Conocí a un señor que tiene mi misma moto, una KTM EXC 450 pero la de él tenía más potencia en la salida, luego de preguntarle porque se comportaba diferente la moto me contó que le había hecho la modificación de 3×3 cambiando las agujas del carburador, al parecer venden un kit para esto, alguien puede darme una guía de que consiste y como consigo uno, sería espectacular
Modificación de 3×3 y agujas de carburador, para que sirve?
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whgandalf
In my opinion, you should be thinking about looking for work. Try to network and see if there are employers were looking for analytics. This is different from analysts. They could be market research companies, companies are looking for pricing decisions, and even productivity.
Look to companies where the culture and business processes are not instinctual. Rather look for companies that require analysis.
Unfortunately, one becomes more Bible as one becomes more familiar with the tools of analysis. This may be SAS, business objects, or any other reporting environment.
In conclusion, a massive degree in analytics should result in a job sooner or later.
whgandalf
The biggest piece of advice I could give is to take a course in microeconometrics/labour econometrics as a part of your course. If your course coordinator won’t let you, beg. If they still won’t let you, then go off-line for a week or two and properly digest Mostly Harmless Econometrics (or if your stats isn’t too good yet, Mastering Metrics). If you want to go and work in health analytics, then replace what I just wrote with the equivalent for research design.
Why learn microemet? Basically, many of the big questions in business are of the form “what will happen if we do x”. Predictive models that aren’t informed by causal reasoning do *terribly* at this question–they answer the question “what do we see happening to y when we see x”. Inferring what will happen to y when you fiddle with x is a difficult task when all your data come from a world in which you did not fiddle with x. Too often we come across people with great technical chops who aren’t even aware they’re making mistakes when answering these questions. Don’t be one of these people.
The second biggest piece of advice would be to not become too enamoured by the sexy end of data science (especially predictive algorithms), but *do spend the time learning this stuff in depth*. Often the simple stuff done well is far more useful to real-world decisionmaking.
Third: read very widely.