Skills Required for Data Scientist
By: Saurabh Dorle
Tags: Machine Learning, Data Science, Data Science Skills

First let’s understand What is data Science?

In simple words, data science is the process of getting insights from structured and unstructured data along with the identification of information from big data. It identifies what the data actually represents & how this information can be transformed into valuable resource in business domain.

Data science possesses various key skills. Let us have a quick look on those points and get familiar with it. Data science requires multiple number of skills, so let’s see what are all those skills and why that specific skill is required?

1. Basic Mathematics :

Having a basic mathematical background initially is necessary for a Data scientist. In building of Machine learning models, developing deep learning models and also to understand the concepts, mathematics plays a vital role. Since most of the ML algorithms have the basis of Statistics, Probability, Linear algebra theories & concepts.

2.Programming:

Now shifting our focus on hands-on skills, knowledge of atleast one programming language is required. Nowadays Python and R languages are ruling the world of Data Science. If you are a beginner then better get started with Python as it is provides a generic approach towards data science. Whereas R language is mainly built by statisticians for statistical analysis purpose. R and Python are state of the art in terms of programming languages oriented towards the field of data science. So, learning them both is an ideal solution.

3. Database :

After we learn to code, it is equally important to learn about databases where most of the data resides. You should be at least at the level of general designing of simple to moderate relational databases and their management. Basic knowledge of SQL can contribute in our journey towards data science. Thus, the data can be taken for analysis in models. While working over cloud system Hadoop can be used. Knowledge of basic distributed system concepts like Pig & Hive would also prove helpful.

4. Machine Learning:

Machine Learning(ML) is teaching machines to perform specific tasks without explicit programming. Machine learns from the data we pass it as input. The process of learning is automatic (on its own) without any human assistance. ML enables analysis of huge volume of data delivering faster & more accurate results. Henceforth, it helps in building different predictive models.

5. Visualization:

As we all know the common human tendency that pictures memorization or understanding is more convincing than theories. Thus, grasping of difficult concepts or identifying new patterns becomes easy after visualization. Similarly, in ML Data Visualization denotes the data, analysis, results, comparisons etc. in pictorial format or graph format. It clarifies the influential factors in business domain for gaining insights in future. Trending tools are Tableau and Power BI.

6. Domain Knowledge:

Knowing the domain in which we are working should be properly known to us. For example, if you work for a finance company, then you must be aware of the finance domain. This domain knowledge indicates specific understanding of their data, models & outcomes. In some service based companies, working has to be done with data from multiple domains.

So, these are some collective skills required for becoming a Data Scientist. In the next post I’ll share detailed description of skills along with the study material. Till then stay tuned with Omni Data Science!