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  Python Libraries-Applications NumPy Python approaches data analysis by array - oriented computing which becomes the basis for all data processing tasks. With NumPy, it is possible to implement multidimensional arrays and apply techniques ranging from simple cleaning and merging to advanced tasks such as linear algebra and statistical analysis. Once installed, NumPy provides the foundation for other libraries such as pandas, scikit-learn, and SciPy. The functionality that is provided in NumPy applies to many data processing tasks. Pandas  The pandas library in Python is used for doing data manipulation. Origin of the library name comes from Panel Data and Statistics. pandas provides efficient ways to: Format data into dataframes which makes it easy to identify and delete specific records, columns or chunks of data using indexing All file types such as csv, json, xml, excel can be called and converted to dataframes with a simple function. pandas uses loc and iloc functions to ...

Building an AI Team A strong AI team forms the basis of a successful business. Any organization who wants to implement AI must first gauge their strength and weakness. Also, with the speedy acceptance of AI applications, organization are not only hiring AI experts, but their entire business strategies are revolving around building an experienced AI team. There are multiple ways to organize AI teams. The teams are structured as   Building an AI team Centralized Distributed Hybrid Characterized by an individual or a team responsible for the control, implementation, and maintenance of the AI strategy. Defined by individual users or business units (BU) maintaining and controlling their own AI plan. Combines a centralized enterprise strategy with a decentralized execution and implementation. Also, allow for a holistic view of AI implementation with central control on policies, framework, reporting, plus best practices for the business units to follow. The individuals or team are respons...

Health Before Wealth: Back and Neck Pain Therapy for Desk-Working Professionals Ah, the glamorous life of a desk worker! You wake up, put on your favorite sweatpants (hey, it's a tough job), and plop down in front of your computer to tackle the day. But wait! Before you dive into that sea of emails and spreadsheets, let’s chat about something that’s probably lurking in your future: back and neck pain. Yes, folks, it’s time to put the “ow” in office! The Dark Side of Desk Jobs Picture this: you’re typing away, and suddenly you realize your neck feels like it’s auditioning for a role in a horror movie—twisted and bent at unnatural angles. Back pain? It’s just your body’s way of saying, “Help me! I wasn’t made for this!” If you’re not careful, you could end up feeling like a pretzel that’s been twisted one too many times. The Price of Ignoring Pain Now, I get it. You’re busy! You’ve got deadlines, meetings, and a caffeine addiction that rivals the most dedicated barista. But ignoring ...

  Comparison: Data Warehouse and Data Lake Structured Data Unstructured Data Data Lake (Raw Data) Reporting Analytics Process Data Sources Data Lake Architecture Analytics Reporting Process Structured Data Reporting Analytics (Extract Transform Load) ETL Data Warehouse (Metadata + Summary + Raw Data) Data Sources Data Warehouse Architecture The comparison between a data warehouse and a data lake is as follows: The figure represents the process of data collection, storage, and reporting in Big Data. Data Warehouse Data Lake Data Structure and transformed Structure/semi-structure, processed/raw Working Structure-ingest-analyze Ingest-analyze-understand Processing Schema-on-write Schema-on-read Storage Expensive when volumes are high Built for low cost storage Flexibility Fixed configuration, not very flexible No particular structure, configure and reconfigure as per your requirements Cost/Efficiency Efficiently uses CPU/IO Efficiently uses storage and processing capabilities at very ...