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Data Analytics News
Medium
19h
129
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Building a Data Analytics platform on AWS with Apache Superset
- The objective is to predict customer churn using past customer information, helping companies to maintain customer satisfaction and financial stability.
- AWS provides scalable and reliable storage solutions via S3, which allows businesses to store, retrieve, and manage their data effectively.
- AWS Glue simplifies the processing of data for analytics by transforming datasets stored in various data sources, streamlining data integration workflows and accelerating data processing.
- Amazon Athena allows users to analyze data directly from Amazon S3 using standard SQL queries, enabling fast and cost-effective results without the need for complex data processing or infrastructure management.
- To ingest, analyze, and visualize data, AWS services such as S3, AWS Glue, and Athena are used in combination with Superset.
- Superset provides analytical capabilities such as data visualization and dashboarding and can be deployed on an EC2 instance using Docker Compose.
- AWS Glue Data Catalog enables effective data organization by defining tables and employing crawlers to automatically populate these tables by extracting schema information from data sources.
- The connection between Superset and Athena can be established using connection strings in the specified format, and data can be added to Superset for each table and respective charts created.
- The exploration of these tools has equipped users with the knowledge to create comprehensive and interactive visualizations, enabling efficient data-driven decision-making.
- AWS S3, Glue, and Athena in combination with Superset demonstrate the power of AWS services and Apache Superset in driving data-driven insights, enabling informed decisions efficiently.
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Medium
20h
107
Types of AI: Unveiling the Diversity in Artificial Intelligence Models
- Artificial intelligence is a vast field of computer science that aims to create intelligent machines capable of human cognitive functions.
- Reactive machines are designed to respond to a specific sequence of predictable inputs without the ability to learn.
- Limited memory machines use historical data temporarily to make decisions, examples include personal assistants and autonomous vehicles.
- Theory of Mind AI is an AI frontier where machines can fully understand human emotions, beliefs, and intentions to engage in social interaction.
- Self-aware AI embodies systems that engage in decision-making, learning, and possess awareness of their own existence equivalent to human-like self-awareness.
- Narrow AI and AGI are classified as artificial narrow intelligence and general intelligence, respectively.
- Superintelligent AI is a hypothetical form of AI that surpasses human intelligence - it is capable of self-improvement that leads to rapid advancements beyond human control.
- AI systems perpetuate social imbalances if they are trained on biased data.
- The black box effect in complex neural networks makes it hard to understand why a certain conclusion was drawn - high transparency is key in areas like healthcare and law enforcement.
- Safeguarding individual privacy becomes more important as AI becomes more deeply rooted in our lives besides AI systems are vulnerable to hacking, leading to potential misuse and security breaches.
- Development of autonomous weapons poses an ethical challenge as machines are designed to make decisions about life or death.
- AI has the potential to revolutionize data analytics when combined with IoT and transform transportation with autonomous vehicles and advanced robotics.
- In summary, Kanerika is an AI company delivering cutting-edge innovative solutions that set the pace and allow businesses to meet and set industry standards.
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Medium
23h
33
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Why Google Sheets and MS Excel are not utilised for Data Exploration ?
- Data exploration often involves pulling data from databases/data warehouses and querying it using SQL.
- Python is used for data manipulation and extraction of relevant information.
- Business Intelligence Tools like Tableau or Power BI are used for data visualization and publishing results.
- Despite features like data viewing, editing, and chart creation, Google Sheets and Microsoft Excel are not commonly used for data exploration in data science.
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Siliconangle
1d
117
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Driving efficiency with AI and cloud solutions: theCUBE analysis from Alteryx Inspire
- The data analytics industry is being transformed by cloud integration, AI, and machine learning.
- Companies are moving analytics operations to the cloud for scalability and cost-efficiency.
- Alteryx Inc.'s tools are driving efficiency and innovation in various industries.
- Practical applications and industry impact were highlighted at Alteryx Inspire.
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Medium
1d
133
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Data-Driven Culture: How to Implement It in Your Company?
- A data-driven culture is crucial for companies dealing with big data.
- It involves using data to guide decision-making and actions.
- Top-level support, training, and investment in technology are important for implementation.
- The future of a data-driven culture includes AI, machine learning, and real-time analytics.
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Medium
1d
361
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From Data to Decisions: Transforming Cardiovascular Care through Predictive Analytics
- The article discusses how predictive analytics can transform cardiovascular care by identifying patterns that can be used for early detection and prevention strategies.
- The article uses the Agile methodology to iteratively develop and refine predictive models based on the data-driven insights obtained during each sprint phase.
- Four models were considered among the best for predicting cardiovascular disease: Random Forest Classifier, Gradient Boosting Classifier, Logistic Regression, and Fine-tuned GBC.
- Random Forest Classifier works well for classification tasks, Gradient Boosting Classifier can deal with imbalanced data sets, Logistic Regression is valuable for understanding the relationship between target and features, and the Fine-tuned GBC model was well-suited for predicting cardiovascular disease and adept at navigating the complex interactions typical of medical data.
- The data for the study was classified into objective, examination, and subjective features, and the best model was found to be GBC.
- The evaluation of these models using AUC and F1 Score metrics offers a thorough analysis of their classification accuracy concerning cardiovascular disease risk.
- The model's ability to enhance early detection of cardiovascular diseases can significantly reduce late-stage diagnosis rates of CVD among the screened population, reducing the burden associated with treatments.
- By preventing advanced stages of cardiovascular diseases through early intervention, the model would lead to a noticeable decrease in the financial burden associated with CVD treatments, such as hospital admissions, surgeries, and long-term care.
- The model would have a practical impact by increasing the rate of early-stage CVD detection, improving patient management and treatment outcomes.
- The results from the Fine-tuned GBC model demonstrated its effectiveness for predicting cardiovascular diseases and its superior prediction accuracy, particularly when integrating various data types.
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TechBullion
1d
255
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Pioneering Invention Through Software Development with Vention CEO and co-founder, Sergei Kovalenko
- Vention Solutions are powering startups and companies with high-value software development solutions to achieve their vision, powered by talented Ventioneers engineers who have expertise in AI/ML, application development, cloud, data analytics, DevOps, IoT, mobile, and web technology.
- As AI continues to grow rapidly, custom software development is rapidly evolving due to increasing demand by unlocking newer capabilities, such as generative AI and chatbots, making organizations focus on problem-solving, streamlining functions, and enhancing user experiences
- Vention seeks to continue promoting education in software development in their endeavors by encouraging sharing of knowledge through learning sessions, reports, and blog posts to consistently upgrade development capabilities
- AI is gradually becoming synonymous with enhancing customer self-service capabilities like chatbots to maintain strong relationships in fintech and other industries while keeping track of regulatory compliance rules
- The VC landscape is in flux, with the Covid crisis slowing down funding. Startups need to show a clear path to profitability, optimize the stability and cash flow and focus on AI technology, especially AI-driven software solutions, to remain appealing to investors.
- Vention provides the CTO as a Service initiative that enables startups to tap into the right talent for strategic leadership and technical oversight, both valuable for startups' success.
- Vention is expanding its services globally and empowering its staff in line with its people-first approach policy to make their clients' high-risk software projects easier.
- Vention is launching the International Invention Day to celebrate innovation and creativity of any ideas that contribute positively to humanity.
- Vention has launched the In-Vention Incubator program, inviting start-ups worldwide. This program offers free software engineering services for three months, reflecting a $150K in-house market value.
- Vention regards their vision every step of the way; they continue to put people at the forefront of their practice, emphasizing skill development, relocation, and fostering a global team. They aim to provide technology leaders with top talent and the flexibility to accelerate their roadmap, innovate faster and scale operations exponentially.
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TechBullion
1d
15
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An Insights Into The Software Development Market: Interview Vention CEO and co-founder, Sergei Kovalenko.
- Sergei Kovalenko, CEO and co-founder of Vention Solutions, offers insights into the state of software development heading into 2024, VC funding landscape’s sparking concerns among startups and the impact of Artificial Intelligence on software developers.
- Kovalenko emphasizes the importance of integrating Artificial Intelligence (AI) capabilities into business models, highlighting Vention's expertise in AI/ML, application development, cloud, data analytics, DevOps, IoT, mobile, and web.
- Vention’s 2024 technology business trends survey says AI is critical to success, with 96% of surveyed companies recognizing AI expertise as essential within their teams.
- Kovalenko points out that seed-stage companies lean towards predictive analytics and chatbots, while scaling companies prioritize predictive analytics, cybersecurity, and task streamlining.
- Kovalenko believes AI integrations such as ChatGPT in the finance industry can help with identifying and managing problematic communications avoiding potential compliance issues.
- Finding and retaining software talent can be challenging, and partnering with a company such as Vention can demonstrate potential for long-term success to secure VC funding.
- Despite slowing VC funding due to unprecedented global events, Kovalenko is optimistic about the future of software development and expects continued growth in the industry due to the increased demand for companies to keep pace with emerging technologies.
- Vention is launching the inaugural International Invention Day and the first-ever In:Vention Incubator, an incubator program offering free custom software development services for three months, recognizing human creativity and ingenuity and showcasing groundbreaking innovations that shape our world.
- Vention is continuing to expand its global footprint and enhance client services to all regions, operating in nearly 15 countries, with the newest software development hub in Mexico City.
- At Vention, passion-driven innovation encompassing our ethos, a people-first approach, and embracing change are what make Vention unique.
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Medium
1d
7
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Unveiling Hidden Connections: The Power of Network Graph Visualization
- Network graphs comprise nodes (representing entities) and edges (depicting connections between entities).
- Network graphs provide insights into complex biological systems, identifying key proteins involved in various pathways.
- Implementation of network graph scenarios in Python can be done using NetworkX.
- Network graph visualization helps uncover hidden patterns and make data-driven decisions.
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Medium
1d
41
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How To Handle Incoming Data Request Like A Pro — A Data Analyst’s Guide
- Understand the request and evaluate feasibility & readiness
- Acknowledge the request and express interest
- Choose the appropriate communication channel and uncover the underlying purpose and motivation
- Understand the desired outcome and tangible deliverables for the project
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2 Likes
Medium
1d
301
Exploring Product Utility Measures — Post #12
- The concept of product utility and its growth in the digital industry is being explored.
- The measure of Consumer and End-User Product Value (CEPV) is important in assessing product utility.
- Product managers and development teams should focus on enhancing user flow and achieving value exchange.
- Future blog posts will delve deeper into this topic.
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Medium
1d
103
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Conquer Career Gaps: 5 Reasons to Pursue Data Science as a Fresh Start
- Data science can be an excellent fit for a fresh start and career gaps should not be seen as a barrier.
- Career gaps can provide valuable transferable skills that are highly valuable in data science.
- Continuous learning and adaptability are important in the field of data science.
- Data science offers diverse opportunities and learning resources without the need for a specific degree.
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Medium
1d
133
Data Analysis with Python: Basic Concepts
- Lists are one of the basic data structures used for data manipulation in Python.
- Functions allow for creating reusable blocks of code in Python.
- Conditions in Python are created using if, elif, and else statements for evaluating situations and performing different actions accordingly.
- Loops in Python, such as for and while loops, allow for repeating operations based on specific conditions or a certain number of times.
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TechBullion
1d
141
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What Factors Have Led To The Rapid Growth Of The Data Warehousing Market?
- Data warehousing plays a vital role in businesses to draw data from distinct sources, trace trends, visualize patterns, and aid in informed decision-making processes.
- Data warehouses are created to support analytical purposes of data-driven decision-making mainly.
- Data Warehousing Consulting offers expertise in designing, optimizing, and managing data ecosystems that align with organizational objectives.
- Data warehousing in a cloud-based context has scalability, agility, and cost-saving opportunities handy.
- Real-time stream processing platforms and IoT technology like Apache Kafka, Apache Flink, etc., enables quick responses to data discoveries.
- Augmented analytics, embedded analytics, advanced visualization, and advanced edge computing are some modern techniques for data warehousing.
- Privacy-preserving analytics techniques, like homomorphic encryption and differential privacy, help in analyzing sensitive data while protecting individual privacy.
- Zero Trust Security Model guarantees that access to the data warehouse is closely regulated and monitored, lowering the risk of unauthorized access and data breaches.
- The CIO (Chief Information Officer) must strive to increase public awareness of the usage of ODFS data warehouses.
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Towards Data Science
1d
154
Image Credit: Towards Data Science
A Whimsical Journey Through Wait Times
- When we start our wait for the next edit on the Wikipedia page, we don't know what kind of page we're on.
- Likewise, when we call customer service and are put on hold, at the start we don't know what kind of customer service we're waiting on.
- The lottery doesn't care how long we've waited for a win, whether we just won or haven't won for a year, our anticipated additional wait until the next win is almost always between 126 days and 133 days.
- Exponential distributions only model 'Lottery-Win'-like data where expected wait remains the same regardless of a person's wait so far.
- Weibull distribution is used for waits that have memory where people expect different waits based on how long they've already waited.
- Carl Herold recommends using a Weibull distribution for analyzing wait times with Python.
- Weibull distribution fits popcorn waits well where the longer people wait, the less they expect to wait.
- Empirical data distribution analysis should be used when data doesn't fit the Weibull distribution.
- Carl Herold is a scientific programming writer on Medium writing on Rust and Python, machine learning, and statistics in about one article per month.
- The article talks about three types of waiting: Wikipedia wait, customer service wait, and weekly lottery ticket.
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