"How do I get started in data science?"
"Here's what you need to know: Linear Algebra, Convex Optimization, Differential Equations, Calculus, Algorithms, Distributed Computing, Databases (SQL & NoSQL), Machine Learning, Probabilistic Modeling, Deep Learning, Natural Language Processing, Data Visualization, Data Structures, and don't forget Scala for functional programming, and Hadoop, and Big Data, and Experimental Design, and Functional Analysis, and......"
Your eyes glaze over as you see yourself going back to a dimly-lit lecture hall for the rest of your life.
You want to be a data scientist
not a PhD in 7 different academic disciplines
You just read yet another a list of books you MUST COMPREHEND, MOOC's you MUST TAKE, and statistical programming libraries you MUST MASTER to get started in data science.
With the plethora of information online, it's immensely unmotivating, overwhelming, and perplexing to navigate a huge list of resources without any context for how they fit together, why they matter, or how they will help you become a data scientist.
Do you really need to know statistical learning theory and functional analysis at a graduate school level to be a data scientist?
You end up spending more time debating what to learn than actually making progress.
Given your specific educational background and work experience, you still don't know what knowledge gaps you have, which you need to close, and which you can ignore.
You don't have time, money, or patience to waste learning things that don't matter.
A data science position (with its high profile and lucrative salary) seems more and more out of your reach with each passing day.
You wish someone would put you our of your misery by telling you specifically what you need to learn (down to the specific key words and topics) to become a data scientist.
How would you feel if you woke up tomorrow with perfect knowledge for how to become a data scientist as fast as possible?
You would be 100% confident on which MOOC's to take, which books to read, and which statistical programming libraries to use.
You'd know the highly specific knowledge gaps you must close given your educational and professional background.
Your money, time, and effort would be spent wisely and to great effect as you could just concentrate on creating highly effective data science projects showcasing your best work.
You'd be motivated, sure, and enthusiastic about having the right data science position within your reach.
It would be like future you (an awesome data scientist) travelled back in time to tell current you all the right things to do and focus on.
You'd be a data scientist in no time and the high profile, lucrative salary, and enjoyment of working with math, computer, and data would be all yours!
Become a Data Scientist without being overwhelmed, perplexed, or unmotivated by having the right highly personalized plan
Done are the days of sitting at a table being surrounded by books on books on books and endless amounts of data science resources.
Throw out that giant stack of textbooks.
You don't have to learn most of the stuff people say you need to learn.
Data Science Getting Started Guide
includes two modules: Data Science Getting Started Guide and Data Science Resume Guide which
will introduce you to several fail-safe processes to figure out what kind of data science jobs appeal to you,
what those jobs suggest you learn, and how to use the data you generate through the research to guide your path to becoming a data scientist.
In the first module, Data Science Getting Started Guide, you'll learn:
- How to figure out the knowledge gaps that MUST be closed in order for your to become a data scientist
- How to develop your perfectly personalized "learning data science" action plan
- What kind of data scientist you are
- How to work backwards from a data science job to your current situation to build your plan
- Which knowledge gaps matter and which you can safely ignore
- Which "become a data scientist" resources you can trust
- How to build the right data science skill sets from day one so that you don't waste time or effort
In the second module, Data Science Resume Guide, you'll learn:
Figuring Out What’s Been Going Wrong
- What’s the purpose of your Data Science Resume
- Six Ways Your Resume Could be Sabotaging You
Reading the Hiring Manager’s Mind
- How to Figure Out What the Job Posting Means
- How to Research Key Issues Facing the Company
- What Are Data Scientists Doing There Today?
- How to Sssess the Hiring Manager’s Real HOPES
Identifying YOU as the Perfect Candidate
- How to Identify Your Complete Set of Strengths
- How to Communicate Your Unique SPEC
Making Your Resume Look the Part
- What Sections to Include (and Not)
- How to Order Your Resume (Case-Specific)
- How to Improve Your Format (including Templates)
- Top 10 Formatting FAQs
Doing Your Skills Justice
- 3 Principles for How to Structure the Skills section
- How to Identify What Skills to List (and Not List)
- Which Projects to include in the Projects section
- How to describe your Project Work
Making Your Experience Come to Life
- What Experiences to List (and Not List)
- How to Structure the Experiences Section
Showcasing Your Education
- What Education/Courses to List (and Not List)
- Guidelines for How Much Detail to Share
Nailing the Cover letter
- What Template to Follow
- How to Tailor Your Cover Letter
- How to Say Something New
- How to Toot Your Own Horn
- How to Make it Readable (by a 10th Grader)
- How to Write to Someone (Ideally Hiring Manager)
Pulling it All Together
- Three Proven Techniques for Self-Review
- 7 Questions to Guide Peer Review
- Final, Pre-Submission Checklist
Hitting Submit with Confidence
Data Science Getting Started Guide will direct you in constructing your own highly personalized plan for what you need to learn and what you can safely ignore - saving you time, effort, and worry.
And most importantly, Data Science Getting Started Guide will give you the straight truth of what matters and what you can ignore to achieve your dream of becoming a data scientist with a high profile and lucrative salary faster than you've ever imagined.
It will be like future you (an awesome data scientist) travelled back in time to tell current you all the right things to do and focus on.
Become a data scientist faster than you think possible!
Getting Started Guide
How would you feel if you woke up tomorrow with perfect knowledge, given your background and education, for how to become a data scientist as fast as possible?
Entry level Data Scientists earn $80-$100k per year. The average US Data Scientist makes $118K. Some Senior Data Scientists make between $200,000 to $300,000 per year.
Start building your data science career today for the cost of a nice dinner. Just $49 gets gives you several fail-safe processes to figure out what kind of data science jobs appeal to you, what those jobs suggest you learn, and how to use the data you generate through the research to guide your path to becoming a data scientist.
Download instantly, read on any device you have, and set yourself up for data science success.
Get the "Getting Started Guide" Now and Start Today
Buy with confidence and without fear. This guide was written to help you. If you're not happy, email email@example.com and you'll receive a refund.
Who wrote this guide?
Sebastian Gutierrez is the author of "Data Scientists at Work", an interview-based book covering the background, work, and thoughts of 16 of the world's leading data scientists. Sebastian co-runs the Data Science Weekly email newsletter which proudly reaches ~22k subscribers a week. Sebastian is one of three moderators for the Data Science subreddit whose community numbers ~25k members. Sebastian has spoken in Data-related conferences in the US, the UK, and Europe. Sebastian Gutierrez holds a BS in Mathematics from MIT and an MA in Economics from the University of San Francisco.
You are awesome and you will be an awesome data scientist. Get started today!