Common threads exists in successful students that have transitioned to data science as a career after a bootcamp program.
This post is a brief ode to the spreadsheet, which paved the way for many to learn about how to organize information, collaborate, and analyze data. Spreadsheets played and/or still play a substantial parts of our analytical life. Data scientists can get a little smug in associating with technical tools, often inclined to discuss the latest and greatest. However, spreadsheets mostly likely still rule part of the workflow. It may be quickly inspecting the data, the best way to share information with non-technical people, or an accessible way to check results.
The popularity of data science in the media makes the combination of established areas of study more accessible and interesting to everyone.
There is no denying that data science helps with online content but for many and most content publishers is often unattainable with their current data sets. Vanity metrics may be readily available but are less flexibility. For instance vanity metrics may not be able to tell you about unique users. Aggregated pageview counts are generally not enough to demonstrate growth and stickiness. Beware of Vanity Metrics (HBR) can also point to the other pitfalls of relying on counting beans on the surface. Most likely, medium to large content publishers are moving from or adding metrics to WordPress plugins or Google analytics tools.
The most important role of the leading data scientist and analytics person is to lay a solid foundation for the rest of the team.
There is a high demand for enterprising data scientist and data professionals to pave the way for data in all types of organizations. Businesses have the FOMO or fear-of-missing-out and the frenzy for talent is exacerbated by the increasing number of data sources, third party tools, and success stories. As businesses orient to data driven cultures, professionals have opportunities to reframe their experience, skills, and abilities to meet those needs.
It turned out to be a wonderful Fall Semester at Georgia Tech through their Online Master in Computer Science (OMSCS) program. As of 2015, OMSCS is a unique MOOC (Massive Open Online Courses) partnership with Udacity. The most promising aspects of OMSCS is its accessible, affordable, and challenging curriculum. I personally applied to the program to dive deeper into machine learning, where it was difficult to do it on my own and when there are no additional stakes. Coursera is a great place to start to get a solid data science and analytics foundation, the courses range from free to super affordable.
Originally posted on LinkedIn Pulse on January 4, 2016.
Social Media Week Chicago 2015 in November featured a master class on business intelligence using social analytics. The three speakers on the panel were from start-up fashion publisher (Clique Media Group, Inc. or CMG), digital marketing agency (Tenthwave Digital), and retail pharmacy chain (CVS Health). The panel discussed ways to leverage social media to make business decisions, gain customer insights, and stay competitive. Insights in social media in this class were leveraged from Digimind’s social listening and analytics platforms, which are known as software as a service (SaaS) products. Read the complication of tweets and panel notes in this storify link. I was invited as a panelist to share successes at CMG, my slides can be viewed here.
Data engineering is a crucial subset of the data science toolbox because access to data is required for high quality analysis and story telling. A data scientist must understand the tasks and time required for data engineering and be prepared to roll up her sleeves, which may include hammering out low-level scripts or developing company-wide software to create a fully functional data science environment. The data can set up successes or failures at an organization. A data scientist’s work is only as good as her access to data. This posts provides questions to evaluate time requirements for engineering the ideal environment, data access, and resources?
Lawyers and Data Scientists share a similar passion for discovery and uncovering the truth
Gone and vanishing are the days that data enthusiasts occupy dark quarters, not seeing the light of day or speak a cryptic language. Data science, the current fancy term for data analytics, mining, and predictive algorithms are giving data nerds more options. But even if data scientists are highly desired, where should they actually work and where can they thrive in the workplace?