Data scientists are data analysts who have technology competence and can solve complex problems. They collect, analyze, and interpret enormous amounts of data and deal with numerous aspects linked to mathematics, statistics, and computer science. They are in charge of providing information that goes beyond statistical analysis. Data scientist positions can be found in both the private and public sectors, including banking, consulting, manufacturing, pharmaceuticals, government, and education.
Typical roles in data science careers
Data science jobs are available in a variety of industries. Data science applications are used in a variety of industries, from targeted marketing to identifying financial fraud. In the business, there are numerous full-time and freelance career options. A data science profession may encompass the following roles:
- Data scientists: They examine, evaluate, model, and interpret data to develop practical plans for organizations to enhance sales and revenue.
- Senior data scientist: This is a higher-level position. These individuals supervise data scientists, provide model development direction, and assist in the creation of an end-to-end process.
- Business intelligence analyst: To acquire a detailed picture of a company’s status, a BI analyst analyses market trends, and business developments. They look at corporate strategies and financial data to see where they might improve.
- Data mining engineer: These specialists analyze data using statistical tools to find connections, patterns, and outliers, which they use to advise businesses on risk reduction and income improvement.
- Data architect: Data architects create strategies for data management systems that keep data sources organized, secure, and accessible. They collaborate closely with system designers and developers.
Skills required in data science
A data scientist’s skill set includes technical and programming knowledge as well as a solid grasp of mathematical principles. To begin developing these abilities, try the following:
- Database tools: When it comes to storing and analyzing data, understanding databases is essential. Learn how to use tools such as Oracle Database, MySQL, and Microsoft SQL.
- Statistical concepts: Statistics is the study of data collection and analysis methods. Understanding this will aid you in interpreting and presenting facts.
- Probability: This mathematical idea aids in the comprehension and prediction of the probability of a situation or event.
- Mathematical analysis: To comprehend limits and related ideas, you employ mathematical techniques such as series, differentiation, integration, and analytic functions.
- Analytics tools: To comprehend limits and related ideas, you employ mathematical techniques such as series, differentiation, integration, and analytic functions.
- Data wrangling: Tools like Flume, Python, Scoop, and R can be used to clean, manipulate, and organize data.
- Machine learning concepts: Data Science and Machine Learning assist you in developing prediction algorithms based on data by analyzing current and historical data and projecting this learning onto a model that predicts likely outcomes.
- Big data tools: Hadoop and Apache Spark are two platforms that can help you cope with massive amounts of complex data. This is because they perform far better than traditional data processing technologies.
- Data visualization: This method is used to provide a graphical representation of facts and information. It assists in gaining useful information. Data visualization is an artistic skill that you may learn via practice.
Job Growth Statistics in Data Science Careers
There are numerous compelling reasons to pursue a data science and statistics degree from Cal U, but probably the most compelling is the field’s current job boom. As a data science and statistics student, you’ll be laying the groundwork for a career that includes:
- Since 2012, job growth has increased by 650 percent (source: LinkedIn).
- By 2026, 11.5 million new employees are expected (source: U.S. Bureau of Labor Statistics).
- The average annual salary for a data scientist is $120,931 (source: Glassdoor), with an average salary of over $84,000 per year for 14 specific data analysis and data science careers linked to Cal U’s statistics and data science degree program (source: Pennsylvania’s State System of Higher Education).
According to a recent LinkedIn research, the fastest-growing careers in data science and data analysis are:
- Job increase of 8X for Machine Learning Engineers: According to Payscale, the average pay is $100,956.
- 5X job growth for data scientists: According to Glassdoor, the average compensation is $120,931.
- Job growth of 7X for Sales Development Representatives: According to Glassdoor, the average pay is $47,973.
- Customer Success Manager: Job growth of 6X, with an average income of $78,818 according to Glassdoor.
- Big Data Developer: 5X job growth, with an average compensation of $85,000 according to PayScale.
- Full Stack Engineer: 5X job growth, with an average compensation of $104,463 according to Glassdoor.
- Unity Developer: 1X job growth, with an average annual compensation of $83,318 according to PayScale.
- Director of Data Science: 9X job growth, with an average income of $158,911 per year according to PayScale.
- Full Stack Developer: 5X job growth, with an average compensation of $76,063 per year according to PayScale.
Conclusion
The technique of extracting relevant information from enormous amounts of data to generate meaningful insights is known as data science. Data scientists are in high demand because they can assist firms in making critical scaling decisions. Learning about various data science occupations will assist you in determining which role is the best fit for you.
