The full article about 51 expert tips for learning big data analytics was written by Molly Galetto. You can find 4 sections in this article.
Big data is everywhere, and small businesses and enterprises alike are making strides in transforming business outcomes through effective big data analytics. For today’s marketing and IT professionals, big data analytics is rapidly becoming an essential yet multi-faceted skill, and those who master big data analytics play a critical role in transforming their companies into data-driven organizations.
Why Master Big Data Analytics?
1. Big data creates career advancement opportunities for IT and other professionals. “Big data is definitely creating tremendous opportunities for the IT pros that know and understand it. That could be in a new role such as a data engineer or simply in a revision of an existing job description — one that makes you more versatile and less dispensable to your employer and will likely generate unexpected opportunities down the road.
“Where do you add these magical skills, especially if your employer isn’t offering training in them? The Internet, of course. Education and skills training has experienced its own share of change lately, and there’s plenty of upside for the knowledge-thirsty IT pro: Loads of readily available, online classes for developing new skills across the technical spectrum. Best of all, many of these learning opportunities come at no cost to students — so the only thing you’re really putting on the line is your time and energy. Admittedly, those are not finite resources — but you can tackle new learning and career advancement chances with minimal risks.” – Kevin Casey, 10 Big Data Online Courses.
You can learn more about the 12 others tips of this section here.
Get an Education in Big Data Analytics
14. Consider a two-year Master’s degree program focused on Big Data analytics. “It’s well documented that there’s a big data talent gap, but what’s being done about it? What’s needed is knowledge and experience. On the first front, hundreds of colleges and universities worldwide are gearing up business analytics, machine learning and other programs aimed at analysis of data in a business context.” – Doug Henschen, Big Data Analytics Master’s Degrees: 20 Top Programs.
You can find more information about the 7 others tips of this section here.
Essential Languages and Skills to Master
21. There are several essential tools of the trade anyone interested in a career in big data analytics should master. “SAS, SPSS, R, and SQL. Start with any tool that you can get access to. Sometimes you will be surprised to find that a Tool that you thought did not exist in your organization actually does. In one of my previous jobs, when I was busy negotiating with SAS for licenses for my team, a colleague of mine, who was an Actuary told me that he had seen a SAS session in one his team member’s PC, sometime back. I followed up with that team member and we found that we had a SAS server already in place waiting to be used!
“Learning is not about knowing everything, but learning substantial portions thoroughly and gaining sound knowledge about what you learn. I would much prefer a candidate who knows a lot about how to run a regression in SPSS, than a person who has half baked knowledge (knows a little bit about CHAID, done a little bit of regression, knows a little bit of SAS and a little bit of SPSS) If you can master one tool and a few modules/techniques of the tool, then you stand a better chance of getting a job and also of being able to get a job done.
“Pick up a tool that is available easily to you and start learning it – SAS, SPSS, R (now available as open source).
“I do not recommend using pirated software though they are now openly available in the market.” – Snehamoy Mukherjee, 5 Tips to build a Career in Analytics and Big Data!
For more explanation about the 12 others tips of this section click here.
Tips for Mastering Big Data Analytics
33. If you’re a business or marketing professional without an in-depth knowledge of the technical jargon typically used in big data analytics tutorials and courses, you can still master big data analytics if you know where to look for the right learning materials. “Intrigued by analytics? Wish you knew more about it? A lot of people search for information, and land on sites that are, well, too geeky. They’re aimed at programmers, people who pride themselves on knowing all the intricacies of their favorite software, or (eek!) math majors. These are not good source for business people aiming to get a grip on the topic.
“Maybe you’ve come across ESPN ’s FiveThirtyEight. This is the right kind of reading for you. These articles, written in normal human English (ok, much better than normal), can be read and understood by any educated adult. Great. Still, there’s a much wider range of analytics topics, and viewpoints, on the web that business readers can understand and put to good use. It’s a matter of knowing where to look.” – Meta S. Brown, 6 (OK, 7) Big Data and Analytics Learning Resources That Business P…, Forbes.
You can learn more about the 19 others tips of this section here.
DSC Resources
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
