Build A Career in Data Science
Seiten
2020
Manning Publications (Verlag)
978-1-61729-624-6 (ISBN)
Manning Publications (Verlag)
978-1-61729-624-6 (ISBN)
Build a Career in Data Science is the top guide to help readers get their first data science job, then quickly becoming a senior employee. Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills readers need alongside their technical know-how in order to succeed in the field.
Key Features
· Creating a portfolio to show off your data science projects
· Picking the role that’s right for you
· Assessing and negotiating an offer
· Leaving gracefully and moving up the ladder
· Interviews with professional data scientists about their experiences
This book is for readers who possess the foundational technical skills of data science, and want to leverage them into a new or better job in the field.
About the technology
From analyzing drug trials to helping sports teams pick new draftees, data scientists utilize data to tackle the big questions of a business. But despite demand, high competition and big expectations make data science a challenging field for the unprepared to break into and navigate. Alongside their technical skills, the successful data scientist needs to be a master of understanding data projects, adapting to company needs, and managing stakeholders.
Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers.
Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.
Key Features
· Creating a portfolio to show off your data science projects
· Picking the role that’s right for you
· Assessing and negotiating an offer
· Leaving gracefully and moving up the ladder
· Interviews with professional data scientists about their experiences
This book is for readers who possess the foundational technical skills of data science, and want to leverage them into a new or better job in the field.
About the technology
From analyzing drug trials to helping sports teams pick new draftees, data scientists utilize data to tackle the big questions of a business. But despite demand, high competition and big expectations make data science a challenging field for the unprepared to break into and navigate. Alongside their technical skills, the successful data scientist needs to be a master of understanding data projects, adapting to company needs, and managing stakeholders.
Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers.
Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.
Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers. Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.
| Erscheinungsdatum | 04.06.2020 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 186 x 235 mm |
| Gewicht | 660 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Informatik ► Web / Internet ► Web Design / Usability | |
| ISBN-10 | 1-61729-624-4 / 1617296244 |
| ISBN-13 | 978-1-61729-624-6 / 9781617296246 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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