Interested in pursuing a future in Data Science? Find resources, events, opportunities, and advice to confirm your interest and kickstart your career.
Explore a career path in Data Science
Data science is one of the fastest growing and most exciting fields out there today, transforming industries by turning raw data into actionable insights. It is interdisciplinary in nature, leveraging statistical modeling, machine learning, and data visualization techniques to extract value from data. Data scientists extract, clean, analyze, and visualize data, culminating in actionable insights that drive decision-making.
Subdisciplines
Data science is a broad field with many different subdisciplines. Read below to learn about some of the most popular specializations.
Data Analytics
Data analytics denotes the latter stages of the data science lifecycle, during which information is analyzed and insights are drawn out using statistical tools and software. Typical outcomes include predictive models, explanations of past events, or data visualizations that communicate insights clearly to stakeholders using statistical and computational tools.
Data Mining
Data mining is the process of gaining information from large datasets through the use of machine learning, statistical analysis tools, and databases. Data miners are skilled at sifting through data to identify patterns and correlations that would not otherwise be readily apparent. Data miners play a crucial role in uncovering hidden insights that empower organizations to make data-driven decisions and gain a competitive edge.
Database Management
Database management involves the development, maintenance, and administration of databases, which are structured systems used to store and organize data. Database managers work to ensure the stability, security, performance, and accessibility of their systems, while continually adapting them to meet the evolving data needs of their organization. Their role is crucial in ensuring that data remains available, secure, and optimized for analysis and business operations.
Machine Learning
Machine learning fits within the data science umbrella as a tool that data scientists use to effectively work with data. Machine learning algorithms automate tasks like prediction, classification, and anomaly detection, allowing data scientists to analyze massive datasets at scale with greater efficiency and accuracy. This field is evolving with advancements in deep learning, allowing for greater capabilities from those tools.
Are you a graduate student working in the fields of artificial intelligence and/or machine learning? Do you have a background in biology or the life sciences? You might be a fit for the following opportunity!
Click here to read this blog post from Jenna Jordan, a former Data Engineer for the City of Boston’s Analytics Team. In her blog, she walks through the full arc of her role, from how she got started to what …
Want to impress recruiters and land your next data science role? Mastering how to run a data science project can set you apart. Check out the key takeaways below and read the full article from Dzidas Martinaitis via the link …
These developing issues should be on every leader’s radar screen, data executives say. Artificial intelligence and data science became front-page news in 2023. The rise of generative AI, of course, drove this dramatic surge in visibility. So, what might happen …
By Mitchell Moise
Mitchell MoiseEmployer Outreach and Engagement Specialist
The dramatic advancements in AI over the past few years are omnipresent in the job search, and this collection of podcasts from MIT’s In Machines We Trust can shed light on ways AI might affect your experience in the job …
