Data Scientists use computer science, statistics, and mathematics to apply machine learning, predictive modeling, and statistical analysis to extract insights and aid decision-making about complex digital data, such as website usage, sales, logistics and customer engagement.
Adaptability, Analytical & Critical Thinking, Basic Computer Competencies, Communication Skills, Creative Thinking, Customer Focus, Dependability, Detail Orientation, Humility, Initiative, Instruction/Teaching Abilities, Integrity, Interpersonal Competencies, Leadership, Lifelong Learning, Mathematics, Problem Solving & Decision-Making, Professionalism, Reading, Science & Technology Understanding, Scheduling & Coordinating, Teamwork & Writing Skills
Can perform basic descriptive and inferential statistical analysis. Can create basic visualizations of data
Can perform more advanced statistical analysis and data mining techniques
Can create complex and informative visualizations of data
Can apply a wide range of statistical and data mining techniques to solve complex problems
Can create highly effective and insightful visualizations of data
Design, implementation, and validation of predictive models that can learn from past data to forecast future trends, behaviors, and outcomes. It requires an understanding of machine learning algorithms and data modeling techniques, and the ability to apply these in various scenarios to support decision-making processes.
Can understand and apply basic machine learning algorithms
Can build and train more complex machine learning models
Can develop and deploy state-of-the-art machine learning models to solve real-world problems
Data wrangling and cleaning are about transforming and mapping data from its raw form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes. This process includes cleaning data to ensure accuracy, completeness, and consistency, which is crucial for reliable analysis.
Can clean and prepare data for analysis
Can handle more complex data wrangling tasks, such as dealing with missing values and outliers
Can automate data wrangling and cleaning processes using tools and techniques like data pipelines
Domain expertise refers to in-depth knowledge and understanding of the specific field or industry in which the data scientist operates. It involves grasping the nuances, trends, and key performance indicators of the domain, which is critical to applying data science techniques effectively and to ensuring that the insights generated are relevant and actionable within that context.
Has a basic understanding of the domain in which they are working
Can apply their data science skills to solve specific problems in their domain
Has deep expertise in their domain and can use data science to drive innovation and decision-making
To be a Data Scientist, you need a high school diploma, with a bachelor’s degree in a data science-related field usually preferred depending on the opportunity. Other preferred and/or required certifications include: IBM Data Science Professional Certificate, Google Data Analytics Individual Qualification (GAIQ), Microsoft Certified: Data Scientist Associate, CompTIA Data+, Cloudera Certified Associate Data Scientist, SQL Essential Training, Python for Data Science and Machine Learning, Machine Learning Foundations, Introduction to Natural Language Processing (NLP), Data Visualization with Python.
Check out our Career Resources Directory to find programs in Indiana that offer opportunities to develop your skills. These include certificate and degree attainment, internship programs, and mentorship to help you prepare for a career in tech.
