Big Data: The Responsibilities Of a Data Scientist
The number of channels that data can be collected from is constantly rising and becoming more easier to access and by 2025, there would be more than 150 trillion gigabytes of data available that needs analysis, according to Forbes.
Despite the amount of data available, its volume is less important as how it is processed and utilized by different businesses is the key focus. Data science has made data more meaningful to businesses as it increases their profit by eight to ten per cent.
The ability of a data scientist to prepare, store, process and manage data makes them highly sort after by many organizations today. Sectors such as health, finance, transport and retail rely on their data management skills to grow effectively as a business.
Big data—as commonly termed—refers to the amount of data collected by businesses on a daily business, whether advertently or inadvertently. This big data can be group into three major categories, structured, semi-structured and unstructured data.
Structured data refers to data that is properly sorted as a model in a spreadsheet (data warehouse) or database, which makes searching for them quite easy.While for unstructured data, it is difficult to search for these data as they are not in a predefined data model. Semi-structured is more of a combination of both structured and unstructured data.
The responsibilities of a data scientistinclude preparing, storing and processing an array of data obtained from various sources such as websites, cloud storage, smart devices, security cameras and many more. However, to begin a career in data science, it is important to hold a bachelor’s degree (and as an added advantage, a Masters degree or PhD) in a related field.
Succeeding in a career as a data scientist entails you continually improve your coding and business skills, such as maths, statistics, decision-making and stakeholder management. It is also important to effectively communicate important data insights to your audiences.
With online education providing the much-needed flexibility for people with multiple responsibilities, it is best to apply now for a data science program if your intentions are to become a data scientist.
Below are the major responsibilities of data scientists in today’s world.
1. Preparing big data
One of the first steps for a data scientist in every organization is preparing big data and its relevant algorithms or models. This involves working together with major stakeholders in your businessto discover the specific information they require from your analysis.
Doing this helps you stay informed on the best way to conduct the entire process and equally identify the best analytical tools to achieve your organizational goals.
However, at the end of every project, it is also the responsibility of a data scientist to utilize data visualization tools to present discoveries. Using these tools allows you to display data in a more engaging and presentable wayin forms of charts, graphs and infographics.
2. Storing big data
Data scientists handle large amounts of data and need storage solutions that can handle these data and be also be flexible enough to upscale as newer streams of information are fed into it. It is also the responsibility of a data scientist to ensure that the storage device provides the required high level of input and output operations per second (IOPS) need in that organization.
For large corporation pulling in large amounts of data, they typically opt for a hyperscale computing environment, while smaller businesses utilize a traditional clustered network attached storage (NAS). Regardless of whichever storage solution your business opts for, your responsibility is to ensure that it can handle large data sets quickly.
3. Processing big data
One of the most important and most sort after skills for data scientists is the ability to process data. Being able to divide larger data streams into smaller bits and study key patterns is a must-have skill for a data scientist in any organization.
Processing big data can help an organization become proactive on a host of critical issues such as identifying cybersecurity threats, fraudulent transactions, unusual activity any many more just from studying data patterns.