11 Types of Data Science Jobs (With Responsibilities)

Data science is the process of gathering and analyzing data to better make decisions and understand situations. Data scientists can specialize in many areas of data science, from business intelligence to statistical analysis. If you’re a data scientist or you want to become one, learning more about the various fields of data science can be beneficial. In this article, we explain what a data scientist does, explore 11 types of data science jobs and provide helpful tips you can use to become a data scientist.

What does a data scientist do?

Data scientists use the information to solve problems and help others make informed decisions. These are some of the responsibilities that a data scientist may manage:

• Create and implement data models

• Coordinate with other professionals

• Present visual data information

• Study new data science technology

• Synthesize data and information

types of data science specializations

These are 11 types of data science specializations you can pursue:

BUSINESS INTELLIGENCE

Business intelligence, a branch of data science, involves data scientists gathering and evaluating information to inform business leaders. The data analysis conducted in business intelligence processes assists organizations in making decisions aimed at achieving success. For instance, business intelligence data scientists might collect data on competitors’ performance, enabling business leaders to formulate effective strategies.

CLOUD COMPUTING

Cloud computing is the practice of providing services, storage, and databases over the internet. Data scientists specialize in collecting and analyzing data stored in cloud-based databases. They also contribute to storing and structuring information in cloud computing data storage locations.

CYBER SECURITY

Cybersecurity data scientists gather information about the security of an organization’s online and internal information. They assess data regarding safety to determine how easily outside sources may access information. Cybersecurity data scientists can help other cybersecurity professionals ensure an organization’s information stays private and secure.

DATA ENGINEERING

Data engineering involves the design and construction of systems for collecting and storing data. Data engineers also develop programs that can transform raw data into processed information, which data scientists can utilize to draw insights. Additionally, data engineers create systems that enable data access for multiple individuals within an organization.

DATA MINING

Data scientists who focus on data mining use software to find patterns in large sets of data. They can search for correlations and anomalies to learn more about accuracy. Finding patterns can also help data miners predict outcomes and understand trends.

DATA VISULIZATION

Data visualization experts focus on taking raw information and presenting it in a way that their colleagues can understand. They may create graphs and figures to demonstrate how data compares and what it means. For example, a data visualization expert may create a presentation to show a business leader how a new product may perform in the market.

MACHINE LEARNING

Machine learning in data science is the process of teaching robots and programs to behave in certain ways. A data scientist who specializes in machine learning uses information and algorithms to help machines understand how to react to problems and scenarios. They develop artificial intelligence that can improve operations for manufacturing and design.

MARKET DATA ANALYTICS

Market data analysts are scientists who focus on how products and services perform within markets. They may gather information about how events affect profits. They can also predict how new products or services may perform and help leaders prepare for what may happen in different world events.

Becoming a data scientist is an exciting and rewarding journey, but it requires a combination of education, skills, and practical experience. Here are some tips to help you become a data scientist:

1. Educational Foundation:
   • Obtain a bachelor’s degree in a relevant field, such as computer science, statistics, mathematics, or a related discipline. A master’s or Ph.D. can be advantageous but is not always required.
2. Learn Data Science Fundamentals:
   • Master the basics of statistics, linear algebra, and calculus.
   • Develop a strong foundation in programming with languages like Python and R.
   • Understand data manipulation and analysis libraries, such as Pandas, NumPy, and Scikit-Learn for Python.
3. Machine Learning and AI:
   • Gain expertise in machine learning and artificial intelligence concepts and algorithms.
   • Learn how to implement machine learning models using frameworks like TensorFlow or scikit-learn.
4. Data Visualization:
   • Acquire skills in data visualization tools such as Matplotlib, Seaborn, and Tableau.
   • Effectively communicate data insights through charts and graphs.
5. Big Data Technologies:
   • Familiarize yourself with big data technologies like Hadoop, Spark, and NoSQL databases.
   • Understand distributed computing concepts.
6. Domain Knowledge:
   • Develop domain expertise in an area that interests you (e.g., finance, healthcare, e-commerce). Data science is more effective when applied in specific contexts.
7. Build a Portfolio:
   • Work on personal or open-source projects to build a strong portfolio showcasing your skills.
   • Participate in Kaggle competitions or data science hackathons to gain experience.
8. Networking:
   • Attend data science meetups, conferences, and webinars to connect with professionals in the field.
   • Join online data science communities and forums to ask questions and learn from others.
9. Online Courses and Resources:
   • Take advantage of online courses, such as those on Coursera, edX, or Udacity, to supplement your learning.
   • Read data science books and research papers.
10. Soft Skills:
    • Develop strong communication and problem-solving skills.
    • Learn to work effectively in multidisciplinary teams.
11. Internships and Job Experience:
    • Seek internships or entry-level positions in data-related roles to gain practical experience.
    • Continuously apply what you’ve learned to real-world problems.
12. Stay Updated:
    • Data science is a rapidly evolving field. Stay updated with the latest trends, tools, and technologies.
13. Certifications:
    • Consider earning certifications in data science, machine learning, or related areas to demonstrate your expertise.
14. Personal Projects:
    • Work on personal data science projects to explore your interests and deepen your skills.
15. Patience and Persistence:
    • Data science can be challenging. Be patient and persistent in your learning journey.

Remember that becoming a data scientist is a gradual process, and there is no one-size-fits-all path. Tailor your learning and experiences to your goals and interests, and continually seek opportunities to grow in this dynamic field.