What does a Data Science do ?
The past few years have been revolutionary in the history of marketing (digital and traditional), with new and enthralling trends captivating the likes of industry leaders. Data science, data analytics, machine learning, artificial intelligence, digital marketing, etc., are some of the recent marketing trends that have created waves in the industry with their peculiar characteristics and scope.
Data science, particularly, has piqued the attention of brand leaders reason of which several brand leaders are planning to incorporate the concept into their marketing and promotional campaign. It is believed that many brands are employing more and more skilled and experienced Data scientists and analytics.
What is Data Science?
Data science is summarized by data gathering, analysis, and interpretation, among others. It is a field of study that combines mathematical and statistical methods to collect and interpret data, which then can be used to solve business problems. Many companies use data science for-
Data science is used in many industries, namely, banking, healthcare, manufacturing, marketing, sales, and others. Since many organizations are finding it difficult to manage the enormous amount of data, the need for talented data scientists is on the rise. The ability of data scientists to turn a sea of data into actionable insight is what makes data scientists an inevitable part of the business organization in the current milieu. Before we delve deep into what data scientists do, let’s understand who data scientist is and what are their roles and responsibilities in an organization.
Who is a Data Scientist?
A person who collects, analyses, and interprets data is the apt answer to what is meaning of a data scientist. However, a data scientist’s job scope extends to more than just data collection and interpretation. They work with different departments to understand business goals and fulfil them by deploying their mathematics and statistics knowledge. Data scientists also play an important role in many business decisions. Besides data science and data analytics, data scientists also undertake these activities:
A data scientist performs a varied set of roles and responsibilities with each project. Below, we have stated some common roles and responsibilities of data scientists.
What is the Role of a Data Scientist in an Organization?
A data scientist must perform these roles and responsibilities daily in a business organization. In fact, this is what a data scientist does to make a splash in the field.
What Skills Are Required for Becoming a Data Scientist?
Statistical analysis
Statistical analysis is an important function of a data scientist’s job profile. Therefore, an aspiring candidate should be well-versed in different statistical analysis processes and techniques to succeed in the job. They must also be prolific at identifying patterns in data and making sense of the pattern.
Machine learning is an active part of several marketing campaigns. However, the novel concept is slowly gaining momentum in the Indian commercial sector. As a data scientist, one must align machine learning techniques to business goals and objectives successfully. You must also be skilled in implementing algorithms and statistical models into the computer to transmit and extract data automatically.
Computer Science
Many mathematical calculations and statical projections have been made easy by computer systems. Therefore, a data scientist must be well-versed in the principle of the computer system. In addition to this, they should be well-equipped with the fundamentals of artificial intelligence, database systems, numerical analysis, and software engineering, among others.
Programming
A data scientist must be skilled to use computer coding languages like C, C++, Java, Python, and R because these languages aid the data analysis process. For example, computer languages like Python and R are widely used to interpret data in interaction graphs and pie charts and make data accessible and understandable to a layman.
Data storytelling
Data Scientists often interact with stakeholders and department heads to present data. Hence, they must be good storytellers to make non-technical audiences comprehend data analysis.
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