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How do I get started with learning data analytics?
Tips for learning SQL, Excel, Python, and Tableau.
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13 answers
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Siddhi Vinod’s Answer
Hello Jenny, I would highly recommend you to initiate your journey in data analytics by exploring the basic level courses on Udemy. This platform has been a great stepping stone for me in establishing my career in the data analytics field.
As you build your theoretical understanding, you can concurrently start practicing SQL Queries, Excel, Python, and Tableau. This will significantly boost your hands-on experience.
Please feel free to reach out if you need further assistance. Thank you!
As you build your theoretical understanding, you can concurrently start practicing SQL Queries, Excel, Python, and Tableau. This will significantly boost your hands-on experience.
Please feel free to reach out if you need further assistance. Thank you!
Updated
Jonathan’s Answer
Many have pointed out that seeking practical experiences and integrating extra training courses to fill in any knowledge gaps is an excellent strategy.
A crucial aspect to highlight is that understanding the workings of coding and maneuvering through these programs is nearly as vital as the detailed, step-by-step method to coding. The coding tools and software we'll be utilizing a decade from now could be vastly different from what we're accustomed to today.
A crucial aspect to highlight is that understanding the workings of coding and maneuvering through these programs is nearly as vital as the detailed, step-by-step method to coding. The coding tools and software we'll be utilizing a decade from now could be vastly different from what we're accustomed to today.
Updated
Allen’s Answer
Understanding data and how to organize and interpret it is the first step. Data modeling courses will help you understand when to put data together, when to separate it and why. Then practice with mysql and excel. While it's great to learn some specific tools, each organization and company has different tools and processes that you'll have to learn, so don't get too caught up in trying to learn them all. Once you know how to manage and manipulate the data you can use any tool.
Also - the most important thing is to make sure your data is accurate and complete and that it comes from a trusted source. I try to go get my own data when possible rather than rely on another source. If the data is wrong or incomplete or misinterpreted it can lead to bad business decisions. Confirmation bias is a big problem - believing data that matches what you already think is true. To counter this I try to look for data that contradicts my opinion and if I can't find any then I know mine is probably correct.
There are other biases to be aware of. This is my favorite example:
In WWII the US military was losing a lot of bombers, so they decided to analyze damage on their planes. Turns out all the damage was on the wings and center fuselage, so the initial conclusion was they needed to reinforce those areas. However, their data was incomplete because the only planes they could survey were the ones that made it back to base. The ones that were shot down could not be analyzed. So the answer was actually the inverse. Damage to the wings and center fuselage were not fatal and the planes made it back to base. So they should reinforce the other areas instead.
Also - the most important thing is to make sure your data is accurate and complete and that it comes from a trusted source. I try to go get my own data when possible rather than rely on another source. If the data is wrong or incomplete or misinterpreted it can lead to bad business decisions. Confirmation bias is a big problem - believing data that matches what you already think is true. To counter this I try to look for data that contradicts my opinion and if I can't find any then I know mine is probably correct.
There are other biases to be aware of. This is my favorite example:
In WWII the US military was losing a lot of bombers, so they decided to analyze damage on their planes. Turns out all the damage was on the wings and center fuselage, so the initial conclusion was they needed to reinforce those areas. However, their data was incomplete because the only planes they could survey were the ones that made it back to base. The ones that were shot down could not be analyzed. So the answer was actually the inverse. Damage to the wings and center fuselage were not fatal and the planes made it back to base. So they should reinforce the other areas instead.
Updated
Julie’s Answer
Hi, Jenny--It's important to gain a technical background in specific coding languages and software like you mentioned, but critical to understand how data is used and managed and why and how it's collected. IBM actually has a great website for students called Skills Build (https://skillsbuild.org/students/course-catalog) that includes a module on data science that dives into the fundamentals on how data is used in different industries. I'd start there.
For technical coding and software skills, there are so many places online offering training, some free, some paid. It can be confusing! As Sarah said, free courses on LinkedIn are a good place to start. I also really like Coursera's "SQL for data science" course, and DataCamp is good, too. Both have costs, but usually a student discount.
And, while I don't have any specific courses for it, building skill in developing insights from data, or being able to tell a story with data, should be on your radar.
Good luck,
Julie
For technical coding and software skills, there are so many places online offering training, some free, some paid. It can be confusing! As Sarah said, free courses on LinkedIn are a good place to start. I also really like Coursera's "SQL for data science" course, and DataCamp is good, too. Both have costs, but usually a student discount.
And, while I don't have any specific courses for it, building skill in developing insights from data, or being able to tell a story with data, should be on your radar.
Good luck,
Julie
Ankita Pimple
Currently Student pursuing MS in Management Information System
18
Answers
Updated
Ankita’s Answer
Sure, here's a brief guide to get started with learning data analytics:
1. Basics First:
- SQL: Learn basic query writing for data retrieval.
- Excel: Get familiar with functions and basic data analysis tools.
- Python: Start with fundamental concepts like variables and operations.
- Tableau: Understand data visualization basics and how to create simple charts.
2. Online Learning Resources:
- Use platforms like Coursera, Udemy, or Khan Academy for courses and tutorials.
3. Practice Regularly:
- Practice writing queries, analyzing data in spreadsheets, coding in Python, and creating visualizations.
4. Engage with Communities:
- Join online forums to ask questions and learn from others.
5. Real-world Projects:
- Work on projects to apply your skills and build a portfolio.
6. Continuous Learning:
- Stay updated with the latest trends and techniques in data analytics.
Keep learning and practicing, and you'll progress in your data analytics journey!
1. Basics First:
- SQL: Learn basic query writing for data retrieval.
- Excel: Get familiar with functions and basic data analysis tools.
- Python: Start with fundamental concepts like variables and operations.
- Tableau: Understand data visualization basics and how to create simple charts.
2. Online Learning Resources:
- Use platforms like Coursera, Udemy, or Khan Academy for courses and tutorials.
3. Practice Regularly:
- Practice writing queries, analyzing data in spreadsheets, coding in Python, and creating visualizations.
4. Engage with Communities:
- Join online forums to ask questions and learn from others.
5. Real-world Projects:
- Work on projects to apply your skills and build a portfolio.
6. Continuous Learning:
- Stay updated with the latest trends and techniques in data analytics.
Keep learning and practicing, and you'll progress in your data analytics journey!
Updated
Sarah’s Answer
Hi - There are some great, free courses available through LinkedIn Learning. You can search and filter results by specific software (e.g., Microsoft Excel) and content level (Introductory, Intermediate, Advanced).
Updated
Julia’s Answer
Hi Jenny! It's great that you want to start learning different data analytics tools and languages; I have used each of the tools you listed throughout my college career and into my consulting career. Online learning platforms like LinkedIn learning or Udemy have tons of very comprehensive and easy to follow trainings that can suit many levels of understanding. There are also some great YouTube videos that are easy to follow along with and great for visual learners. As always, there's nothing better than learning by doing so I'd suggest just clicking around in each platform, following tutorials step by step, and practicing as much as possible! Good luck!
Updated
Suraj’s Answer
Starting with SQL exercises to perform aggregations, pivoting and normalization could be one of the steps. You could refer this Problem and Solution. https://github.com/need-data-community0021/sql-_date_time_case_study_on-flight-dataset
Updated
Godfrey’s Answer
Data analytics is a good path to future career prospects,as everything is derived from data,be it decision making, intervention and therefore need for great data analysis.
People who foresee data analytics as a career need to have basic computer skills and they also need to good statistical data analysis such as Excel,SAS, Tableau,among other analytical modules.
One can learn data analytics online free ,some are offered in YouTube tutorials which can be a basis.
However Massachusetts institute of technology is one of the best institution that offers data analytics classes that one can persue.
People who foresee data analytics as a career need to have basic computer skills and they also need to good statistical data analysis such as Excel,SAS, Tableau,among other analytical modules.
One can learn data analytics online free ,some are offered in YouTube tutorials which can be a basis.
However Massachusetts institute of technology is one of the best institution that offers data analytics classes that one can persue.
Updated
Justin’s Answer
I agree with all the other answers in terms of developing your technical capabilities.
As you do, however, keep in mind that these are tools that are ultimately used to help solve business problems and/or identify opportunities. You should make sure you understand how analytics will be used and interpreted... especially when getting into visualization. One thing you can do early on as you are learning technologies, make sure you learn design principles and take a course or two on Human Computer Interaction. This can be the difference between impactful analytics vs spewing out data and charts.
Learn design principles through the platforms that others have recommended and take a course or two on Human Computer Interaction
As you do, however, keep in mind that these are tools that are ultimately used to help solve business problems and/or identify opportunities. You should make sure you understand how analytics will be used and interpreted... especially when getting into visualization. One thing you can do early on as you are learning technologies, make sure you learn design principles and take a course or two on Human Computer Interaction. This can be the difference between impactful analytics vs spewing out data and charts.
Justin recommends the following next steps:
Updated
Jeff’s Answer
Hey there. There are many learning sources out there for data analytics. LinkedIn learning is a great source I recommend, along with Udemy. These two sources offer many courses, so try to narrow down your learning criteria. For example, within Python, you can learn coding, visualizations and the many different machine learning techniques. Also, Tableau is a great tool for data analytics and there many visuals you can use to enhance your analysis. Many courses may offer learnings on using visuals in Tableau and Excel, such as pie charts or bar charts, so be sure to understand many different visuals and how they can be effective in different situations. Also, try to pick courses that have an example use-case or sample data that helps you better learn how to visualize the data. Good luck!
Updated
Dhiraj’s Answer
There are some great points that people have added here. I will add few from my experience that really helped me grow in DS career.
As a Data Scientist you are often tasked with finding a solution to a problem by doing Descriptive, predictive or prescriptive analysis. Each aspect of DS requires different skills, however there are common themes across all.
Learning to be curious: Extremely important to stay curious when solving a problem and asking the right questions. Jumping off to the solution without frontloading your thought process can often lead to half-baked solutions, especially when you are solving new and complex problems.
Clearly defining the problems current state, future state (end outcome) and the challenges/gaps you need to address to get to the future state.
As you think about building muscles around various tools and techniques, I would highly encourage you to also start thinking about how to approach variety of problems.
As a Data Scientist you are often tasked with finding a solution to a problem by doing Descriptive, predictive or prescriptive analysis. Each aspect of DS requires different skills, however there are common themes across all.
Learning to be curious: Extremely important to stay curious when solving a problem and asking the right questions. Jumping off to the solution without frontloading your thought process can often lead to half-baked solutions, especially when you are solving new and complex problems.
Clearly defining the problems current state, future state (end outcome) and the challenges/gaps you need to address to get to the future state.
As you think about building muscles around various tools and techniques, I would highly encourage you to also start thinking about how to approach variety of problems.
Updated
Michael’s Answer
Hi Jenny,
One can start learning about data analytics and the various data tools online. There are tutorials that can be found via www.udemy.com, www.youtube.com, Google, LinkedIn Learning (www.linkedin.com), etc. to learn about Tableau, R, Python, SQL, Excel, Google Sheets, etc.
If you have interests in becoming a Data Analyst or Data Scientist, 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
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 and Data Scientists 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?
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 or as a Data Scientist!
One can start learning about data analytics and the various data tools online. There are tutorials that can be found via www.udemy.com, www.youtube.com, Google, LinkedIn Learning (www.linkedin.com), etc. to learn about Tableau, R, Python, SQL, Excel, Google Sheets, etc.
If you have interests in becoming a Data Analyst or Data Scientist, 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
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 and Data Scientists 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?
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 or as a Data Scientist!