Data Scientist Career

Planning and Analysis

 

Data Scientist

Career Area: Planning and Analysis

Occupation Group: Data Analysis and Mathematics

Salary

Percentile wages tell how much a certain percentage of an overall population in a geographic area or within a given industry or field makes. The percentile wage estimate is the value of a wage below which a certain percent of workers fall.

An example would be the 25th percentile, 25 percent of workers employed in that occupation earn less and 75 percent earn more than the estimated wage value. At the 75th percentile, 75 percent of workers employed in that occupation earn less and 25 percent earn more than the estimated wage value.

A typical Data Scientist earns the following wages (national and state):

State

The average salary in North Carolina for those pursuing this career is $109,419

*The salaries depicted here are representative of the range of salaries posted in job listings over the past year. Living wage in North Carolina is $30,000.

National

The average salary in the United States for those pursuing this career is $110,855

*The salaries depicted here are representative of the range of salaries posted in job listings over the past year. Living wage in North Carolina is $30,000.

What Does a Professional in this Career Do?

Utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.

Employment Trends

The job demand and job growth statistics shown here were derived from job posts over the past year. Expected job growth projections are extrapolated from year-over-year job post listing history.

Job demand and job growth is expected at the following rates:

Growth
North Carolina958+29.1%
Nationwide31388+19%

Skills

A professional in this position typically utilizes the following skills in the course of everyday work in this exciting and challenging field:

Baseline Skills

The following are baseline skills every Data Scientist is expected to have in order to experience success in this field:

  • Research: Research comprises creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of humans, culture and society, and the use of this stock of knowledge to devise new applications. It is used to establish or confirm facts, reaffirm the results of previous work, solve new or existing problems, support theorems, or develop new theories.
  • Communication Skills: The ability to convey information to another effectively and efficiently.
  • Teamwork / Collaboration: A Collaboration is a purposeful relationship in which all parties strategically choose to cooperate in order to achieve shared or overlapping objectives.
  • Problem Solving: Problem solving consists of using generic or ad hoc methods, in an orderly manner, for finding solutions to problems.
  • Creativity: Creativity is a phenomenon whereby something new and somehow valuable is formed.

Specialized Skills

These skills are specific to working in this career:

  • Data Science: Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
  • Python: Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.
  • Machine Learning: Machine learning is the subfield of computer science that, according to Arthur Samuel, gives computers the ability to learn without being explicitly programmed. Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term machine learning in 1959 while at IBM. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.
  • SQL: SQL ( ESS-kew-EL or SEE-kwl, Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS).
  • Apache Hadoop: Apache Hadoop ( ) is an open-source software framework used for distributed storage and processing of dataset of big data using the MapReduce programming model.

Distinguishing Skills

Any Data Scientist that possesses the following skills will stand out against the competition:

  • Keras: Working experience of Keras. Keras is an open source neural network library written in Python. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
  • Classification Algorithms: Working experience of Classification Algorithms. In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
  • Pandas: In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis.
  • Pipeline (Computing): Working experience of pipelines. In computing, a pipeline is a set of data processing elements connected in series, where the output of one element is the input of the next one.
  • Model Building: Model building as a hobby involves the creation of models either from kits or from materials and components acquired by the builder.

Salary Boosting Skills

A professional who wishes to excel in this career path may consider developing the following highly valued skills:

  • Long Short-Term Memory (LSTM): Working experience of Long Short-Term Memory (LSTM), which are a building unit for layers of a recurrent neural network (RNN).
  • Time Series Analysis: Working experience of Time Series Analysis, which comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language).
  • Attribution Modeling: Working experience of Attribution Modeling. An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions.

Education

This career typically requires the following level of education. The numbers presented in the pie charts below were derived from actual job posts over the past year. Not all job postings list education requirements.

.
Education Level%
Bachelor's Degree27%
Master's Degree65%
Doctoral Degree8%

Experience

This position typically requires the following level of experience. The numbers presented in the pie charts below were derived from actual job posts over the past year. Not all job postings list experience requirements.

Experience Required%
0 to 2 years23%
3 to 5 years52%
6 to 8 years15%

Many of the programs offered through NC State are designed for working professionals who need additional credentials to enhance existing work experience.

Students who do not have the expected level of experience may wish to look into internship and employment opportunities.

Common Job Titles

It is possible to find work in this field in positions commonly listed as the following job titles:

  • Data Scientist
  • Associate Data Scientist
  • Deputy Director - Commercial Data Science & Analytics
  • Data Science Manager
  • Junior Data Scientist

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