Skills You Need To Become A Data Scientist
Data science is the backbone of today’s successful businesses. The fierce competition amongst firms is anchored on the timely access to accurate data concerning customer needs and evolving technological landscape. That has increased the demand for data scientists whose job specification entails providing the necessary information to assist companies in taking decisive actions and gaining a competitive advantage.
As a data scientist, you’ll require a lot of data to develop, implement, and deploy machine-learning algorithms. Advanced statistical methods are equally essential to do predictive analytics and get useful insight from the data. Often, the data scientist will also dabble in deep learning to make use of the latest tech developments, such as neural networks and the likes. All of this needs a mathematically inclined thinking style and programming skills.
Having said all that, here is a review of must-have skills if you consider becoming a data scientist.
i) Education Requirements
The first step to becoming a data scientist is to acquire a solid education background. These include:
Masters or Ph.D.: Data scientists are usually highly educated, with most of them possessing either a master’s degree or a PH.D. Therefore, you should be prepared to undergo rigorous training in complex IT and data-related educational materials to attain this honor. These include computer science, statistics, social sciences, and physical sciences. Academic excellence in these disciplines will give you sufficient knowledge in analyzing and processing vast amounts of data.
Specialized Training: Learning computer science does not end with obtaining a master’s Degree or PH.D. You need training in programs that are specific to the job area in which you will specialize. For instance, you can learn how to use Apache Hadoop software or big data querying. Advancing and obtaining a higher qualification training in these specific areas will keep you on the path of becoming a qualified data scientist.
ii) Technical Skills
Specific technical skills are mandatory to become a data scientist. Here is a list of the top must-have technical skills:
Computer Programming: Data Science relies on programming skills to process raw data into insights. Although there are no rules on which programming language you need to learn, R and Python are the most preferred choices. Most importantly, you may want to choose a programming language that serves the problem at hand, so learning a few could come handy. You can pick from programming languages such as Java, R, Python, SQL, Julia, and Scala.
Data Mining skills:
Organizations rely on an influx of information from many sources. These include social media, personal details in subscriptions, emails, and general traffic over the internet. The data scientist should be able to extract only the vital information from these sources and derive meaningful information that might create value for the organization.
A data scientist encounters a lot of data logging, which requires an individual with a background of intensive data analysis. The complexity of the data might necessitate multiple assessments that could only be performed by an individual acquainted with software engineering skills.
If the company produces data-driven products, software engineering knowledge is critical to help set the system right.
To be considered a full-stack data scientist, you need to know programming, statistics, maths, visualization, data management, and everything that pertains to data science. Almost 80% of your work as a data scientist is to prepare data for processing. Therefore, you will be dealing with large volumes of data, which you have to manage.
In this case, database management includes programs that help you to index, edit, and process requests made between the database and linked programs. Using a database management system, you can also retrieve or store data, so if you consider becoming a data scientist, this is a skill you require. Some of the popular database management systems in data science include Oracle, MySql, PostgreSQL, SQL Server, IBM DB2, and NoSQL databases.
Data visualization means a graphical representation of the findings from your data set. Necessarily, data visualizations are a form of communication that helps you to explore your data to the conclusion. So, creating data visualizations allows you to understand the real value of data, from which you can get meaningful information.
For data visualization, you need to understand how to use bar charts, line plots, heat maps, histograms, Geo maps, relationship maps, time series, and other visualization methods. However, you don’t need to worry about all these methods because you can use tools such as Google Analytics, Ms. Excel, PowerBI, SAS, Fusion Charts, and QlikView to generate a visual representation of your data.
While working as a data scientist, you will often require cloud computing products to manage and process data. You can use programs such as Google Cloud, Azure, and AWS, which provide access to frameworks, operational tools, databases, and programming languages. Considering data science entails working with large volumes of data, understanding cloud computing would make your work easier. You can use cloud computing for:
- Data mining
- Data acquisition
- To customize data variables for performance.
Other technical skills you need include Microsoft Excel, Machine Learning, and Data Wrangling.
iii) Non-Technical Skills
Besides these fundamental guidelines on how to become a data scientist, you also require non-technical interpersonal skills. Among essential skills in this category include:
In addition to technical skills, you could also benefit from having the desire to acquire new knowledge. You should, therefore, ask questions because, as a data scientist, you will spend most of your time gathering and preparing data. Data science is an evolving field, so you need to regularly update your knowledge by reading books on new technologies and methods. Curiosity will help you to interpret data more accurately.
Data visualization and communication:
Knowing data visualization tools such as Tableau, ggplot, or matplotlib will make you a better data scientist. It will help you make the data more presentable and convincing when explaining it to its users. You should also be capable of communicating and interpreting your findings to the relevant audiences in a way that makes sense to them.
The data scientist's function is complementary. Your work involves sharing insights with company executives, managers, designers, and teams in different departments. All this is necessary for the practical application of your findings, so being able to work with others will make your work easier. You’ll need to be easy to collaborate with on different projects.
Strong Communication Skills:
While you may be the most qualified data scientist, you should be able to communicate effectively. Communication is crucial because, in some instances, you will be required to share your insights with non-technical users of data.
The Bottom Line
As a data scientist, you must fit strategically within your organization by possessing the requisite technical and non-technical skills. Technical skills offer you vital skills that you will need to work with large volumes of data, while non-technical skills will help you collaborate with different teams to help the organization make the most of your findings. This way, you will carve for yourself a competitive job and stay abreast of the changing information needs of current organizations.