Top 10 Most Wanted Data Science Skills in 2021

For professionals who are going to acquire their own skills in data science, it is essential to understand the needs of the industry and build a skill to fill this gap.

About 1.145 trillion MB of data is generated worldwide every day. Companies across the industry are looking for talent to process, evaluate and filter information relevant to their business from this growing data. With the rapid advancement of technology, more and more companies are digitizing their operations, resulting in increasing demand for professionals with both technical skills and business expertise to utilize the data resources we have today.

For professionals who are going to acquire their own skills in data science, it is essential to understand the needs of the industry and build a skill to fill this gap.

The most sought after skills and tools for data science professionals in 2021 are:

  1. SQL: Structured Query Language (SQL) is used to communicate and extract data types from databases. A data analyst needs to know SQL because they will need it to access data from the company’s database. Thus, it becomes the most important skill for a data science professional. Learning SQL is beginner-friendly and does not require prior knowledge of database or programming language.
  2. Python: Created in the 1990’s, Python is seen as a primary language that every data science professional should know and is easier to learn than other languages. Data science professionals use Python for application development, statistical programming (for clearing, analyzing and visualizing big data), web development, dynamic binding, dynamic typing and web scraping, among other tasks.
  3. And programming: R is a free open source software used to extract, resize and analyze data from a large portion of the data. Data scientists, data miners and statisticians use R for statistical data analysis and machine learning visualization. This programming language is used for data analysis in industries such as healthcare, banking, IT and e-commerce.
  4. Machine Learning: Machine learning is a branch of artificial intelligence (AI) that enables engineers to create programs and powerful robust data analytics algorithms that enable machines to mimic human intelligence. Currently, machine learning is in high demand because it is used to develop systems that can detect patterns in large data sets, predict the course of events, and help arrive at conclusions based on data metrics.
  5. In-depth learning: Deep learning is a subset of machine learning and skills need to be acquired to build a career in data science. Deep learning is mainly used for speech and image recognition, NLP (natural language processing) and robotics. Through in-depth education, data science professionals can advance their careers in defense, industrial automation, medical research, and electronics, among other sectors.
  6. Spark: Created in 2014, Spark is a framework of unified computing engines and a library set for parallel data processing. It is the most actively developed open-source engine for big data processing. It supports multiple programming languages ‚Äč‚Äčlike Python, SQL, Java and R. Spark makes it easy to start and scale up to big data processing and runs anywhere from desktop to a cluster of thousands of servers.
  7. Data Visualization: The use of visual representations such as graphs and charts for informational insights often allows for greater clarity and identification of patterns. While data visualization may not be an important skill that is asked in job descriptions, how to present your work and visually demonstrate analysis and insights is considered a baseline for data science professionals. Dumb is one of the most popular data visualization tools used by data scientists. This tool supports numerous data sources and allows the analytics to be converted to a dashboard for color visualization, making data models and reports more convenient. Thus, it is a widely accepted tool because it provides flexibility to data scientists.
  8. Cloud: There is a high demand for cloud skills for IT professionals as companies move their IT infrastructure to the cloud, especially in work-to-anywhere models with epidemic-induced action. The primary skills to become well versed in the cloud are Amazon Web Services (AWS), Java, Azure, Linux, DevOps, Docker, and Infrastructure as a Service (IaaS). Cloud computing is expected to grow in the coming years as more and more companies relocate their operations.
  9. Mathematics and statistics: Having accurate knowledge of calculus, linear algebra, statistics and probability is essential for data analysis, data sorting and data visualization. A statistician is responsible for collecting, analyzing and interpreting data, which will then interact with stakeholders, thereby contributing to an organization’s operational strategies.

And most importantly,

Business Skills: A survey conducted by edtech platform Scaler found that more than 80% of data scientists struggle primarily in their careers because real-world datasets are much more fragmented, non-standard and complex than the models they work with when training. More than 95% of respondents to this survey highlighted the need for data scientists to properly solve open business problems, which requires practical experience in addition to training and simulation. So business intelligence and intelligence are very important for a data scientist to work effectively.

According to Job Search Engine Indeed, job search for the role of data scientist in India is on the rise, with a 35 percent increase between July 2020 and July 2021. Their data also highlights a 50 percent increase in searches for business intelligence developers, a role that is equally important in assisting an organization in the decision-making process.

It is quite clear that data science is the industry right now for technology professionals around the world. What they need to succeed and build a strong career is the right skills for the job.

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