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(¤"U1s"1 "11I³U(1 1 i U 1 ( ¤ " U 1 s " 1 ɚ " { q ɞ ¤ (ĈƇÝĭńĈāōžƤĈŸžƇŕGžGŸŕŋ ɀɇɕɀɄɃ1ŋÝĭń¤ƍùžûŸĭùĈŸž *2/* -)о Ѷ ) $"#Ҋ 4$)" * Ѷ ) 0/0- Ҋ-**!*0- - - '$/ / $ ) ѵ *( Follow me on LinkedIn for more: Steve Nouri https://www.linkedin.com/in/stevenouri/ COPYRIGHT NOTICE Copyright © EliteDataScience.com, Challenger Media LLC ALL RIGHTS RESERVED. This book or parts thereof may not be reproduced in any form, stored in any retrieval system, or transmitted in any form by any means—electronic, mechanical, photocopying, recording, or other- wise—without prior written permission of the publisher, except as provided by the United States of America copyright law. TABLE OF CONTENTS - - - * - - - CH. 1 - LAUNCHING YOUR CAREER 1.1 - What do I need to know in order to become a data scientist? / How do I land a job as a data scientist? 1.2 - What are the most relevant tools to learn TODAY in terms of commercial value? 1.3 - What’s the most efficient way to learn DS / ML as a busy professional? 1.4 - How do I switch careers as quickly as possible? 1.5 - How do I build a portfolio of real-world projects? CH. 2 - ROLES AND REQUIREMENTS 1.2 - What is the difference between Data Science, Machine Learning, AI, Data Analysis, and Deep Learning? 2.2 - How much math should I learn for DS / ML? 2.3 - Do you need an advanced degree / CS degree / math degree to become a successful data scientist? 2.4 - What makes a good data scientist? 2.5 - Am I too old / too young to become a data scientist? CH. 3 - BEST ADVICE FOR ________? 3.1 - People with business backgrounds seeking to enter the field? 3.2 - Students seeking to enter the field? 3.3 - People with software engineering backgrounds seeking to enter the field? 3.4 - Someone with no relevant work experience seeking to enter this field? 3.5 - Someone seeking to transition from data analyst to data scientist? CH. 4 - FUTURE-PROOFING YOUR CAREER 4.1 - What does the career path of a data scientist look like? 4.2 - Should I use libraries / pre-existing solutions, or should I code algorithms from scratch? 4.3 - How can I stay abreast with the latest tools and best practices given the rapid pace of this industry? 4.4 - Will DS/ML be automated in the future? How can I future-proof my skills and career? 4.5 - How can I use DS or ML to make money from home? / Are there remote opportunities? © EliteDataScience.com , All Rights Reserved 2 Welcome to EliteDataScience.com’s Data Science Career Guide! When we surveyed 29,265 subscribers on our email list, one of the most common questions was, “How do I get started in data science and machine learning?” We’ve compiled this guide of FAQs to help you do just that… and much more. We hope that you’ll use this guide to jumpstart your journey and cut the learning curve. Let’s start with how to build a rock-solid foundation of practical skills and knowledge. Then, later in this guide, we'll cover specific tips for people of various backgrounds. To start: 1. Read the rest of this guide in its entirety. We surveyed 29,265 subscribers on our email list, and these are the most common questions we’ve received. Chances are that you have a few of these questions as well. 2. Circle back to the answer for the question, “What’s the most efficient way to learn DS / ML as a busy professional?” In that answer, we outline what we’ve found to be the most efficient roadmap for learning these skills. 3. Get your hands wet immediately. We’ve prepared several tutorials for you to get started, and we recommend diving into them ASAP. You can find the full list of links and resources later, but here are a few important ones to look out for: a. Data Science Primer: The Core Steps of the ML Workflow b. Tutorial #1: Python for DS Ultimate Quickstart Guide c. Tutorial #2: Intro to Machine Learning with Python and Scikit-Learn Throughout this guide, we’ll also have some external links to additional resources or articles. We recommend reading through the complete guide first, and then checking them out afterwards. You’ve made an outstanding career decision to start learning more about DS & ML (even if you decide it’s not for you). So without further ado, let’s keep going! © EliteDataScience.com , All Rights Reserved 3 CH. 1 - LAUNCHING YOUR CAREER - - - * - - - 1.1 What do I need to know in order to become a data scientist? / How do I land a job as a data scientist? While there are a variety of positions that could fall under DS, we've categorized them into two types: Business Data Scientists and Product Data Scientists. First, we’ll address the core skills that every data scientist needs. Then, we’ll address those categories separately. There are also hybrid roles that require the skills from both the business and the product side. Finally, please note that we’re not trying to provide an exhaustive list of everything you might run into. Instead, our goal is to list the core skills within each category that will give you the biggest bang for your buck. There are only 24 hours in a day... and you still need to sleep, eat, work, go to school, and/or spend time with family and friends. So we’re going to introduce the core skills that will get you a foot in the door. And yes, some employers will have more requirements. But if you lock down the following core skills, you WILL be able to land a high-paying job in this field, guaranteed. All Data Scientists 1. Data Analysis / Exploratory Analysis - First, you need to be able to analyze data and extract key insights. You should do this before any modeling or building any product. That includes data visualization and calculating key summary statistics. Proper exploratory analysis guides you throughout the rest of your project. © EliteDataScience.com , All Rights Reserved 4 2. Data Preprocessing - Includes extracting, cleaning, transforming, aggregating, and de-aggregating data. In other words, be comfortable developing raw data into a more useful format for analysis. 3. Applied Machine Learning - It doesn’t matter if you’ll directly be doing the modeling or not... machine learning is one of THE central technologies within this field. Applied ML includes data exploration & cleaning, feature engineering, algorithm selection, and model training. Business Data Scientist Business data scientists improve business profitability through data analysis, predictive modeling, and testing. For business data scientists, the emphasis is on the insight that you can derive from the data. Examples include: ● Marketing - Building predictive models and bidding strategies for ad markets like Google Adwords or Facebook Ads ● Investing - Using stock price data, global macro-economic indicators, and machine learning to predict stock prices ● Strategy - Using clustering to find “similar” test and control stores for a chain-wide experiment ● Operations - Building models that predict customer churn, allowing the company to proactively reach out Aspiring business data scientists should add the following core skills to their skillset: 4. Domain Knowledge - Data science is never done in a vacuum. You will always be applying your DS skills in a domain (e.g. Marketing or Finance) to drive real business value. You either need to have domain knowledge or the desire to acquire domain knowledge. In fact, it’s not uncommon for DS interviews to include case interviews. © EliteDataScience.com , All Rights Reserved 5 5. Communication and Presentations - As a business data scientist, arriving at the right data-driven answer is only half the battle. The other half is communicating your insights to key stakeholders to get buy-in. In fact, your job has many similarities with management consulting. Product Data Scientist Product data scientists build ML / AI tools and software. They train models, build prototypes, and integrate ML solutions into other parts of the software. For product data scientists, the emphasis is on the product that you build. Examples include: ● E-Commerce - Building and integrating a dynamic pricing model into an e-commerce platform ● Entertainment - Building a recommendation engine to recommend other movies a user might enjoy ● Banking - Building a fraud detection system after analyzing large numbers of credit card transactions ● SaaS - Building a chatbot platform that uses natural language processing (NLP) to provide smarter chatbots Aspiring product data scientists should add the following core skills to their skillset: 4. Software Development Basics - You won't need to know as much about software development as a full-stack engineer. But product data scientists usually work closely with software engineers... so you’ll need to be able to speak a shared language. Be familiar with concepts like agile development, version control, and software uploads/Science et Technologie/ data-science-guide.pdf
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- Publié le Apv 16, 2021
- Catégorie Science & technolo...
- Langue French
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