Muhammed Ismail

Someone used to ask me why do I passionate in training people. I said, training is a slow yet rewarding process. The long-term impact it has on individuals and organization can be incredibly rewarding. 🙂 Thank you for this wonderful award! #powerbi #powerautomate #powerapps #powerplatform #learning #training #dataanalysis #fromFinancetoFinance

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When it comes to Data & Analytics, it's a truism that Data Quality (DQ) is crucial. After all, Garbage In=Garbage Out, no matter how sophisticated your algorithms. However, limiting the focus to DQ alone isn't enough. We need to start looking at another type of DQ: Decision Quality. Value creation happens not when an insight is produced, but when real business decisions are made or actions are taken. Therefore, the other DQ that matters is Decision Quality. Quite often, it's not just about the accuracy and precision of the insights we provide as analysts but also about ensuring we collaborate with our business partners to properly frame the questions in the first place. What are your thoughts on the topic? Check out this video for more insights: https://lnkd.in/gajVdepY #dataquality #analytics #decisionquality

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Our latest News of the Month is out! Lots of Power BI and Spotfire news this month, all the E&P software firms seem to have been quiet this year. Come on y'all, do something interesting! https://lnkd.in/g9hQtimS #dataanalytics #oilandgas

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Tutorials for Data Science Beginners Lesson 1 :Divide the data into train and test set Step 1 : Import the Data using pandas , e.g. pd.read_csv Step 2 :Take Care of Missing Data using Scikit Imputer Step 3: Divide them into matrix of features and Dependent Variable vector ,use Pandas iLoc Step 4 : Import Train_Test_Split from Scikit Learn with inputs as matrix of features and dependent variable vector, the output will be 4 datasets 1. 2 Training Data Sets 2. 2 Test Data Sets Step 5: Apply Feature Scaling

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🚀 Data Integration Insights: ETL vs. ELT 📊 Understanding the nuances between ETL and ELT methodologies can revolutionize your data strategies! Here's a closer look: 1. ETL (Extract, Transform, Load):   - Extract data from diverse sources, like databases or applications.   - Transform data to align with operational requirements, often involving data cleansing, normalization, and aggregation.   - Load processed data into a target system, typically a data warehouse. 2. ELT (Extract, Load, Transform):   - Extract data from various sources, similar to ETL.   - Load raw data directly into a target system, such as a data lake or cloud data warehouse.   - Transform data within the target system using processing capabilities like SQL, Spark, or other data processing frameworks. ⚡️ Key Differences: - ETL emphasizes data transformation before loading, often preferred for structured data and traditional data warehousing. - ELT loads raw data first and transforms it later within the target system, suitable for big data and cloud-based analytics platforms. 🔍 Ready to optimize your data workflows? Let's connect and explore the best approach for your organization! #DataIntegration #ETL #ELT #DataAnalytics 💼🔗

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With the guidance of Maven Analytics, I have started brushing up on my skills. For a start, I present a nice little project. https://lnkd.in/gV3EKt5T The next one is going to be using Power BI, which is much more intense and insightful. Don't change the channel :)

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Definitely some good options for managing your #DataPipelines out there. If you’re #SQL-forward, I’d recommend an #ELT approach with Fivetran , Portable , Airbyte combined with dbt Labs or SQL Mesh by Tobiko Data on top of Snowflake. If you’re #Python-centric I’d recommend looking into Apache Airflow , Dagster Labs, or Mage. If you’re trying to stick with your cloud provider Microsoft Azure Data Factory with Microsoft Fabric or Amazon Web Services (AWS) Glue and Redshift are great options. If you’re looking for a platform that combines #DataScience, #DataEngineering, and #Business Intelligence, I’d take a look at Dataiku or Databricks. If you’re more interested in #LowCode, I’d recommend looking at Matillion. Or if you’re Jeff Hughes , you’ll say “F it” and roll with some good ole #PowerShell and #SQLServer!

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Mastering Power BI: Essential Interview Questions and Answers Dive into the world of Power BI with this comprehensive guide to common interview questions and expert answers. Whether you're a beginner or an experienced user, this article equips you with the knowledge and insights needed to ace your next Power BI service interview. Explore key concepts, best practices, and practical tips to showcase your proficiency and land your dream job in the dynamic field of data analysis and visualization. Pdf credit goes to respective owner Follow for more resources #data #analytics #powerbi #service

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2 Minute Data Science : Forecasting -Predictive Modelling Lesson 2 : Regression models (both linear and non-linear) are used for predicting a real value, e.g Salary. If your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values. Regression technique vary from Linear Regression to SVR and Random Forests Regression. Here are the steps to implement it with code, continuing from last example #Load the Data Set dataset = pd.read_csv('Salary_Data.csv') print(dataset) YearsExp Salary 0       1.1  2345.0 1       1.3  6789.0 2       1.5  910112.0 #X : Matrix of Feature #y : Dependent Variable vector(Independent Variable) which you will be predicting X = dataset.iloc[:,:-1].values y = dataset.iloc[:,-1].values #Spliting into Train and Test from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1) #Implementing Linear Regression from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train,Y_train) #Making a single prediction (for example the salary of an employee with 12 years of experience) print(regressor.predict([[12]]))

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Started my journey on becoming certified in Databricks. First goal was to complete their "Get Started with Data Analysis on Databricks" course. I'm planning on starting my "Databricks Certified Data Analyst Associate" learning followed by sitting the Exam. But before then I'm aiming to pass the DP-600 - Having Power BI skills with Fabric and Databricks would be the gold standard in my opinion. Lots of learning ahead 😃 #PowerBI #DataBricks

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At BI4YOU , the journey of knowledge is boundless!🎓💡 Just took my first stride into Snowflake! Wrapped up the Snowflake Hands-On Essentials Data Warehousing Workshop, acquiring essential skills for effective data management, including: 🔹 Creating, editing, and dropping Snowflake databases, schemas, tables, views, file formats, and stages. 🔹 Loading and querying JSON data with finesse, utilizing Path notation and Cast statements. 🔹 Crafting and refining compute resources (Snowflake Warehouses) for optimal performance. 🔹 Mastering various data loading techniques, from insert statements to Snowflake's Load Data Wizard and COPY INTO statements. Ready to tackle the next set of challenges! #Snowflake ❄️ #Cloud #Engineering #AWS #AZURE #GCP #DataEngineer #DataAnalytics #DataWarehouse #ETL #IA #ML #🚀

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It is time for types of analytics to evolve and add 'Participative Analytics' to the list! Here’s why… More than 2 decades ago, when I started into analytics as a profession, it used to be either Descriptive or Predictive analytics. The expectation from an analytics solution at that time was to use data to either describe what has happened or predict what is likely to happen. Over time with better computing power, better algorithms, and appetite to trust a machine to investigate why an event has happened (diagnose) or recommend next best action (prescribe); diagnostic and prescriptive analytics got inserted into types of analytics. And, for a very long time until recently it is about doing one or more of the four out of descriptive, diagnostic, predictive and prescriptive analytics. At the core, analytics is about using data to help in better decision making. It is who plays which role, between human and analytical system (machine/model) that largely defines type of analytics. For example, if the analytical solution tells what is likely to happen but does not suggest what actions to take to prevent or promote happening of that event, then it is doing predictive analytics. Then, when and how does analytics become participative? Consider a game of doubles in tennis where one of the players is a robot and the other three are humans. Which pair do you think would win? While most of us might say that the duo of robot + human should beat the humans only pair. It is likely, but not possible to say with 100% surety. A lot depends on how well the robot in the team is trained in the rules & skills of the game and how effective it is while co-playing with the other three humans. The same applies to each human player in the game. So, both machine and humans are participating in taking decisions during the game based on the actions of each other (information or data generated). If you compare this to a business scenario, the pair of robot and human are two team members in a company whereas the opponent pair are their counterparts from another competitor firm. Each of them is either individually or collaboratively taking decisions to act based on the actions of their teammate and the actions of their competitors, with the sole objective to succeed (win)! Analytics can no longer be limited to being prescriptive but needs to be built to be 'Participative'. Given the exponential growth in capabilities of artificial intelligence solutions along with tech infrastructure that can process and generate results with blink of an eye (almost); such analytics solutions are a possibility. Fair to say, it is time to add ‘Participative Analytics’ to the list of types of analytics? What you think could be various use cases of Participative Analytics? #artificialintelligence, #typesofanalytics, #participativeanalytics, #aiml, #actionableanalytics Check comments for illustration of types of analytics (Image credit: www.freepik.com)

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🎉 Celebrating My Databricks Certified Data Engineer Professional Certification! 🎉 I’m thrilled to announce that I’ve successfully passed the Databricks Certified Data Engineer Professional exam! 🚀✨ This certification validates one’s ability to perform advanced data engineering tasks using Databricks. Here’s a glimpse of what the certification covers: Databricks Tooling: 20% Data Processing: 30% Data Modeling: 20% Security and Governance: 10% Monitoring and Logging: 10% Testing and Deployment: 10% If you’re passionate about advance data engineering, I highly recommend pursuing this certification! 🌟 #Databricks #DataEngineering #CertifiedProfessional

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Project Description: High-Level KPI Report for Massachusetts General Hospital (MGH) Project Goals: The primary goal of this project was to analyze a subset of patient records to generate a high-level KPI report for the executive team at Massachusetts General Hospital (MGH). The purpose of the report was to provide stakeholders with critical insights into the hospital's recent performance, focusing on key metrics such as patient admissions, average hospital stay durations, average costs per visit, and insurance coverage rates. These insights were intended to inform strategic decisions, optimize operational efficiency, and enhance patient care. Business Needs: MGH required a comprehensive analysis to understand its performance trends better and identify areas for improvement. Specifically, the hospital needed to: ·      Track the number of patient admissions and readmissions over time. ·      Determine the average duration of patient stays. ·      Calculate the average cost per visit. ·      Assess the proportion of procedures covered by insurance. Addressing these needs was crucial for MGH to maintain high standards of care, manage costs effectively, and ensure that a significant portion of procedures were covered by insurance, thus reducing financial burdens on patients. Discovering and Presenting Meaningful Insights: To achieve the project goals, the following steps were undertaken: 1. Data Collection and Cleaning: ·      Patient records from 2011 to 2022 were collected. ·      Data was cleaned and pre-processed to ensure accuracy and consistency. 2. Data Analysis: ·      The total number of patient admissions was calculated, revealing 974 admissions between 2011 to 2022. ·      Monthly admission trends were analyzed, identifying February as the peak month for patient encounters. ·      Geographic analysis showed Suffolk County as the region with the most admissions, predominantly for ambulatory cases. ·      The average duration of patient stays was determined to be approximately 7 hours. ·      Cost analysis indicated an overall average cost per visit of $3600, with patients aged 19-35 incurring about twice the average cost. ·      Insurance coverage analysis revealed that approximately 68% (19084) of procedures were covered by insurance, with Medicare being the top insurer, primarily serving the white population aged 65 and above. 3. Visualization and Reporting: ·      The findings were compiled into a comprehensive KPI report, utilizing visualizations to highlight key trends and insights. ·      The report was presented to the executive team, providing clear, actionable insights to inform decision-making. Through this project, we provided MGH with valuable insights into their operational performance, helping them to enhance patient care, manage costs, and improve overall efficiency. Maven Analytics #mavenhospitalchallenge Link: https://lnkd.in/epXgVCDC

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I recently published an article on Medium exploring the latest feature enhancements in Power BI for May 2024. From improved data visualization capabilities to enhanced performance and new integration options, user interaction, and overall usability. Check out the full article for an in-depth look at these features and tips on how to leverage them for data analysis needs. 📖 Read the full article #PowerBI #DataAnalytics #BusinessIntelligence #DataVisualization #TechUpdates

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