Welche innovativen Möglichkeiten gibt es, Datenbereinigung und -visualisierung in der Aus- und Weiterbildung einzusetzen?

A tip I can give here is to document everything. Data communication is about trust as well, and not knowing which steps you took to clean the data, you might as well leave some errors in it. When documented, also discuss these steps with the stakeholders. Maybe it’s an opportunity to optimize a business process, or maybe it highlights the need for a data quality report. So communicate what you do, why you do it, and your audience will thank you.

In my experience, the key to having clean data is first ensuring that information entering a database is as clean as possible from the start. This will save substantial amounts of time in the long run, as fixing issues with data at the source (e.g. adding form validation to guide users submitting information) may alleviate much of the need to introduce complex data cleaning methods later on. Secondly, having a clear line of communication with users is crucial. Knowing what data is important, and what should be removed, establishing standardised and useful naming conventions, and understanding the intricacies of how the data is used, can only be achieved by collaboration and a shared understanding of how the data will ultimately be used.

Over time, businesses amass a significant amount of unnecessary data that must be updated to ensure its relevance and verified for accuracy and completeness. Detecting any deficiencies can be accomplished using various reporting tools or spreadsheet software. Addressing these gaps will inevitably require human involvement, although artificial intelligence may offer some assistance to a certain extent.

While data visualization is widely recognized as an effective means of conveying information and insights to enhance learning and teaching processes,it should be complemented with critical thinking and contextual understanding to avoid misinterpretation or oversimplification of complex data. Additionally, there may be differing perspectives on what constitutes essential skills within data literacy. Some may prioritize technical skills such as coding and statistical analysis, while others may emphasize the ability to critically evaluate data sources, identify biases, and communicate findings effectively.

Some Data Cleaning tips for Excel or Power BI Users: - Spot anomalies like missing values, duplicates, or inconsistencies. - Use functions like IFERROR(), ISBLANK(), or filters. - Utilize Excel's "Remove Duplicates" or Power BI's "Remove Rows" in Power Query. - Employ TEXT(), DATEVALUE(), or custom formatting in Excel; "Format" in Power Query. - Manually correct or use SUBSTITUTE(), TRIM(), PROPER() in Excel; "Replace Values" or "Split Column" in Power Query. - Use functions/formulas in Excel; Power Query for transformation tasks. - Implement data validation rules in Excel; DAX formulas or Power Query for Power BI. - Keep a log of the process in both Excel and Power BI. - Schedule periodic reviews to ensure ongoing data quality.

Data visualization involves transforming raw data into graphical or visual representations, such as charts, graphs, maps, or dashboards, to facilitate understanding, analysis, and interpretation. Visualizing data allows for the presentation of complex information in a clear and intuitive manner, making it easier to grasp patterns, trends, and relationships within the data.

In today's data-driven world, education and training institutions accumulate vast amounts of data from various sources such as student records, assessments, learning management systems, and feedback surveys. However, this data is often messy, incomplete, or inconsistent, making it challenging to derive meaningful insights. Data cleaning, also known as data preprocessing, involves the process of identifying and correcting errors, inconsistencies, and inaccuracies in the data to ensure its reliability and usability. One innovative application of data cleaning in education is in personalized learning.

Data visualization is not just about presenting charts and tables; it's an art form that focuses on: - Effective Communication: Transforming data into clear, concise visuals. - Meeting Business Needs: Tailoring KPIs and visuals to address specific business questions. - Targeted Audience: Designing with the audience in mind for maximum impact. - Correct and Accurate Representations: Ensuring data integrity in the visual presentation.

There is a quote that I came across in a Google data analysis training that I believe summarizes the purpose of data visualization; audience needs to understand the visual in less than 5 seconds. In that context, visualization needs to be short, light, relevant and accurate. Another rule that I keep in mind is not to allow myself to show off with an impressive visual. Communication serves a purpose and this is not to boost ones ego. It is about communicating a message in an impactful way.

What makes data visualization really hit the mark? It's all about keeping it crystal clear and straightforward. Think about it like explaining something to a friend – you'd want to make it easy to get, right? That's the essence of effective data visualization – making complex information simple and easily digestible. Let me break it down for you. - Simplicity and Clarity : Keep it simple, use clear visuals, and ensure your message is easily understood. - Relevance and Context : Effective data visualization, like storytelling, prioritizes what matters most to your audience, offering context tailored to their interests and needs. - Accuracy and Precision: Stick to reliable data, avoiding confusion or distortion.

Communication and language are no longer big barriers when it comes to visualizations. One can easily teach to a large audience using charts and graphs. By performing simple data cleaning and visualization, open ended teaching is encouraged. Students thrive when they are challenged to think and interact. This promotes cognitive and analytical skills, which in turn creates passion towards the subject. One by one, they'll soon be excited to learn more.

Data cleaning and visualizing is key for data literacy. with the help of clean data visualization non tech people can understand and read the data effectively and take decision. Messy data can lead to wrong insights and wrong decision. Make sure to clean the data before analyze and visualize. Data literacy is like communicating with data and as a data analyst our job is to break the barrier between large data and stakeholder. For this we have to be creative and good storyteller and making simple yet great dashboards can help others to communicate with data effectively

A alfabetização de dados é essencial no mundo atual, onde os dados impactam todas as áreas da vida. Dominar essa habilidade permite tomar decisões informadas, resolver problemas e inovar, ao mesmo tempo em que desenvolve habilidades como pensamento crítico e colaboração. Para melhorar a alfabetização de dados, é importante integrar práticas de limpeza e visualização de dados na educação e no treinamento, promovendo uma cultura de dados desde cedo. Isso pode ser feito através da participação em projetos, colaboração com outros especialistas, acesso a fontes de dados abertos e busca de oportunidades de aprendizado que correspondam aos objetivos individuais.

Data cleaning involves removing inconsistencies, errors, and irrelevant data from datasets, enhancing their quality for analysis. Techniques include handling missing values, removing duplicates, and standardizing formats. Visualization then transforms cleaned data into intuitive charts or graphs, aiding comprehension. Techniques like scatter plots, histograms, and bar charts help uncover patterns and trends. Together, these processes improve data literacy by making information accessible and understandable, facilitating informed decision-making.

Building data literacy in education and training requires making data cleaning and visualization tools accessible to both teachers and students. Low-code or no-code platforms, coupled with age-appropriate training materials, empower educators to become data facilitators. Students can be active participants in the data cleaning and visualization process, building data literacy skills that are directly applicable in future careers. Further with AI Assistive Coding and Content Generation, Students can make quick conceptual prototypes and projects, much faster - across multiple subject areas. This can foster huge innovation across school grades.

Data cleaning and visualization play crucial roles in enhancing education and training👌 Data Cleaning, for example, can identify at-risk students by cleaning and analyzing attendance records, assignment submissions, and test results. Identify trends of absenteeism or poor performance. & Data Visualization will Create visual dashboards to help educators track student progress. Early warnings can let them intervene quickly and provide specialized assistance Remember, effective data cleaning and visualization empower educators, administrators, and students to make informed decisions and enhance the learning experience. 📊📚🔍

Pour le nettoyage, j’utilise Monsieur Propre et Skip Lessive 3 en 1. Pour la visualisation, j’allume mon écran, et les données apparaissent comme par magie.

Here are two approaches to outliers. Method 1 Divide the data into 4 equal parts Q1 represents the 25th percentile Q2 represents the 50th percentile Q3 represents the 75th percentile The IQR is the difference between the Q3 and Q1 Outliers are data that fall below Q1 – 1.5 IQR and data that falls above Q3 + 1.5 IQR Advantage: eliminate data that was likely to be an anomaly. Method 2 Convert the data into categorical variables by grouping the data into ranges. Eg suppose the data set ranged from 1 to 50. Ranges are 1 to 10, 11 to 20, 21 to 30, 31 to 40, 41 to 50 Then use the categorical variable as the input for the model. Advantage: All data is considered and the impact of the anomaly if it exists would be small.

Beyond the basics, consider the human element. Data isn't just numbers; it's stories about people, trends, and the world. Embrace the unexpected insights and the 'why' behind the data. Remember, every chart or cleaned dataset tells a human story. Dive into those stories, be curious, and let them guide your learning and sharing.

Some tips to keep in mind beside Data cleaning and Data Visualisation: - Ensure diverse, reliable sources are seamlessly integrated. - Establish policies for privacy, compliance, and access control. - Utilize statistical methods and ML for insights and predictions. - Efficiently organize data with suitable storage solutions. - Optimize processing and query performance for scalability. - Enable secure data sharing and collaborative analysis. - Document sources, definitions, and transformations. - Continuously monitor data quality and address issues. - Empower users with training and resources. - Solicit feedback to iterate and improve data processes.