Top Data Scientist Skills You May Need In 2023
With advancements in technology, the old methods are no longer in function. The world has adapted to fast-paced technology. It has seeped into our lives so swiftly that we barely have any task or chore that does not involve any use of technology. We have technology everywhere, from controlling the light bulbs in our house through our phones to cleaning our homes. Our offices, hospitals, education, and even our leisure time activities revolve around technology and gadgets. We all have our online presence alongside businesses that have now become more digital due to technology.
Millions of people today have access to the internet and contribute to the data pool every passing second. 37% of the world’s population has access to social media platforms. Instagram has more than eight million users and two million active users using the platform daily. Every day, users worldwide contribute to the data pool by adding 2.5 quintillion bytes of data. Imagine the size of the data pool growing so enormously and how much data it offers and manages. To look into the valuable information and manage such a big chunk, we rely on data scientists to facilitate our lives.
Since 2012, the data scientist’s role has grown by over 650%, and by 2026, there will be 11.5 million jobs in this field. The field has become more lucrative than before, painting an optimistic picture for the jobs in 2022 and beyond. The recent openings in the tech industry are more about the machine or artificial learning.
These jobs demand skills in the pre-modeling and post-modeling phases. You only need a masters in data science online or on-campus if you have a knack for understanding and managing data. Besides your academic qualifications, the following skills can help you excel in your career.
SQL refers to structured query language. SQL is the universal language in the database realm that everyone should know if they are working or handling data. Be it a data scientist, data engineer, or data analyst. They cannot move forward in their careers if they do not know SQL. This structured query language is essential in extracting data from the database, creating data pipelines, and manipulating data. It plays a substantial role in the pre-analysis or pre-modeling phases of data handling or data cycle.
With solid SQL skills, you can strengthen your analysis regarding modeling and visualization. It helps you manipulate and extract data in advanced ways. Companies working with petabytes of data require more scalable and efficiently written queries for professional purposes.
It is one of the hottest and preferred skills when it comes to the tech industry. It is a go-to programming language to learn over R, and you can also use R wherever you need. Python is currently the most preferred programming language in big data companies. It is simple and easy to learn, and it can handle giant sets of data. Learning Python can serve as a building block for applications that involve constructing machine learning models, manipulating data, or writing DAG files. Python syntax is easy to learn, and it is an effective analyzing tool for data scientists.
3. Data visualization
Data visualization entails the data being presented visually in graphs, infographics, or any other unconventional ways. Data storytelling and data visualization goes side by side. Suppose you are a data scientist knowing how to present and explain the information that is in the form of infographics. In that case, you can be a potential hire for many tech companies looking for such skills. It is vital to develop your data visualization and data storytelling skills to pitch your ideas and models as a data scientist. It will also help you communicate with people who are not tech-savvy to understand what is happening in the model you have presented to them.
4. Machine learning
Upon reading this term, we think it is something for computers or systems that do not involve any human interaction. The reality is otherwise. If we look at the prerequisites for the data scientist’s role, machine learning will be on the list. Machine learning can help you solve data-related problems and manipulate data tailored to your requirements. Building a skill set that involves neural networks, decision trees, reinforced learning, or logistic regression can help a data scientist to excel in their career. Machine learning is an umbrella term entailing different aspects. Choose the one that best aligns with your job role.
5. Business Knowledge
As a data scientist, you must assume that you only need technical skills tailored to your niche, but it is more than that. As a data scientist, you will work in a different business organization outside the tech industry. Suppose you are helping a company to plan its strategies based on the data that you have extracted. In that case, it is vital to develop business knowledge. Businesses rely on the information they receive to plan their projects, target their revenue, or grow in general.
In a fast-paced world, where data management has become a challenging task, technology development has paved the way for many potential job opportunities in the tech industry. With billions of bytes of data, businesses and other organizations rely on the data pools to devise plans and strategies. Staying ahead of the curve and projecting for better growth, data scientists play a substantial role in providing and extracting accurate information tailored to an organization’s professional needs. With skills and academic degrees, data scientists can explore many potential career pathways.
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