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Featured Resume Example: Data Analyst
Attentive Data Analyst offering sharp time management, organizational and assistance skills. Personable individual willing to work hard and take on any task. Pursuing full-time role involved in data analysis and research.
- Research and transform information from raw data into an easily understood analysis that identifies trends and insights for the organization.
- Pinpoint a set of variables to evaluate and work with when deciding on the range of analysis and scope of information sought.
- Use a variety of sources inside and outside of the company to collect, aggregate and analyze data.
- Interpreted information from a series of database investigations to make predictions and recommendations for a company’s scope of work.
- Discussed results of database analysis with various members of management in an organization, and led staff members to realize the significance of the data.
- Discovered industry trends based on data collection methods and analysis strategies, and used the information to help the company make production and product adjustments to increase efficiency by 12 percent.
SUMMARY OF QUALIFICATION
- Expertise in computer programming languages such as Java, Python and C++, and database programs like Hadoop and MongoDB
- Outstanding use of problem-solving methods to help design the best strategies of measuring information and reviewing the results
- Excellent skills in analytical analysis and proven ability to read and interpret different points of data
- Documentation Management
- Report writing
- Research project support
- Quality assurance standards
- Database maintenance
- Data Entry
- Data Analysis
Top 4 Characteristics of a Best-in-Class Data Analyst Resume
- Summary Keep your summary short and sweet, explaining your top skills and how you’ve applied them up to now. Think of this section as an “elevator pitch” that demonstrates what value you can add to the company. For example: “Thorough data analyst with a solid engineering background, passionate about improving the success of businesses.”
- Skills Your resume should feature a combination of technical and soft skills. Some of the more popular attributes recruiters look for include:
• Knowledge of database languages like Python or SQL
• Proficiency in Google Sheets or Microsoft Excel
• Knowledge of data visualization software like Qlik or Tableau
• Mathematical and statistical skills
• Leadership, problem-solving and decision-making skills
- Work history In this section, emphasize your achievements, giving specific details on how you’ve contributed to a company’s success. For example: “Used SPSS software to analyze data and conduct research for 2 major products, helping to increase sales by 14%.”
- Education A bachelor’s degree in business analytics, computer science, data science or a related field is usually a necessary requirement for a data analyst. Some other certifications and courses you should look to feature include:
• Bachelor of Science in Mathematics
• BBA, Business Statistics
• Open Certified Data Scientist
• Certified Analytics Professional
• Certification of Professional Achievement in Data Sciences
• Certified Data Management Professional
• Certified Health Data Analysts (for professionals in the healthcare sector)
• Intellipaat Big Data Hadoop Certification
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This professionally-styled template uses subtle dotted lines to separate each section, while the use color fonts for the job seeker name and section headings place the focus on important info.
Data Analyst Resume FAQs
1. What are the skills you should emphasize for this specific job?
Feature technical and soft skills in equal measure, including some of the following popular attributes:
- Data cleaning and preparation
- High level of mathematical ability
- Creating data visualizations
- Presentation skills along with written and oral communication skills
- Knowledge of the domain
- Problem-solving skills
- Knowledge of statistical programming languages such as SQL, Oracle, Python
- Interpersonal and teamwork skills
- Ability to analyse and interpret data
- Accuracy and attention to detail
- Machine learning
- Proficiency in Microsoft Excel
- Data reporting
- Predictive analysis
- Optimization and heuristics
- Developing risk simulations and operational analysis
2. What are some examples of training and certifications that fit this specific resume?
Certified professionals are very much in demand in the data science industry. Some certifications that will enable you to stand out from the crowd and enhance your key skills include:
- Intellipaat Big Data Hadoop Certification
- Cloudera Certified Professional
- MCSE Certification in Data Management and Analytics by Microsoft
- Hortonworks Hadoop Certification
- Open Certified Data Scientist
- MongoDB Certified Developer Exam
- Certification in EMC Data Science and Big Data Analytics
- SAS Certified Data Scientist
- Data Science Council of America certification
- Oracle Business Intelligence Certification
- Mining Massive Data Sets Graduate Certification
- Certification of Professional Achievement in Data Sciences
3. How should you format your resume?
Choose your resume format based on these guidelines:
- Data analysts with a lot of experience should use the chronological format, which features an extensive work history section.
- If you have limited experience, but have the required skill sets and qualifications, use a functional format, in which you can present your skills in different categories (e.g., technical skills, soft skills), along with highlights from your academic training.
- The combination format is a hybrid of the chronological and functional formats, and highlights both your relevant experience as well as skills. Use this format if you have a few years of experience, or are coming from a different career field, but have transferable skills and experiences you can feature.
4. How should you craft your resume if you’re looking to take the next step forward in your career?
Keep looking to improve your analytical capabilities to advance your career. Formal education, boot camps, online courses and advanced degrees will help you obtain greater knowledge of important areas such as machine learning, neuro-linguistic programming (NLP) and supervised/unsupervised learning. Also look to expand your expertise into areas that are of prime importance to your employer, such as healthcare, biology, stock markets or advertising.