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Data Analytics News
Medium
1w
54
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What are the Secret Ingredients that make Dashboard Filters so Effective?
- Filters in dashboards are interactive components that allow users to refine and customize the data displayed within a dashboard.
- User-friendly dashboard filters can be used to personalize data by allowing users to select specific criteria to focus on.
- Filters are effective in dashboards because they allow users to narrow down the data displayed on a dashboard.
- Several types of filters are commonly used in dashboards to help users interact with and customize the data they view.
- Interactive chart elements can function as filters, for instance, by choosing a specific area within a treemap or a column in a column chart.
- Choosing the right filters for your dashboard can be a daunting task, but it is important to get it right.
- Filters allow you to narrow down the data displayed in your dashboard, making finding the information you need easier.
- Here are some best practices for using interactive filters in dashboards
- The HR team uses this visualization to track, analyze, and report on the productivity and effectiveness of the employees.
- In conclusion, filters are a powerful tool that can be used to make dashboards more effective.
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Medium
1w
50
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Unlocking Insights Through Visuals: An Introduction to Data Visualization
- Data visualization is crucial for effective communication and understanding of data insights.
- Visualization helps in Exploratory Data Analysis (EDA) and generating hypotheses.
- Matplotlib and Seaborn are essential Python libraries for data visualization.
- Matplotlib provides flexibility while Seaborn adds abstraction and aesthetics.
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Medium
1w
186
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PCSO Lottery Draw Results: Your Highest Chance of Winning (Probably) — A Pair Article
- The dataset is a PCSO lottery draw result data frame.
- Descriptive statistics and correlations were analyzed for the prize amounts and number of winners.
- Exploration was conducted on total winners, lottery games, and prize values.
- May showed a slightly higher probability of winning based on historical data.
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Medium
1w
152
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House Prices Prediction: A Machine Learning Approach
- Using Machine Learning methods to analyse dataset sourced from Kaggle, patterns and relationships in London housing market were discovered
- A statistical summary of the dataset was generated and a missing values matrix was plotted to understand anomalies in the data
- The Random Forest Regressor model was trained to predict house prices using features such as area, number of bedrooms, number of bathrooms, number of receptions, latitude, and longitude
- The model achieved notable results, evidenced by a variance score of 0.821, indicating its ability to explain a substantial portion of the variance in house prices.
- The feature importance plot highlights that area in sq ft is the most influential predictor of house prices, while latitude and longitude are also important
- Decision Tree Regressor model, which works by recursively partitioning the feature space, was also used to predict house prices.
- After comparing the metrics of both models, it became clear that the Random Forest Regressor outperforms the Decision Tree Regressor in predicting house prices.
- Both models used the 'ffill' method to handle null values in the features columns
- Despite being highly effective in predicting house prices, there is room for improvement to enhance accuracy and capture housing market complexities.
- Geocoding technique using Nominatim API was used to convert house postcodes into latitude and longitude coordinates to incorporate geographic information for enhancing the depth and effectiveness of machine learning models.
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Medium
1w
288
Hypothesis Testing
- The null hypothesis is a statement that we assume to be true unless there is strong evidence suggesting otherwise.
- The alternative hypothesis is a statement that contradicts the null hypothesis and is accepted as true only when there is strong evidence to back it up.
- The significance level is a critical threshold used in hypothesis testing to determine whether to reject the null hypothesis.
- The p-value helps us see how strong the evidence is against our initial guess in hypothesis testing.
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Medium
1w
360
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Astronomical Data Analysis
- Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information.
- Astronomical data analysis requires handling incomplete and inconclusive data, often characterized as noise.
- The size of astronomical data makes it challenging to maintain without machine assistance.
- Astronomers need digital data analysis skills, knowledge of data structures, programming features, and data visualization techniques.
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Medium
1w
123
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Using Datawrapper to Make Custom Data Visualization More Efficient
- Datawrapper wasn’t made to create complex interactive visualizations like custom data tools.
- Recently, Urban Institute combined the built-in capabilities of Datawrapper with the data-handling capabilities of a custom JavaScript framework called Svelte.
- One example project was building a county-level map of North Carolina that would enable users to explore demographic information, access to health care and other data relevant to the state’s immigrant community.
- Datawrapper charts aren’t designed to be the main figure within a complex data tool or dashboard.
- Svelte dispatches data from the chart interaction (click/hover) and uses it to set a writable variable with one of Svelte’s reactive stores. A second read-only store is derived from the county-specific information in the underlying source data.
- Urban Institute cleaned and transformed an Excel file provided by the research team in R. They set up two reactive stores using the cleaned and converted data: a writable store for setting the active county and a read-only derived store for all county data.
- Outside of data display components, the main component of the application is the Datawrapper map iframe, which is embedded in the parent webpage. This explains how interaction events are captured.
- Svelte’s dispatch() function passes event information to the parent component and Urban Institute plans to incorporate Datawrapper’s web components to improve load time compared with iframes for chart switching.
- This approach helps Urban avoid complex custom visualization tools while still making impressive, interactive dashboards.
- This project highlights the value of combining the capabilities of Svelte and Datawrapper to build interactive maps with dynamic information tables.
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Medium
1w
407
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The Advanced Visualization Plots that Data Scientists Use
- KS Plot (Kolmogorov-Smirnov Plot): Assess distributional differences between datasets.
- SHAP Plot (SHapley Additive exPlanations Plot): Evaluate feature significance in model predictions.
- OC Curve (Operating Characteristic Curve): Illustrate the balance between true positive and false positive rates in classification.
- Precision-Recall Curve: Show the trade-off between precision and recall in classification.
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Medium
1w
195
Unlocking the Power of Science and AI: Scientific Machine Learning
- Physics Informed Neural Networks (PINNs) in Scientific Machine Learning (SciML) combine the knowledge of physics with the abilities of neural networks.
- SciML can be used for various real-world problems such as detecting gravitational waves and predicting the spread of diseases like Covid-19.
- Explore SciML through a comprehensive book and Julia's ecosystem, known for its speed and simplicity.
- Join the world's first bootcamp dedicated to Scientific Machine Learning to enhance your skills.
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Medium
1w
144
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Unlocking the Power of String Similarity, i introduces: Pedro Thermo Distance & Similarity
- Pedro Thermo Distance algorithm extends beyond the static nature of conventional methods like the Levenshtein distance.
- The algorithm introduces a flexibility that allows it to adapt its evaluation strategy based on the “temperature” of the match sequence.
- Each character comparison either heats up or cools down a conceptual thermometer, which influences the scoring of subsequent character matches or mismatches.
- The ‘thermometer’ acts as a dynamic gauge of the text alignment’s “temperature,” influenced by the sequence of edits.
- This approach allows Pedro Thermo Distance to adapt more fluidly to the context of text comparison, providing a more nuanced and context-sensitive analysis than traditional methods.
- Pedro Thermo Distance evaluates the consistency of characters, adapting its response based on the flow of both correct and incorrect sequences.
- Pedro Thermo Distance and Similarity offer a new lens through which to view data, adapting to our complex and dynamic data environment.
- The tool is prepared to elevate work involved in refining AI models, cleansing vast datasets, and exploring the genetic threads of life itself.
- The ability to quickly and accurately assess text similarity can dramatically impact the effectiveness of information retrieval systems, AI responsiveness, and data integrity checks.
- Pedro Thermo Distance & Similarity stands out by setting a new standard in text analysis technology.
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Medium
1w
242
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Installing Apache Superset on Windows using Ubuntu WSL
- To install Apache Superset on Windows using Ubuntu WSL, follow these steps:
- Enable WSL by running a command in PowerShell as Administrator.
- Install Ubuntu from the Microsoft Store and create a new user account.
- Update package lists, install dependencies, and create a Python virtual environment in Ubuntu.
- Install Apache Superset and configure the superset config file.
- Initialize the Superset database, create an admin user, and load example data.
- Start the Superset server in Ubuntu and access the Superset UI in a Windows web browser.
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14 Likes
Medium
1w
357
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Soaring in Data: Exploring the Airline Market in Colombia
- The author explored the airline market in Colombia by sourcing data from Colombia's Special Administrative Unit of Civil Aeronautics (Aerocivil).
- They spent a significant amount of time cleaning and standardizing the data using powerful tools.
- The author hosted the data on AWS for secure and convenient access.
- They used the web-based tool Sigma for data visualization and analysis, which provided a streamlined and familiar user interface.
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21 Likes
Medium
1w
72
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Most Pirated Magazines on Sci-Hub
- Sci-Hub is a database that contains millions of research documents, including both free and private materials.
- The website allows users to access documents via DOI (Digital Object Identifier) search.
- The most pirated magazines on Sci-Hub include publishers like Elsevier BV and Springer.
- A notebook on Kaggle provides a step-by-step process to extract information from Sci-Hub.
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Medium
1w
387
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D3 Pie Chart for React Native
- To create a D3 pie chart in React Native, you need to add the d3 and SVG packages to your project.
- Create a file for the component, such as PieChart.tsx, and define the necessary interfaces for data and component props.
- Define the properties for the PieChart component, including data, width, height, outerRadius, innerRadius, and padAngle.
- Use the pie and arcGenerator functions to generate the necessary components for rendering the pie chart.
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Cloudblog
1w
196
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Accelerating CDC insights with Dataflow and BigQuery
- Data-driven companies are increasingly infusing real-time data into their applications and user experiences.
- Historically, customers have had to manage temp tables and schedule merge statements to keep their systems in sync, which can be a lot of work and prone to failures.
- To solve these problems, BigQuery includes native CDC support, which reduces complexity and makes results available to analysts immediately, accelerating time to insight.
- Customers have a comprehensive range of options for CDC workloads depending on their use case.
- Dataflow along with the new Dataflow at-least-once streaming mode can drastically simplify CDC pipelines and reduce costs.
- There are many use cases where you might consider using Dataflow for your CDC pipeline, such as computing statistics on a subset of the input data, triggering mechanisms, detecting anomalies, joining star schema relational databases, writing to destinations other than data warehouses, etc.
- BigQuery CDC functionality can be used with Dataflow’s BigQueryIO connector by ensuring the destination table follows the CDC prerequisites outlined in the BigQuery documentation.
- Each incoming element should provide two types of information: the actual data fields for a particular data row in the table and the information about whether this is an upsert or delete operation for that row.
- The semantics of the connector are fairly intuitive and the sequence number in the RowMutationInformation object is important.
- The STORAGE_API_AT_LEAST_ONCE method can result in a faster and cheaper pipeline when used in a pipeline with the Dataflow streaming engine enabled.
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