ANALYSTDHIREN

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, ...

User Research on Retailer - Analysis of Challanges and Possibility in adoption for E-Commerce platform for thier on-growing business. I have carried out this research by using DESK Research mentioned, where we reached multiple of retailer with a standardised questionear, which was articulated in such a way that we could get the expected outcome as needed, below are the glimpse of my research.

  UNDERSTANDING THE DATA ANALYTICS PROJECT LIFE CYCLE To achieve the desired results when working on data analytics projects, there are a few fixed tasks that must be completed. In order to effectively lead data to insights, we are going to create a data analytics project cycle, which will be a collection of standardised data-driven activities. Sequences for successfully attaining the goal using input datasets should be followed by the stated data analytics processes of a project life cycle. The steps in this data analytics process could include problem identification, dataset design and collection, data analysis, and data visualisation. The data analytics project life cycle stages are seen in the following phases Identifying the problem Designing data requirements Pre-processing data Performing data analysis Visualizing data Lets get to deep dive in understanding the above phases Identifying the Business Problem: By doing data analytics using web datasets for expandin...

International Sales Dasboard 1 :  Finding : Year Slicer, Sales by Segment, Sales by market, Loss by Product (Created a Measure), Profit by product ( Created a Measure), Region wise sales Map, Top ten customers by Profit. Steps Followed: Step 1: Connecting Data Step 2: Analyzing table & Relationship Step 3: Data Cleaning - Power Query editor (DAX) Step 4: Developing Model Step 5: Report Building Dashboard 2 : Resharing  Re-created dashboard using NASA space traveled data from Kaggle. This dashboard was created by using Power Query. With the Power Query, connections are made between different files and used as a data model in Power Pivot.

Hey, I hope you'll know the squid game, lets apply the same concept on SQL for data manipulation.'

Hey Guys, Have you wondered how netflix recommends you the videos you might ne intrested to watch. Let me show you have it workss Stayy tunned .............. 👉In Netflix videos are recommended based on members interests using ML algorithms 👉Here let us have a look at the important components of Netflix ML Architecture. 👉 Axion Fact Store: Axion fact store is part of Netflix Machine Learning Platform, the platform that serves machine learning needs across Netflix. First part of below image shows how Axion interacts with Netflix’s ML platform. 👉 Fact: A fact is data about the members or videos. An example of data about members is the video they had watched or added to their My List. An example of video data is video metadata, like the length of a video. 👉Compute application: These applications generate recommendations for the members. They fetch facts from respective data services, run feature encoders to generate features and score the ML models to eventually generate recommendatio...

While coding and computer simulations are extremely important in machine learning, the exercises in the book can (mostly) be solved with pen and paper. The focus on penand-paper exercises reduced length and simplified the presentation. Moreover, it allows the reader to strengthen their mathematical skills. Michael U. Gutmann The exercises collected here are mostly a union of exercises that I developed for the courses “Unsupervised Machine Learning” at the University of Helsinki and “Probabilistic Modelling and Reasoning” at the University of Edinburgh. The exercises do not comprehensively cover all of machine learning but focus strongly on unsupervised methods, inference and learning.  Link :  DRIVE