Data science is considered a new buzzword in the technology world, promising great pay and good job growth. But what exactly is data science? What does a data scientist do?
Data science has become more common since big data continues to be increasingly important to how companies make decisions.
Becoming a data scientist might be analytically satisfying, intellectually challenging, and allow you to be at the front of new technological advances.
Data science involves the practice of analyzing large volumes of often unstructured data, intending to produce meaningful insights. Data science approaches, tools, and algorithms can be applied to all business problems.
You can use data to learn which customer segments prefer which products, optimize sales funnels, determine the kinds of discounts the clients appreciate the most, etc.
Also, data science can be applied to the internal operations of companies and organizations to enhance their productivity.
Data scientists use data to understand and explain the things around them and help their organizations make better decisions.
They also decide the queries their team should ask and explore how to answer them utilizing data. They develop predictive models to theorize and forecast.
Data scientists can take on different roles and tasks, depending on their institutions and industries. However, they can often be responsible for:
- Find trends and patterns in datasets to explore insights
- Make data models and algorithms to forecast results
- Leverage machine learning (ML) techniques to enhance the quality of data and product offerings
- Communicate recommendations to senior staff and other teams.
- Deploy many data tools, such as R, SAS, SQL, or Python, in data analysis.
- Stay up to date on innovations in data science.
The working environment for data scientists may differ depending on the companies and which industry they work in.
Although scientists often work full-time schedules during business hours, they may sometimes need to work extra hours in the evenings to ensure they have completed all tasks required for current projects.
Here are some common characteristics of experts in this field:
- A long time sitting at a desk.
- Using scanners, printers, computers, and other office equipment.
- Present solutions or conclusions about their analyzes to other experts, executives, customers, and other stakeholders.
These experts work for the government, computer systems design, related services, development & research, for universities, or software publishers.
Degree and Certification
To get a position related to a data scientist, you must fulfill educational requirements, which may vary depending on the job level and the industry.
To become one data scientist, you will need at least a master’s degree. But many large companies and organizations prefer candidates who have a master’s degree or even a doctoral degree.
Some preferred bachelor’s programs for aspiring data scientists include computer engineering, computer science, information technology, applied statistics, or math.
People with no degrees can take advantage of some organizations’ and universities’ certification programs. Besides traditional certification and degree programs, boot camps and online self-guided learning courses are also available.
Certifications are optional, but they can give you a competitive edge over other candidates and make you more desirable to employers.
Employers may also require their candidates to have several years of industry experience. You can prepare for this by approaching small and medium businesses for internship opportunities. It will help you acclimate to professional environments and bring your academic skills into practice.
You need a head for business strategy. It includes the ability to understand business issues and analysis from the standpoint of a solid problem statement.
It refers to the graphical representation of data using visual elements, such as graphics, maps, charts, infographics, and more. Data scientists must be able to visualize data using some tools, such as d3.js, Tableau, and ggplot.
Machine learning can help analyze a large amount of data utilizing data-driven models and algorithms. It can also automate important parts of data scientists’ jobs, such as cleaning data by getting rid of redundancies.
It is one domain-specific programming language made for querying and managing data held in a relational database management system. SQL can be used to read and retrieve data from a database or insert/update new data.
It is the most adaptable and most popular programming language in the data science field. It can handle anything, including data mining, website construction, and more.
It is one integrated suite of software facilities for calculation, graphical display, and data manipulation. It is more popular in academic contexts than Python.
Instead of aiming for perfection, data scientists often cultivate the discipline and patience to teach themselves new things and learn new concepts quickly. Data science is always changing at a fast pace.
As an expert in the field, you will need to collaborate with different teams to understand their requirements and gather their feedback to arrive at solutions.
Depending on your level of expertise in your role, you may also have to work with data architects, data engineers, and data scientists.
These skills are crucial in the form of oral communication, writing, and data visualization. Good storytelling skills mean that analytical solutions are communicated concisely, clearly, and to the point.
Good communication skills are crucial in most professions. As a data scientist, you will need to understand the business requirements or problems at hand, probe stakeholders for more data, and communicate key insights about the data.
Here are some of the most common data science tools used by data scientists to carry out their data operations:
- Apache Spark
The demand for data scientists is always high and tends to increase over time as more and more organizations adopt cloud systems in their operations.
According to Glassdoor, “data science” is the third most desired career in the US.
Meanwhile, LinkedIn ranked it as one of the top most promising jobs in the US and reported a 56% increase in the job opening.
According to recent surveys, the COVID-19 pandemic has not negatively impacted data science opportunities.
Specifically, 50% of data and analytics science organizations have suffered no impact (approximately 42 %) and have actually grown in size (approximately 7.6%) during this pandemic.
The average annual salary for a data scientist is $124,540 in the United States.
This average salary might vary significantly depending on many factors, such as years of experience, qualifications, location, and company.
Here are some advantages and disadvantages:
- Excellent Job Prospects: The demand for data scientists is always high. Hence, there has never been a better time to get into the field of data science.
- Versatility: A career in data science is very versatile, allowing you to get the opportunity to work in various fields.
- Involve Challenging and Varied Work: No one wants to be bored in their jobs. Fortunately, the data scientist role will bring challenging work to keep your brain active.
- Master Every Element of Data Science is challenging: Data science is a very wide field, meaning it is impossible to master elements of this field.
- Experience Ethical Issues: Many ethical issues surrounding collect and use of data. So as a data scientist, you might potentially lose some friends.
- Require High Commitment and Continual Learning: The data science field is constantly evolving, meaning you will have to keep up with the latest advances and best practices continually.
It is not easy to become a data scientist. It is intellectually rigorous and has a steep learning curve, but it is worth your effort and time.
The short answer is Yes. Data science is a dream career with various opportunities for advancements in the future.
Nowadays, the demand for data scientists is high, salaries are competitive, and the perks are numerous. LinkedIn called Data Scientist “The most promising career,” while Glassdoor called it “the best job in America.”
The tasks of data scientists and data analysts can seem similar: Both find patterns or trends in data to produce new ways for their organizations to make good decisions about operations.
But the data scientist tends to have more responsibility and is typically regarded as more senior than the data analyst.
The data scientist is typically expected to make their questions about the data, while the data analyst might support teams that already have set purposes.
Data scientists might spend more time developing models, incorporating advanced programming, and utilizing machine learning to find and analyze data.
Many data scientists might begin a career as data statisticians or analysts.
Becoming a scientist in this field generally requires some formal training. Here are some basic steps to consider.
- Step 1: Get a data science degree.
- Step 2: Sharpen relevant skills.
- Step 3: Get an entry-level data analytics job.
- Step 4: Prepare for a data science interview.
If you want to increase your salary in this area, you can consider the options:
Higher education: A data scientist who obtains a doctoral degree can generally qualify for a more advanced position and higher salary.
Leadership roles: A senior data scientist oversees junior data scientists in addition to his regular responsibilities. They will also receive higher wages.
Change industry: You can consider changing to another sector that is more competitive and pays higher salaries. Social networking companies, such as Facebook and Google, often pay decent wages to data scientists. The same goes for financial manufacturers or organizations as well.
Data science might be ‘the sexiest job of the 21st century with fat salaries. You can start your career in this field right now with a proper bachelor’s degree.
Becoming a data scientist might require some training, but a challenging and in-demand career will be waiting at the end.