Data processing

Guest post by Eric Schwartzman, founder and CEO of Comply Socially, which helps employers manage the risk and capitalize on the opportunities of social media in the workplace. Follow him on Twitter @EricSchwartzman The online Boston Marathon bombing witch-hunt last week dragged social media down to a new low. Social media has become “the cocktail party from hell,” writes Maureen Down in her column “…with the flood of information jeopardizing meaning.” When everyone’s talking about a crisis…

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When external data from multiple sources is added to the existing data set to enhance the quality and richness of the data. this value adding process is known as data enrichment #dataenrichmentservices #dataenrichment

Over the past couple of years, the business world has become increasingly focused on data because businesses are generating loads of data in all of its every-day operations and processes. Data has now become an intangible asset and businesses are now recognizing the intrinsic value of data. Businesses are using this data to gain real-time […] The post Difference Between Business Intelligence and Data Science first appeared on Difference Between.

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The IABAC certification process hones a critical skill set, including proficiency in data architecture and data pipelines. As a Certified Data Engineer, you'll be equipped to create, maintain, and optimize data infrastructure systems. This encompasses the entire data lifecycle, from its inception to its final destination, ensuring that all data is accurate, complete, and secure. Your work ensures the smooth functioning of key processes, such as data warehousing and database management.

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Discover the key stages of the data analytics process and learn how to effectively collect, prepare, analyze, and visualize data. This systematic approach is essential for businesses and organizations seeking to derive insights and make informed decisions based on their data. With our expert guide, you'll gain a comprehensive understanding of each stage and learn valuable tips and techniques to help you succeed in your data analytics endeavors.

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Data Science Techniques :- 1. Data collection techniques : - web scrapping - data mining - surveys - using APLs - data acquisition 2. Data cleaning techniques : - imputation of missing values - outlier detection and treatment - encoding categorical variables - feature scaling 3. Data visualization techniques : - bar graph - histograms - scatter plots - heatmaps - box plots - line graph - pie chart 4. Machine Learning techniques : - supervised learning - unsupervised learning - semi-superv...

Ever think about what powers AI like ChatGPT? 🌿 It’s time to dig into its environmental footprint! Our deep dive uncovers the surprising sustainability challenges of these data centers. Let’s chat (pun intended)! 💻🌱 Alt Text: A state-of-the-art data center with rows of servers illuminated by soft blue lights.

❌ Struggling to land your dream data analysis job? 👉 Save this post for essential tips on creating job-winning data projects! When working on your projects: 1️⃣ Document your process - Clear documentation shows your approach and thought process. 2️⃣ Use data visualization - Effective visuals communicate your findings clearly. 3️⃣ Highlight actionable insights - Show how your data can drive real-world decisions. ✅ Pro tip: Share your projects on GitHub or create a personal portfolio…

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Discover the power of JSON in web development! Our comprehensive guide covers syntax, data types, and best practices. Perfect for beginners and pros alike. #JSON #WebDevelopment #CodingTips Learn more: https://blog.shubhamgohar.com/json-explained-a-comprehensive-guide-to-syntax-and-usage/

As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.