How Can Data Science Help To Boost Career In 2022

Everyone wants a data scientist of their own. These bunch of professionals, popping up from a relatively new field, are in-demand among every sector of the industry including media, technology, finance, or healthcare. The job role ‘Data scientists’ is voted among the top 3 jobs in America for the year 2022. With tones of opportunities for a high-paying career and a massive business demand for talent in the domain, data science has emerged as a popular choice among professionals and graduates looking for better career opportunities.

But great pay is not the sole motivator for candidates to enroll in data science training programs. The job of a data scientist is intellectually challenging and satisfying. Plus, the professional has a forefront in advanced technologies. A data scientist earns close to $190k in the United States as of March 15, 2022, according to Glassdoor.

Why Choose To Become A Data Scientist?

Over the last decade, industries have been gradually taking a data-driven decision-making initiative. With consumers changing their buying habits in the current time, dependency on data science has become essential for businesses to function.

A professional making a career switch into data science could make high salaries and solve complex problems. Plus, a data professional would solve unique issues that make the job more enjoyable.

Job Description Of Data Scientist

As a data scientist, you might be assigned the following tasks

1. Mine and analyze data sets to find hidden patterns with the help of data tools

2. Develop data models to present the insights

3. Communicate recommendations to the non-technical members of the team

4. Run optimization drives to improve product development and marketing techniques

Job Growth And Salary Of Data Scientists

Data science is a diverse field that can touch upon multiple industries and disciplines. On average, a data scientist can earn up to $190k annually. With more industries taking a data-driven business initiative, there is a massive chance of expansion in the field. It will lead to more jobs and opportunities for talents who can crunch numbers and present valuable insight based on those findings.

Some of the standard job titles associated with the data scientist are:

• Data Analyst

• Data Engineer

• Data Architect

• Data Scientist

• Business Analyst

• Machine Learning Engineer

These roles often interwind into a single job with a unique title.

How To Become A Data Scientist?

According to the best data science bootcamp, a professional requires formal training to become a data scientist. Ideally, a data science degree could make it easier to land a job, followed by some time at a coding bootcamp to polish hard data skills.

Here is a step-by-step guide for becoming a data scientist:

1. Getting the right skills

Anyone planning to become a data scientist needs to acquire a degree of specialization in a field such as mathematics, statistics, data analysis, or programming. Apart from that, fundamental knowledge of Python, SQL, and Excel is also necessary. As a data scientist, you will employ these tools frequently to filter and organize raw data. Understanding data visualization tools like Tableau and Power BI could also be helpful.

2. Learn the Programming Languages

Data scientists use specialized data cleaning, mining, and modeling tools and manage a huge chunk of data. Python and SQL are two main tools used by them for their purpose. Enrolling in the best data science bootcamp can help you learn more about data science fundamentals.

Data scientists are increasingly using machine learning, a subset of Artificial intelligence, to create algorithms that teach the computer to learn without explicit programming. Some of the tools used in machine learning mostly depend on running the algorithm.

3. Practice visualization skills

A data scientist is expected to create charts and graphs that detail an algorithm's findings to viewers. Proficiency in using tools like Tableau, Power BI, and Plotly is necessary. Excel is also an important tool and practically unavoidable for a data scientist.

4. Polish the communication skills

Proficiency in hard data skills is essential for a professional data scientist. But for, a successful data scientist is expected to come with excellent communication skills and the ability to share ideas. After all, data scientists cannot expect groundbreaking changes in the industry until they can communicate their findings to the non-technical members of the team. If communication is not your forte, rest assured it can be improved with practice. Start with delivering the presentation to a friend or training before the mirror, before moving before the group setting.

5. Work on data science projects to build portfolio

Once you have acquired the basics of programming and visualization skills, it's time to put them to use. Practice projects that need a wide range of skills, such as Excel and SQL to manage databases and Python and R to analyze data and arrive at new insights.

During the practice session, try working on different types of data, including texts, images, and videos. As a beginner, working on different data sets will increase your proficiency and strengthen your portfolio.

P.S. Instead of showcasing your work on your website, try GitHub. GitHub is a public platform that can display the entire process of arriving at the results while simultaneously boosting your profile.

6. Start at entry-level positions

One of the biggest advantages of starting in a related entry-level job is the exposure you get while getting paid. From there, work your way up to the top position with experience. These roles set you up to work heavily on data cleaning and analysis.

Conclusion

Data science is a relatively new and fast-growing field. It is one of the fastest-growing job sectors in the global economy. Becoming a data scientist requires some training, leading to an in-demand, high-paying role. SynergisticIT runs the best data science bootcamp in los angeles for candidates who aspire to become data scientists. The program aims at teaching the essential hard and soft skills expected from a data scientist. In this course, students learn how to use different tools such as Python, NumPy, model deployment, and tableau, which are essential for data mining, cleansing, and predictive analysis.