Practice, practice, practice
Yes, “practice makes a man perfect” is the exact phrase that fits at the moment. If you want to become a master in Data Science you just need to invest your time in practice.
Well, there are some other aspects on which a data scientist should focus on. In this post, we are giving a brief introduction of some points and will discuss it in-depth in a later post.
Data Processing & cleaning
A data scientist has to deal with many kinds of data which includes various file formats, frequency of update, cleaning process, aggregation level, data size etc.
Before going on the next step i.e Analysis and modelling, data scientists spend huge time on this thing.
It is a very time-consuming process to make sense of any data. Therefore the ability to correctly automate data processing is one of the important skill set that a data scientist desired.
If we talk in percentage, a good data scientist spends 80% of the time on Data processing & cleaning.
Analysis & modelling
If a person has some decent knowledge of mathematics and statics can easily develop this skillset.
Because this step involves a deep understanding of mathematics and statics to create and use various analytical or predictive machine learning models.
It will be good if you gain some knowledge about advanced machine learning techniques like Support vector machine, XGboost, neural nets etc.
we have seen students who are good in these technology makes helps them to achieve excellence in statistics and mathematical models including logical regression, linear optimisation and Bayesian probability that we have studied earlier.
If you are unable to understand these techniques, you can join our Data science Course in Delhi, where you can explore all the concepts related to data science.
A data scientist needs to tackle data wrangling, optimisation along with visualisation to create correct insights, multiple model testing, predictions and decisions. And you know what all these activities have to do regularly with varying scale and capacity of the organisation. Therefore having knowledge of scripting programming languages can help a data scientist in a better way.
R and Python are the most famous language used by the data scientist, Hence both have great use in Data Science.
And the best thing is that you can master it by joining the Python course in Delhi
In most of the educational institute, teachers and experts taught STATA & basics of R or Python language, but if we talk about practical use, it requires more from you. You should have clear how to write code in Python or R scripts
You can also enrol yourself in our Django course in Delhi
Tackling with data at scale
Huge data and production algorithms need highly optimised scripts compared to a testing environment where perform data cleaning, machine learning testing etc.
This optimised environment requires the right database to save your data, correct configuration of the machine where scripts may run easily, selection of perfect libraries and package to perform such kind tasks.
Although all tasks are not compulsorily performed by the data scientist it is a concern of data engineers.
But if you are working in a small company or as an independent researcher you should have this skillset.
Knowledge of domain & capability of detecting patterns
Probably you will enjoy this skill the most while entering in the data science field. All other components of this stream are for generally for tech seavy but this is most relevant to the human intuition and domain study. Thus, it is still very far from automation.
You have to critically examine the patterns and back them by data, follow the process flow and connect data & mathematical models to the demand of the industry.
Well, these skills are inherent in economists, statistician and mathematician and can be used in any industry or domain assigned if one is passionate to learn about the specific domain.
Also read: Python for data science (for interlinking)
Communication & Visualisation
When it comes to translate the models or any prediction that you have built into actionable insights, communication plays a very crucial role here for any data scientist. To design any course of action based on data, you need to have good communication skill.
Anyway, communication skill plays a very crucial role in any stream, so having excellency in it will be plus for any data scientist.
Now come to the visualization, there is a phrase a picture is worth a thousand words is perfect to use here.
When you have to make your audience understandable about huge and complex data, having a good visualization skill is a must. Because its a fact that humans generally understand the visualised data and patterns in comparison to text in any language.
It is good if we are sharing our knowledge with others using multiple platforms like blogs, books, written articles. Contributing to the R or Python language by speaking at any seminar or building data dashboards etc.
Because sharing your knowledge also strengthen your skillset. If you want to get successful in this field, join Data science training institute in Delhi.
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