Buch, Englisch, 255 Seiten, PB, Format (B × H): 152 mm x 229 mm
Buch, Englisch, 255 Seiten, PB, Format (B × H): 152 mm x 229 mm
ISBN: 978-1-4842-0359-0
Verlag: SPRINGER NATURE
Data scientists are in demand as never before, and if you have the skills in advanced analytics, big data, machine learning, and project management, you can pretty much write your own ticket. Even better, those with skills in computer science, statistics, business intelligence, and any of the other disciplines that underlie data science can, with additional education in key topics, find work as data scientists. Getting Into Data Science: Tools and Techniques for Professional Success shows you how.
Data scientists help companies navigate in a business world in which data now rules and those who can find it, extract it, shape it, and make profitable use of it gain sizable advantages over competitors. Given the speed of technological change, companies in all industries need help more than ever before to create value from the mountains of data their organizations generate. They need data scientists. In this book, author William Ford, a data scientist himself, shows that data scientists, like professionals in other transformational periods, are indispensable change agents that help organizations solve immediate problems, manage challenges, and bridge the gap into new fields of economic opportunity.
But there’s a catch. you need to develop the chops to call yourself a data scientist credibly. You need the hard skills related to data science, including skills in computer science, math, machine learning, statistics, and more. You also need some soft skills, li
ke the ability to convey ideas, lead a project team, and coach other people. Those going out on their own to consult also also need business skills—like the ability to market yourself or identify a strategic point of leverage that a project can exploit.
Getting into Data Science strives to help budding data scientists understand which skills they need to master to position themselves in roles that help organizations thrive in our data-driven world. This book shows you:
- The skills and education required for success as a data scientist
- The tools and methods data scientists use to solve problems, prototype analytic variables, and find opportunities in data sets
- How to develop the project management, organizational, and "soft" skills data scientists need to prosper whether working as an employee or as a consultant
- How to help your organization or consulting clients become data driven on a project-by-project basis
This book will help you develop the essential career skills required to enter the exciting field of data science whether you want to work for a leading tech company or startup, or choose to work as a consultant in some capacity. In short, this book will outline how to become upwardly mobile as a data scientist. For those with the right skills, the time to move into this field is now—while the data science fire is hot.
What You'll Learn
- M
ethods and tools data scientists use to solve problems
- How to help companies become data driven and capitalize on their data stores
- How to structure initial analytic engagements to minimize risk and maximize value
- How to identify problems, write proposals, manage projects, and get results
- How to add to or bolster your data science skills
- How to use a foundation in analytics to minimize project risk
Who This Book Is For
Budding data scientists, computer scientists, statisticians, business information specialists and analysts, and others seeking to develop their data and business skills and become a sought-after data science professional.
Zielgruppe
Popular/general
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Introduction
Chapter Goal: To chart the evolution and rise of the data scientist as an invaluable, highly paid employee or consultant, to outline job and career prospects for data scientists, and to show how data scientists add value in the data-driven business world.
Chapter 1: Becoming a Data Scientist
Chapter Goal: To show what it takes to become a data scientist, and how computer scientists, statisticians, analysts, and others with the right skills and additional education and experience can call themselves data scientists legitimately.
- What data scientists do all day and how they do it
- Data science as a calling
- Positioning yourself to drive organizational change via analytics
- Outline of compatible roles for data scientists and the value that each adds to organizations
- Ways of working as a data scientist: employee, dedicated consultant, freelance project worker
Chapter 2: Building a Foundation as a Data Scientist
Chapter Goal: To outline the general analytic skills that are required of a data scientist, how to get them, and how to augment your existing skill set.
- Identifying your strengths and weaknesses in computer science, statistics, analytics, software development, and other disciplines necessary to be an effective data scientist
- What
and where you should study - Ways to gain experience as a data scientist
- Getting exposure to the different aspects of data science
- Confronting the trade-off between horizontally and vertically focused data science
- Keeping your education up to date and acquiring new skills
Chapter 3: Understanding the Tools and Methods Data Scientists Use
- Supervised learning
- Unsupervised learning
- Tools of the coding data scientist, including Python and R
- Tools of the applications data scientist, including RapidMiner/RapidAnalytics, Excel, MatLab, SAS, Pentaho, etc.
- General problem-solving techniques
Chapter 4: Succeeding as a Data Scientist
Chapter Goal: To outline project management methods for data science, and to show that classification—machine learning techniques designed to minimize risk—is the preferred method for initial projects.
- Initial projects are critical to your success
- Understand that classification is essential
- Define your message and prepare the stakeholders for new analytic insights
- Scope the project and manage risk
- Identify the problem to be solved - Job #1: Figure out the problem that must be solved
- Tools for problem identification
- Methods for problem identification
- Developing a solution for
the problem at hand - Manage expectations
- Drive the project
- Deliver models and train the stakeholders to understand their value
Chapter 5: Managing Advanced Data Science Projects
Chapter Goal: To outline methods and provide insights on managing complex and difficult data science challenges.
- What constitutes an advanced project
- How to approach a complicated challenge
- Define your message and prepare the stakeholders for new analytic insights
- Scope the project and manage risk
- Manage
expectations - Drive the project
- Deliver models and train the stakeholders to understand their value
Chapter 6: Working Through People to Get Results
Chapter Goal: To outline the soft skills that will be required whether you are working in house as a data scientist or as some form of consultant.
- Learn how to motivate change
- Learn how to coach
- Learn to work a case
- Learn how to sell yourself
Chapter 7: Learning from Case Studies
Chapter Goal: To review real-world cases, show what worked and what did not work, and understand how to apply the lessons to future assignments.
- Customer churn models don’t work if they identify dead people
- Interpret data correctly
- How not to manage a project
- The combative stakeholder
- The endless engagement
- Solve difficult problems
- Deliver value to the organization
Chapter 8: Taking Your First Steps as a Data Science Employee
Chapter Goal: To show how to get hired as a product- or service-focused consultant, and what to do once you’re on board. This will be the least-understood role but possibly the greatest opportunity for readers.
- Introduction to the environment (positives/negatives)
- Landing the job (cases, math, subject matter)
- Possible roles and typical projects and how to solve them
- Career path
- Prospering at a product/service company
Chapter 9: Signing on with a Services Firm
Chapter Goal:
To show how to be hired as a traditional consultant (think Bain, BCG, McKinsey) and what to do once you’re on board.
- Introduction to the environment (positives/negatives)
- Landing the job (cases, math, subject matter)
- Possible roles and typical projects
- Career path
- Prospering at a consulting firm
Chapter 10: Going It Alone as an Independent Consultant
Chapter Goal: To discuss getting work and prospering as an independent consultant
- Introduction to the environment (posit
ives/negatives) - Landing the job (cases, math, subject matter)
- Possible roles and typical projects
- Career path
- Prospering as an independent consultant
Chapter 11: Setting Your Sights Higher as a Data Scientist
Chapter Goal: To help you develop your career. You’re now established as a data scientist and have plenty of work and challenges. Is that all there is for the rest of your career? Not by a long shot.
- Keep learning
- Potential subsequent roles - Evangelism
- Management
- C-level
- Working toward your own business
Appendix: The Tools of the Trade
- Problem-solving flow charts
- Technical descriptions of work methods and tools
- Example statement for work
- Tips on negotiating contracts
- Master services agreements
- Example delivered slide deck
- Getting started with Python
- Getting started with Pandas
- Getting started with PySpark
