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Data Science: 11 Things you’re Forgetting to Do

Data Science: 11 Things you’re Forgetting to Do

The scope and scale of Data science in today’s IT scenario is unmeasurable. It is present everywhere; most of the companies base their decision on the decision of their data scientist expert. Today the number of applicants who are enrolling in Data Science Online Training is phenomenal. However, just enrolling into a good training will not ensure you a success at it unless you learn to use this in a right way. In this blog we are covering 11 important points that developers forget to implement most of the time. Have a look at them and see if you too are forgetting to use any of them.

  • Think it and Do it: Data science is all about structured approach. You need to be structured with your methodology or else you can easily lose your way in the mountains of data that you will encounter every day. You need to think in a structured way and this is something that no Data science Training in Chennai can teach. In pursuit of quick results most experts are forgetting this fundamental approach. Make sure before you start your work on any piece of data, you first structure your data well.
  • Keeping everyone in Loop: Role of a data scientist is crucial in every organization they are in centre of action all the time; this makes all the more important that they remain connected with everyone in their team. Most experts are forgetting this role these days and remain concentrated on their bit only, which isn’t correct. Data scientist must ensure they are in loop all the time and are working in proper sync with their team.
  • Working on Presentation: A good Data Science Training will definitely teach how to draw out or read the data but will never teach on how to present it clearly. A data scientist must keep in mind that most of the time he is presenting his data to non-technical people. Therefore, it is imperative that he must present it in a manner that is well understood and appreciated by everyone concerned.
  • Keep Enhancing: Like all IT fields, Data science also demands kaizen i.e constant learning. Most experts take things for granted. No matter how skilled you are in this field you need to keep evolving and keep yourself up to date. You can update yourself by learning Python or any other function like ARIMA. You can learn all this while continuing your job or through a good Datascience training online.
  • Safeguard your Data: Yes, Data analysis is your core responsibility but data security is equally important. Most experts forget and take this lightly only to regret it later. Ensure that you are not only managing your data but also securing it.
  • ML algorithms are Important but not Everything: People who are into Data science at times value ML algorithms too much. No doubt they are important but to base everything on it is also not wise. If you want to be an expert at this you must add some more variable in your kitty you must adapt to new approaches to extract data. One single approach will not take you far in this field. This is something you must always remember.
  • Work more with Combination: Combining models and algorithms together can produce great wonders. It can make tasks simpler and can get it done much quicker. However, most experts prefer the same tried and tested path. It is imperative to learn and innovate when at work especially in this field. Good creativity in this sector will only open lucrative opportunities for you.
  • Be good at Data Cleaning: Any information can form a data but you have to be specific with what information you want to use. This is where Data cleansing becomes so important. An unwanted data can really upset your rhythm of data processing; you really need to be careful about it. Some experts start immediately on data without segregating it. This is a wrong practise which you must avoid to enjoy good flow of work.
  • Learn all the major Algorithms: Learning goes a long way in procuring a good career in this field. There are in total 6 major algorithms in this field namely: Random forest, Logistic regression, Neural network, FTRL, XG boost and SVM. At times, people are content with 2-3 algorithms and that is where they make a mistake. To be master you need to learn all the tricks of the trade. Make sure you do avail a good Data science training in Chennai to keep yourself ahead in this race all the time.
  • Good understanding of Statistics: Statistics is imperative in this field; you don’t have to be a master at it but must have some understanding to make your analysis more comprehensive. Developers are forgetting the essence of this stream and are paying price for it. So, make sure you know at least fundamentals of statistics to keep growing in this field.
  • Start acquiring knowledge in Deep Learning: This is where the stream is now headed. Deep learning will soon make a huge impact and it is wise to prepare for it right away. Some people in Data science forget that in this field things can change very quickly. So they need to keep themselves updated with the trends. Data science is growing and growing fast so it is imperative that people who are in it grows with it.

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