3 answers
sukanya
Student
Birmingham, Alabama
1
Question
Asked
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Reqiure career guidence for a Individual with Prior QA looking to find a job in Analytics?
Hi i.m an international student graduated from UAB masters in data science. I worked as a quality analyst for fedex prior to my masters . While working as a QA my interest in the BA and DA position grew how they work and started gaining knowledge in it and motivated me to pursue my masters. Now after completing my masters its become really hard to find a job as both BA and DA. I have worked on python, R and tableau projects and put that in the resume but it doesnot help. i dont have experience as a data analyst. What should i do?
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3 answers
Collins Lucky
Data Entryspecialist, Cost analysis, Repairing of all kinds of smartphones, Apple, & Samsung mobile.
48
Answers
Bonny, RV
Updated
Collins’s Answer
Hi Sukanya,
Here's a friendly guide to boost your chances of securing a BA or DA position:
1. **Showcase Your Skills:** Highlight your analytical prowess and data-focused tasks from your QA role.
2. **Create a Portfolio:** Display your projects involving Python, R, and Tableau, providing comprehensive explanations.
3. **Get Hands-On Experience:** Seek internships, co-ops, or volunteer work in the field of data analytics.
4. **Make Connections:** Participate in industry gatherings and network with professionals on LinkedIn.
5. **Earn Certifications:** Secure relevant certifications from resources like Coursera or edX.
6. **Personalize Your Applications:** Make your resume and cover letter unique for each job application.
7. **Utilize Career Services:** Take advantage of UAB’s career services for resume critiques and job placement assistance.
8. **Start at the Beginning:** Apply for entry-level roles or internships in data analytics to gain experience.
9. **Hone Your Data Analysis Skills:** Consistently practice problems on platforms like Kaggle or LeetCode.
10. **Get Ready for Interviews:** Prepare to discuss your projects and your approach to problem-solving.
Here's a friendly guide to boost your chances of securing a BA or DA position:
1. **Showcase Your Skills:** Highlight your analytical prowess and data-focused tasks from your QA role.
2. **Create a Portfolio:** Display your projects involving Python, R, and Tableau, providing comprehensive explanations.
3. **Get Hands-On Experience:** Seek internships, co-ops, or volunteer work in the field of data analytics.
4. **Make Connections:** Participate in industry gatherings and network with professionals on LinkedIn.
5. **Earn Certifications:** Secure relevant certifications from resources like Coursera or edX.
6. **Personalize Your Applications:** Make your resume and cover letter unique for each job application.
7. **Utilize Career Services:** Take advantage of UAB’s career services for resume critiques and job placement assistance.
8. **Start at the Beginning:** Apply for entry-level roles or internships in data analytics to gain experience.
9. **Hone Your Data Analysis Skills:** Consistently practice problems on platforms like Kaggle or LeetCode.
10. **Get Ready for Interviews:** Prepare to discuss your projects and your approach to problem-solving.
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
5777
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hello Sukanya!
Career Guidance for Transitioning from QA to Analytics
Transitioning from a Quality Analyst (QA) role to a position in Business Analytics (BA) or Data Analytics (DA) can be challenging, especially when you have recently completed your master’s degree in data science. However, with the right approach and strategy, you can leverage your existing skills and experiences to secure a position in analytics. Here’s a step-by-step guide to help you navigate this transition effectively.
1. Leverage Your Existing Skills
Your background as a QA professional provides you with several transferable skills that are highly valuable in analytics:
Attention to Detail: As a QA analyst, you have honed your ability to identify discrepancies and ensure quality, which is crucial in data analysis.
Problem-Solving Skills: Your experience in troubleshooting issues can translate well into identifying trends and anomalies in data.
Understanding of Processes: Familiarity with business processes will help you understand the context of the data you’re analyzing.
2. Build Relevant Experience
While you may not have direct experience as a Data Analyst, there are several ways to build relevant experience:
Internships or Entry-Level Positions: Look for internships or entry-level roles that focus on data analysis. These positions often require less experience and can provide valuable hands-on learning.
Freelance Projects: Consider taking on freelance projects or volunteering for non-profits that need help with data analysis. Websites like Upwork or Freelancer can connect you with potential clients.
Personal Projects: Create your own projects using publicly available datasets. Document your findings and methodologies on platforms like GitHub or personal blogs to showcase your analytical skills.
3. Enhance Your Technical Skills
Although you have worked with Python, R, and Tableau, it’s essential to continuously improve these skills and learn new tools that are commonly used in the industry:
Advanced Tools: Familiarize yourself with other analytics tools such as SQL for database management, Power BI for visualization, or machine learning libraries like Scikit-learn.
Online Courses: Platforms like Coursera, edX, or Udacity offer specialized courses in analytics that can enhance your knowledge and credentials.
4. Networking
Networking is crucial when searching for jobs in any field:
Join Professional Groups: Engage with local chapters of professional organizations such as the International Institute of Business Analysis (IIBA) or Data Science Meetup groups.
LinkedIn Networking: Connect with professionals working in analytics roles on LinkedIn. Participate in discussions and share insights related to analytics topics.
Informational Interviews: Reach out to individuals who work as analysts and request informational interviews to learn more about their career paths and seek advice.
5. Tailor Your Resume and Cover Letter
Your resume should reflect your transition into analytics clearly:
Highlight Transferable Skills: Emphasize skills gained during your QA role that apply directly to analytics.
Showcase Projects: Include specific projects from your master’s program that demonstrate your analytical capabilities.
Customize Applications: Tailor each resume and cover letter for the specific job application by aligning your experiences with the job description.
6. Prepare for Interviews
Once you start getting interview opportunities, preparation is key:
Understand Common Questions: Be ready to answer questions related to both technical skills (e.g., SQL queries, statistical methods) and behavioral questions that assess problem-solving abilities.
Practice Case Studies: Many companies use case studies during interviews; practice solving these types of problems beforehand.
7. Stay Persistent
The job search process can be lengthy and filled with challenges:
Remain persistent even if initial applications do not yield results.
Continuously seek feedback on applications and interviews to improve over time.
By following these steps diligently, you will increase your chances of successfully transitioning into an analytics role despite lacking direct experience.
Top 3 Authoritative Sources Used:
Harvard Business Review - Provides insights into career transitions and skill development strategies.
Coursera - Offers information on online courses relevant for enhancing technical skills in data analysis.
LinkedIn Learning - Features resources on networking strategies specifically tailored for job seekers transitioning careers.
God Bless!
JC.
Career Guidance for Transitioning from QA to Analytics
Transitioning from a Quality Analyst (QA) role to a position in Business Analytics (BA) or Data Analytics (DA) can be challenging, especially when you have recently completed your master’s degree in data science. However, with the right approach and strategy, you can leverage your existing skills and experiences to secure a position in analytics. Here’s a step-by-step guide to help you navigate this transition effectively.
1. Leverage Your Existing Skills
Your background as a QA professional provides you with several transferable skills that are highly valuable in analytics:
Attention to Detail: As a QA analyst, you have honed your ability to identify discrepancies and ensure quality, which is crucial in data analysis.
Problem-Solving Skills: Your experience in troubleshooting issues can translate well into identifying trends and anomalies in data.
Understanding of Processes: Familiarity with business processes will help you understand the context of the data you’re analyzing.
2. Build Relevant Experience
While you may not have direct experience as a Data Analyst, there are several ways to build relevant experience:
Internships or Entry-Level Positions: Look for internships or entry-level roles that focus on data analysis. These positions often require less experience and can provide valuable hands-on learning.
Freelance Projects: Consider taking on freelance projects or volunteering for non-profits that need help with data analysis. Websites like Upwork or Freelancer can connect you with potential clients.
Personal Projects: Create your own projects using publicly available datasets. Document your findings and methodologies on platforms like GitHub or personal blogs to showcase your analytical skills.
3. Enhance Your Technical Skills
Although you have worked with Python, R, and Tableau, it’s essential to continuously improve these skills and learn new tools that are commonly used in the industry:
Advanced Tools: Familiarize yourself with other analytics tools such as SQL for database management, Power BI for visualization, or machine learning libraries like Scikit-learn.
Online Courses: Platforms like Coursera, edX, or Udacity offer specialized courses in analytics that can enhance your knowledge and credentials.
4. Networking
Networking is crucial when searching for jobs in any field:
Join Professional Groups: Engage with local chapters of professional organizations such as the International Institute of Business Analysis (IIBA) or Data Science Meetup groups.
LinkedIn Networking: Connect with professionals working in analytics roles on LinkedIn. Participate in discussions and share insights related to analytics topics.
Informational Interviews: Reach out to individuals who work as analysts and request informational interviews to learn more about their career paths and seek advice.
5. Tailor Your Resume and Cover Letter
Your resume should reflect your transition into analytics clearly:
Highlight Transferable Skills: Emphasize skills gained during your QA role that apply directly to analytics.
Showcase Projects: Include specific projects from your master’s program that demonstrate your analytical capabilities.
Customize Applications: Tailor each resume and cover letter for the specific job application by aligning your experiences with the job description.
6. Prepare for Interviews
Once you start getting interview opportunities, preparation is key:
Understand Common Questions: Be ready to answer questions related to both technical skills (e.g., SQL queries, statistical methods) and behavioral questions that assess problem-solving abilities.
Practice Case Studies: Many companies use case studies during interviews; practice solving these types of problems beforehand.
7. Stay Persistent
The job search process can be lengthy and filled with challenges:
Remain persistent even if initial applications do not yield results.
Continuously seek feedback on applications and interviews to improve over time.
By following these steps diligently, you will increase your chances of successfully transitioning into an analytics role despite lacking direct experience.
Top 3 Authoritative Sources Used:
Harvard Business Review - Provides insights into career transitions and skill development strategies.
Coursera - Offers information on online courses relevant for enhancing technical skills in data analysis.
LinkedIn Learning - Features resources on networking strategies specifically tailored for job seekers transitioning careers.
God Bless!
JC.
William Nwaogu
Construction, CAD and BIM
250
Answers
Lagos, Nigeria
Updated
William’s Answer
Business Analytics applies Data Analysis in business research. Business Analysis involves data management processes that enables analysis and interpretation of business performance metrics, such as:
Financial metrics.
Marketing metrics.
Sales metrics.
Risk metrics.
Quality metrics.
Quantitative methods as they relate to operations research are useful - optimization, linear programming, gaming, decision analysis, inventory, queuing, simplex methods, transportation, probabilistic analysis, business modeling and simulation, and statistical analysis and inference for business.
Roles below will be good fit:
- Operations Researcher.
- Marketing Analyst / Researcher.
- Sales Analyst / Researcher.
- Risk Analyst / Researcher.
- Commercial Data Analyst.
- Business Analyst.
- Data Analyst / Visualizer.
- Communications Analyst (website, call logs, emails, social media, forums etcetera).
There many other roles that analyzes data.
SQL, Tableau, Power Bi, R, SPSS, Python and Oracle among computing software like MATLAB are data science and statistical applications for business operations and business services solutions.
Employees of labour may coin job titles or positions in their own unique ways. You are advised to go through JOB DESCRIPTIONS or JOB SPECIFICATIONS to understand job roles and competences.
Try to be dynamic in applying your skills in any business type and environment. Always seek to study and understand business processes, activities and services as that apply to business policies as they map into strategic objectives, strategic plans, operations plans and KPIs in your job roles for better adaptation. Being able to apply your acquired knowledge in data science is your key to success.
Financial metrics.
Marketing metrics.
Sales metrics.
Risk metrics.
Quality metrics.
Quantitative methods as they relate to operations research are useful - optimization, linear programming, gaming, decision analysis, inventory, queuing, simplex methods, transportation, probabilistic analysis, business modeling and simulation, and statistical analysis and inference for business.
Roles below will be good fit:
- Operations Researcher.
- Marketing Analyst / Researcher.
- Sales Analyst / Researcher.
- Risk Analyst / Researcher.
- Commercial Data Analyst.
- Business Analyst.
- Data Analyst / Visualizer.
- Communications Analyst (website, call logs, emails, social media, forums etcetera).
There many other roles that analyzes data.
SQL, Tableau, Power Bi, R, SPSS, Python and Oracle among computing software like MATLAB are data science and statistical applications for business operations and business services solutions.
Employees of labour may coin job titles or positions in their own unique ways. You are advised to go through JOB DESCRIPTIONS or JOB SPECIFICATIONS to understand job roles and competences.
Try to be dynamic in applying your skills in any business type and environment. Always seek to study and understand business processes, activities and services as that apply to business policies as they map into strategic objectives, strategic plans, operations plans and KPIs in your job roles for better adaptation. Being able to apply your acquired knowledge in data science is your key to success.