Data Analyst Resume that Communicates Passion and Purpose

Do you enjoy working with numbers and information? Perhaps you like diving headfirst into research and gathering data. Maybe your passion projects in the past have involved a lot of research and data gathering. Add to that, you studied mathematics or statistics in college. Maybe you are considering a job as a data analyst? 

Bear in mind that a data analyst is different from a business analyst. A data analyst gathers data from different sources and translates it into more understandable language which is then reviewed and used as a basis for decision-making. A business analyst uses the information that a data analyst generates to address an organization's concern.

Below are some of the usual tasks of a data analyst:

• In charge of constructing and maintaining databases
• Data mining and cleaning for analysis
• Analyzing and interpreting data for reports
• Explores new data collection and analysis techniques
• Preparing reports for other business units to use

Resume writing tips for this position

Career summary/objective

When writing your summary, highlight your experience analyzing information or research. Emphasize your proficiency in programming languages and data research methods.

Example:

I am a numbers person. Spreadsheets and data make sense to me. I welcome the challenge of translating data into understandable information. I have extensive background as a data analyst. Along with harvesting data, I have also used data to tell stories. I do not just gather information. As a data storyteller, I have harnessed the power of data to tell compelling stories and help organizations solve problems, improve processes, and plan its next steps for business.

My undergraduate background is in economics and my previous work experience has made me well-versed in two database languages "“ Python and R. I am proficient in Microsoft Excel and Google Analytics. I am further improving my data storytelling skills so I can create stronger narratives  that stakeholders can appreciate. Currently, I am finishing my postgraduate studies in statistics where I was introduced to machine learning.

Emphasize your strengths related to the job in your Work Experience

Data analysts are typically inclined to solve problems and possess an analytical mind. They are expected to be detail oriented as they will have to spot trends and patterns in the data they collect. Note this in your experience and highlight the specific kind of work you have done that involves data.

Example:

XYZ Inc., Philippines

Data analyst, July 2018 - present

• Harvests data and analyzes it for review by business analysts
• Creates predictive models based on data
• Works on forecasting and demand of the company's products
• Prepares reports and presents them as requested by senior management

YYZ Technologies, Philippines

Data analyst, May 2017 - June 2018

• Part of the team who designed the program to enhance the company's data privacy policies
• Harvested data and generated reports on a regular basis
• Spearheaded the department's project on creating best practices on data management

ABC & Associates, Philippines

Researcher, April 2017 - April 2017

• Lead researcher for projects
• Utilized SPSS to analyze quantitative data gathered
• Wrote results and recommendations of research projects and presented them to clients

Key skills

A data analyst usually has experience with the following: data warehousing data mining,

handling of data models, and analyzing large data sets. He or she should also be able to write reports based on these data and communicate them effectively. 

Two soft skills that are emerging among practitioners are data storytelling and data visualization. Data storytelling is the practice of creating a narrative that is compelling and easy to understand. It goes hand in hand with data visualization or being able to present data in a graphical manner.

Other technical skills

Data analysts have knowledge in database languages such as Python, R and SQL. He or she must also be well-versed in tools such as Microsoft Excel and Google Analytics. While not serving as lead project managers, data analysts must be familiar with project management methodologies because they are usually part of teams that use these.

Example:

Extensive experience analyzing large datasets and handling data models. Intermediate knowledge in machine learning. Strong background in IBM SPSS, Python, R, Microsoft Excel and Google Analytics. Well-versed in data storytelling and data visualization. Familiar with the following project management methodologies: Agile, Kanban, Six Sigma, and Waterfall.

Education

Data analysts usually have a background in economics, mathematics, or statistics.

Example:

MS in Statistics

University of the Philippines Diliman, Philippines

BS in Economics

University of Manila, Philippines

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