Data science has bloomed out as the hottest field of the decade. People are in the progress to make it as their career due to its demand, job satisfaction and its value as a professional. Moreover it acts as the open source to step-in regardless the background. Are the one who is curious on strategies that lead the queries to a particular suggestion? Do you go crazy behind the algorithms which conclude to a specific outcome? You are a way up for it, some of the skills that are to be developed are

Programming knowledge:

Dr.Goutam Chakraborty, Professor at Oklahoma State University suggests that in order to extract the data and to do the ETL side of the data various coding languages such as Python, R, SAS are employed. Along with that Database management, Data manipulation, querying acts as the vital aspects.

Background in Mathematics:

According to management Guru Peter Drucker, you cannot manage what you cannot measure. Multivariable calculus and linear algebra are the vital concepts through which the implementation of the data from facts takes place.

Basics on Probability and Statistics:

Probability is the occurrence of the events which deals with the likelihood. For example, by working on the tweets of the person, one’s likes and dislikes could be analysed. This may lead to the prediction of one’s future tweets through the advanced analysis and modelling of the structured data. While dealing with the Big data which is complex, vast, and unstructured, Data wrangling has to be applied based on the statistics. This is nothing but the interpretation of the data, which is done by probability notations.

Dealing with Algorithms:

Works on the problems, Firstly, classification algorithm enables to choose between the options based on their outcome. E.g. - Is it A or B? Anomaly detection algorithm is for the fraud identification. E.g. - Is this message from the internet typical? Machine learning technique is employed in Regression algorithm which is the quantitative analysis. E.g. –What will the fourth quarter sales? For organization of the data from the known facts Clustering algorithms are used. E.g. – Which viewers like the same types of movies? Lastly, Reinforcement learning algorithm which works on Artificial Intelligence is used for prediction. E.g. – In self-driving system, when the yellow light glows, brake or accelerate?

Presentation skills:

The visualization of the data must be easier to grasp, as it the most essential aspect. Through this, reports can be designed with the insights. The unique and creative ideas create an impact among the listeners. Tools such as Excel, Tableau, Qlikview are quite handy to work on.

Form a community:

The best way to commence is by building up your own network related to data science, things like Python meetups, Visualization meetups. Another possibility is by attending conferences regarding your research. Join the online communities like O’Reilly data newsletter, Kaggle, and KDNuggets, Podcasts which acts as the resources to plug you into the happening in the data science community.

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