Data Analyst To Develop Facial Recognition – Skills and Education Requirements

by admin on November 20, 2018

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data analyst

A data analyst uses data to answer specific questions, translating numerical results into easily understood language and visualizations. This information is used to give insight to other members of a team which can be used to make more informed decisions. Teams that include data analysts can be found in the private sector, government, and academia. The ability to analyze and model data opens the door to many possible career paths.

These are skills used by operations analysts, marketing analysts, and financial analysts, just to name a few. Business decisions regarding sales, marketing, logistics, or costs are better made based on data that has been collected and analyzed. Data analysis provides scientists insight into the results of experiments. Health departments hire data analysts to monitor diseases and prevent outbreaks.

Duties of a Data Analyst

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Data analysts perform a lot of duties, which vary depending on their specific area of specification. At some companies, analysts work within teams with other analysts, data scientists, and data engineers. At other companies, there may be a single analyst who must answer all data related questions the company has.

In an academic setting, students may have access to a few analysts to help them sort through their research and come to conclusions based on the data they have collected. They may also work closely with research scientists to develop the experiments in a way that they provide the right data to answer the proposed research question.

Regardless of the arena, the skills required can be broken down into four major skill areas: analytical, communication, critical thinking, and math skills.

 

data analysis done by data analysr

Analytical Skills

Data analysts must be able to recognize trends or patterns in complex datasets. This requires the use of programs such as Tableau to crunch numbers and produce figures. The information from these products is then analyzed within the context of related information. For example, current trends in transportation costs might be considered in the context of annual fuel pricing trends.

Just running numbers through a set of formulas is not enough to provide a good analysis. Many modeling techniques are only appropriate if the data fit specific criteria. Deciding what method to use to analyze a dataset also varies by the specific question one is trying to answer. Therefore, it is important that an analyst is able to apply what they know to determine what methods they will use.

Communication Skills

Data analysts must have strong communication skills. Sometimes, they are one of only a few people working on a team or at a company that understand the statistical techniques necessary to work with data. Presenting analyzes entirely in statistical jargon may leave coworkers confused. Therefore, it is necessary to be able to bridge the gap between the numbers and field appropriate lingo.

An analyst may be called upon to present the trends they have found in the data to large groups of people. Public speaking experience is advantageous for those who wish to advance their career. Additionally, the ability to produce clear visualizations of analysis for such presentations or publication adds to the value of an analyst.

Critical Thinking

After using analytical skills to pick the appropriate methods to analyze the data, an analyst must be able to look at these trends and come to support existing conclusions or reach new ones. This might mean considering new sources of data or looking at the data in a novel way to confirm new conclusions. Regardless of the outcome, critical thinking requires not only seeing what information supports a conclusion but considering the cases that may be made against a conclusion. This multidimensional mode of critical thinking is necessary to advance in the advent of big data.

Math

The basis of analyzing data is a strong background in mathematics. There are many ways to analyze any given dataset. It is important to understand the underlying mathematics used in these methods. This enables an analyst to have a deeper understanding of the output and what it may imply about the data.

Numeracy skills also help an analyst determine if an output is realistic for the parameters set and the inputted data. Like with any interface between humans and mathematics, small errors can significantly alter the outcome. Something as simple as leaving out a negative sign can skew model. Having a conceptual idea of what the output should be given characteristics of the dataset and model behavior is important.

Overarching Skill: Attention to Detail

On top of all these skills, analysts must have significant attention to detail. Individual pieces of data may carry with them some deviation from the mean. Careful attention to how data fit an overall trend not only prevents mistakes, it can provide novel insights into a problem or potential solution.

Making the connections between the analysis, the conclusions, and the communication of those conclusions also requires a large amount of attention to detail. Some of the phrases used in statistics have very precise meaning. If the specific ways of interpreting an analysis are not followed and then well communicated, the conclusions drawn could be incorrect. Then decisions made based on those conclusions would also be incorrect.

job requirement

Education Requirements

Academic

A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. Most analysts will have degrees in fields like math, statistics, computer science, or something closely related to their field. Strong math and analysis skills are needed.

A Bachelor’s degree in mathematics, economics, computer science, statistics, biostatistics, or information management is necessary for most entry-level jobs. Continuing education with a masters degree in one of these fields or an associated field such as public health or bioinformatics may be necessary for upper-level jobs or to move into associated fields such as data science. Regardless of the degree, strong mathematical and analytical skills are necessary.

In addition to mathematical and analytical skills, big data has made computer programming skills more necessary for working with data. While working with spreadsheets is one method many analysts use, a broader set of skills is becoming more necessary. Some ability to program is becoming necessary. Many online learning platforms provide courses in data storage, statistical software, and languages that support statistical analysis. Experience using two or more of the following platforms or programs is recommended: Excel, SQL, Tableau, SAS, S+, SPSS, R, and Python. Depending on the specific analytical field, different systems may be used to collect, retrieve, and analyze data, depending on how it is collected and accessed.

Experience

The best way to demonstrate competency as an analyst is to have experience as a data analyst. This may seem obvious, but to some, it may sound intimidating. Luckily, there is a lot of publically available data to practice analyzing. The benefit of analyzing public data is an aspiring analyst can build a portfolio. It is also possible to gain experience outside of the classroom through internships or offering to analyze data for ongoing studies for classmates or professors.

In an ideal world, a job candidate would have experience:

  • Modeling data
  • Using reporting packages (Business Objects)
  • Manipulating databases (SQL)
  • Programming (Python, R)
  • Using statistical packages for analyzing datasets (Excel, SPSS, SAS)
  • Collecting and organizing large amounts of data
  • Communicating large amounts of information while maintaining attention to detail and accuracy
  • Developing queries
  • Writing reports
  • Creating visualizations
  • Presenting findings

Salary

The salary of an analyst varies greatly across experience levels, the type of analysis, and who an analyst works for. Snagajob.com cites the Bureau of Labor Statistics (BLS) regarding the median annual salaries of different types of analysts as:

Financial

$35.75 per hour or $74,350 annually

Market Research

$29.12 per hour or $60,570 annually

Operations Research

$34.12 per hour or $70,960 annually

Analysts often work for a specific employer, but they can also work as contractors or freelancers. As their experience as well as the projected demand for data-savvy individuals increases, it is likely the job options for an analyst will also broaden.

Conclusion

 In a world growing more and more reliant on data, and with more data available, the job prospects for a data analyst are bright. Business Intelligence (BI) and Artificial Intelligence (AI) are fields that use analysis to drive immediate decisions and machine learning. Algorithms such as those used by Google, Facebook, and Instagram to curate what the user sees are based on the analysis of data. The filters used by Snapchat required data analysis to develop facial recognition.

Strong analytical skills, attention to detail, and critical thinking are required in all aspects of a career as an analyst. These skills can be developed as one gains the more formal academic skills such as computer programing and mathematics necessary for a career as a data scientist. For those with an academic background in their current career field that way to add analyzing data to their skill set, there are many online learning options available. Also, many of the more popular tools used, such as R, Python, and the guides developed to learn them, are open source.

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