As the field becomes more in demand, it becomes more appealing to both students and professionals. These include people who are not data scientists, but who are very intrigued by data and data science, leading them to wonder what data science and big data abilities are needed for careers in the data science field.
Also, as it is a vast field with several sub-disciplines, such as data preparation and exploration, data representation and transformation, data visualization and presentation, predictive analytics, machine learning, and so on. For newcomers, the question is “What skills do I need to become a data scientist?”
As we know, we need both technical vs non-technical skills to become data scientists. In this article, we’ll discuss both the skills firstly we’ll start with the technical ones.
- Data Visualisation
Companies generate large volumes of data regularly. In order to make the information easily understandable, this information must be converted into a simpler form. People have a difficult time understanding raw data as opposed to images such as charts and graphs. A picture is worth a thousand words, as the expression says,
Data scientists use ggplot, d3.js, matplotlib, and tableau to visualize their data. You can use these tools to convert complex project outcomes into an easy-to-understand format.
- Unstructured Data
Working with unstructured data is crucial for data scientists. The unstructured data consists of information that does not fit into a database table. A few examples include videos, blog articles, customer reviews, social network posts, video feeds, and audio. These are all collections of lengthy texts.
People generally refer to unstructured data as “black analytics” because of its complexity. Working with unstructured data allows you to uncover insights that can help you make better decisions. A data scientist should be capable of analyzing and manipulating unstructured data across many platforms.
- Apache Spark
Apache Spark has quickly become the most popular big data tool on the planet. Spark is a Hadoop-like platform for computing large amounts of data. The only difference between Spark and Hadoop is its speed. Spark is quicker. Unlike Hadoop, Spark caches its computations in memory rather than on disc, which makes it faster since Hadoop reads and writes data to disc.
In order to accelerate the execution of complex algorithms, Apache Spark was designed primarily for data science. In the case of a large amount of data, it aids in dispersing data processing and thus saves time. It also helps data scientists handle large, unstructured data volumes. It can be applied to a single machine or a group of machines.
It is defined as a desire to know more about something. As a data scientist, you should ask questions about data, since you spend approximately 70% of your time gathering the knowledge regarding the same and preparing it. Due to the rapidly evolving nature of data science, you will need to learn more to keep up.
To keep your expertise up to date, you should read relevant books on data science trends and review online content. Despite the vast amount of information available on the internet, it is vital that you understand it all. Curiosity is one of the skills you’ll need as a data scientist. For example – It is possible, that you will not be able to gain much insight from the data you have gathered so far. You can comb through the data to find answers and new information out of curiosity.
Data scientists cannot work alone. The tasks you will have to perform include working with firm executives to build strategies, product managers to develop better products, marketers to launch more effective campaigns, and client and server software engineers to create data pipelines and streamline workflows. To be successful, you must work with everyone in the company, including your customers.
Basically, you’ll create use cases with your teammates so that you can understand the business goals and the data you’ll need to address challenges.
- Ability to communicate
Companies looking for data scientists would like someone who is able to communicate their findings clearly and fluently to non-technical departments, such as marketing or sales. By providing companies with quantitative insights and knowing the needs of non-technical colleagues, a data scientist will be able to manage data effectively. Our latest flash survey provides more information on communication abilities for quantitative experts.
These were some of the crucial skills that will give you a brief regarding the skills that you need to become a data scientist.