Applied Scientist vs. Data Scientist

Applied Scientist vs. Data Scientist: A Comprehensive Comparison

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The fields of artificial intelligence (AI), Machine Learning (ML), and Big Data are rapidly growing, and with them come several exciting career opportunities. Two of the most popular roles in these fields are Applied Scientist and Data Scientist. While they have some similarities, they are distinct roles with 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 explore these differences in detail.

Definitions

An Applied Scientist is a professional who applies scientific principles to solve practical problems in the industry. They work on developing and implementing machine learning models, algorithms, and software systems for various applications, such as natural language processing, Computer Vision, speech recognition, and Robotics. They are responsible for designing, Testing, and evaluating the performance of these systems and ensuring they meet the business requirements.

On the other hand, a Data Scientist is a professional who uses statistical and computational techniques to extract insights and knowledge from large datasets. They work on collecting, cleaning, and processing data from various sources, such as databases, social media, and IoT devices. They then analyze this data to identify patterns, trends, and correlations that can help organizations make informed decisions.

Responsibilities

The responsibilities of an Applied Scientist and a Data Scientist differ significantly. While both roles involve working with data and Machine Learning models, their focus and scope are different.

Applied Scientist

An Applied Scientist is responsible for the following:

• Designing and developing machine learning models and algorithms for various applications
• Testing and evaluating the performance of these models and algorithms
• Implementing these models and algorithms into software systems and applications
• Collaborating with cross-functional teams, such as software engineers, data scientists, and product managers, to ensure the models meet the business requirements
• Staying up-to-date with the latest Research and developments in the field of machine learning and AI

Data Scientist

A Data Scientist is responsible for the following:

• Collecting, cleaning, and processing large datasets from various sources
• Analyzing this data to identify patterns, trends, and correlations
• Creating statistical models and algorithms to predict future outcomes and trends
• Communicating the insights and findings to stakeholders through visualizations and reports
• Collaborating with cross-functional teams, such as business analysts, software engineers, and product managers, to ensure the insights are actionable and valuable to the organization

Required Skills

The skills required for an Applied Scientist and a Data Scientist are different, although they do share some common skills.

Applied Scientist

The skills required for an Applied Scientist include:

• Strong understanding of machine learning algorithms and Statistical modeling
• Proficiency in programming languages such as Python, Java, and C++
• Experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
• Knowledge of natural language processing, Computer Vision, speech recognition, and robotics
• Strong problem-solving skills and the ability to think creatively
• Excellent communication and collaboration skills

Data Scientist

The skills required for a Data Scientist include:

• Strong understanding of Statistics and Data analysis techniques
• Proficiency in programming languages such as Python, R, and SQL
• Experience with Data visualization tools such as Tableau and Power BI
• Knowledge of machine learning algorithms and techniques
• Strong problem-solving skills and the ability to think critically
• Excellent communication and collaboration skills

Educational Backgrounds

The educational backgrounds required for an Applied Scientist and a Data Scientist are different, although they do share some common backgrounds.

Applied Scientist

The educational backgrounds required for an Applied Scientist include:

• A bachelor's degree in Computer Science, Mathematics, or a related field
• A master's degree or Ph.D. in Computer Science, machine learning, or a related field
• Experience in developing and implementing machine learning models and algorithms

Data Scientist

The educational backgrounds required for a Data Scientist include:

• A bachelor's degree in statistics, Mathematics, or a related field
• A master's degree or Ph.D. in Statistics, data science, or a related field
• Experience in Data analysis and statistical modeling

Tools and Software Used

The tools and software used by an Applied Scientist and a Data Scientist are different, although they do share some common tools.

Applied Scientist

The tools and software used by an Applied Scientist include:

• Machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn
• Programming languages such as Python, Java, and C++
• Cloud computing platforms such as AWS, Azure, and Google Cloud
• Natural language processing tools such as NLTK and spaCy
• Computer vision tools such as OpenCV and TensorFlow Object Detection API

Data Scientist

The tools and software used by a Data Scientist include:

• Data visualization tools such as Tableau and Power BI
• Programming languages such as Python, R, and SQL
• Statistical modeling tools such as SAS and SPSS
• Big data processing tools such as Hadoop and Spark
• Cloud computing platforms such as AWS, Azure, and Google Cloud

Common Industries

The industries that employ Applied Scientists and Data Scientists are different, although there is some overlap.

Applied Scientist

The industries that employ Applied Scientists include:

• Technology companies, such as Google, Amazon, and Microsoft
• Healthcare companies, such as Pfizer and Johnson & Johnson
• Financial services companies, such as JPMorgan Chase and Goldman Sachs
• Automotive companies, such as Tesla and Ford
• Robotics companies, such as Boston Dynamics and iRobot

Data Scientist

The industries that employ Data Scientists include:

• Technology companies, such as Google, Facebook, and Apple
• Healthcare companies, such as UnitedHealth Group and CVS Health
• Financial services companies, such as American Express and Visa
• Retail companies, such as Walmart and Amazon
• Government agencies, such as the CIA and NSA

Outlooks

The outlooks for Applied Scientists and Data Scientists are positive, with both roles projected to grow significantly in the coming years.

According to the Bureau of Labor Statistics, the employment of computer and information Research scientists, which includes Applied Scientists, is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. This growth is driven by the increasing demand for AI and machine learning in various industries.

Similarly, the employment of Data Scientists is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. This growth is driven by the increasing amount of data generated by organizations and the need to extract insights and knowledge from this data.

Practical Tips for Getting Started

If you are interested in pursuing a career as an Applied Scientist or a Data Scientist, here are some practical tips to get started:

Applied Scientist

• Learn the fundamentals of machine learning and Statistical modeling
• Gain experience in programming languages such as Python, Java, and C++
• Familiarize yourself with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn
• Build a portfolio of machine learning projects that demonstrate your skills and experience
• Network with professionals in the industry and attend conferences and meetups to stay up-to-date with the latest developments in the field

Data Scientist

• Learn the fundamentals of statistics and data analysis techniques
• Gain experience in programming languages such as Python, R, and SQL
• Familiarize yourself with data visualization tools such as Tableau and Power BI
• Build a portfolio of data analysis projects that demonstrate your skills and experience
• Network with professionals in the industry and attend conferences and meetups to stay up-to-date with the latest developments in the field

Conclusion

In conclusion, the roles of an Applied Scientist and a Data Scientist are distinct, with different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started. While both roles are exciting and offer many opportunities, it is essential to understand the differences and choose the one that aligns with your interests and career goals.