Data Analysts are at the front line of client engagement. They spot patterns in data and create a narrative around their findings. Data Analysts use Artificial Intelligence and other querying processes to interpret existing data and then structure it for the purpose of maximizing the best business value for their clients.
Data Analysts are at the front line of client engagement. They evaluate a client’s data and determine what’s useful to the client’s request, and what’s still required. This assessment allows them to interpret existing data and then structure it for the purpose of maximizing the best business value for their clients utilizing Artificial Intelligence or other querying processes. Once Data Analysts go through the initial evaluation process, they may request additional data from the clients in order to get the most effective results for their clients.
So what does a Data Analyst do? Data Analysts organize and assess data so that the team is able to reach logical and meaningful conclusions. In order to do so, they need to be able to spot patterns in the data and create a narrative around their findings. This helps executives, marketers and other stakeholders, who may not have a data science background, understand the findings.
Data Analysts organize information into quantifiable content, so their skills need to be heavy on math and statistics. They also need to be:
A Data Analyst undergraduate degree usually falls into Bachelors of Science Math, Statistics, Computer Science, Business Management, Finance or Economics. Those are the degrees that give Data Analysts a head-start in the job market because each one emphasizes statistics and analytical skills.
Because they’re considered to be Junior Data Scientists, they may wish to consider that Data Scientists earn the following Bachelors of Science degrees:
Even with a B.S., entry-level Data Analysts pursue additional training to enhance their skillset, thereby improving their marketability.
Data Analysts need to look at the purpose of the data relative to the goals of the project. For example, if a client wants to determine which customer is most likely to place orders at a specific average order value, then the supplied data should provide historical information on the value of orders from all customers; however, the client wants to learn more about a specific customer segment, and so the Data Analyst would need to locate previous customer segments that can be identified and then isolated. Here are some of the top technical skills that Data Analysts rely on:
Data acquisition, application and enrichment are part of the daily life of a Data Analyst. In order to create predictive, statistical and spatial analytics with repeatable results using the same intuitive user interface, they need Alteryx.
Being able to automate some of the more tedious processes are key for Data Analysts, and KNIME is a great tool that assists in predictive analytics. Its primary function is to integrate various components for machine learning and data mining through a modular data pipelining concept; while the graphical user interface allows for an easier assembly of nodes for data preprocessing, modeling and data analysis and visualization.
Python, Java, Perl and C/C++ are all languages that can serve a Data Analyst well. Using R to analyze data creates a series of steps, including programming, transforming, discovering and modeling, which can communicate the results in ways that are more preferable and useful to the query set. Python is more popular than R, although both of these open-source languages are equally viable. In addition, SAS remains in use and continues to see strong support overall within the various Data Management fields.
SQL (structured query language) is the foundation of complex queries because most big data systems use it, along with additional proprietary extensions for more customized use. Even so, the standard SQL commands such as "Select," "Insert," "Update," "Delete," "Create" and "Drop" can still be used to accomplish most tasks – a universal language to master.
Big data calls for big scaling capabilities, and so Data Analysts should be familiar with NoSQL such as MongoDB or HBase. These systems work quickly with large volumes of data and can scale accordingly for a more customized approach.
Apache Hadoop, Hive or Pig are great additions to a Data Analyst’s capabilities. Here’s why: Hadoop is built on clusters of commodity computers, providing an easy way for storing and processing data without format requirements.
The bigger the data, the slower the process, so speed is key. Apache Spark is also popular because it’s faster than Hadoop – a boon when running extremely complex algorithms. Familiarity with cloud-based tools can also be a great assistance to Data Analysts. Amazon S3 is one of the more popular ones.
Being able to communicate their findings is one of the job requirements of Data Analysts, and in order to do so, they need to be familiar with the wide assortment of tools that are at their disposal.
Tableau is an essential software package that can present the data and showcase the derived insights. It provides a wide array of tools that allow Data Analysts to drill down further and see the results in a variety of visual formats.
ggplot2 allows Data Analysts to plot trends on a graph with unique color-coding to help distinguish between key points. The findings can then be processed directly as a PDF or object that can be easily disseminated to shareholders.
Unstructured data from reviews, social media comments and email can be a gold mine of information for Data Analysts, but it doesn’t always fit neatly into traditional data tables. That’s why Data Analysts should be able to leverage the capabilities of ETL.
How much does a Data Analyst make per year? Like most fields, it depends on your experience, location and skillset. Some of the leading job-search companies published their findings:
PayScale: The Average Data Analyst Salary
Glassdoor: Data Analyst Salaries
Xperra Data Analysts review data for suitability for selected use case. They sometimes recommend to clients how to capture and aggregate data that doesn't exist in order to have data that is useful for analytics purposes. They develop systems for collecting data and then compile their findings into meaningful reports that can provide additional insight into existing conditions and help predict future trends.
They’re your team of experts who are responsible for:
Xperra’s Data Analysts gather data from a variety of sources to identify market patterns and trends, and then discover how that information can be leveraged to answer questions and solve problems. When your business can see the big picture and apply those learnings to understand future developments, you’ll be more equipped to meet and exceed your future projections.