The data scientist job has been ranked 6^th in this year’s WEF report for the fastest-growing jobs. Why? Because undoubtedly more and more businesses are relying on data-driven decision-making processes to make their operations more efficient and enhance their customer’s user experience. Data Scientists play an important role in doing so. Data Science is a growing field that involves different processes right from collecting data, to analyzing and processing so that data science professionals can make a decision favoring a company’s growth. If you are looking to go for a data science career but are confused about what the life of a data scientist is, and what his day-to-day activities are, then this article is for you.

What is a data scientist?

Answering the important question first – what is a data scientist? A data scientist is a professional who uses various techniques that includes statistical, computational, and analytical techniques to extract meaningful insights and knowledge from large, complex datasets. They are proficient in technical skills like programming languages, and data science tools, have relevant data science education, and also possess important interpersonal skills like good communication, teamwork, business, domain knowledge, etc. Data scientists work in a wide range of industries, including healthcare, finance, marketing, and technology, and their role has become increasingly important as organizations rely more on data-driven decision-making.

Day-to-day life of a data scientist?

Now talking about the roles and responsibilities of a data scientist, it is as follows:

1. Understanding the Business Problem

Data Scientists are the ones who lead different data science projects within an organization. The first step in any data science project is to understand the business problem that needs to be solved. This involves working closely with stakeholders to identify the key questions and objectives of the project and defining the scope and requirements.

2. Collecting and Cleaning Data

Once the problem has been identified, the next step is to collect and clean the relevant data. This can involve extracting data from various sources, such as databases, APIs, and web scraping. Data cleaning involves removing any errors, duplicates, or inconsistencies in the data to ensure that it is accurate and reliable.

3. Exploring and Visualizing Data

After collecting and cleaning the data, the data scientist will typically explore and visualize the data to gain insights and identify any patterns or trends. This can involve using statistical methods, such as descriptive statistics and data visualization tools, to understand the data and generate useful insights. Students can learn all these by enrolling in any good data science course.

4. Developing and Implementing Models

The next step in the data science process is to develop and implement models that can be used to solve business problems. This can involve using machine learning techniques, such as regression, classification, and clustering, to build predictive models that can be used to make informed decisions.

5. Evaluating the performance of the models developed

After the models have been developed and implemented, the data scientists will typically evaluate their performance to ensure that they are accurate and reliable.

6. Communicating results

The final step in the data science process is to communicate the results of the project to stakeholders. This can involve preparing reports and presentations summarizing key findings and insights and providing recommendations for future action.

Top skills and education required for a data scientist

Data Scientists are in huge demand now and millions of students are turning towards this career. Many top institutes provide data science education and courses specifically designed for data science careers. These courses and certifications cover all the skills, knowledge, and practical education required to become an efficient data scientist. Students wanting to enter the data science field can opt for degrees like a Bachelor’s or Master of Science, Data Analysis, Mathematics, or Computer Science. MBA in data science is also a preferable data science course. After completing these courses, the student must expect to gain the following data science skills:

• Machine learning
• Data visualization
• Python
• Statistics
• Data Analysis, etc.

Conclusion

A Career in Data science is very exciting. Data Scientists form up a major role in providing an accurate decision-making process that is backed by insights driven by huge amounts of data. Right from identifying the problem to solving the problem, data scientists have to work through each step without any chances of error. They need to work with talented teams of data analysts, business analysts, data engineers, programmers, software developers, etc. Combined with these technical professionals, data scientist has to use their business acumen and domain knowledge to devise a model meeting stakeholders’ expectations. Sounds exciting isn’t it? So, if you are starting your data science career then be prepared for this exciting roller coaster ride.