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Data Analytics News
Medium
7d
166
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Navigating the Crypto Market: Data Analysis Made Easy with WhalePortal
- WhalePortal is a reliable and authentic data analysis platform tailored for Bitcoin and cryptocurrency traders.
- It offers a variety of tools such as heatmaps, sentiment heatmaps, fear and greed indexes, and buy/sell ratio, empowering traders with investor psychology and market sentiment.
- The platform also provides educational resources, including articles, guides, and blogs, to enhance trading skills.
- WhalePortal is suitable for both experienced investors and beginners, offering advanced data analysis tools for informed decision-making.
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Medium
7d
378
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Making Data Beautiful is not Enough!
- Data visualisation best practices and the use of design process to create impactful designs are discussed.
- Data artists are beating A.I in mapping data and creating beautiful and impactful infographics.
- Data storytelling goes beyond visually-appealing data charts and focuses on communicating real outcomes.
- Fostering empathy in data storytelling practices is essential for creating narratives that inspire action and drive meaningful change.
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Medium
7d
99
Data Downtime, is the new oil leaking?
- Data downtime is a critical issue impacting businesses.
- Causes of data downtime include software bugs, human errors, cybersecurity incidents, maintenance and upgrades, data integration issues, and natural disasters.
- Data observability is crucial for managing data effectively.
- The five key pillars of data observability are freshness, quality, volume, schema, and lineage.
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Medium
1w
104
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Unlock Weather Insights: How You Can Use Power BI with OpenWeatherMap — Part 17
- Next, we will format and position the button.
- Adding the humidity icon to the button.
- Using a text box to display humidity percentage information.
- Setting font color and finishing the integration of humidity data.
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Medium
1w
87
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A Brief Theoretical Approach to Understanding One-Way ANOVA (Analysis of Variance)
- One-way ANOVA aims to determine whether there is enough evidence to suggest that the observed between-group variability is significantly greater than the expected within-group variability.
- It partitions the total variability in the dataset into sum of squares between (SSB) and sum of squares within (SSW).
- The F-statistic is calculated by comparing the mean squares between (MSB) and mean squares within (MSW).
- If the calculated F-statistic exceeds the critical value, we reject the null hypothesis and conclude that there are significant differences between the group means.
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Medium
1w
12
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Everything You To Know About Logistic Regression — Part 1
- Logistic regression is used when the dependent variable is binary (0 or 1) and the independent variable can have any number of classes.
- Logistic regression returns continuous values as output, which are later converted into binary values based on a threshold.
- The prerequisites for logistic regression include the understanding of probability, odds, and log odds.
- Logistic regression is used to analyze the relationship between a dichotomous dependent variable and categorical or numeric independent variables.
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Medium
1w
259
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Unveiling HR Insights: An Analytics Dashboard Built with SQL and Power BI
- The HR Analytics Dashboard project involves an HR employee dataset that provides a rich source of information for analyzing various aspects of the workforce and deriving meaningful insights.
- To ensure data quality and consistency, the dataset undergoes a thorough cleaning and preprocessing process using SQL.
- The project aims to answer various questions about HR employee data using SQL queries, which address key questions such as employee count based on gender, race/ethnicity, age distribution, and company turnover rate, among others.
- To bring the insights to life and facilitate interactive exploration, the project utilizes Power BI to create a visually compelling HR Analytics Dashboard.
- The dashboard presents key visualizations derived from the SQL data analysis, which provide a starting point for further investigation and can guide HR strategies and initiatives.
- During the project, data cleaning, filtering, data quality, and data exploration-related challenges were encountered, emphasizing the significance of these areas in ensuring the accuracy and reliability of analysis.
- The project highlights the importance of data quality, data exploration, and visual storytelling in deriving meaningful insights from HR data.
- By leveraging these tools, organizations can gain valuable insights into their workforce, enabling data-driven decision-making and strategic HR initiatives.
- The HR Analytics Dashboard project demonstrates the power of combining SQL for data analysis and Power BI for data visualization in the context of HR analytics.
- The HR Analytics Dashboard project is an invitation to explore the dashboard further by visiting the GitHub repository and collaborate on advancing HR analytics practices.
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Medium
1w
163
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Unveiling the Magic of Web Scraping: A Beginner’s Journey on Google Colab (A Comprehensive Guide)
- In this article, you will learn the basics of web scraping, including why it is essential, how it works, and how to do it effectively using your favorite web scraping tool.
- Before you start, ensure that you have your favorite web scraping tool at the ready, and pull up the Jupyter Notebook from the author’s GitHub repository.
- Web scraping is essential for researchers, businesses, and data analysts to collect the data they need for their studies, monitor competitors' prices, and analyze information.
- Choosing a suitable website is paramount for successful data extraction. Factors like website structure, data consistency, and anti-scraping measures can significantly impact the scraping process.
- The article focuses on scraping job listings from TimesNowJob. To scrape individual job listings, the author uses BeautifulSoup to parse the HTML content of the webpage.
- To identify elements for scraping, one should know basic HTML tags commonly used in web scraping such as ,,, etc.
- The scrape_with_pagination function navigates through a specified number of pages and gathers various job details like the title, company name, experience requirements, and more. It then stores the information in a structured format.
- To bypass anti-scraping measures, adopt techniques such as rotating user agents, using proxy servers, and implementing delays between requests.
- Navigate the legal and ethical landscape of web scraping responsibly, and respect website terms of service, robots.txt files, and copyright laws when scraping data.
- In conclusion, web scraping is an essential skill that data professionals need, and this article has provided the basics to get started.
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Medium
1w
154
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A Beginner’s Guide to Combining Dataframes with Pandas
- This article explains how to merge tables using Pandas in Python.
- The inner join is the most common type of join used, returning a dataframe with only rows that have matching values in both dataframes.
- If columns being joined have different names, left_on and right_on can be used to specify both names.
- A left join will return all rows in the left table and only matching rows in the right table, while a right join does the opposite.
- An outer join (or full join) will return all records that have a match in either table.
- The indicator argument can be set to True to create a new column indicating which dataframe each row belongs to.
- The concat() method can be used to concatenate dataframes vertically (by rows) or horizontally (by columns).
- By default, concat() keeps the original indexes of the separate dataframes, but this can be changed with the ignore_index argument.
- To concatenate dataframes horizontally (by columns), the axis argument needs to be set to 1.
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Medium
1w
389
Working with Heterogeneous Information Networks part5(Machine Learning 2024)
- Heterogeneous graph neural networks (HGNNs) have recently shown impressive capability in modeling heterogeneous graphs.
- A novel heterogeneous graph model called MULAN is proposed, which includes a type-aware encoder and a dimension-aware encoder.
- The type-aware encoder compensates for the loss of node type information and leverages graph heterogeneity in learning node representations.
- The dimension-aware encoder captures the latent interactions among diverse node features and encodes comprehensive information of graph heterogeneity, node features, and graph structure in node representations.
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Medium
1w
410
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Data Analysis Project — Customer Income and Expenditure Analysis to Guide Product Strategy
- Mitron Bank wants to introduce a new line of credit cards to broaden their product offerings and reach the financial market.
- AtliQ Data Services proposed a pilot project to analyse a sample dataset of 4000 customers' online spending and other details before implementing the full project for Mitron Bank.
- The analysis of the provided sample data will guide Mitron Bank in tailoring the credit cards to customer needs and market trends.
- By personalising the features of its credit card based on the insights associated with customers' monthly spending, Mitron Bank can encourage the use of its credit card as the most preferred mode of payment.
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Medium
1w
386
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Building a Scalable Data Engineering Pipeline for YouTube Data Analysis
- Understanding customer behavior, market trends, and emerging patterns is crucial for staying ahead in a competitive market.
- This blog post discusses building an end-to-end data engineering pipeline for YouTube data analysis.
- The goals include data ingestion, data lake architecture, ETL design, and reporting.
- The project focuses on building a scalable data engineering pipeline tailored for YouTube data analysis using AWS services.
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23 Likes
Medium
1w
100
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Machine Learning: Transforming the Financial Landscape
- Fraud Detection: ML algorithms are effective in combating financial fraud by analyzing transaction patterns, customer behavior, and data to detect anomalies.
- Risk Assessment: ML algorithms help financial institutions assess creditworthiness by analyzing financial history, credit scores, and relevant data.
- Algorithmic Trading: ML-driven algorithms revolutionize market operations by analyzing market data, news feeds, and social media sentiment for accurate trades.
- Customer Relationship Management: ML algorithms analyze customer data to personalize financial offerings and enhance customer relationships and loyalty.
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6 Likes
Medium
1w
349
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Visualizing your data with Tableau
- This article offers an overview of the workspace with different available features to create the first Tableau dashboard using open-source datasets.
- The tips dataset from Python is introduced, which provides a data description.
- It is advised to frame a set of questions you would like to answer from this dataset and its corresponding dashboard.
- Simple column charts are constructed to answer summary questions.
- Visualizations such as scatter plots, histograms, and data tables are used to explore the given dataset and answer complex questions.
- Finally, the Tableau dashboard is created and insights are drawn from it.
- Readers are encouraged to dive deeper into the features and functionalities of Tableau that can be accessed from its official website.
- It is a useful tool for data analysis, and this article serves as a good starting point to get started with Tableau.
- The article provides step-by-step instructions on creating a variety of visualizations, such as scatter plots, histograms, and data tables.
- Creating a dashboard and insights from it are also discussed.
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Medium
1w
219
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The Statistical Checklist: 15 Mistakes Every Data Scientist Should Evade
- Confusing Statistical Significance with Impact
- Ignoring Essential Data Transformations
- Overlooking Model Assumptions
- Inadequate Handling of Missing Data
- Excessive Reliance on Automated Model Selection
- Employing Overly Complex Models Unnecessarily
- Data Leakage in Model Training
- Disregarding the Simplicity of Model Parsimony
- Ignoring Temporal and Spatial Dependencies
- Undercommunicating Model Uncertainty
- Neglecting Nonlinearity in Variable Relationships
- Overfitting Predictive Models
- Misinterpreting Model Outputs
- Overlooking External Validity
- Mismanaging Data Granularity
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