Indeed, reports that job searches for Data Scientist positions in India have increased by 35%. Additionally, their data shows a 50% surge in searches for Business Intelligence Developers.
Data Science is the industry to be in right now for IT workers all over the world. All they need are the necessary abilities for the job to thrive and develop a promising career.
Every day, the world generates approximately 1.145 trillion megabytes of data. Companies from all industries are looking for someone who can process, assess, and filter essential data for their businesses from the ever-increasing amount of data available.
With the rapid evolution of technology, more businesses are digitising their operations, resulting in a growing demand for individuals with both technical and business acumen to exploit the vast amounts of data available today.
The following are the most in-demand skills and tools for data scientists in 2021:
1) SQL:
SQL is a structured query language that is used to interface with databases and extract data kinds. A data analyst will need to know SQL to retrieve data from a company’s database.
As a result, it becomes the most critical talent for a data scientist. SQL is simple to learn and requires no prior understanding of databases or programming languages.
2) PYTHON:
Python, created in the 1990s, is often regarded as the primary language that every data science practitioner should be familiar with, and it is straightforward to learn compared to other languages.
Data scientists use Python for various activities, including application development, statistical programming (to clean, analyse, and visualise massive data), web development, dynamic binding, dynamic typing, and web scraping.
3) R PROGRAMMING:
R is a free open source programme that may extract, transform, and analyse data in enormous amounts. R is a statistical data analysis and machine learning visualisation language used by data scientists, miners, and statisticians.
This programming language is used in various areas, including health care, IT, Banking, and e-commerce.
4) MACHINE LEARNING:
Machine learning is a branch of Artificial Intellect (AI) that aids engineers in developing programmes and data analytics techniques that allow machines to mimic human intelligence.
Machine learning is in high demand right now because it’s being used to create systems that can forecast the path of events by discovering patterns in large data sets and assisting with data matrices.
5) DEEP LEARNING:
Deep Learning is a subset of machine learning that is a must-have ability for anyone interested in a data science profession. Deep understanding is primarily utilised in speech and image recognition, natural language processing, and robotics.
Data scientists can advance their careers in defence, industrial automation, and machine learning by deep understanding.
6) SPARK:
Spark is a framework of unified computing engines and libraries for parallel data processing launched in 2014. It is the open-source engine for extensive data processing that is most actively developed, and python, SQL, Java, and R are among the programming languages supported.
Spark is simple to get started with and scale up to massive data processing, and it can run on anything from a single computer to hundreds of servers.
7) DATA VISUALISATION:
Visual representations like graphs and charts typically provide increased clarity and pattern recognition when communicating data insights.
Though data visualisation isn’t required ab most job descriptions, knowing how to present your work and visually display analysis and insight is considered a minimum requirement for data scientists. Tableau is a data visualisation tool that data scientists widely use.
This application works with various data sources and can convert analyses into dashboards for colourful visualisation, making it easier to create data models and reports. As a result, it is a widely used tool since it provides versatility to data scientists.
8) CLOUD:
As businesses move their IT infrastructure to the cloud, cloud skills for data scientists are in great demand, especially as the pandemic-induced transition to work-from-anywhere models continues.
Amazon Web Services (AWS), Java, Azure, Linux, DevOps, Docker, and Infrastructure as a Service are the most critical cloud skills to have (IaaS).
Cloud computing is predicted to expand in popularity in the coming years as more businesses shift their operations to the cloud.
9) STATISTICS AND MATHEMATICS:
A solid understanding of calculus, linear algebra, statistics, and probability is required for data analysis, data sorting, and data visualisation.
A statistician is in charge of gathering, analysing, and interpreting data subsequently conveyed to stakeholders and contributing organisation’s operational strategies.
10) BUSINESS ACUMEN:
According to a poll done by EdTech platform Scaler, over 80% of data scientists struggle in their first few years since real-world datasets are significantly more fragmented, non-standard, and sophisticated than the samples they deal with in training.
More than 95% of survey respondents correctly identified the necessity for data scientists to handle open-ended business problems, requiring real experience and training and simulation. The importance of business acumen and intellect cannot be overstated.
It is critical for people who want to pursue a career in data science to understand the industry’s requirements and develop a skill set to fill the gap.
