What are Technical Skills?

Technical skills refer to the knowledge and expertise needed to accomplish complex actions, tasks and processes relating to computational and physical technology as well as a diverse group of other enterprises. Those who possess technical skills are often referred to as "technicians", with the expression referring to audio technicians, electronics technicians, market technicians, computer technicians, engineering technicians and a variety of other designations. Technical skills are practical ones, typically related to the fields of mechanics, information technology, mathematics and science.

Technical skills also refer to the expertise of a certain type of market participant who uses technical analysis signals to buy and sell stocks, bonds, futures and other financial instruments.

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Technical Skills

Key Takeaways

  • Technical skills are sets of abilities or knowledge used to perform practical tasks in the areas of mechanics, science, mathematics and information technology.
  • In finance, technical skills may also refer to those utilized by investors and analyst who follow the procedures of technical analysis.
  • In most cases, the acquisition of advanced technical skills requires specialized training or education.

How Do Technical Skills Work?

Technical skills can refer to the ability to perform tasks that require the use of certain tools, whether tangible or intangible, and the technology required to master their intended uses in a variety of scenarios. In this regard, the knowledge in a technical skills capacity is seen as practical in nature because it allows an individual to complete a designated task in a real world, not theoretical, manner. Given the growth of technology within worldwide and local economies, the need for diverse technical skills and knowledge is likely to continue to grow into the foreseeable future.

Technical Skills Education and Training

The acquisition of advanced technical skills requires specific education or training, often with a hands-on learning component and many advanced topical elements. Technical skill requirements are listed for the majority of career fields, with the highest concentrations being employment in areas involving scientific, technological, engineering, computational and mathematical capabilities.

Within the financial markets, trader and investor participation in the technical analysis skill set requires the use of various mathematical and pattern recognition tools. These include the ability and expertise to determine what historical data is required and how it needs to be applied in order to elicit the required information.

Most technical analysis applications in relation to market and other financial activities are designed to digest historical information measured in days, weeks, months or years and use the output to predict future directional outcomes in specific financial instruments.

An Example of Technical Skills

In finance, technical skills include an array of knowledge topics that include computing abilities, quantitative analysis and various financial market forecasting techniques. Technical analysis requires a variety of mathematical skills, often advanced in nature, to produce price chart analysis and model trends that look at historical information to predict future price movements. Technical skills in this context usually refer to an individual who possesses the knowledge and expertise to complete the mathematical tasks required to gather the historical data, produce a data model set and perform directional analysis on the various outputs.

For example, to produce a linear regression model for technical analysis, the market analyst must have the skills and knowledge necessary to properly gather the historical data, perform any necessary calculations and use the output to generate a visual representation of current and legacy historical data.

Once the linear regression is complete, the market analyst needs the skills and expertise needed to extrapolate future market activity based on the directional patterns identified in the model set(s).