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Identifying the Right Role for Yourself

Data science and artificial intelligence are exciting, growing fields with a lot to offer prospective job seekers. However, even with the massive growth in technology and positions, there are still many barriers to entry. This course explores today’s challenges and opportunities within data science and artificial intelligence, the varying skills and education necessary for some commonly confused positions, as well as the specific job duties associated with various in-demand roles. By taking this course, learners will be able to discover which role and industry best fit their skills, interests, and background as well as identify any additional education needed, both of which will prepare them to apply and interview for DS/AI positions.

By the end of this course, students will be able to:

• Identify the required skills, education, and experience for various DS/AI roles.

• Recall the similarities and differences between various commonly confused DS/AI roles.

• Describe a data science/artificial intelligence role that aligns with personal goals and area of interest.

• Assess what additional skill training is needed to enter a specific DS/AI role.

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Syllabus

Data Science and Artificial Intelligence Field & Roles

Welcome to Module 1, Data Science and Artificial Intelligence Field & Roles. Now that you’ve finished school or accumulated some initial experience in the data science and/or artificial intelligence fields, you’d probably like to find a full-time, long-term position. In this module, we’ll discuss the current DS/AI landscape, some of the common challenges of landing a DS/AI role, and the basic experience and education you will need to be considered for a DS/AI role. We’ll close the module with a discussion about your current DS/AI experience, education, and goals for the future. We’ll use this as a benchmark to reflect on at the end of the course and specialization.

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what should give you pause

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Explores current challenges and opportunities within data science and artificial intelligence, which is standard in industry

Taught by Camille Funk, recognized for work in data science and artificial intelligence

Helps learners discover which role best fits their skills, interests, and background, which is useful for personal growth and development

Strengthens an existing foundation for intermediate learners in data science and artificial intelligence

Examines today’s challenges and opportunities within data science and artificial intelligence, which highly relevant in an academic setting

This course explicitly advises students to take other courses first as prerequisites

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Reviews summary

Navigating ds/ai career paths

According to learners, this course is a highly valuable and practical guide for understanding the vast and often confusing landscape of Data Science and Artificial Intelligence careers. Students particularly appreciate how it clarifies the distinctions between various roles, such as Data Scientists, Analysts, and Engineers, which is a common pain point for those entering the field. The course excels at helping individuals identify the right role for their skills and interests and pinpointing any necessary additional education or training. While it provides a solid foundational overview, some indicate it serves best as an introductory guide rather than a deep technical dive, making it especially beneficial for those new to the field or considering a career transition.

Most beneficial for those exploring new careers.

"If you're already an experienced professional in data, some parts might feel a bit too basic, but it's fantastic for career switchers."

"This course is ideal for someone just starting their journey or considering a career change into data science and AI."

"As someone with some background, I found the initial modules a little introductory, but the overall structure is still useful."

Offers a comprehensive introduction to the DS/AI job market.

"A great starting point for anyone who is new to the data science and AI job market and unsure where to begin."

"The course provides a good foundational understanding of the various roles without getting bogged down in too many technical details."

"It's perfect for gaining an initial grasp of the industry landscape and what different positions involve."

Aids in aligning personal skills with career paths.

"This course provided a clear roadmap for me to figure out which DS/AI role best suits my background and interests."

"I now have a much better idea of what additional skills I need to acquire to transition into my desired data role."

"It helped me assess my current standing and determine concrete steps to prepare for DS/AI job applications."

Defines and distinguishes common DS/AI job roles.

"The course really helped me understand the differences between a data scientist, analyst, and engineer, which I was completely confused about."

"I finally have clarity on what each role entails. It's so helpful to see the similarities and differences laid out clearly."

"It cleared up a lot of misconceptions I had about various ML and AI positions, making it much easier to target my job search."

Focuses on career guidance, not technical skills.

"I was hoping for more specific guidance on the technical skills required for each role, but it's more about job descriptions."

"Don't expect coding exercises or deep dives into algorithms; this course is purely for understanding job roles and career paths."

"It tells you 'what' to learn for a role, but not 'how' to technically implement or acquire those skills."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Identifying the Right Role for Yourself with these activities:

Review basic data analysis concepts (e.g., descriptive statistics, data visualization)

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Refresh your knowledge of data analysis concepts to enhance understanding of course materials and improve learning outcomes.

Browse courses on Data Analysis

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Review notes or textbooks on descriptive statistics.
Practice creating data visualizations using a tool like Tableau or Google Data Studio.
Complete online tutorials or exercises on data analysis concepts.

Solve practice problems and coding exercises

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Reinforce your understanding of data science and artificial intelligence concepts by solving practice problems and coding exercises.

Browse courses on Coding Exercises

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Join online platforms like LeetCode or HackerRank for coding practice.
Solve practice problems related to data structures, algorithms, and machine learning.
Participate in coding competitions or hackathons to test your skills.

Follow tutorials on specific data science or artificial intelligence techniques

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Enhance your skills by following guided tutorials, deepening your understanding of specific techniques and their applications.

Browse courses on Data Science Techniques

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Identify a specific technique you want to learn, such as machine learning algorithms or natural language processing.
Find reputable online tutorials or courses that cover the technique.
Follow the tutorials and complete the exercises to apply the technique.

Three other activities

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Contribute to open-source projects related to data science or artificial intelligence

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Apply your skills in a practical setting by contributing to open-source projects, enhancing your portfolio and demonstrating your capabilities.

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Identify open-source projects on platforms like GitHub related to data science or artificial intelligence.
Review the project documentation and identify areas where you can contribute.
Write code, fix bugs, or improve documentation to contribute to the project.
Submit your contributions for review and feedback.

Develop a data science or artificial intelligence project portfolio

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Showcase your skills and demonstrate your learning outcomes by developing a portfolio of data science or artificial intelligence projects.

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Identify a project that aligns with your interests and skills.
Gather data, build models, and analyze results using appropriate techniques.
Create a report or presentation to document your project and its findings.
Share your project on platforms like GitHub or LinkedIn.

Mentor junior students or colleagues in data science or artificial intelligence

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Enhance your understanding by mentoring others in the field, reinforcing your knowledge and fostering a supportive learning community.

Browse courses on Mentoring

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Identify opportunities to mentor junior students or colleagues in data science or artificial intelligence.
Share your knowledge and expertise, providing guidance and support to mentees.
Review mentees' work, providing feedback and constructive criticism.
Encourage mentees to ask questions and seek clarification.

Career center

Learners who complete Identifying the Right Role for Yourself will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for building and maintaining machine learning models. A successful Data Scientist must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become a Data Scientist because it provides an introduction to the field of data science and covers topics such as data collection, data analysis, and machine learning.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their findings to help businesses make decisions. A successful Data Analyst must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become a Data Analyst because it provides an introduction to the field of data science and covers topics such as data collection, data analysis, and machine learning.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers are responsible for building and deploying machine learning models. They work closely with Data Scientists to develop and implement machine learning solutions to business problems. A successful Machine Learning Engineer must have a strong foundation in computer science, mathematics, and statistics. This course may be useful for someone who wishes to become a Machine Learning Engineer because it covers topics such as machine learning algorithms, model deployment, and cloud computing.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. They ensure that data is available to Data Scientists and Data Analysts in a timely and efficient manner. A successful Data Engineer must have a strong foundation in computer science and software engineering. This course may be useful for someone who wishes to become a Data Engineer because it covers topics such as data warehousing, data pipelines, and cloud computing.

See salaries and explore the career path for Data Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers are responsible for designing and developing artificial intelligence systems. They work on a variety of projects, from self-driving cars to facial recognition software. A successful Artificial Intelligence Engineer must have a strong foundation in computer science, mathematics, and statistics. This course may be useful for someone who wishes to become an Artificial Intelligence Engineer because it covers topics such as artificial intelligence algorithms, natural language processing, and computer vision.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Architect

Data Architects are responsible for designing and managing data systems. They work with stakeholders to understand their data needs and develop data solutions that meet those needs. A successful Data Architect must have a strong foundation in computer science, database design, and data warehousing. This course may be useful for someone who wishes to become a Data Architect because it covers topics such as data modeling, data integration, and data governance.

See salaries and explore the career path for Data Architect

Business Analyst

Business Analysts are responsible for understanding the business needs of an organization and developing solutions to meet those needs. They work with stakeholders to gather requirements, analyze data, and develop recommendations. A successful Business Analyst must have a strong foundation in business analysis, project management, and data analysis. This course may be useful for someone who wishes to become a Business Analyst because it covers topics such as business requirements gathering, data analysis, and solution development.

See salaries and explore the career path for Business Analyst

Cloud Engineer

Cloud Engineers are responsible for designing, building, and maintaining cloud computing systems. They work with customers to understand their cloud needs and develop cloud solutions that meet those needs. A successful Cloud Engineer must have a strong foundation in computer science, networking, and cloud computing. This course may be useful for someone who wishes to become a Cloud Engineer because it covers topics such as cloud architecture, cloud security, and cloud deployment.

See salaries and explore the career path for Cloud Engineer

Data Science Manager

Data Science Managers are responsible for leading and managing data science teams. They work with stakeholders to define data science goals and develop strategies to achieve those goals. A successful Data Science Manager must have a strong foundation in data science, leadership, and management. This course may be useful for someone who wishes to become a Data Science Manager because it covers topics such as data science leadership, team management, and project management.

See salaries and explore the career path for Data Science Manager

Data Scientist II

Data Scientists II are responsible for developing and implementing machine learning models. They work closely with Data Scientists I to develop and implement machine learning solutions to business problems. A successful Data Scientist II must have a strong foundation in computer science, mathematics, and statistics. This course may be useful for someone who wishes to become a Data Scientist II because it covers topics such as machine learning algorithms, model deployment, and cloud computing.

See salaries and explore the career path for Data Scientist II

Data Analyst II

Data Analysts II are responsible for collecting, cleaning, and analyzing data. They use their findings to help businesses make decisions. A successful Data Analyst II must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become a Data Analyst II because it provides an introduction to the field of data science and covers topics such as data collection, data analysis, and machine learning.

See salaries and explore the career path for Data Analyst II

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software systems. They work on a variety of projects, from web applications to mobile apps. A successful Software Engineer must have a strong foundation in computer science and software engineering. This course may be useful for someone who wishes to become a Software Engineer because it covers topics such as software design, software development, and software testing.

See salaries and explore the career path for Software Engineer

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They use their findings to help businesses make decisions. A successful Statistician must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become a Statistician because it covers topics such as data collection, data analysis, and machine learning.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts are responsible for developing and implementing mathematical models to solve business problems. They work on a variety of projects, from risk management to portfolio optimization. A successful Quantitative Analyst must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become a Quantitative Analyst because it covers topics such as financial modeling, data analysis, and machine learning.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts are responsible for developing and implementing mathematical models to improve the efficiency of business operations. They work on a variety of projects, from supply chain management to logistics. A successful Operations Research Analyst must have a strong foundation in mathematics, statistics, and computer science. This course may be useful for someone who wishes to become an Operations Research Analyst because it covers topics such as mathematical modeling, data analysis, and optimization.

See salaries and explore the career path for Operations Research Analyst

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Identifying the Right Role for Yourself.

Data Science for Business

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Provides a comprehensive overview of data science and its applications in business. It covers the fundamental concepts of data mining, data analysis, and machine learning. The book is written in a clear and concise style, and it is packed with real-world examples.

Identifying the Right Role for Yourself

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What's inside

Syllabus

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Reviews summary

Navigating ds/ai career paths

Activities

Career center

Reading list

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