Saturday, January 9, 2016
Learning with Treehouse for only 30 minutes a day can teach you the skills needed to land the job that you’ve been dreaming about.
I can honestly say that besides technical knowledge, Treehouse also gave me a certain mindset, and while I generally am a very ambitious person, with each completed course I felt energized to better myself even more.
Is there any advice you’d like to share with new students who are aspiring developers?
I’ve been only for about 2+ years in this industry and I could already write tens of pages about what should and should not be done by aspiring developers, but here’s a few:
If you’re offered a project you know nothing about, take it, you’ll learn after.
Be passionate about it. If your brain doesn’t get “turned on” by new concepts, libraries, programming languages, you should not be doing it.
Get ready to learn continuously. The web is like the Universe. Ever expanding. Consequently the same has to happen to your knowledge and skill-set.
Teach others and code forward ( code forward is a concept I came up with and it comes from “pay it forward”; it basically means do a few projects for free once in a while for people who deserve it ).
Accept failure as a necessary step in self-betterment.
Research before asking questions and know when to ask. Putting in even hours or days for finding the solution will always be more rewarding in the long-run than asking a question on StackOverflow waiting to be spoon-fed the answer. That being said, asking has its place especially in a team-based environment or if the deadlines are (as they often are) very tight.
Go to as many interviews as you can. If you tell a recruiter (agent) that you’d like to go to the interview even if just for the sake of the experience, they’ll respect you for that, and will do their best to land you an interview. That being said, don’t just rely on recruitment agencies, show some initiative and contact companies on your own too. It might just be the detail that gets you hired.
Finally, keep learning, especially when you feel discouraged.
Friday, November 14, 2014
These tips are provided by , who brings 20 years of varied data-intensive experience working with successful start-ups, small companies across various industries, and eBay, Visa, Microsoft, GE and Wells Fargo.
1. Leverage external data sources: tweets about your company or your competitors, or data from your vendors (for instance, customizable newsletter eBlast statistics available via vendor dashboards, or via submitting a ticket)
2. Nuclear physicists, mechanical engineers, and bioinformatics experts can make great data scientists.
3. State your problem correctly, and use sound metrics to measure yield provided by data science initiatives.
4. Use the right KPIs (key metrics) and the right data from the beginning, in any project. Changes due to bad foundations are very costly. This requires careful analysis of your data to create useful databases.
5. Fast delivery is better than extreme accuracy. All data sets are dirty anyway. Find the perfect compromise between perfection and fast return.
7. Big data has less value than useful data.
8. Use big data from third party vendors, for competitive intelligence.
12. You don't have to store all your data permanently. Use smart compression techniques, and keep statistical summaries only, for old data. Don't forget to adjust your metrics when your data changes,.
14. Always include EDA and DOE (exploratory analysis / design of experiment) early on in any data science projects. Always create a . And follow the traditional .
15. Data can be used for many purposes:
· quality assurance
· to find actionable patterns (stock trading, fraud detection)
· for resale to your business clients
· to optimize decisions and processes (operations research)
· for investigation and discovery (IRS, litigation, fraud detection, root cause analysis)
· machine-to-machine communication (automated bidding systems, automated driving)
· predictions (sales forecasts, growth and financial predictions, weather)
17. Data + models + gut feelings + intuition is the perfect mix. Don't remove any of these ingredients in your decision process.
18. Leverage the power of compound metrics: KPIs derived from database fields, that have a far better than the original database metrics. For instance your database might include a single keyword field but does not discriminate between user query and search category (sometimes because data comes from various sources and is blended together). Detect the issue, and create a new metric called keyword type - or data source. Another example is , a fundamental metric that should be created and added to all digital analytics projects.
19. When do you need true real time processing? When fraud detection is critical, or when processing sensitive transactional data (credit card fraud detection, 911 calls). Other than that, (with a latency of a few seconds to 24 hours) is good enough.
20. Make sure your sensitive data is well protected. Make sure your algorithms can not be tampered by criminal hackers or business hackers (spying on your business and stealing everything they can, legally or illegally, and jeopardizing your algorithms - which translates in severe revenue loss). An example of business hacking can be found in section 3 i.
Wednesday, November 5, 2014