ANALYSTDHIREN

Data Analytics for Healthcare Industry: Transforming Patient Outcomes The healthcare industry generates vast amounts of data every day, including patient records, medical images, and clinical trials. This data holds immense potential to improve patient outcomes, but it can also be overwhelming to analyze without the right tools and expertise. That’s where data analytics comes in - it allows healthcare providers to turn raw data into meaningful insights and drive better decision-making. Here’s a closer look at how data analytics is transforming the healthcare industry: Improving Diagnosis and Treatment Data analytics can help providers to identify trends and patterns in patient data that might not be immediately obvious. For example, data analysis can reveal correlations between certain symptoms and diagnoses, or help doctors identify which treatments are most effective for specific patients. This can lead to more accurate diagnoses and personalized treatment plans, resulting in better ...

Looking to improve your understanding of neural networks? Look no further than activation functions! These functions determine the output of a neural network and can help it recognize patterns in data. From ReLU to Tanh to Mish, there are a variety of activation functions to choose from.

If you're looking to learn Business Analysis, 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 is a great place to start with as it is a great source of free videos that can help you learn BA skills and increase your knowledge. I have compiled some very useful 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐯𝐢𝐝𝐞𝐨𝐬 in a sequential manner. Do watch the videos in sequence and tag me if it helped you. 1. Software Development Life Cycle: https://lnkd.in/g5sj7uYX 2. Role of Business Analyst in each phase of SDLC: https://lnkd.in/gJ6ymEU2 3. Difference between Agile and Waterfall: https://lnkd.in/gAhQ55yQ 4. Understanding Scope of the Project: https://lnkd.in/gt4iawKt 5. How to create Use Case Diagram for Scope Modelling: https://lnkd.in/gS3UkGGd 6. Stakeholder Analysis and Mapping: https://lnkd.in/gaHFpxYR 7. Types of Requirements: https://lnkd.in/gzmjJFPQ 8. Difference between Functional and Non-Functional Requirements: https://lnkd.in/garfYP8h 9. How To Gather Requirements: https://lnkd.in/g4khWeMY 10. Business Process Flow Modelling: https://ln...

99% of the SQL you write as a data analyst is not complex. Most of it comes from a short list of simple skills. Here is cheat sheet I assembled of must know SQL for analytics… SELECT: The foundation of all SQL queries - Allows you to retrieve the data - Choose the columns you want to display in the SELECT statement - Separate columns with a comma WHERE: Filter your results based on criteria - AND - OR - NOT - IN (list) - =, <, >, etc. GROUP BY: Group rows that have the same values and aggregate data using aggregate functions - COUNT - SUM - AVG - MIN - MAX JOINS: Combine data from multiple tables to form a single output - INNER JOIN - LEFT JOIN   CASE WHEN: Allows you to perform conditional logic - Example: CASE WHEN animal_type = 'Lion' THEN 'Carnivore' WHEN animal_type = 'Giraffe' THEN 'Herbivore' ELSE 'Omnivore' END AS diet_type WINDOW FUNCTIONS: Perform calculations across rows rather than just within a group by - ROW_NUMBER - RANK - DE...

Description : Used machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Model : Random Forest  Rank : Top 3% Globally :  https://www.kaggle.com/competitions/titanic/leaderboard# Accuracy - 81.10% Project Link :  https://github.com/AnalystDhiren Final Submission result : 0 = No , 1 = Yes

Business Intelligence vs. Data Science – Same thing, different semantics?  Overlap :  Both disciplines are data-heavy; data both in the input and the output and deliverables will be very data-focused and both contingent on proper data management. They both require data engineering. Finally, the both utilize data visualization to convey the data story. Difference :  if it wasn’t for the big difference in methods, tools, and approach, I don’t think we would have seen them as two different fields. The major difference is that while BI focused on past and actual data, DS tries to predict the future. That subtle difference in focus requires two very different approaches. DS applies a much more explorative approach, often using unstructured data, whereas BI mainly works with structured data in a descriptive way. Future While the data analytics field matures, I am confident that the two disciplines will converge more and more. There is a vast overlap in handling data pipelines, ...