10 answers
Grant
Student
1
Question
Asked
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How can get job as a data analyst as an undergraduate?
I have degree in computer science and data science certification and I want to build a career in data science ,
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10 answers
David Allen
Data Analytics
10
Answers
Dallas, Texas
Updated
David’s Answer
Securing a job as a data analyst while still an undergraduate is achievable with strategic planning and proactive steps. Here's a roadmap to help you:
1. Develop Skills and Knowledge: Take relevant coursework in statistics, mathematics, and computer science. Learn programming languages like Python and SQL. Gain hands-on experience with data manipulation and analysis through projects and internships.
2. Build a Strong Portfolio: Showcase your data analysis projects on platforms like GitHub or Kaggle. Highlight your skills and experiences in your resume and cover letter, quantifying your achievements whenever possible.
3. Gain Practical Experience: Seek internships or part-time roles in data-related positions. Network with professionals in the field, and utilize university resources such as career services and alumni networks to find opportunities and receive guidance.
1. Develop Skills and Knowledge: Take relevant coursework in statistics, mathematics, and computer science. Learn programming languages like Python and SQL. Gain hands-on experience with data manipulation and analysis through projects and internships.
2. Build a Strong Portfolio: Showcase your data analysis projects on platforms like GitHub or Kaggle. Highlight your skills and experiences in your resume and cover letter, quantifying your achievements whenever possible.
3. Gain Practical Experience: Seek internships or part-time roles in data-related positions. Network with professionals in the field, and utilize university resources such as career services and alumni networks to find opportunities and receive guidance.
Samir Husain
Sr Business Analyst
8
Answers
Jersey City, New Jersey
Updated
Samir’s Answer
You have the right foundation to kickstart an analytics career. I would recommend focusing on the following
Develop Skills: Learn relevant tools like Python, R, SQL, and Excel.
Build Portfolio: Work on projects showcasing your data analysis abilities.
Gain Experience: Seek internships or part-time roles in data-related fields.
Network: Connect with professionals and join relevant groups.
Tailor Applications: Customize resumes and cover letters for data analyst positions.
Prepare for Interviews: Practice common data analyst interview questions.
Try HackerRank for SQL/Python practice
Participat in Kaggle data challenges to get hands on data wrangling experience
Develop Skills: Learn relevant tools like Python, R, SQL, and Excel.
Build Portfolio: Work on projects showcasing your data analysis abilities.
Gain Experience: Seek internships or part-time roles in data-related fields.
Network: Connect with professionals and join relevant groups.
Tailor Applications: Customize resumes and cover letters for data analyst positions.
Prepare for Interviews: Practice common data analyst interview questions.
Samir recommends the following next steps:
Patrick Meyer
Executive - Data Analytics and AI
750
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Grant, I want you to understand that your dream of becoming a data analyst right after college is not only possible, but also highly achievable with the right plan and determination. While it's true that some entry-level data analysis roles might ask for prior professional experience, there are numerous ways for you to boost your chances of securing a job in this exciting field immediately after graduation.
Firstly, make the most of your computer science degree and data science certification. Use them to highlight your technical know-how and analytical prowess. Show off your relevant coursework, projects, and accomplishments that prove your ability to work with data, derive insights, and effectively communicate your findings. Employers appreciate practical experience with programming languages like Python, R, SQL, and data visualization tools like Tableau or Power BI. So, don't shy away from showcasing your skills in these areas.
Moreover, Grant, remember that real-world experience gained through internships, co-op programs, or freelance projects can greatly improve your resume and give you a taste of actual data analysis situations. Look for opportunities to use your skills in a professional environment, such as contributing to data-centric projects at your university, teaming up with research labs, or volunteering for non-profit organizations that need data analysis help. These experiences not only boost your credentials but also hone your practical problem-solving skills and give you valuable industry insights.
Networking is another key factor in landing a data analysis job as a student. Make the most of career fairs, industry events, and networking sessions to meet professionals in the field and learn about potential job or internship openings. Join relevant student groups, online communities, and professional associations focused on data science and analytics to broaden your network and stay informed about industry trends and job opportunities. Building connections with mentors, professors, and alumni can provide invaluable guidance and referrals, opening doors to career opportunities in data analysis.
Lastly, Grant, tailor your job hunt to focus on roles that match your skills, interests, and career aspirations. Look into companies and sectors that prioritize data-driven decision-making and innovation, like tech firms, financial services, healthcare, or e-commerce. Modify your resume and cover letter to highlight your relevant experience and qualifications, and be ready to demonstrate your technical skills and problem-solving ability during interviews through case studies, coding tests, or data analysis tasks.
By capitalizing on your academic background, gaining hands-on experience, networking wisely, and targeting suitable job openings, you can set yourself up for a successful start to your career in data analysis right after college. Stay active, persistent, and flexible in your job search, and don't hesitate to seek advice and mentorship to maximize your chances of success in the vibrant and rewarding world of data science.
Firstly, make the most of your computer science degree and data science certification. Use them to highlight your technical know-how and analytical prowess. Show off your relevant coursework, projects, and accomplishments that prove your ability to work with data, derive insights, and effectively communicate your findings. Employers appreciate practical experience with programming languages like Python, R, SQL, and data visualization tools like Tableau or Power BI. So, don't shy away from showcasing your skills in these areas.
Moreover, Grant, remember that real-world experience gained through internships, co-op programs, or freelance projects can greatly improve your resume and give you a taste of actual data analysis situations. Look for opportunities to use your skills in a professional environment, such as contributing to data-centric projects at your university, teaming up with research labs, or volunteering for non-profit organizations that need data analysis help. These experiences not only boost your credentials but also hone your practical problem-solving skills and give you valuable industry insights.
Networking is another key factor in landing a data analysis job as a student. Make the most of career fairs, industry events, and networking sessions to meet professionals in the field and learn about potential job or internship openings. Join relevant student groups, online communities, and professional associations focused on data science and analytics to broaden your network and stay informed about industry trends and job opportunities. Building connections with mentors, professors, and alumni can provide invaluable guidance and referrals, opening doors to career opportunities in data analysis.
Lastly, Grant, tailor your job hunt to focus on roles that match your skills, interests, and career aspirations. Look into companies and sectors that prioritize data-driven decision-making and innovation, like tech firms, financial services, healthcare, or e-commerce. Modify your resume and cover letter to highlight your relevant experience and qualifications, and be ready to demonstrate your technical skills and problem-solving ability during interviews through case studies, coding tests, or data analysis tasks.
By capitalizing on your academic background, gaining hands-on experience, networking wisely, and targeting suitable job openings, you can set yourself up for a successful start to your career in data analysis right after college. Stay active, persistent, and flexible in your job search, and don't hesitate to seek advice and mentorship to maximize your chances of success in the vibrant and rewarding world of data science.
Param Singh
Management Consulting
5
Answers
Hayward, California
Updated
Param’s Answer
Getting a job in data science can be both exciting and challenging. Here are some steps you can take to increase your chances of landing a job in this field:
1. **Gain the Necessary Education**: Most data science positions require at least a bachelor's degree in a relevant field such as computer science, statistics, mathematics, or engineering. Many employers also prefer candidates with advanced degrees like a master's or Ph.D. in data science or a related discipline.
2. **Develop Strong Skills**: Data science requires a combination of technical and soft skills. Make sure you're proficient in programming languages commonly used in data science such as Python, R, SQL, and others. Additionally, skills in statistics, machine learning, data visualization, and big data technologies like Hadoop and Spark are valuable.
3. **Build a Portfolio**: Create a portfolio showcasing your data science projects. This could include personal projects, academic projects, or freelance work. Having a portfolio demonstrates your skills and gives potential employers concrete examples of your abilities.
4. **Gain Practical Experience**: Look for internships, co-op programs, or entry-level positions that provide hands-on experience in data science. Practical experience is highly valued by employers and can help you build your skills and network in the field.
5. **Network**: Attend industry events, conferences, and meetups to connect with professionals in the field. Join online communities and participate in discussions on platforms like LinkedIn, GitHub, and Kaggle. Networking can help you learn about job opportunities and get referrals from people in the industry.
6. **Stay Updated**: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Follow blogs, podcasts, and online courses to continue learning and expanding your skillset.
7. **Customize Your Resume and Cover Letter**: Tailor your resume and cover letter to highlight your relevant skills and experiences for each job application. Emphasize specific projects or achievements that demonstrate your ability to solve problems using data science techniques.
8. **Prepare for Interviews**: Be prepared to answer technical questions related to data science concepts, algorithms, and programming languages. Practice coding exercises, case studies, and behavioral questions commonly asked in data science interviews.
9. **Consider Specializations**: Data science is a broad field, and there are many specialized roles within it such as machine learning engineer, data analyst, data engineer, etc. Consider focusing on a specific area of interest and gaining specialized skills and experience in that area.
10. **Be Persistent**: Landing a job in data science can take time and effort. Don't get discouraged by rejection and keep improving your skills and applying for relevant opportunities.
By following these steps and staying dedicated to your goals, you can increase your chances of securing a job in data science. Good luck!
1. **Gain the Necessary Education**: Most data science positions require at least a bachelor's degree in a relevant field such as computer science, statistics, mathematics, or engineering. Many employers also prefer candidates with advanced degrees like a master's or Ph.D. in data science or a related discipline.
2. **Develop Strong Skills**: Data science requires a combination of technical and soft skills. Make sure you're proficient in programming languages commonly used in data science such as Python, R, SQL, and others. Additionally, skills in statistics, machine learning, data visualization, and big data technologies like Hadoop and Spark are valuable.
3. **Build a Portfolio**: Create a portfolio showcasing your data science projects. This could include personal projects, academic projects, or freelance work. Having a portfolio demonstrates your skills and gives potential employers concrete examples of your abilities.
4. **Gain Practical Experience**: Look for internships, co-op programs, or entry-level positions that provide hands-on experience in data science. Practical experience is highly valued by employers and can help you build your skills and network in the field.
5. **Network**: Attend industry events, conferences, and meetups to connect with professionals in the field. Join online communities and participate in discussions on platforms like LinkedIn, GitHub, and Kaggle. Networking can help you learn about job opportunities and get referrals from people in the industry.
6. **Stay Updated**: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Follow blogs, podcasts, and online courses to continue learning and expanding your skillset.
7. **Customize Your Resume and Cover Letter**: Tailor your resume and cover letter to highlight your relevant skills and experiences for each job application. Emphasize specific projects or achievements that demonstrate your ability to solve problems using data science techniques.
8. **Prepare for Interviews**: Be prepared to answer technical questions related to data science concepts, algorithms, and programming languages. Practice coding exercises, case studies, and behavioral questions commonly asked in data science interviews.
9. **Consider Specializations**: Data science is a broad field, and there are many specialized roles within it such as machine learning engineer, data analyst, data engineer, etc. Consider focusing on a specific area of interest and gaining specialized skills and experience in that area.
10. **Be Persistent**: Landing a job in data science can take time and effort. Don't get discouraged by rejection and keep improving your skills and applying for relevant opportunities.
By following these steps and staying dedicated to your goals, you can increase your chances of securing a job in data science. Good luck!
Ellaine Tsz Ying Ho
Business Intelligence Developer
2
Answers
New York, New York
Updated
Ellaine Tsz Ying’s Answer
I think that it's great that you have an interest in the data and analytics field since it is very popular and in demand in recent years, and will continue to be in the future.
To become a data analyst as an undergraduate, focus on foundational education by pursuing a degree in math, computer science, or a related field as mentioned by many other folks on this thread. Then, you might want to develop technical skills in statistics, R or Python programming, SQL, and data visualization. You can get access to some free and also paid courses online through platforms like LinkedIn, Coursera, Datacamp and many others. Other than developing those skills, you also want to work on real-world projects to apply your knowledge, and have a few under your belt when it comes to interviewing and showcasing your abilities. All of those can make an impact on your understanding of the field, subject matter, and ultimately build a career in data analytics.
To become a data analyst as an undergraduate, focus on foundational education by pursuing a degree in math, computer science, or a related field as mentioned by many other folks on this thread. Then, you might want to develop technical skills in statistics, R or Python programming, SQL, and data visualization. You can get access to some free and also paid courses online through platforms like LinkedIn, Coursera, Datacamp and many others. Other than developing those skills, you also want to work on real-world projects to apply your knowledge, and have a few under your belt when it comes to interviewing and showcasing your abilities. All of those can make an impact on your understanding of the field, subject matter, and ultimately build a career in data analytics.
Chris Powers
Sr. Director of Engineering
8
Answers
Chicago, Illinois
Updated
Chris’s Answer
A hiring manager who is looking to fill an entry level role is worried about these things:
1. Can this person actually get stuff done?
2. Can this person learn what they need to learn on-the-job?
3. Is this person someone I want to interact with daily?
So how do you demonstrate to a hiring manager that you have skills, can learn what you don't know, and are cool to work with?
Generally I would recommend:
1. Create a portfolio of data science work you've done, either class projects or personal projects you've worked on. This shows the hiring manager you do have some skills and can bring a project through to a successful conclusion.
2. Keep learning outside of classes, and be ready to tell your interviewers about what you have been learning and the initiative that demonstrates.
3. Don't underestimate the importance of soft skills, communication, and personal warmth. Be the friendly coworker that you would want to work along side.
1. Can this person actually get stuff done?
2. Can this person learn what they need to learn on-the-job?
3. Is this person someone I want to interact with daily?
So how do you demonstrate to a hiring manager that you have skills, can learn what you don't know, and are cool to work with?
Generally I would recommend:
1. Create a portfolio of data science work you've done, either class projects or personal projects you've worked on. This shows the hiring manager you do have some skills and can bring a project through to a successful conclusion.
2. Keep learning outside of classes, and be ready to tell your interviewers about what you have been learning and the initiative that demonstrates.
3. Don't underestimate the importance of soft skills, communication, and personal warmth. Be the friendly coworker that you would want to work along side.
Srishti Singh
Data Analyst
1
Answer
Pullman, Washington
Updated
Srishti’s Answer
Considering your academic background in Computer Science and your certification in data science, you're already well on your way to carving out a successful career in Data Science and Analytics. Here are some constructive steps to help you achieve your career goals.
Firstly, it's crucial to create a portfolio that effectively showcases your skills and accomplishments. A well-crafted resume is certainly beneficial, but it's equally important to demonstrate your abilities in other ways. For instance, using GIT to display your projects not only provides a platform for your work but also demonstrates your technological proficiency, as GIT is widely used by data science professionals across various industries.
Another platform I'd recommend is Kaggle, an online data science competition and community platform. It allows you to contribute to projects of your choosing and include these projects in your GIT and resume. You'll find projects of varying difficulty levels, enabling you to start with simpler ones and gradually take on more challenging ones. This strategy not only gives you more projects to showcase but also helps you enhance your data science skills. These skills include Python, R, data science/analytics tools and libraries, Spark, data wrangling, AWS, and more.
Building a robust network is another vital step. This can be achieved through community involvement (Kaggle can be of assistance here too), LinkedIn, and attending relevant conferences and events.
In today's world, data is a valuable asset across all industries, be it tech, finance, or consumer sectors. Therefore, there's enormous potential for growth in this field. So keep enhancing your profile, networking with professionals, and seizing opportunities as they come. Your journey in data science is just beginning, and the possibilities are endless!
Firstly, it's crucial to create a portfolio that effectively showcases your skills and accomplishments. A well-crafted resume is certainly beneficial, but it's equally important to demonstrate your abilities in other ways. For instance, using GIT to display your projects not only provides a platform for your work but also demonstrates your technological proficiency, as GIT is widely used by data science professionals across various industries.
Another platform I'd recommend is Kaggle, an online data science competition and community platform. It allows you to contribute to projects of your choosing and include these projects in your GIT and resume. You'll find projects of varying difficulty levels, enabling you to start with simpler ones and gradually take on more challenging ones. This strategy not only gives you more projects to showcase but also helps you enhance your data science skills. These skills include Python, R, data science/analytics tools and libraries, Spark, data wrangling, AWS, and more.
Building a robust network is another vital step. This can be achieved through community involvement (Kaggle can be of assistance here too), LinkedIn, and attending relevant conferences and events.
In today's world, data is a valuable asset across all industries, be it tech, finance, or consumer sectors. Therefore, there's enormous potential for growth in this field. So keep enhancing your profile, networking with professionals, and seizing opportunities as they come. Your journey in data science is just beginning, and the possibilities are endless!
Michael Richtberg
Product management & engineering
16
Answers
Austin, Texas
Updated
Michael’s Answer
Absolutely, you have a fantastic foundation to kickstart your journey. By maintaining a clear focus and actively seeking ways to boost your data science skills, you're bound to make great strides. The industry is currently experiencing a monumental shift, with groundbreaking AI technology spearheading our future. At the heart of this revolution is data, and data scientists are in high demand. This is the perfect moment to seize the opportunity and fill the gap in the IT market where there is a shortage of talent in this particular area.
Look for AI/ML classes and take advantage of those resources to build your credentials. LinkedIn has over 250 new AI courses available for free.
Michael recommends the following next steps:
Giacomo Paini
Dental hygienist
37
Answers
Vicenza, Veneto, Italy
Updated
Giacomo’s Answer
I am in the same situation
It s difficult
You need experience
Maybe as volounter
Or a better certification
American market is very competitive
It s difficult
You need experience
Maybe as volounter
Or a better certification
American market is very competitive
Jonathan Ammirati
Consulting
7
Answers
Washington, Washington
Updated
Jonathan’s Answer
Along with the points mentioned previously, it's crucial to establish yourself as an effective communicator within the field of data analysis. The ability to accurately understand and respond to the specific queries of your colleagues, clients, and superiors regarding data analysis is vital.
Moreover, many business roles require data analysts to summarize, organize, and convey findings in a straightforward and succinct way. These communication abilities are typically best developed through consistent practice and practical, "hands-on" experience.
Moreover, many business roles require data analysts to summarize, organize, and convey findings in a straightforward and succinct way. These communication abilities are typically best developed through consistent practice and practical, "hands-on" experience.