Deep Digital Solutions Group’s Post

"Beyond the Algorithm: Industrial Analytics Frameworks Every Data Scientist Needs" In this article, I dive into the essential analytics frameworks that have shaped my experience working with industrial data. From predictive maintenance to process optimization, these frameworks are key to driving real-world impact in complex environments. Whether you're in manufacturing, energy, or data science, these insights can help you bridge the gap between theory and practice. Curious how you can elevate your data science game with the right frameworks? Check out the article below!

A data engineer oversees the management of data, from collection through to transformation, distribution, and consumption. However, it's essential to understand that data engineering truly revolves around "deeply" grasping how data can be a valuable asset to your organization. It involves bringing together "scattered" and uncovered knowledge and insights and "presenting" them in the most easily understandable way. Below is a great book to start this journey by Joe & Matt. #dataengineering #dataanalytics #datascience #learning #book #data

Ever wondered how raw data turns into actionable insights? This GIF brilliantly illustrates the Data Engineering Pipeline, a crucial process in the data world. Imagine a streamlined workflow where data is: Collected: Gathered from various sources. Cleaned: Scrubbed for accuracy and consistency. Transformed: Converted into a useful format. Stored: Safely housed for analysis. Data engineers are the architects of these pipelines, ensuring that data flows smoothly and remains reliable for analysts and scientists. This behind-the-scenes work is essential for turning raw information into meaningful data. #DataEngineering #DataPipeline #DataTransformation #DataIngestion #DataStorage #DataCleaning #BigData #DataScience #DataManagement #DataAnalysis

Data engineering is the backbone of data science, but how can organisations ensure that their data engineering efforts align with business objectives? By defining clear data strategies and governance frameworks. Data engineering with purpose. 🌟 #DataStrategy #DataGovernance #DataEngineering

Analytics engineering doesn’t eliminate the need for data analytics and data engineering. It’s simply filling a gap that has existed for a while. #analyticsengineering #dataengineering

Data engineering is the backbone of data science, but how can organisations ensure that their data engineering efforts align with business objectives? By defining clear data strategies and governance frameworks. Data engineering with purpose. 🌟 #DataStrategy #DataGovernance #DataEngineering

Data engineering is the backbone of data science, but how can organisations ensure that their data engineering efforts align with business objectives? By defining clear data strategies and governance frameworks. Data engineering with purpose. 🌟 #DataStrategy #DataGovernance #DataEngineering

Data engineering is the backbone of data science, but how can organisations ensure that their data engineering efforts align with business objectives? By defining clear data strategies and governance frameworks. Data engineering with purpose. 🌟 #DataStrategy #DataGovernance #DataEngineering

📊 Methods of Normalizing Data In the world of data science, normalization is a crucial step to ensure that features (variables) are on a similar scale.  Here are three common methods: Simple Feature Scaling: Divide each value by the maximum value in the feature.  Formula: (x{new} = {x{old}} / {x{max}}). Min-Max Scaling: Shift and rescale values to a range between 0 and 1.  Formula: (x{new} = {x{old} - x{min}} / {x{max} - x{min}} ). Z-score Standardization: Transform values to have a mean of 0 and standard deviation of 1.  Formula: (x{new} = {x{old} - {mean}} / {standard deviation}). Remember, choosing the right normalization method depends on your data and problem!  📈 #DataScience #MachineLearning #DataAnalysis