5 DATA SCIENCE SKILLS TO MASTER IN 2021
Data science and artificial intelligence have taken over the modern, digital world in several domains. With the increasing amount of data, businesses worldwide rely heavily on data science and analytics. 2021 is the best time to hone some emerging data science skills to land a top gig as a data scientist and stay competitive. Data science and analytics jobs are growing year on year and will continue to do so, aided by the increase in digitization and challenging market conditions.
Factors Influencing the Demand for Data Scientist Jobs
The three main factors which influence any job ranking are demand, supply, and growth. Examining these in the data analytical technology field will clearly show us the need for data scientists is in 2021. Data scientists rank number three in the most sought-after jobs because of their high demand, which is constantly increasing without any signs of slowing down. It’s not just the FANG companies like Google, Netflix, Amazon, and Facebook that rely on data-driven decision-making. Still, even start-ups today depend on data to maximize profits and improve customer satisfaction by using data science to create accurate algorithms.
As data science is still a relatively new field, the supply of data scientists is quite low. This low supply has resulted in many open positions for data scientists in the job market. Data scientist jobs have been seeing a rise since 2012, and since then, the demands for this position are constantly growing. Therefore, data scientists are a crucial requirement for the tech industries and will continue to be in light of its high demand and lower supply. Data science is about using novel tools and machine learning techniques to extract the necessary knowledge from data. This requires specific skill sets that you need to have if you seek work as a data scientist.
5 Data Science Skills to Master In 2021
- Programming Language
Proper knowledge of coding and programming is one of the most fundamental skills data scientists have. Data scientists can use diverse programming languages to build machine learning models, but Python, R, SQL, Julia, Java, and Scala are the most popular. Python and R are the two preferred and favorite programming languages among data scientists for their simplicity and ease of use. For example, Python offers a fantastic data science library, Pandas, that helps any data scientist do wonders with data manipulation and analysis. Its functionality makes it the most used package, and the fact that it has become a standard data structure for machine learning models only helps in its popularity.
- Data Visualization and Storytelling
Data has an important story to tell about every business. Data visualization means presenting the data visually, either in graphs, pie charts, scatter plots, bar charts, line plots, heat maps, etc. Data storytelling skills imply taking the visualizations to the next level with applications like Tableau, PowerBI, and Google Analytics to communicate the gleaned business insights in the form of a story.
- Database Management
Database management is about retrieving, manipulating, editing, and transforming the required datasets. It also helps in testing the data once the model has been built. Some of the standard database management systems used today are SQL, Oracle, MySQL, Cassandra, and MongoDB.
- Docker
Docker is a deployment and application running platform for machine learning models. Model deployment is rapidly becoming just as necessary as model building. The reason behind it is that this deployment integrates the model with the product, giving it a more significant business value.
5. Business AcumenÂ
Having business knowledge is extremely important for a data scientist because without knowing the business goals and objectives, it is impossible to decide whether the analysis is heading in the right direction. A data scientist without domain knowledge of the business is like an egghead.
With tough competition in the data science industry, polishing your fundamentals and honing your skills through data science projects will build a strong foundation for you and help you stand out. The more you work on data science projects, the faster you will master all these data science skills and kick-start your career as a data scientist.
