The year 2020 was been quite a peculiar anomaly for whatsoever was taking place globally. The COVID-19 pandemic specifically has created a new path to work from home with the help of the latest and advanced technology. Even in the present year 2021, the COVID-19 pandemic is still continuing in many places. But the best thing is data science is continuing to grow more in each and every industrial sector. In this article, let’s understand the best path to become a big data scientist.

Job role of a data scientist

A data scientist mainly does the following tasks on a daily day-to-day basis irrespective of the industry one works in:

• Getting patterns and trends in datasets for unrevealing insights
• Developing algorithms and data models to forecast results
• Using the several Machine Learning (ML) procedures to improve the quality of data or product offerings
• Communicate recommendations to other teams and senior members
• Deploy data tools, such as Python, R, SAS, or SQL in data analysis
• Stay on top of innovations in the field of data science

Essential technical and non-technical skills to be a data scientist

The most vital skills are computing, statistical analysis, mining, and processing massive data sets. This also has valuable data extraction. There are several data scientists who has Masters or a Ph.D. in engineering, computer science, and statistics. Having a strong educational background gives them a proper foundation to be aspiring data professionals. It is better to acquire a better knowledge of several programming languages for data science and to be a success in the field such as statistics and mathematics.

• Programming Languages

An individual must have sound knowledge of several data science programming languages like C/C++, SQL, Python, Java, and Perl. The data science languages serve the data scientists to well-organize the unstructured data sets. This will also be useful in transforming the data into meaningful insights. In the current scenario, many are considering Python and R languages as they are very easy to use.

• Data Storytelling and Data Visualization

Data storytelling combined with data storytelling is among the top skills that are needed to be mastered by every data science professional. This is a crucial part of the data science process where they will be compared to their data engineering colleagues. As they take up unique roles like interacting with project stakeholders for giving the results of a data science project, having compelling data visualization is the perfect process to deliver the results that come from an ML algorithm. It is an essentiality of the final data storytelling. Always keep looking for the latest data visualization techniques by using the Python libraries and R packages to get an effective outcome.

• Communication Skills

One should hone the communication skills so that they well-communicate and share the knowledge among their team members who might not have the same skillset.

• Business Acumen

Possessing strong business acumen will also channel the technical skills. An aspiring data scientist should have the ability to discern the problems and potential challenges which are required to be solved for the growth of the company.

Opportunities in Data Science

Data Science is considered among the most attractive career opportunities. Globally, there are plenty of big data scientist career opportunities for data-based roles.

Job role of a senior data scientist

The senior data scientist’s role in data science in comparison to a non-senior data scientist is they provide advanced expertise on several essential areas that are useful for the firm’s growth. The key responsibilities that are part of their day-to-day job role are:

• Staying updated about the latest advancements of data science and adjacent fields to ensure that there are better results.
• Suggesting, and managing data-driven projects that are valuable for a business's interests.
• Collating and cleaning data from different entities so that it can be used by junior data scientists.
• Formulating creative ideas for leveraging the business’s vast collection of data in the databases.
• Monitoring the performance of junior data scientists and giving them required practical guidance.
• Managing the activities of the junior data scientists, and making sure that they properly execute their job responsibilities, which should be aligned with the business’s vision and objectives.
• Finding and applying advanced statistical procedures to obtain actionable insights.
• Collaboratively working with junior data scientists for building the latest and improved analytics systems such as from prototyping to production.
• Cross-validating models and delegating works to junior data scientists to get better outcomes and also for completing the projects on time.
• Producing and disseminating non-technical reports, which detail the accomplishments and limitations of every project.

Data Science Certification benefit As there is a boom in data science many individuals are looking for multiple career opportunities, one among them is, doing a data scientist certification. There are several institutes that offer certifications that will give better latest industry exposure and advanced quality projects so that one can stand apart from the crowd. Many institutes offer certification programs that will make you a highly valuable professional. Few of the institutes that offer the best data scientist certification programs in data science are:

• DASCA (Data Science Council of America)
• Coursera

To summarize

The field of data science is growing impressively. There are numerous opportunities where a person can establish themselves as a data scientist. By enhancing one’s skills, gaining knowledge on the right tools, doing a certification that enhances the career will be very useful to perform in a better way and to grow in their respective careers.