Looking forward to land a data science job? Here are 4 data science technical skills you need to master in 2019!
By Saheli Roy Chowdhuri
Data Science and Big Data Analytics are witnessing an upward trajectory since the past few years and 2019 is all set to embrace both. These technologies provide companies with incredible insights into their key target audience and helps them hire the best talent via predictive analytics. Data analytics also makes it easier for them to understand the efficiency of their business model.
Embracing this technological bandwagon and digital revolution, companies are hiring skilled data scientists with a handsome pay.
So, if you are aspiring to land a data science job in a big tech company, then master these 4 technical skills by joining a data science course:
1. Programming Languages and Tools
In a nutshell, the task of a data scientist is to acquire relevant data from an unorganised set of data and deliver the key insights into an understandable form for everyone in the company. Accordingly, to process and visualise the important data, the analytics professionals use a wide array of data programming languages and tools such as R, Hadoop, Python etc.
These common programming tools/languages make work easier for both the data scientist as well as the company.
2. Statistical Analysis
A data scientist should be adept at Statistical Analysis. A large part of his task involves collecting relevant data and showcasing it in a consumable form to non-data professionals.
A data scientist should be proficient at performing statistical tests like estimations, distributions, and drawing statistical significance of big data sets. While most of these things are today done with the use of software, one needs to be strong at statistics in order to input the logic.
3. Data Visualization
It is said that a picture speaks a thousand words. It’s true even in the field of mathematics and statistics. Data scientists must have the ability to visualize the relevant data and present it in a meaningful form. This helps those who are not familiar with raw data and statistics.
A data scientist should represent data in a way that’s easy for everyone to decipher. It should convey the problem as well as the solution, thereby making people understand the significance of the data available. There are tools like Tableau and Qlikview that help in presenting their findings in a visually appealing form.
4. Machine Learning
Machine Learning is about writing algorithms based on large data sets that can either make predictions or take actions. For instance, if you are employing machine learning techniques for the banking sector, you can write an algorithm that can then automatically classify clients into good prospects or bad prospects.
For those who are interested in economics and statistics, machine learning can be a very fascinating field to deep dive in.
The other non-technical aspects that one needs to work upon too are communication skills, creativity, and developing business understanding. But data science is a core technical field and mastering the technical skills of this field is of utmost importance.