Data scientists use data to explain and understand the phenomenon around them. This makes it possible for them to aid companies and organizations in making the right decisions.
Working as a data scientist not only provides analytical satisfaction and the ability to understand modern technological advances but also challenges the intellect. The job demand is increasing daily as more organizations realize how useful they can be for channeling appropriate decisions.
If you’re interested in becoming a data scientist, you may want to consider a data science graduate certificate online. There are also always options for a Data Science Roadmap. However, if you wish to learn more about the profession and why you should become one, keep reading to learn more.
Data Scientist Overview
Data science focuses on analytics. A data scientist’s responsibilities include analyzing, collecting, and interpreting data to aid an organization in decision-making. The role combines both technical and traditional jobs like those of a computer programmer, scientist, mathematician, and statistician.
Advanced analytical techniques such as predictive modeling, application of scientific principles, and machine learning are used to analyze large amounts of data. A data scientist also tests/develops hypotheses, makes inferences, and analyzes customer/market trends, stock trades, equipment maintenance, medical conditions, cybersecurity threats, revenue opportunities, and financial risks. They also look out for fraud. Also, they can prove valuable for government agencies, sports teams, healthcare providers, academic institutions, and other organizations.
Role and Responsibilities
Data scientists deduce the questions a team should ask and determine how these can be answered using an organization’s data. They work to develop predictive models that aid in forecasting and theorizing. Other responsibilities of a data scientist include:
- Looking for trends and patterns in data sets to obtain insights
- Designing data models and algorithms to forecast outcomes
- Communicating with and providing recommendations to senior staff and company teams
- Using ML (Machine Learning) strategies to improve the quality of product offerings and data
- Deploying data tools like R, SAS, Python, Or SQL in the data analysis stage
- Staying ahead of innovations in the data science sector
Differences Between a Data Analyst and Data Scientist
The role of a data scientist and data analyst may seem similar as both look for trends/patterns in data to channel better decision-making regarding organizational operations. However, data scientists generally have more responsibility and authority and are considered senior to data analysts.
Data scientists are expected to come up with their own questions regarding data, while data analysts often have set goals and team support. A data scientist spends additional time using machine learning, developing models, or using advanced programming to discover/analyze data. Most initially start off as a statistician or a data analyst.
Data Scientist Career Outlook
Data scientists earn $126,404 on average per year in the United States and the demand for data professionals is quite high. Mathematical science and scientist jobs are expected to grow by 31% and statistician jobs by 33% between 2021 and 2031, according to the United States Bureau of Labor Statistics.
These rates are much greater than the average job growth rate for all jobs which is just 5%. This great demand links to the popularity of big data and many corporations realizing its importance.
Steps to Become a Data Scientist
1. Get a Degree
Employers expect academic credentials when you apply for a data scientist job to ensure you have the right skills and knowledge. Getting a bachelor’s degree in a related field, such as statistics, data science, or computer science, is a good idea.
If you already have a bachelor’s degree and wish to study further to polish your skills, then a Master’s in data science is your best bet to become a true expert. Master’s degree holders gave a deeper understanding of machine learning, algorithms, statistics, forecasting, and modeling. They are well-equipped to research a topic they consider important. The best part is that these degrees can also be completed online.
2. Polish Your Skills
Once you have the right education, you can opt for admission into a short or online course to further develop your skills. Some skills that you can turn your attention to include:
Data scientists use programming languages to skim and analyze large batches of data. Popular programming languages they use include Python, R, SQL, and SAS.
Data scientists use machine learning along with deep learning to improve data quality and predict outcomes for future datasets more accurately.
These skills help create graphs and charts using tools like Tableau, PowerBI, and Excel.
Data scientists cannot change gear if they are unable to communicate their findings accurately. They should be able to share ideas and results verbally as well as in written form.
Some employers appreciate data scientists that have an understanding of big data and software frameworks like Hadoop and Apache Spark.
3. Apply for a Job
Applying for an entry-level job when you are new is a good idea. This will allow you to gain expertise so you can seek more advanced positions later on. You can start as a data engineer, business intelligence analyst, data analyst, or statistician initially. After working for some time, you can obtain further education and apply for a data scientist’s job with a shining resume.
Data scientists are experts that have years of experience in the data science field. Not only are they paid well, but their demand is also steadily rising. Obtaining a data science degree or another related degree simply means a promising career.