What is Data science?
In the Data Science domain, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the aim to gain better insights.
Organizations can incorporate these insights to act and improve future outcomes. There are numerous rapidly evolving technologies to help analyze data and build models. In a remarkably short time, there has been rapid progress from desktops to hosting massive parallel warehouses with huge volumes of data; this way, there is a palpable transformation from in-database analytics functionalities in relational databases to unstructured big data tools.
Analytics on unstructured or semi-structured data is becoming increasingly important to incorporate sentiment and other useful information written in natural language into predictive models; this often leads to significant improvements in model quality and accuracy.
Emerging analytics approaches seek to automate the steps in model building and application, making machine learning (ML) technology a necessary evolution towards modern Data science.
Successful ML projects require a combination of algorithms + data + team, and a very powerful computing infrastructure.
Data Scientist ranks among the top three emerging jobs
Although Data science as a field has existed for several decades, the rapid growth of artificial intelligence (AI) in business in the last five years has generated a demand for data scientists that far surpass the availability of trained professionals. Today, 63% of executives cite a lack of talent as a prime barrier to adopting AI technology[1]. This talent gap is an opportunity for aspiring professionals and a challenge for companies striving for a competitive advantage in the market.
According to the LinkedIn Emerging Jobs report[2], 2020, Data Scientist has topped the ‘Emerging Jobs’ list for three years running and is projected to grow at 37% annually. It’s a specialty that’s continuing to grow significantly across all industries, attributed to the evolution of previously existing jobs and increased emphasis on data in academic research.
What skills does a Data scientist need to be successful?
Data Science is a cross-disciplinary set of skills found at the intersection of statistics, computer programming, and domain expertise. It comprises three distinct and overlapping areas:
- Statistics, to model and summarize data sets
- Computer science, to design and use algorithms to store, process and visualize data
- Domain expertise, necessary to formulate the right questions and to put the answers in context
- Other skills often missed are:
- Leadership
- Teamwork
- Communication
