Skills Every Data Scientist Aspirant Should Learn
Data Scientist is termed as ‘Sexist Job of the 21st century’. As this field has become so popular now-a-days, I thought I should write on what should be the most important skills/responsibilities that every Data Scientist or Data Scientist aspirant should have. Let’s discuss how to become data scientists.
What are the Roles and Responsibilities of a Data Scientist?
Data Scientists are like doctors of big data. They take a huge amount of messy data points (Structured and unstructured) and organize them with their skills in math, statistics and programming. They apply all their analytics skills to uncover hidden insights from messy data. In other words, Data Scientists use their knowledge of modelling and statistics to transform data into meaningful insights which helps to create opportunities for customer retention.
Data Scientists must have technical and non-technical skills to do their job in the most efficient way. 3 stages of technical skills in Data Scientist are:
- Data Capturing & pre-processing
- Data Analysis & pattern recognition
- Presentation & Visualization.
To perform, above 3 stages, one should know about 3 categories of tools – tools to pull the data, tools to analyse the data and finally tools to present the insight from data in form of visualization. Here are different tools available:
Tool for Data Pulling and pre-processing
This is the very basic and must skill required for a Data Scientist, regardless you are using structured or unstructured data.
Big Data Technology
Data scientists need to know about different big data technologies. To read more about this, you can check here.
Python is the most popular language for data scientists. It is an interpreted, object-oriented programming language with dynamic semantics. It is a high level language with dynamic binding and typing.
Tools for Data Analysis & Pattern Matching
There are various tools available in the market but this will depend upon your statistical knowledge. There are some tools available in the market which are used for more advanced statistics and some for more basic statistics
Lots of big revenue generating companies used SAS (mostly used in banking sectors like Amex, ICICI etc). To use this tool, basic understanding is good. In SAS, you can manipulate the equation very easily. In SAS, many statistical functions can be performed (like Linear regression)
R is most popular in the statistical world. It is an open-source tool and language that is object oriented, so you can use that anywhere. Most of the things are implemented in R, hence R is the first choice of almost every data scientist.
Machine learning(ML) is the most demanding and most useful tool the data scientists must have. ML algorithms extensively used for advanced analytics, predictive analytics, advanced pattern matching. KNIME, Weka or Jupyter Notebook are the few important examples of Machine Learning tools, however there are alot of ML tools available in the market. To understand in detail about ML with Python, you can here.
Tools for Visualization
After performing deep analysis, one must know how to present it. It gives the appropriate insight which helps to make business decisions. To read in detail, you can check here.
I hope you enjoy reading this blog. I am waiting for your valuable feedback and share the topics on which you want me to post a blog.
Keep reading and learning!!!