Multitasking work of a data scientist

Companies are aggressively using data science to be market leaders. Data is streamed from a variety of sources including the web, social media, customer reviews, internal databases, and government data sets. But just having that data stored will in no way help companies use the data they need to analyze it. Analyzing data is not an easy job as trends are hidden.

The data science industry is drawing revenue from all industries, both domestic and international. Revenue of $1.27 billion was earned in the last year alone and is projected to reach $20 billion by 2025. This sudden growth is because big data is proving to be of great value to the business. Some of the uses are:

Help understand market demand.
Help in the innovation of new products and services.
Helps in customer retention and satisfaction.
Helps communicate the brand to customers.
Help in digital marketing and social media.
It aids in real-time experimentation and monitors business performance.

ROLES OF DATA SCIENTISTS:

Data scientists are data organizers who seek meaning in collected data. A data professional has many roles in their daily data activities. As the entire data process is a pipeline of many interlinked steps, one data scientist can do them all together or separate experts are appointed to complete the process. Some of the functions performed by them are:

Conduct research and frame a problem that is relevant to the market.
Collect data from various internal and external sources such as the web, internal databases, data sets available on the Internet, or customer reviews on social media platforms.
Clean and extract the data from all inconsistencies, such as gaps and incorrectly entered figures, time zone differences, etc.
Explore the data from all directions to find any kind of behavioral patterns or hidden trends in it. For this, many tools are used that are programmed for exploratory data analysis.
Use statistical and mathematical models and tools to learn data deeply and prepare it for predictive decision making.
Create new algorithms that are also called machine learning, where data is used to automate work.
Communicate inferences learned using data visualization tools and present them in a way that can be understood by management.
Proper understanding will lead to making actionable decisions and finding solutions that can be practically applied.

Different companies have different tasks lined up for their data analysis, but most of the activities remain similar.

SKILLS OF A DATA SCIENTIST:

Data scientists need to have several skills up their sleeves. But the most important of them is to have a curious mind and an analytical mindset. Searching for a question and then, like a detective, searching for answers from a large amount of data is no joke. Basic traits like patience, curiosity, and contextual understanding can help you succeed. The rest of the knowledge is technical and can be learned and practiced. Some of the necessary skills are: