“Data Analytics Handbook” is a new resource meant to inform young professionals about the field of data science. Written by a group of students at UC Berkeley: Brian Liou, Tristan Tao, and Elizabeth Lin, Edition One of the book includes in-depth interviews with Data Scientists & Data Analysts at: Facebook, LinkedIn, Yelp, BigML, Cloudera, and many more. Edition Two includes interviews with CEOs and Managers from Y-Hat, BigML, Cloudera, Mode Analytics, Flurry, and many more.
- Communication skills are underrated. If you can’t present your analysis into digestible concepts for your CEO to understand, your analysis is only useful to yourself.
- The biggest challenge for a data analyst isn’t modeling, it’s cleaning and collecting. Data analysts spend most of their time collecting and cleaning the data required for analysis. Answering questions like “where do you collect the data?”, “how do you collect the data?”, and “how should you clean the data?”, require much more time than the actual analysis itself.
- A Data Scientist is better at statistics than a software engineer and better at software engineering than a statistician. The greatest difference between a data scientist and a data analyst is the understanding of computer science and conducting analysis with data at scale. That being said, data scientists only need a basic competency in statistics and computer science. Not all data scientists are Ph.D.’s, and newly developed tools are empowering more and more people to be able to do data science.
- The data industry is still nascent, if you want to work with a variety of stakeholders in a more freeform role, the time to do so is now. Data scientists and data analysts all say they interact with a many parts of the company from engineering to business intelligence to product managers. The roles of data scientists and data analysts are largely undefined and vary by your own skill set and the company’s needs.
- Both roles require a curiosity about working with data, a quality more important than your technical abilities. The ability to discover trends and patterns previously unseen is what truly makes you valuable. Having a curiosity enables you to ask the creative questions necessary for transcendent analysis. As practice, when given a dataset, ask yourself what questions do you have about the data and how would you answer them.
Both editions of the handbook are available free for download HERE.
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