Applied Scientist vs. Business Data Analyst

Applied Scientist vs Business Data Analyst: A Comprehensive Comparison

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Are you interested in a career in the AI/ML and Big Data space but unsure which role to pursue? The Applied Scientist and Business Data Analyst roles are two positions that are often confused with each other. While they both deal with data, they have different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers. In this article, we will provide a thorough comparison of the two roles to help you make an informed decision.

Applied Scientist

Definition

An Applied Scientist is a professional who uses scientific methods, algorithms, and data analysis to solve complex problems in various industries. They are responsible for designing, implementing, and testing machine learning models to solve real-world problems. Applied Scientists use their expertise in mathematics, statistics, Computer Science, and domain-specific knowledge to develop solutions that meet the needs of their clients.

Responsibilities

The responsibilities of an Applied Scientist include:

• Identifying business problems that can be solved using Machine Learning techniques
• Designing and implementing machine learning models
• Analyzing data and presenting insights to stakeholders
• Collaborating with cross-functional teams to develop solutions
• Conducting experiments and Testing hypotheses
• Staying up-to-date with the latest Research and developments in the field of machine learning

Required Skills

The required skills for an Applied Scientist include:

• Strong background in Mathematics and statistics
• Proficiency in programming languages such as Python, R, and Java
• Knowledge of machine learning algorithms and techniques
• Experience with Data analysis and visualization tools such as Tableau and Excel
• Excellent communication and collaboration skills
• Ability to work in a fast-paced environment and manage multiple projects simultaneously

Educational Background

Most Applied Scientists have a graduate degree in computer science, Statistics, mathematics, or a related field. Some may also have a Ph.D., particularly if they are working in research and development.

Tools and Software Used

Some of the tools and software used by Applied Scientists include:

• Python and R programming languages
• TensorFlow, PyTorch, and other machine learning frameworks
• Jupyter Notebook for data analysis and visualization
• SQL and NoSQL databases for data storage and retrieval
• Cloud platforms such as AWS, Azure, and Google Cloud for deploying and scaling machine learning models

Common Industries

Applied Scientists can work in a variety of industries, including:

• Healthcare
• Finance
• Retail
• Manufacturing
• Technology

Outlook

The outlook for Applied Scientists is positive, with a projected job growth of 15% from 2019 to 2029. The demand for machine learning experts is expected to increase as more companies adopt AI/ML technologies to improve their operations and gain a competitive advantage.

Practical Tips for Getting Started

To get started as an Applied Scientist, you can:

• Earn a graduate degree in computer science, statistics, mathematics, or a related field
• Gain experience in machine learning by working on personal projects or contributing to open-source projects
• Build a strong portfolio showcasing your machine learning projects and skills
• Attend conferences and networking events to meet other professionals in the field

Business Data Analyst

Definition

A Business Data Analyst is a professional who uses data analysis to help businesses make informed decisions. They are responsible for collecting, analyzing, and presenting data to stakeholders in a way that is easy to understand. Business Data Analysts use their expertise in statistics, Data visualization, and communication to provide insights that can help improve business operations and increase profitability.

Responsibilities

The responsibilities of a Business Data Analyst include:

• Collecting and analyzing data from various sources
• Creating visualizations and reports to communicate insights to stakeholders
• Identifying trends and patterns in data
• Providing recommendations to improve business operations
• Collaborating with cross-functional teams to develop solutions
• Staying up-to-date with the latest trends and developments in data analysis

Required Skills

The required skills for a Business Data Analyst include:

• Strong background in statistics and data analysis
• Proficiency in programming languages such as SQL, Python, and R
• Knowledge of data visualization tools such as Tableau and Power BI
• Excellent communication and collaboration skills
• Ability to work in a fast-paced environment and manage multiple projects simultaneously

Educational Background

Most Business Data Analysts have a bachelor's degree in statistics, mathematics, Economics, or a related field. Some may also have a graduate degree, particularly if they are working in a specialized area such as healthcare or finance.

Tools and Software Used

Some of the tools and software used by Business Data Analysts include:

• SQL for data retrieval and manipulation
• Python and R programming languages for data analysis
• Tableau and Power BI for data visualization
• Excel for data analysis and reporting
• Cloud platforms such as AWS, Azure, and Google Cloud for data storage and retrieval

Common Industries

Business Data Analysts can work in a variety of industries, including:

• Healthcare
• Finance
• Retail
• Manufacturing
• Technology

Outlook

The outlook for Business Data Analysts is positive, with a projected job growth of 25% from 2019 to 2029. The demand for data analysts is expected to increase as more companies collect and analyze data to gain insights into their operations and customers.

Practical Tips for Getting Started

To get started as a Business Data Analyst, you can:

• Earn a degree in statistics, mathematics, economics, or a related field
• Gain experience in data analysis by working on personal projects or contributing to open-source projects
• Build a strong portfolio showcasing your data analysis projects and skills
• Attend conferences and networking events to meet other professionals in the field

Conclusion

In summary, Applied Scientists and Business Data Analysts are two roles that deal with data but have different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers. Applied Scientists use their expertise in machine learning to develop solutions to complex problems, while Business Data Analysts use their expertise in data analysis to help businesses make informed decisions. Both roles offer promising career opportunities for those interested in the AI/ML and Big Data space.