievement rate and other data could be converted into a more intuitive chart (such as converting the 88% achievement rate from a simple number to a ring chart). If it was to reflect the ranking and other content, the table could be converted into a more intuitive bar chart. 2. ** PowerPoint presentation is a skill that can be learned ** - PowerPoint presentation was not an art but a skill, and there were ways to learn it. For example, he could participate in a 14-day work-type PowerPoint rapid improvement class to learn a series of knowledge points such as style building, typography, animation adjustment, data presentation, and so on. ** 3. Big data-related content display (Take the smart digital power big data platform as an example)** 1. ** Platform Construction Concept ** - It revolved around the three core concepts of data assetization, asset valuation, and business dataization. Build a data warehouse system, service layer, data calculation layer, and data product layer to realize the full process management from data collection to data application. 2. ** Data Integration ** - It supports a variety of data sources, such as Oracle, Mystical, HBase, and other database, as well as industrial agreements such as Opc-Modbus. It could perform full data extraction, increment extraction, and extraction under specified conditions. It also had data cleaning and integration functions. 3. ** Data Management ** - Using tool components such as indicator reporting tools, self-service analysis platforms, data visualization, and machine learning algorithms to provide comprehensive support for data governance. 4. ** Data application ** - It is widely used in production and operation auxiliary analysis, electricity sales transaction data analysis, AI fault analysis and other diverse business scenarios. 5. ** Data Management ** - Through rule configuration, quality reports, quality inspections, and other means to achieve closed-loop management of data quality, improve the overall quality of data. 6. ** Data analysis component ** - Including data general report, self-service analysis platform and machine learning platform, the whole process of big data machine learning can be solved through model definition wizard. 7. ** Data Service Platform ** - Kettle web visualization configuration is provided to send data to KAFKO or convert it into an interface file for the caller to access the data without coding. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
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