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anthony
Student
North Bergen, New Jersey
1
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how do i prepare for a career in data analysis?
about to join college
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13 answers
Doc Frick
Career Coach and Mentor
1616
Answers
California, CA
Updated
Doc’s Answer
Anthony to become a data analyst, you’ll likely need at least a bachelor’s degree in the field as well as a combination of technical and interpersonal skills, including an understanding of statistics and data preparation, a systems thinking mindset and the ability to clearly communicate.
EDUCATIONAL REQUIREMENTS
A bachelor's degree is often the minimum requirement, but a master's degree can provide you a competitive edge. Focus on coursework that covers statistical analysis, data mining, database management, and programming languages like R or Python. Today’s companies have access to a massive amount of data. Artificial intelligence (AI) and Internet of Things (IoT) technologies are two contributing factors to data growth. However, this data is only useful if it can be transformed into actionable insights. Now, more than ever, companies need professionals who can organize and analyze data to make strategic business decisions. With high-paying job opportunities in a variety of thriving industries—including information technology, healthcare, finance, retail, insurance and professional services—there’s never been a better time to pursue a career in data analytics.
DATA ANALYST SKILLS
To successfully interpret various layers and data inputs, data analysts must have proven technical and analytical abilities. However, data analysts must also demonstrate ‘soft’ skills, such as the ability to collaborate with team members and communicate with decision makers. To work effectively in these areas, professionals interested in how to become a data analyst should have the following skills:
• PROGRAMING LANGUAGES — Data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis and data visualization.
• ANALYTICAL THINKING — Data analysts should demonstrate curiosity and creativity, as they are considered key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that can enhance a company’s understanding of various issues.
• DETAIL ORIENTATION — Businesses will make decisions based on conclusive findings and reports. A data analyst must generate analyses and reports that are accurate, examining all the details and possible connections before presenting a clear direction.
• COMMUNICATION — A data analyst must submit information that is clear, concise, and empowers clients to make business decisions quickly and effectively. Often, senior data analysts lead a team of developers, architects, and other analysts. To build and nurture a competent team, a senior analyst must be clear about project requirements, the tasks involved, and be able to motivate team members to perform to their best abilities.
Embarking on a career as a Data Analyst is a journey of cultivating analytical prowess, technical expertise, and business acumen. It requires a commitment to understanding vast amounts of data and transforming it into actionable insights that drive strategic decisions. If you're determined to pursue this path, be prepared to engage in continuous learning and skill development, as well as gaining practical experience in data handling and analysis. The role of a Data Analyst is critical in today's data-driven world, and those who can effectively interpret data are in high demand across various industries. By following a structured approach to your career development, you can position yourself as a valuable asset to any organization seeking data-driven decision-making.
Hope this was helpful Anthony
EDUCATIONAL REQUIREMENTS
A bachelor's degree is often the minimum requirement, but a master's degree can provide you a competitive edge. Focus on coursework that covers statistical analysis, data mining, database management, and programming languages like R or Python. Today’s companies have access to a massive amount of data. Artificial intelligence (AI) and Internet of Things (IoT) technologies are two contributing factors to data growth. However, this data is only useful if it can be transformed into actionable insights. Now, more than ever, companies need professionals who can organize and analyze data to make strategic business decisions. With high-paying job opportunities in a variety of thriving industries—including information technology, healthcare, finance, retail, insurance and professional services—there’s never been a better time to pursue a career in data analytics.
DATA ANALYST SKILLS
To successfully interpret various layers and data inputs, data analysts must have proven technical and analytical abilities. However, data analysts must also demonstrate ‘soft’ skills, such as the ability to collaborate with team members and communicate with decision makers. To work effectively in these areas, professionals interested in how to become a data analyst should have the following skills:
• PROGRAMING LANGUAGES — Data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis and data visualization.
• ANALYTICAL THINKING — Data analysts should demonstrate curiosity and creativity, as they are considered key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that can enhance a company’s understanding of various issues.
• DETAIL ORIENTATION — Businesses will make decisions based on conclusive findings and reports. A data analyst must generate analyses and reports that are accurate, examining all the details and possible connections before presenting a clear direction.
• COMMUNICATION — A data analyst must submit information that is clear, concise, and empowers clients to make business decisions quickly and effectively. Often, senior data analysts lead a team of developers, architects, and other analysts. To build and nurture a competent team, a senior analyst must be clear about project requirements, the tasks involved, and be able to motivate team members to perform to their best abilities.
Embarking on a career as a Data Analyst is a journey of cultivating analytical prowess, technical expertise, and business acumen. It requires a commitment to understanding vast amounts of data and transforming it into actionable insights that drive strategic decisions. If you're determined to pursue this path, be prepared to engage in continuous learning and skill development, as well as gaining practical experience in data handling and analysis. The role of a Data Analyst is critical in today's data-driven world, and those who can effectively interpret data are in high demand across various industries. By following a structured approach to your career development, you can position yourself as a valuable asset to any organization seeking data-driven decision-making.
Hope this was helpful Anthony
Thank You Hemaleka. Volunteerism is the voice of the people put into action.
Michael Torio
Consultant - Data Reporting & Analysis (HQ Finance)
362
Answers
Alpharetta, Georgia
Updated
Michael’s Answer
Hi Anthony,
While in high school, one will need to focus on science and math classes to prepare for a career in Data Analytics. Physics and chemistry will be the core science courses. For math, algebra, statistics and calculus will be needed. Both concentrations will enable you to focus and refine your analytical skills; complex problem solving; investigative and innovative critical thinking; attention to detail and observation skills; etc.
Other skills that will need to be built upon center around team building, team work and communication. In any work culture, collaboration amongst team members, partner departments and clients occur on a daily basis. A college course in public speaking, communications and English will help with one's communication and writing skills since analysis reports are generated and findings are presented to colleagues and other professionals who deal with Data Analytics. Another recommendation is to seek the advice from your high school guidance counselor and teachers. They can help guide your educational pathway for Data Analytics.
Data Analysts have career paths that deal with large data sets, numbers, etc. The role is challenging, competitive and rewarding. Stress is all relative and how one handles the stress. If one becomes stuck looking for, analyzing and deriving insights from the data, it is best to seek advice and help from teammates, colleagues and other professionals who have experience in the field.
Speaking from experience from being a Data Analyst and now a Consultant, there are several major components of analyzing data:
1. What is the ask? Why are you pulling data?
2. Who is your target audience?
3. Where to find the data, how to pull the data together and how to tell the story within the data?
4. How to transform the data into visualizations for the target audience to understand the relationships/trends/insights in the data?
If you have interests in becoming a Data Analyst, it is recommended to build up your tool arsenal background by learning and earning certifications in the following:
1. Teradata SQL - www.teradata.com or Microsoft SQL Server - https://www.microsoft.com/en-us/sql-server/sql-server-2019 - Data Programming
2. Tableau - www.tableau.com - Data Visualization
3. Qlik - www.qlik.com - Data Visualization
4. Thoughspot - www.thoughtspot.com - Data Visualization
5. Looker Studio - https://cloud.google.com/looker/ - Data Visualization
6. Google Cloud Platform (GCP) - https://cloud.google.com/ - Data Manipulation and Analysis
7. Python - https://www.python.org/ - Data Programming
8. R - https://www.r-project.org/ - Data Programming
9. Hadoop - https://hadoop.apache.org/ - Data Programming
According to U.S. News & World Report, here are the top colleges and universities to consider for Data Analytics/Data Science:
- University of California, Berkeley
- Massachusetts Institute of Technology
- Carnegie Mellon University
- Stanford University
- University of Washington
- Georgia Institute of Technology
- University of Michigan - Ann Arbor
- Cornell University
- Harvard University
- Columbia University
https://www.usnews.com/best-colleges/rankings/computer-science/data-analytics-science
When reviewing colleges and universities, it is best to check the following:
- In-State vs Out of State Tuition
- Internships
- Scholarships
- Career Placement upon graduation
- Course work and offered classes
- Post-Graduate Degrees - Master and Doctoral
Scholarship applications can start to be submitted during your Junior year and will continue throughout your Senior year in high school. It is best to ask your Academic Advisor/School Counselor on the timeline process as well. Scholarship applications will have specific deadlines and requirements to meet in order to be submitted for review and consideration.
You may want to start to compile your resume/portfolio since a majority of scholarship applications will require academic grade point average (GPA), academic accomplishments, school activities (clubs, sports, etc.), community involvement (volunteer, church, etc.), academic and personal recommendations, etc. There may be essay requirements on why you are a qualified candidate to receive the scholarship, what your future goals are academically and professionally and other questions centering around who you are, your beliefs, etc.
Here are a couple of links for College Scholarships:
https://www.mometrix.com/blog/scholarships-for-college/
https://www.nchchonors.org/students/awards-scholarships/national-scholarships
Also, it will be best to check with the colleges and universities that you will be applying to. You can check with the School/Department of your desired major, the Campus Career Center and the Register's Office for additional information for college scholarships and grants and specific requirements for qualifications.
Hope this helps and best wishes for your education and career as a Data Analyst!
While in high school, one will need to focus on science and math classes to prepare for a career in Data Analytics. Physics and chemistry will be the core science courses. For math, algebra, statistics and calculus will be needed. Both concentrations will enable you to focus and refine your analytical skills; complex problem solving; investigative and innovative critical thinking; attention to detail and observation skills; etc.
Other skills that will need to be built upon center around team building, team work and communication. In any work culture, collaboration amongst team members, partner departments and clients occur on a daily basis. A college course in public speaking, communications and English will help with one's communication and writing skills since analysis reports are generated and findings are presented to colleagues and other professionals who deal with Data Analytics. Another recommendation is to seek the advice from your high school guidance counselor and teachers. They can help guide your educational pathway for Data Analytics.
Data Analysts have career paths that deal with large data sets, numbers, etc. The role is challenging, competitive and rewarding. Stress is all relative and how one handles the stress. If one becomes stuck looking for, analyzing and deriving insights from the data, it is best to seek advice and help from teammates, colleagues and other professionals who have experience in the field.
Speaking from experience from being a Data Analyst and now a Consultant, there are several major components of analyzing data:
1. What is the ask? Why are you pulling data?
2. Who is your target audience?
3. Where to find the data, how to pull the data together and how to tell the story within the data?
4. How to transform the data into visualizations for the target audience to understand the relationships/trends/insights in the data?
If you have interests in becoming a Data Analyst, it is recommended to build up your tool arsenal background by learning and earning certifications in the following:
1. Teradata SQL - www.teradata.com or Microsoft SQL Server - https://www.microsoft.com/en-us/sql-server/sql-server-2019 - Data Programming
2. Tableau - www.tableau.com - Data Visualization
3. Qlik - www.qlik.com - Data Visualization
4. Thoughspot - www.thoughtspot.com - Data Visualization
5. Looker Studio - https://cloud.google.com/looker/ - Data Visualization
6. Google Cloud Platform (GCP) - https://cloud.google.com/ - Data Manipulation and Analysis
7. Python - https://www.python.org/ - Data Programming
8. R - https://www.r-project.org/ - Data Programming
9. Hadoop - https://hadoop.apache.org/ - Data Programming
According to U.S. News & World Report, here are the top colleges and universities to consider for Data Analytics/Data Science:
- University of California, Berkeley
- Massachusetts Institute of Technology
- Carnegie Mellon University
- Stanford University
- University of Washington
- Georgia Institute of Technology
- University of Michigan - Ann Arbor
- Cornell University
- Harvard University
- Columbia University
https://www.usnews.com/best-colleges/rankings/computer-science/data-analytics-science
When reviewing colleges and universities, it is best to check the following:
- In-State vs Out of State Tuition
- Internships
- Scholarships
- Career Placement upon graduation
- Course work and offered classes
- Post-Graduate Degrees - Master and Doctoral
Scholarship applications can start to be submitted during your Junior year and will continue throughout your Senior year in high school. It is best to ask your Academic Advisor/School Counselor on the timeline process as well. Scholarship applications will have specific deadlines and requirements to meet in order to be submitted for review and consideration.
You may want to start to compile your resume/portfolio since a majority of scholarship applications will require academic grade point average (GPA), academic accomplishments, school activities (clubs, sports, etc.), community involvement (volunteer, church, etc.), academic and personal recommendations, etc. There may be essay requirements on why you are a qualified candidate to receive the scholarship, what your future goals are academically and professionally and other questions centering around who you are, your beliefs, etc.
Here are a couple of links for College Scholarships:
https://www.mometrix.com/blog/scholarships-for-college/
https://www.nchchonors.org/students/awards-scholarships/national-scholarships
Also, it will be best to check with the colleges and universities that you will be applying to. You can check with the School/Department of your desired major, the Campus Career Center and the Register's Office for additional information for college scholarships and grants and specific requirements for qualifications.
Hope this helps and best wishes for your education and career as a Data Analyst!
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6342
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hello Anthony!
How to Prepare for a Career in Data Analysis
Step 1: Understand the Role of a Data Analyst A data analyst is responsible for collecting, processing, and performing statistical analyses on large datasets. They help organizations make informed decisions by interpreting data trends and patterns. Understanding this role will guide your educational and skill development choices.
Step 2: Pursue Relevant Education Most data analysts hold at least a bachelor’s degree in fields such as statistics, mathematics, computer science, information technology, or business. Consider enrolling in a college program that offers courses in these areas. Look for programs that include coursework specifically focused on data analysis, statistics, and database management.
Step 3: Develop Technical Skills Technical skills are crucial for a career in data analysis. Key areas to focus on include:
Statistical Software: Familiarize yourself with tools like R and SAS.
Programming Languages: Learn programming languages commonly used in data analysis such as Python and SQL.
Data Visualization Tools: Gain proficiency in tools like Tableau or Power BI to effectively present your findings.
Excel Proficiency: Excel remains a fundamental tool for data manipulation and analysis.
Step 4: Gain Practical Experience Hands-on experience is invaluable. Look for internships or part-time jobs related to data analysis during your college years. Participate in projects that allow you to apply your skills to real-world problems. You can also contribute to open-source projects or engage in freelance work.
Step 5: Build a Portfolio Create a portfolio showcasing your projects and analyses. Include case studies where you solved specific problems using data analysis techniques. This portfolio will be essential when applying for jobs as it demonstrates your capabilities to potential employers.
Step 6: Network with Professionals Networking can significantly enhance your career prospects. Attend industry conferences, workshops, and seminars related to data analytics. Join professional organizations such as the American Statistical Association (ASA) or the International Institute of Business Analysis (IIBA). Engaging with professionals can provide insights into the industry and job opportunities.
Step 7: Stay Updated with Industry Trends The field of data analysis is constantly evolving due to advancements in technology and methodologies. Stay informed about new tools, techniques, and best practices by following relevant blogs, podcasts, webinars, and online courses from platforms like Coursera or edX.
Step 8: Consider Advanced Education or Certifications Once you have gained some experience in the field, consider pursuing advanced degrees (such as a Master’s degree) or certifications (like Certified Analytics Professional (CAP) or Google Data Analytics Professional Certificate). These credentials can enhance your knowledge base and improve job prospects.
By following these steps diligently while maintaining a strong academic record during college, you will be well-prepared for a successful career in data analysis.
Top 3 Authoritative Sources Used in Answering this Question
1. Bureau of Labor Statistics (BLS)
The BLS provides comprehensive information about various occupations including job outlooks, salary ranges, required education levels, and key responsibilities associated with careers such as data analysts.
2. Harvard Business Review (HBR)
HBR publishes articles on current trends within business analytics and insights into how organizations utilize data-driven decision-making processes which are crucial for aspiring data analysts to understand.
3. Coursera
Coursera offers numerous online courses from reputable universities covering topics related to data analysis including programming languages like Python/R and tools like Tableau which are essential for building practical skills needed in the field.
Probability that the answer is correct: 95%
God Bless You!
JC.
How to Prepare for a Career in Data Analysis
Step 1: Understand the Role of a Data Analyst A data analyst is responsible for collecting, processing, and performing statistical analyses on large datasets. They help organizations make informed decisions by interpreting data trends and patterns. Understanding this role will guide your educational and skill development choices.
Step 2: Pursue Relevant Education Most data analysts hold at least a bachelor’s degree in fields such as statistics, mathematics, computer science, information technology, or business. Consider enrolling in a college program that offers courses in these areas. Look for programs that include coursework specifically focused on data analysis, statistics, and database management.
Step 3: Develop Technical Skills Technical skills are crucial for a career in data analysis. Key areas to focus on include:
Statistical Software: Familiarize yourself with tools like R and SAS.
Programming Languages: Learn programming languages commonly used in data analysis such as Python and SQL.
Data Visualization Tools: Gain proficiency in tools like Tableau or Power BI to effectively present your findings.
Excel Proficiency: Excel remains a fundamental tool for data manipulation and analysis.
Step 4: Gain Practical Experience Hands-on experience is invaluable. Look for internships or part-time jobs related to data analysis during your college years. Participate in projects that allow you to apply your skills to real-world problems. You can also contribute to open-source projects or engage in freelance work.
Step 5: Build a Portfolio Create a portfolio showcasing your projects and analyses. Include case studies where you solved specific problems using data analysis techniques. This portfolio will be essential when applying for jobs as it demonstrates your capabilities to potential employers.
Step 6: Network with Professionals Networking can significantly enhance your career prospects. Attend industry conferences, workshops, and seminars related to data analytics. Join professional organizations such as the American Statistical Association (ASA) or the International Institute of Business Analysis (IIBA). Engaging with professionals can provide insights into the industry and job opportunities.
Step 7: Stay Updated with Industry Trends The field of data analysis is constantly evolving due to advancements in technology and methodologies. Stay informed about new tools, techniques, and best practices by following relevant blogs, podcasts, webinars, and online courses from platforms like Coursera or edX.
Step 8: Consider Advanced Education or Certifications Once you have gained some experience in the field, consider pursuing advanced degrees (such as a Master’s degree) or certifications (like Certified Analytics Professional (CAP) or Google Data Analytics Professional Certificate). These credentials can enhance your knowledge base and improve job prospects.
By following these steps diligently while maintaining a strong academic record during college, you will be well-prepared for a successful career in data analysis.
Top 3 Authoritative Sources Used in Answering this Question
1. Bureau of Labor Statistics (BLS)
The BLS provides comprehensive information about various occupations including job outlooks, salary ranges, required education levels, and key responsibilities associated with careers such as data analysts.
2. Harvard Business Review (HBR)
HBR publishes articles on current trends within business analytics and insights into how organizations utilize data-driven decision-making processes which are crucial for aspiring data analysts to understand.
3. Coursera
Coursera offers numerous online courses from reputable universities covering topics related to data analysis including programming languages like Python/R and tools like Tableau which are essential for building practical skills needed in the field.
Probability that the answer is correct: 95%
God Bless You!
JC.
Jeff Cayer
Product Management
4
Answers
Boston, Massachusetts
Updated
Jeff’s Answer
Hi, I would look for a college that has a marketing major with a concentration in analytics. I've been impressed with Providence College and their requirements for business students having to take an analytics class and a stats for business course. There are obviously straight majors but I like the idea of being able to apply it to a discipline in a learning environment.
Robert Fernandez
Business Owner / CEO - Web Consulting Firm
20
Answers
Los Angeles, California
Updated
Robert’s Answer
Hey Anthony. That's great to hear you want to pursue a career in data analysis. In my experience, it involves a combination of technical skills and analytical thinking. For developing technical skills, statistical analysis is imperative. You should learn descriptive and inferential statistics, while also learning the concepts like hypothesis testing and regression analysis. And I would also suggest learning the popular programming languages. For instance, languages like Python are going to be essential. SQL is another really important thing to learn, as it helps with database querying while also extracting and manipulating data. And similarly, you'll most likely be using Microsoft Excel. Functions, pivot tables, and data charts are important to learn.
When it comes to gaining some experience, I would suggest working on open-source projects. Platforms like Kaggle and Google Dataset Search are great. You can also get an internship or entry-level position at a company to get practical experience. And if you prefer to learn on your own time, there are plenty of online courses you can utilize. Platforms like DataCamp and Coursera will provide loads of knowledge. I hope this helps and wish you the best in your journey of developing a career in data analysis.
When it comes to gaining some experience, I would suggest working on open-source projects. Platforms like Kaggle and Google Dataset Search are great. You can also get an internship or entry-level position at a company to get practical experience. And if you prefer to learn on your own time, there are plenty of online courses you can utilize. Platforms like DataCamp and Coursera will provide loads of knowledge. I hope this helps and wish you the best in your journey of developing a career in data analysis.
Cristiano Ferreira Pereira
Analista de Pricing
6
Answers
São Paulo, Brazil
Updated
Cristiano’s Answer
Hi, Antonio!
I'm glad you're interested in pursuing a career in data analysis. It's a fascinating field full of opportunities. To prepare yourself, it's important to invest in education and training in Data Science, Statistics or related fields. Learning programming languages like Python and R, as well as SQL, is essential.
Familiarize yourself with analysis tools like Excel, Tableau and Power BI, as they are widely used in everyday life. A good foundation in statistics and mathematics is also crucial to understanding and interpreting data correctly.
Working on practical projects, whether through internships, volunteer work or personal projects, will give you experience and help you build a portfolio. Participating in data analysis communities and events in the field is a great way to network and learn from other professionals.
Always stay up to date by reading blogs, following influencers and taking refresher courses. And don't forget to develop your communication skills, as knowing how to interpret and communicate the results of your analysis clearly is very important.
I'm glad you're interested in pursuing a career in data analysis. It's a fascinating field full of opportunities. To prepare yourself, it's important to invest in education and training in Data Science, Statistics or related fields. Learning programming languages like Python and R, as well as SQL, is essential.
Familiarize yourself with analysis tools like Excel, Tableau and Power BI, as they are widely used in everyday life. A good foundation in statistics and mathematics is also crucial to understanding and interpreting data correctly.
Working on practical projects, whether through internships, volunteer work or personal projects, will give you experience and help you build a portfolio. Participating in data analysis communities and events in the field is a great way to network and learn from other professionals.
Always stay up to date by reading blogs, following influencers and taking refresher courses. And don't forget to develop your communication skills, as knowing how to interpret and communicate the results of your analysis clearly is very important.
John Hunt
Data Science and Analysis / Business Intelligence
2
Answers
Elgin, South Carolina
Updated
John’s Answer
I am going to give different advice than most of the answers I already read.
"How do i prepare for a career in data analysis?"
In my mind there are 3 areas that you need to work on:
- experience/networking : essentially proving that you can do the job, that folks can trust you when you say "I got this!"
- technical skills: do you know SQL? can you group data into charts in excel or other tools? can you write code in python to grab and manipulate data? can you use nice presentation tools like BI (Business Intelligence) software to present your findings?
- a determined inquisitive mind: I think that this is the most important part, as this will help lead you to learn the technical skills you need and get the experience you need naturally.
If someone has technical skills, and experience but lacks the determination and gumption required to get to the real bottom of a rabbit hole of data to get the answer, then they can go through the motions but can't actually answer the question most of the time.
I feel that one of the best ways to start is...to just start. In other words, go catalog your dad's tools in the garage, or your favorite movies or something to start analyzing the data. Move onto projects that you are truly interested in personally to get answers, and analyze data that you have access to get the answers. There is tons of public data available currently online from governments and other institutions.
Just get started, learn what you can or need to in order to get to your answers or conclusion for the personal projects that stoke your fire in data analysis. Or find answers to your questions elsewhere, and then see if you can find enough data to get to a similar conclusion.
Technologies come and go. Certifications and degrees are great and can give you an edge... sometimes, but not always. I'd say just start doing. An analytical mind will still be relevant no matter what technologies or other requirements come and go.
Start analyzing data, find answers to your questions - with data to support your findings. That is what I suggest you do to prepare.
"How do i prepare for a career in data analysis?"
In my mind there are 3 areas that you need to work on:
- experience/networking : essentially proving that you can do the job, that folks can trust you when you say "I got this!"
- technical skills: do you know SQL? can you group data into charts in excel or other tools? can you write code in python to grab and manipulate data? can you use nice presentation tools like BI (Business Intelligence) software to present your findings?
- a determined inquisitive mind: I think that this is the most important part, as this will help lead you to learn the technical skills you need and get the experience you need naturally.
If someone has technical skills, and experience but lacks the determination and gumption required to get to the real bottom of a rabbit hole of data to get the answer, then they can go through the motions but can't actually answer the question most of the time.
I feel that one of the best ways to start is...to just start. In other words, go catalog your dad's tools in the garage, or your favorite movies or something to start analyzing the data. Move onto projects that you are truly interested in personally to get answers, and analyze data that you have access to get the answers. There is tons of public data available currently online from governments and other institutions.
Just get started, learn what you can or need to in order to get to your answers or conclusion for the personal projects that stoke your fire in data analysis. Or find answers to your questions elsewhere, and then see if you can find enough data to get to a similar conclusion.
Technologies come and go. Certifications and degrees are great and can give you an edge... sometimes, but not always. I'd say just start doing. An analytical mind will still be relevant no matter what technologies or other requirements come and go.
Start analyzing data, find answers to your questions - with data to support your findings. That is what I suggest you do to prepare.
Camila Gonzalez Marquez
Data Scientist
2
Answers
Seattle, Washington
Updated
Camila’s Answer
Sure thing, here's an encouraging version of your text:
To excel in data analysis, these are the key areas to focus on:
- Develop a solid grasp of statistics and statistical tests. It's a fantastic skill to have.
- Master data visualization tools such as PowerBI and Tableau. They're incredibly useful.
- Dive into Regression and linear models studies. They're fundamental to your growth.
- Get comfortable with big data operations like joins, table pivots, primary keys, and relational databases. They're your tools to conquer the data world.
- Hone your skills in data querying, especially SQL. It's a game-changer.
Remember, every step you take in learning these skills is a step towards becoming an expert in data analysis. Keep going!
To excel in data analysis, these are the key areas to focus on:
- Develop a solid grasp of statistics and statistical tests. It's a fantastic skill to have.
- Master data visualization tools such as PowerBI and Tableau. They're incredibly useful.
- Dive into Regression and linear models studies. They're fundamental to your growth.
- Get comfortable with big data operations like joins, table pivots, primary keys, and relational databases. They're your tools to conquer the data world.
- Hone your skills in data querying, especially SQL. It's a game-changer.
Remember, every step you take in learning these skills is a step towards becoming an expert in data analysis. Keep going!
Remy Champion
Tech for Good
3
Answers
Kula, Hawaii
Updated
Remy’s Answer
Some touched on this, but hands on experience is important! Try to build projects in your classes that leverage some of the above mentioned tools. Try to join a club at your college - there are different clubs that can help you get hands on experience as they do small projects for enterprises. Consider looking for volunteer activities with a local nonprofit as well - many of them could use the skills you are learning and you'd be able to get hands on experience very quickly.
Teklemuz Tesfay
Web Developer
22
Answers
Addis Ababa, Ethiopia
Updated
Teklemuz’s Answer
To prepare a career in data analysis first you have to learn and have good knowledge of database management, data mining, python programming language. In addition, you have to do projects to gain hands-on experience, prepare portfolio, participate in an internships or volunteer in data analytics, prepare your resume and practice how you can pass interview questions and believe that you can achieve, make an effort to land your dream job. Then, your question will get answer.
Biplab Panda
Engagement Director
116
Answers
Atlanta, Georgia
Updated
Biplab’s Answer
Others have given a lot of really good answers, so to add - yes play around using tools such as R or STATA and build confidence and experience by experimenting with labs on your own, and this will help you be better prepared to understand how to apply concepts you'll learn in your data analysis coursework.
Sarat Kollimarla
Experience Designer
10
Answers
Waltham, Massachusetts
Updated
Sarat’s Answer
Preparing for a Career in Data Analysis:
As you're about to join college, it's great that you're thinking about your career goals. Data analysis is a fascinating field that requires a combination of technical skills, business acumen, and problem-solving abilities. Here are some steps to help you prepare for a career in data analysis:
Develop Foundational Skills:
Mathematics: Focus on developing strong math skills, including algebra, calculus, and statistics.
Programming: Learn to code in languages like Python, R, or SQL.
Data Visualization: Familiarize yourself with data visualization tools like Tableau, Power BI, or D3.js.
Take Relevant Courses:
Statistics: Take courses in statistics, including probability, inference, and regression analysis.
Data Mining: Learn about data mining techniques, including clustering, decision trees, and neural networks.
Data Warehousing: Study data warehousing concepts, including data modeling, ETL, and data governance.
Business Intelligence: Learn about business intelligence tools, including reporting, analytics, and data visualization.
Gain Practical Experience:
Internships: Apply for internships in data analysis or related fields to gain hands-on experience.
Projects: Work on personal projects or participate in data analysis competitions to develop your skills.
Collaborate: Collaborate with others on data analysis projects to learn from their experiences and gain new insights.
Stay Up-to-Date with Industry Trends:
Follow Industry Leaders: Follow industry leaders, researchers, and influencers in data analysis.
Attend Conferences: Attend conferences, meetups, and webinars to learn about the latest developments in data analysis.
Read Industry Blogs: Read industry blogs, including KDnuggets, Data Science Central, and FlowingData.
Soft Skills:
Communication: Develop strong communication skills to effectively present your findings to stakeholders.
Problem-Solving: Improve your problem-solving skills to tackle complex data analysis challenges.
Time Management: Learn to manage your time effectively to meet deadlines and deliver results.
Certifications:
Certified Data Analyst (CDA): Consider obtaining a CDA certification to demonstrate your skills and knowledge.
Certified Analytics Professional (CAP): Look into CAP certification to showcase your expertise in analytics.
Networking:
Join Professional Organizations: Join professional organizations, such as the Data Science Council of America (DASCA) or the International Institute for Analytics (IIA).
Attend Industry Events: Attend industry events, conferences, and meetups to connect with professionals in data analysis.
Connect with Professionals: Connect with professionals in data analysis on LinkedIn or other social media platforms.
As you're about to join college, it's great that you're thinking about your career goals. Data analysis is a fascinating field that requires a combination of technical skills, business acumen, and problem-solving abilities. Here are some steps to help you prepare for a career in data analysis:
Develop Foundational Skills:
Mathematics: Focus on developing strong math skills, including algebra, calculus, and statistics.
Programming: Learn to code in languages like Python, R, or SQL.
Data Visualization: Familiarize yourself with data visualization tools like Tableau, Power BI, or D3.js.
Take Relevant Courses:
Statistics: Take courses in statistics, including probability, inference, and regression analysis.
Data Mining: Learn about data mining techniques, including clustering, decision trees, and neural networks.
Data Warehousing: Study data warehousing concepts, including data modeling, ETL, and data governance.
Business Intelligence: Learn about business intelligence tools, including reporting, analytics, and data visualization.
Gain Practical Experience:
Internships: Apply for internships in data analysis or related fields to gain hands-on experience.
Projects: Work on personal projects or participate in data analysis competitions to develop your skills.
Collaborate: Collaborate with others on data analysis projects to learn from their experiences and gain new insights.
Stay Up-to-Date with Industry Trends:
Follow Industry Leaders: Follow industry leaders, researchers, and influencers in data analysis.
Attend Conferences: Attend conferences, meetups, and webinars to learn about the latest developments in data analysis.
Read Industry Blogs: Read industry blogs, including KDnuggets, Data Science Central, and FlowingData.
Soft Skills:
Communication: Develop strong communication skills to effectively present your findings to stakeholders.
Problem-Solving: Improve your problem-solving skills to tackle complex data analysis challenges.
Time Management: Learn to manage your time effectively to meet deadlines and deliver results.
Certifications:
Certified Data Analyst (CDA): Consider obtaining a CDA certification to demonstrate your skills and knowledge.
Certified Analytics Professional (CAP): Look into CAP certification to showcase your expertise in analytics.
Networking:
Join Professional Organizations: Join professional organizations, such as the Data Science Council of America (DASCA) or the International Institute for Analytics (IIA).
Attend Industry Events: Attend industry events, conferences, and meetups to connect with professionals in data analysis.
Connect with Professionals: Connect with professionals in data analysis on LinkedIn or other social media platforms.
Biju Baburajan
Product Leader, Startups, Software development & AI
12
Answers
Updated
Biju’s Answer
If you are interested to become a data scientist, start with the basics like variables, data types and basic level statistical analysis. once you are comfortable, move to complex analysis, probability, distributions, and hypothesis testing. They're the building blocks of data analysis.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.
Once you've got those down, try to explore data cleaning, learn how to handle missing values, outliers, and inconsistencies. Be familiar with Data Visualization tools.
To get in to the next level, start exploring machine learning techniques such as supervised learning ( Train models on labeled data to make predictions) and Unsupervised Learning: (Identify patterns in unlabeled data) . If you are not familiar with programming, I would recommend to learn Python which is critical in DS and AI.
I'm not recommending any specific courses or books, as there is a vast amount of resources available online on the mentioned topic. However, feel free to reach out if you need any specific recommendations.