Introduction
We will introduce 8 rules of new age analytics learning in this post. If you are at risk for your future analysis, you should keep it in your heart:
Rule 1: Open devices are included
Open openings will increase your presence every day, for the right reasons. They are useful, with large communities to support and develop high-speed developments. They are also the tools available for professionals (or independent professionals) who work on their own.
You can read more detailed comparison about this tool against SAS here. Except for those who are in the focus group discussions (with great concern including work), learning R (or Python) is almost your duty.
Therefore, if you want a long-term job search, learn from one of these now!
Rule 2: Free demotion and inquiry will be normal
Free self-test scanners will be normal (if not). What I want to say is that more companies will provide a basic part of their equipment. For example, QlikView offers a personal copy like a free download, but you will need to purchase a list if you want to share with your councillors. Larger questions (taken from Google) provide questions about the size of the free information.
How important is it?
Well, you can get any tools you want at the beginning. You can test the equipment before purchasing/enforcing them. In addition, you can enhance your learning by downloading and testing this tool.
Rule 3: A deep learning of at least one subject will increase your skills
Especially then, the first part of your profession. To separate yourself, you will need a specialist at least one place. If you are a smart business, you need to understand all the available tools, techniques and animals. Same as Big Data Experts and Data Professionals. The level of change will not allow you to specialize on all these issues.
You are free to choose what you prefer, as each of them specializes in professional aspects.
Rule 4: However, in leadership positions, you will need a broader perspective
Leaders are expected to know what's going on in general and how it can help organizations. Then, at some point, when you have a deep knowledge of the subjects, you should be especially careful in other areas (although high).
Rule 5: Specific views and stories will be made by the best analysts in the rest
With the growing number of every second data, you no longer can rely on bar graphs and pie sculptures to tell you. The new creative expression helps with effective and effective stories. If infographics, graphical representations of web pages or geospatial maps, these are all better than that they have a comparative effect on a bar/table that says the same.
Rule 6: Coordination of the curriculum machine is key
Computer engineering is important for basic essentials. If a careless Google or smartphone is trying to understand their needs or on the surface on the surface to monitor their health permanently, all this requires the need for human-simplification.
I think that employment opportunities can be divided into two categories:
1. Data collection and fluid information
2. Data flow analysis to get personalized ideas and experiences
Rule 7: Data Science competitions are opportunities to learn and showcase your talent
Rule 7: Data Science competitions are opportunities to learn and showcase your talent
I like these competitions, and I hope you also do it. Unfortunately, I do not have much time to like it. But these are ways to get along with your partner. Look at the different types of spaces in different competitions on Kagle and understand what I'm talking about, that you've learned through these competitions. In addition to the extra money, they may be hiring your employment.
Rule 8: Finally, but at least, regularly learn
This is clear! The more you learn, the better. The important thing is to learn regularly and consistently. If it is Coursera courses, Youtube tutorials, blogs or iPython notebooks on Github, just time to learn more.
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