Guest blog post.
After reading many blog posts, articles and books, I have collected ingredients of data science! Moreover, I've classified them with a purpose of easily making a cook named as data science with below lists for whom wants to construct own career road map! Maybe, you wonder "why I am not giving the recipes of it" is because I do not have any real life experience.
Creative mix of basic ingredients - not unlike data science - to produce delicious curried black spaghetti
Firstly, we most probably have encountered a person with pure data warehousing or machine learning background identifying them as data scientist. This is because their areas highly nested with ds. But, what about other ds, mostly, related areas:
Data science related areas
If we look more closely at any of these areas, we'll be more likely to grasp basics of ds, and to meet core activities done on data scientists' daily life. For this purpose, let's look at general tasks done on data mining:
General data mining tasks
Until now, I assume I have, implicitly, give some intuition to you, and I'm sure you have already seen a writings about `What is Data Science?`. However, outside the definitions done on any sources, let's take a look at ingredients, skills, of it. While doing this, don't forget the dynamics of life; that is what I have listed below is likely to change over time.
Skills and technical knowledge required
Trends
As an additional part, I believe "how you start and end your activities" will highly correlate with quality of your work. Therefore, before putting hand on real data science job, it seems to be armed with theoretical background on below key, common, points be rescue many life from playing around undeployable applications. Here, I'm willing to light on common pitfalls raised from not considering;
Even if reaching to end of the post, we still need a set of practices and techniques in order to be gradually close to deployable solution of our planned product. In twitter, data science blogs or associations, they somehow agree on Agile as data scientists' methodology. I think one of the reasons is embedded cycling bahavior of DS. By cycling behavior, I mean we start from basic model, doing experiences, tuning knobs, and then analyzing test results. Afterwards, we continue with decision done on experiences and test results until deployable product. As a last point, here Agile methodologies are;
As a final word, I have done this research to clear my career road map and put light on each dark corner. It helps me a lot, and I wish it will help you, too. However, I admit this blog post needs more attention than what I've spend. So, please feel free to comment.
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