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Data Analyst

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.

What is a Data Analyst?

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.

 

Skills That Set Them Apart

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:

  • Skilled collaborators. The most successful Data Analysts foster relationships from every department in order to understand what the data needs to measure. It may not be enough to know when customers are buying – an easy enough query – but the real reveal may be why customers buy at that particular time. Analysts can help you discover the less obvious insights when they know which questions to answer.
  • Solid communicators. If you can’t explain it simply, you don’t understand it well enough. Data Analysts have to know what the data shows mathematically, but more importantly, they must be able to apply it to the real-world scenarios. They have to be able to demonstrate how the data answers the query and then discuss what it all means in layperson’s terms.
  • Bottom-line experts. Many times, stand-alone data means very little until it’s compared with other information. For example, a year-end report can stand on its own, providing important feedback for bottom-line results, but it cannot tell you how your company’s progressed over the past five years, nor can it help you predict what may be in store for the years ahead.

 

The Educational Foundation That Sets the Stage

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: 

  • 32% in Math and Stats
  • 19% in Computer Science

Even with a B.S., entry-level Data Analysts pursue additional training to enhance their skillset, thereby improving their marketability.

Specialized Training

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:

 

Tools

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.

Alteryx
Data ETL & Data Wrangling Commercial

Alteryx

Alteryx is the only quick-to-implement end-to-end data analytics platform for your organization that allows data scientists and analysts alike to solve business problems faster than you ever thought possible.

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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.

KNIME Analytics Platform
Data ETL & Data Wrangling FREE Open Source

KNIME Analytics Platform

KNIME Analytics Platform is a powerful free open source data mining tool which enables data scientists to create independent applications and services through a drag and drop interface. It can serve well as a business intelligence resource, which can be used f...

4

Python

Python,  JavaPerl 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

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.

 

NoSQL

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.

 

Big Data Computation Networks 

Apache HadoopHive 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. 

 

Data Visualization

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.  

Tableau
Data Visualization Commercial

Tableau

Tableau is a Business Intelligence tool created to help anyone see and understand their data. Connect to almost any database, drag and drop to create...

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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.

FusionCharts is a JavaScript-based formatting software that charts both web and mobile platform data into three-dimensional graphs. According to New Gen Apps, more than 80% of Fortune 500 companies use it.

 

Non-traditional Data Corralled with Fuzzy Logic

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.

The Average Data Analyst Salary

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

What to Expect from a Wyzoo Data Analyst

Wyzoo 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: 

  • Collecting huge amounts of data from 1st-, 2nd- and 3rd-party sources
  • Analyzing data to identify patterns and create narratives to support them
  • Producing internal and external reports to help identify key findings
  • Collaborating with team members to help communicate important results
  • Creating visualizations of the data through charts and graphs

 

Wyzoo’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. 

Resources

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