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Analytical data miner skills for your resume and career

Updated January 8, 2025
4 min read
Quoted expert
Ambrose Kidd III
Below we've compiled a list of the most critical analytical data miner skills. We ranked the top skills for analytical data miners based on the percentage of resumes they appeared on. For example, 12.0% of analytical data miner resumes contained python as a skill. Continue reading to find out what skills an analytical data miner needs to be successful in the workplace.

15 analytical data miner skills for your resume and career

1. Python

Python is a widely-known programming language. It is an object-oriented and all-purpose, coding language that can be used for software development as well as web development.

Here's how analytical data miners use python:
  • Instructed students in the use of R, Python, SQL, Excel, and Tableau.
  • Created categorization models in Python to identify customer loss and metrics to identify customers prior to discontinuation for improved retention.

2. Tableau

Here's how analytical data miners use tableau:
  • Used Tableau to create patient reports tailored for providers and their medical teams.
  • Delivered Tableau Training sessions to staff.

3. Visualization

Here's how analytical data miners use visualization:
  • Implemented clinical reporting programs that were utilized by both clinical and data management teams which aided in data visualization and reporting.
  • Collaborate with other Engineers to determine reporting and data visualization requirements and develop features that add new functionality to the platform.

4. Data Analysis

Here's how analytical data miners use data analysis:
  • Assisted in investment selection by recommending property purchases after reviewing data analysis.
  • Performed data analysis to validate data warehouse changes in HR and Payroll production data.

5. Power Bi

Here's how analytical data miners use power bi:
  • Created multiple data visualization solution for initial Power BI business preview.
  • Developed Reporting solutions using Power BI.

6. BI

Here's how analytical data miners use bi:
  • Worked as a data architect delivering BI solutions for the agriculture and bio-tech industry to support predictive analysis and information-based decision-making.
  • Collected and analyzed BI end-user requirements and evaluated the feasibility of these requirements from a technical perspective.

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7. Metrics

Metrics are quantitative assessment measures that are commonly used to evaluate, compare, measure, and track results, performance, or production. Business metrics are used to evaluate performance, compare results, monitor and assess the success or failure of various business processes, and track relevant data to improve business outcomes.

Here's how analytical data miners use metrics:
  • Analyzed waterfall model using course level data for the corporate business segment in order to provide retention and success rate metrics.
  • Sorted SQL patient data in order to find metrics needed for patient quality reports.

8. SAS

SAS stands for Statistical Analysis System which is a Statistical Software designed by SAS institute. This software enables users to perform advanced analytics and queries related to data analytics and predictive analysis. It can retrieve data from different sources and perform statistical analysis on it.

Here's how analytical data miners use sas:
  • Trained new employees on both SAS programming and data processing perspectives.
  • Developed SAS programs for demographic data processed in UNIX.

9. Java

Java is a widely-known programming language that was invented in 1995 and is owned by Oracle. It is a server-side language that was created to let app developers "write once, run anywhere". It is easy and simple to learn and use and is powerful, fast, and secure. This object-oriented programming language lets the code be reused that automatically lowers the development cost. Java is specially used for android apps, web and application servers, games, database connections, etc. This programming language is closely related to C++ making it easier for the users to switch between the two.

Here's how analytical data miners use java:
  • Develop applications using ASP, .NET, Java Script and HTML.
  • Provided expert analysis of the appropriateness and correctness of statistical analysis routines used in an automated HR application written in Java.

10. Cloud

Cloud is a server that is accessed over the internet. There are different programs and software that also run on these servers. These clouds can be accessed from anywhere in the world as they are not present in your computer storage, but have their online servers. Cloud consists of data centers all across the world.

Here's how analytical data miners use cloud:
  • Used R word cloud and regression models to determine the types of restaurants with good Yelp reviews
  • Designed and developed ingestion framework to cleanse, validate, transform and load data on to GCS and BigQuery on cloud.

11. PowerPoint

Here's how analytical data miners use powerpoint:
  • Developed PowerPoint presentations and various marketing collateral for Membership Director to present at Chamber budget meetings and Women in Commerce conferences.
  • Produce graphical analysis and PowerPoint presentations for director level management and litigation team to be utilized as legal documentation of compliance.

12. SQL Server

Here's how analytical data miners use sql server:
  • Designed and developed SQL Server 2000 database to manage all computer equipment within Verizon Communications global data centers and infrastructure.
  • Developed and maintained applications using extensive knowledge of Microsoft SQL Server and Visual Basic for automation of data import/export processes.

13. Data Quality

Here's how analytical data miners use data quality:
  • Identify various data quality issues and provide feedback to senior management and emphasized during testing process.
  • Designed data quality star schema which included a fact table with approximately 1.6 billion records and several dimensions.

14. VBA

Visual Basic for Applications or, as it is commonly shortened to, VBA is a certain method of using Microsoft's event-driven programming language known as Visual Basic.

Here's how analytical data miners use vba:
  • Innovated new techniques for campaign analysis and developed automated reporting solutions using VBA macros to speed report generation and delivery.
  • Created Trigger Mapping workbook by writing Excel VBA Macros to extract Mortgage-Backed Security trigger data automatically from over 5000 documents.

15. ETL

Here's how analytical data miners use etl:
  • Analyzed different options for heterogeneous database connectivity and defined policies for dealing with different source databases with ETL team.
  • Created automated reports on inventory defect and associate behavioral trends using Amazon's Data Warehouse and proprietary ETL manager.
top-skills

What skills help Analytical Data Miners find jobs?

Tell us what job you are looking for, we’ll show you what skills employers want.

What analytical data miner skills would you recommend for someone trying to advance their career?

Ambrose Kidd IIIAmbrose Kidd III LinkedIn profile

Associate Teaching Professor, University of Missouri - St Louis

Having a broad skill set and being prepared to learn on the job. The highest salaries will be for those that get their foot in the door and then demonstrate critical thinking and leadership skills.
Do you want to know more?
Read more about what experts have to say about analytical data miner

List of analytical data miner skills to add to your resume

Analytical data miner skills

The most important skills for an analytical data miner resume and required skills for an analytical data miner to have include:

  • Python
  • Tableau
  • Visualization
  • Data Analysis
  • Power Bi
  • BI
  • Metrics
  • SAS
  • Java
  • Cloud
  • PowerPoint
  • SQL Server
  • Data Quality
  • VBA
  • ETL
  • Data Integrity
  • Azure
  • Extraction
  • MATLAB
  • Financial Data
  • Oracle Sql
  • Teradata
  • Pandas
  • QA
  • R
  • Business Problems
  • Data Warehouse
  • PL/SQL
  • Regression
  • HBase
  • Text Mining
  • Macro
  • Amazon Web Services
  • DB2
  • Ssis
  • Development Environment
  • Data Source
  • JavaScript
  • Transform Data
  • SSAS
  • HTML
  • XML

Updated January 8, 2025

Zippia Research Team
Zippia Team

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Updated January 8, 2025

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