Computer Science, Information Technology, Information Science and Data Science can often be seen as interchangeable terminology for similar work.  While they all use technology, there are different focuses in each field which discern them from one another.  However, due to their similarity, we have linked to the related pages from here.

 

Data Science

With the ever-growing collection of data by everything from your smart watch to your bank, Big Data is increasingly important is today’s society.  Data Science is a growing field, projected at a much higher rate than many others over the next decade, simply due to the sheer amount of data available and how it is being used to inform business, marketing, product design and more. Data is only as good, however, as the use we put it to, leading to an increase in data science need.

“Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. “ (Wikipedia)

Data Science incorporates applications from statistics, computer science, data analysis, data visualization, math, communication, business and multiple other fields.  Not only is it an industry which gathers, organizes, analyzes and utilizes data; people in this field are also responsible for communicating that data out to clients or other constituents, creating visually appealing and understandable presentations to a variety of audiences and applying the conclusions out to inform business decisions.

 

Some duties performed by data scientists might be:

  • Analyzing, manipulating, cleaning and processing large sets of data using statistical software
  • Applying feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
  • Creating reports and presentations for business use
  • Creating new, experiential frameworks to collect data
  • Building machine learning pipelines and personalized data products to better understand a business and its customers to make better business decisions.
  • Building models using elaborate algorithms to explain or predict behavior to best inform decisions.
  • Developing data driven solutions to address complex problems

 

Some Job Sectors Include:

  • Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis
  • Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
  • Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
  • Business intelligence specialists: Identify trends in data sets
  • Data architects: Design, create, and manage an organization’s data architecture

 

Education

Most positions available in data science require a Master’s degree—often in Data Science, Data Analytics or another related field.  A background in Math, Computer Science, Statistics , Machine Learning and Data Analysis is beneficial.

 

Technology used in this industry:

Data Visualization: Tableau, PowerBI,

Data Analysis:

Coding Languages: Python, Java, R

Data Science Platforms: MATLAB, IBM Watson Studio, RapidMiner

 

Skills and abilities used in the field:

  • Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection. Descriptive and inferential statistics
  • Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
  • Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
  • Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL.
  • Data storytelling: Communicate actionable insights using data, often for a non-technical audience.
  • Business intuition: Connect with stakeholders to gain a full understanding of the problems they’re looking to solve.
  • Critical thinking: Apply objective analysis of facts before coming to a conclusion.
  • Interpersonal skills: Communicate across a diverse audience across all levels of an organization.
  • Probability

1st Tier:

Career Resources

  • Kaggle: Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.
  • Digital Analytics Association: Advancing the use of data to understand and improve the digital world through professional development and community.
  • Institute for Operations Research and the Management Sciences (INFORMS): INFORMS is dedicated to promoting best practices and advances in operations research, management science, and analytics. As the field's largest professional association, INFORMS and its members work to improve operational and decision-making processes, and outcomes.
  • National Consortium for Data Science: The National Consortium for Data Science (NCDS) is a collaboration of leaders in academia, industry, and government formed to address the data challenges and opportunities of the 21st century.

 

Majors and Programs

The most popular degree for becoming a data scientist is a degree in Data Science.  An advanced degree is preferential, and an undergraduate major in computer science, statistics, machine learning, mathematics, or data analysis is a great foundation for advanced learning. Also, the business knowledge needed to apply the information learned from the data cannot be underestimated, so a background in business, economics or finance can also be helpful.

 

You might also see:

Data Analytics and Policy

Business Analytics

Data Science

Data Analytics Engineering

Business Intelligence and Analytics

 

 

Jobs and Experiences

Big Tech:

Obviously, big technology companies are major employers of data scientists:

Google, Facebook, Microsoft, Amazon, Oracle, IBM, just to name a few.

 

Finance:

The finance industry--banks, investment firms, insurance firms, and the real estate sector--uses data science to calculate risk, detect fraud, and predict market activity. Especially as the shift to financial technology happens and more transactions are happening virtually, these organizations will be increasing hiring.

Examples: Goldman Sachs, Palantir Technologies, Bank of America, Fidelity

 

Professional Services:

Data science is used to optimize operations for many types of organizations, often through professional services firms (including lawyers, advertising professionals, architects, accountants, financial advisers, engineers, and consultants). Data scientists in the professional services industry assist clients in the collection, management, and analysis of data and then interpreting that data to create actionable next steps.

Examples: Liberty Mutual Insurance, Mass Mutual, Bain & Company, BCG, Deloitte, Accenture

 

 

 

 

2nd Tier:

Job titles in field

Data Scientist

Data Analyst

Data Architect

Data Engineer

Business Intelligence Specialist/Developer

Machine Learning Engineer

Machine Learning Scientist

Applications Architect

Enterprise Architect

Data and Analytics Manager