The Data Science Workflow & Job Roles Explained (Data Engineer vs. Analyst vs. Scientist) скачать в хорошем качестве

The Data Science Workflow & Job Roles Explained (Data Engineer vs. Analyst vs. Scientist)

Confused about the difference between a Data Engineer and a Data Scientist? In this video, we break down the end-to-end Data Science Workflow. We start with turning a business problem into customer segmentation and move through data collection, EDA, and model deployment. I also explain the specific job descriptions for each stage, using real-world examples like Amazon Alexa and Snapchat Filters to show how AI models are deployed in the real world. This is Lesson 10 of our Free Placement Course. 📌 TIMESTAMPS: 00:00 - Intro: The Data Science Workflow 00:20 - Step 1: Formulating a Business Problem into a Data Problem 01:23 - Customer Segmentation Example (Marketing ROI) 02:40 - Data Collection: Database vs. Data Set 03:45 - Preparing the Data & Statistical Analysis (Mean, Median, Mode) 04:19 - Exploring Data (EDA) & Prediction 04:43 - Part 2: Job Roles in the Data Science Process 05:00 - Data Engineer: Collection & Cleaning 05:10 - Data Analyst: Cleaning & EDA 05:25 - Machine Learning Engineer: Building & Deploying Models 05:39 - Deployment Examples: Amazon Alexa (Product) vs. Snapchat (Feature) 06:55 - The All-Rounder: What a Data Scientist Does 🔗 RESOURCES: Join the Placement Prep Course: [   / @freeplacementcourse  ] #DataScience #MachineLearning #DataAnalyst #DataEngineer #CareerAdvice #AI #TechPlacement #EDA #SnapchatAI