10 Must Have Data Science Skills, Updated. An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.
It has been a year and a half since Linda Burtch of Burtch Works wrote 9 Must-Have Skills You Need to Become a Data Scientist, a post which outlined analytical, computer science, and non-technical skills required for success in data science, along with some resources for gaining and improving these skills. While this post is still relevant and quite popular, I thought I would take a shot at updating it, taking into account the direction of data science developments over the past 18 months.
Non-Technical Skills 1. Education Burtch provides some numbers related to the educational level of data scientists, indicating that 88% of Data Scientists have, at minimum, a Master's degree. That said, there is likely much more variety of education levels held by those considering themselves data scientists. 2. 3. 4. 5. Business Intelligence solutions architecture. Disclaimer The materials in the article are consistant with the products available from IBM up to January 2005.
IBM products introduced or made available after that date are not covered. Back to top Introduction In today's warehouse environment, organizations are more successful with sound architectures. In the mid to late 1990's, IBM introduced a Blueprint for Data Warehousing that aided data integrity and process consistency through the persistent data store (Central Data Warehouse or CDW). Now information consumers are demanding timely answers to more complex questions that require processing of data from a variety of sources. This article is a presentation of the latest and best available toolsets and approaches that will aid the BI specialist in being able to source data and assimilate it into information that will provide value to the information consumer.
Prerequisites. Beyond Informing, Making Decisions, by Neil Raden. Understanding Analytical Types and Needs. By Neil Raden, January, 2013 Purpose and Intent “Analytics” is a critical component of enterprise architecture capabilities, though most organizations have only recently begun to develop experience using quantitative methods.
As Information Technology emerges from a scarcity-based mentality of constrained and costly resources to a commodity consumption model of data, processors and tools, analytics is quickly becoming table stakes for competition. This report is the first of a two-part series. (Part II will cover analytic functionality and matching the right technology to the proper analytic tools and best practices.) Executive Decision Making - The Five Phases. Interactive Periodic Table of Machine Learning Libraries. Periodic Table of Operators - Documentation v5 - Documentation V5. The Operator is the basic unit of an Alpine Data Mining Analytic Flow.
In order for data analysts to quickly review all the available Operators, Alpine has produced its own interactive Periodic Table of Operators, found here: This Periodic Table groups the Alpine Data Science Operators into the following categories: Load/Data ExtractionSampleExplore TransformModelPredict and ScoreValidateTools A list of all the Alpine Operators within each category is found here: Periodic Table of Operators - Documentation v4 - Documentation V5. The Operator is the basic unit of an Alpine Data Mining Analytic Flow.
In order for data analysts to quickly review all the available Operators, Alpine has produced its own interactive Periodic Table of Operators, found here: This Periodic Table groups the Alpine Data Science Operators into the following categories: Load/Data ExtractionExplore TransformSampleModelPredict and ScoreTools A list of all the Alpine Operators within each category is found here: The Agile Marketing Revolution: Using Automated, Cross-Channel Campa… Origami logic-periodic-table-of-marketing-signals. Marketing Intelligence, Analytics, Reports, Dashboards. Marketing Signal Measurement, Analytics, Reports, Dashboards. The biggest bottleneck to insights is not having the data you need, when you need it.
When your marketing signals are scattered across hundreds of different accounts, reports, and spreadsheets, digging through them to find and compile information can be a process that takes days if not weeks. To make things harder, gathering just any data isn’t good enough. You’ve heard the expression “garbage in, garbage out”: if your signals are of subpar quality, the insights you gain from them will also be shallow, delayed, or worse yet, misleading. In order to get to the right insights, you need to have the right signals that are accurate, complete, and up to date. Origami’s Signal Harvesters not only automate the collection of signals across all of your channels, media, platforms, and other data sources, but we also actively manage and optimize the quality of the signals we fetch.
Infographic depicting unique differences between data scientists and business analysts.
Find out what type of professional is needed to meet your organization’s needs. By Anmol Rajpurohit, @hey_anmol Big data is big business, and organizations everywhere are scrambling to deal with the valuable mountain they have at their fingertips. Knowing how to gather data is one thing. Knowing what to do with it is another matter altogether. Making the most of this digital goldmine to optimize outcomes and meet business goals requires some very advanced skills that many organizations don’t yet have within their ranks. Infographic continues on page 2.