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I need help with data analyst training!?
I'm currently self training for Data Analyst, but I feel like I don't know what to continue, or should I learn all programs needed for Data Analyst and then just apply? What should I do to speed up my training ?
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25 answers
Updated
Rachana’s Answer
Hello Jimmy,
Start by learning the core tools: Excel/Google Sheets, SQL, and a bit of Python plus data visualization with Tableau or Power BI; you can use free courses on Coursera, YouTube, to do this step‑by‑step.
Then pick a few real datasets ( government/open data), practice cleaning, analyzing, and visualizing them, and build 3–5 small projects you can show on GitHub or in a portfolio to prepare for entry‑level data analyst roles.
Start by learning the core tools: Excel/Google Sheets, SQL, and a bit of Python plus data visualization with Tableau or Power BI; you can use free courses on Coursera, YouTube, to do this step‑by‑step.
Then pick a few real datasets ( government/open data), practice cleaning, analyzing, and visualizing them, and build 3–5 small projects you can show on GitHub or in a portfolio to prepare for entry‑level data analyst roles.
Updated
Kurt’s Answer
I am in business software area and reporting and data analytics is part of every project. Like Alec said, getting experience with tools and some hands on is a good plan. Many companies are leaning toward performing business analytics using the Cloud based tools offered by AWS and GCP as examples. Getting self training in that area would help to add skills to your resume. Data analytics is done more with software tools these days, so learning those tools is valuable, however, knowing the design of using the data to provide robust reports to end users and managers is another area to learn.
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Dee’s Answer
Self-Training for Data Analyst — What to Do Next
First, the honest answer to your question
You do not need to learn every tool before applying. That is one of the most common mistakes self-taught analysts make — waiting until they feel "ready enough," when in reality, readiness comes from doing, not from completing more courses. The goal is to become employable, not omniscient.
Here is the clearer picture: learn the core stack well, build proof of work, and apply while you are still learning.
The Core Stack — What Actually Matters
Focus on these in order of priority. Do not move to the next until you are genuinely comfortable with the current one.
1. Excel / Google Sheets — If you have not already, make sure you are solid here. PivotTables, VLOOKUP/XLOOKUP, basic formulas, and data cleaning. Almost every analyst role touches this daily regardless of seniority.
2. SQL — This is the single most important skill for a data analyst. Every company stores data in databases. If you can query, filter, join, and aggregate data confidently, you are already ahead of a large portion of applicants. Practice on Mode Analytics, SQLZoo, or LeetCode's SQL problems.
3. Python or R — Python is the stronger long-term choice. Focus on pandas for data manipulation and matplotlib or seaborn for visualization. You do not need to be a software engineer — you need to be comfortable cleaning data, running analyses, and automating repetitive tasks.
4. A Visualization Tool — Power BI or Tableau. Pick one and go deep. Employers want someone who can turn raw numbers into a clear story for a non-technical audience. Build at least two or three dashboards you are proud of.
5. Statistics Fundamentals — Mean, median, distributions, correlation, hypothesis testing, and basic regression. You do not need a statistics degree, but you need to understand what the numbers are actually telling you and when they are misleading you.
What Will Speed Up Your Training
This is where most self-learners lose time — they watch tutorials passively and confuse familiarity with skill. Watching someone code is not the same as being able to code.
Work on real datasets immediately. Stop using tutorial datasets the moment you understand the basic concept. Go to Kaggle, Google Dataset Search, or data.gov and find messy, real datasets in topics you actually care about — sports, finance, health, whatever interests you. Cleaning and analyzing data you chose yourself teaches ten times faster than following along with someone else's clean CSV.
Build projects, not certificates. A certificate from Coursera tells an employer you completed a course. A GitHub repository with three solid analysis projects tells them you can think, structure a problem, and communicate findings. Employers look at portfolios. Build yours early, even when the work feels rough.
Follow the analysis cycle, not just the tools. For every project, practice the full loop: ask a question, find or collect data, clean it, analyze it, visualize it, and write up what you found in plain language. That cycle is what the job actually is. Tools are just the instruments.
Set weekly deliverables, not just study hours. Instead of saying "I will study 10 hours this week," say "I will finish a SQL analysis of this dataset and write up three findings by Sunday." Output-based goals build momentum far faster than time-based ones.
Get feedback on your work. Post your projects on GitHub, share them in data communities on Reddit (r/datascience, r/learnpython), or connect with analysts on LinkedIn and ask for a quick review. Feedback accelerates improvement in ways that solo studying simply cannot.
When to Start Applying
Start applying once you have all three of the following — not before, but also not a moment after:
You are comfortable writing SQL queries involving joins, aggregations, and subqueries. You have at least two to three projects in a portfolio that go through the full analysis cycle. You can explain your work clearly in plain language to someone non-technical.
You do not need Python mastery. You do not need to know every feature of Tableau. You do not need a degree. Entry-level analyst roles expect someone trainable with strong fundamentals and demonstrated curiosity — your portfolio is the proof of both.
Apply to roles slightly above your current confidence level. The interview process itself will show you exactly what gaps to close, faster than any course will.
A Practical Weekly Structure
If you are training full-time or close to it, here is a structure that produces results:
Monday and Tuesday — focused skill practice on whichever tool you are currently developing. Work through a real problem, not a tutorial.
Wednesday — project work. Continue or start a personal analysis project using a dataset you chose.
Thursday — SQL practice. Do five to ten problems on LeetCode or SQLZoo regardless of where you are in your other learning.
Friday — review and document. Write up what you built or analyzed this week as if you were presenting it to a manager. Push it to GitHub.
Weekend — read. Follow data analysts on LinkedIn, read case studies, look at how companies use data in industries that interest you. Context makes the technical skills meaningful.
The Mindset Shift That Changes Everything
Stop thinking of yourself as a student and start thinking of yourself as a junior analyst who is building evidence of their work. Students wait until they are ready. Analysts ship work, get feedback, and improve. The tools are learnable on the job — the habit of solving problems with data is what you are really building right now.
You are closer than you think. The fact that you are asking how to move faster means you already have the drive. Direct that drive at real problems and real output, and you will be employable sooner than most people who started before you.
First, the honest answer to your question
You do not need to learn every tool before applying. That is one of the most common mistakes self-taught analysts make — waiting until they feel "ready enough," when in reality, readiness comes from doing, not from completing more courses. The goal is to become employable, not omniscient.
Here is the clearer picture: learn the core stack well, build proof of work, and apply while you are still learning.
The Core Stack — What Actually Matters
Focus on these in order of priority. Do not move to the next until you are genuinely comfortable with the current one.
1. Excel / Google Sheets — If you have not already, make sure you are solid here. PivotTables, VLOOKUP/XLOOKUP, basic formulas, and data cleaning. Almost every analyst role touches this daily regardless of seniority.
2. SQL — This is the single most important skill for a data analyst. Every company stores data in databases. If you can query, filter, join, and aggregate data confidently, you are already ahead of a large portion of applicants. Practice on Mode Analytics, SQLZoo, or LeetCode's SQL problems.
3. Python or R — Python is the stronger long-term choice. Focus on pandas for data manipulation and matplotlib or seaborn for visualization. You do not need to be a software engineer — you need to be comfortable cleaning data, running analyses, and automating repetitive tasks.
4. A Visualization Tool — Power BI or Tableau. Pick one and go deep. Employers want someone who can turn raw numbers into a clear story for a non-technical audience. Build at least two or three dashboards you are proud of.
5. Statistics Fundamentals — Mean, median, distributions, correlation, hypothesis testing, and basic regression. You do not need a statistics degree, but you need to understand what the numbers are actually telling you and when they are misleading you.
What Will Speed Up Your Training
This is where most self-learners lose time — they watch tutorials passively and confuse familiarity with skill. Watching someone code is not the same as being able to code.
Work on real datasets immediately. Stop using tutorial datasets the moment you understand the basic concept. Go to Kaggle, Google Dataset Search, or data.gov and find messy, real datasets in topics you actually care about — sports, finance, health, whatever interests you. Cleaning and analyzing data you chose yourself teaches ten times faster than following along with someone else's clean CSV.
Build projects, not certificates. A certificate from Coursera tells an employer you completed a course. A GitHub repository with three solid analysis projects tells them you can think, structure a problem, and communicate findings. Employers look at portfolios. Build yours early, even when the work feels rough.
Follow the analysis cycle, not just the tools. For every project, practice the full loop: ask a question, find or collect data, clean it, analyze it, visualize it, and write up what you found in plain language. That cycle is what the job actually is. Tools are just the instruments.
Set weekly deliverables, not just study hours. Instead of saying "I will study 10 hours this week," say "I will finish a SQL analysis of this dataset and write up three findings by Sunday." Output-based goals build momentum far faster than time-based ones.
Get feedback on your work. Post your projects on GitHub, share them in data communities on Reddit (r/datascience, r/learnpython), or connect with analysts on LinkedIn and ask for a quick review. Feedback accelerates improvement in ways that solo studying simply cannot.
When to Start Applying
Start applying once you have all three of the following — not before, but also not a moment after:
You are comfortable writing SQL queries involving joins, aggregations, and subqueries. You have at least two to three projects in a portfolio that go through the full analysis cycle. You can explain your work clearly in plain language to someone non-technical.
You do not need Python mastery. You do not need to know every feature of Tableau. You do not need a degree. Entry-level analyst roles expect someone trainable with strong fundamentals and demonstrated curiosity — your portfolio is the proof of both.
Apply to roles slightly above your current confidence level. The interview process itself will show you exactly what gaps to close, faster than any course will.
A Practical Weekly Structure
If you are training full-time or close to it, here is a structure that produces results:
Monday and Tuesday — focused skill practice on whichever tool you are currently developing. Work through a real problem, not a tutorial.
Wednesday — project work. Continue or start a personal analysis project using a dataset you chose.
Thursday — SQL practice. Do five to ten problems on LeetCode or SQLZoo regardless of where you are in your other learning.
Friday — review and document. Write up what you built or analyzed this week as if you were presenting it to a manager. Push it to GitHub.
Weekend — read. Follow data analysts on LinkedIn, read case studies, look at how companies use data in industries that interest you. Context makes the technical skills meaningful.
The Mindset Shift That Changes Everything
Stop thinking of yourself as a student and start thinking of yourself as a junior analyst who is building evidence of their work. Students wait until they are ready. Analysts ship work, get feedback, and improve. The tools are learnable on the job — the habit of solving problems with data is what you are really building right now.
You are closer than you think. The fact that you are asking how to move faster means you already have the drive. Direct that drive at real problems and real output, and you will be employable sooner than most people who started before you.
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Nithya’s Answer
Feeing stuck during self‑training is normal, and it usually means you’re ready to shift from learning everything to learning the right things in the right order.
The fastest way to speed up your data‑analyst training is to focus on a small set of core skills, build real projects with them, and start applying once you have a portfolio that proves you can do the work.
Instead of trying to learn every tool, focus on the essentials: Excel for cleaning and exploring data, SQL for querying databases, Python for deeper analysis, and Power BI or Tableau for dashboards.
Once you understand the basics, shift quickly into projects and pick datasets, ask real questions, analyze them, and build visualizations.
This is what employers care about most. You don’t need to master every tool before applying, you need evidence that you can think like an analyst, clean messy data, find insights, and communicate them clearly.
The fastest way to speed up your data‑analyst training is to focus on a small set of core skills, build real projects with them, and start applying once you have a portfolio that proves you can do the work.
Instead of trying to learn every tool, focus on the essentials: Excel for cleaning and exploring data, SQL for querying databases, Python for deeper analysis, and Power BI or Tableau for dashboards.
Once you understand the basics, shift quickly into projects and pick datasets, ask real questions, analyze them, and build visualizations.
This is what employers care about most. You don’t need to master every tool before applying, you need evidence that you can think like an analyst, clean messy data, find insights, and communicate them clearly.
Updated
Ryan’s Answer
Hey Jimmy!
There's definitely nothing wrong with learning at your own pace and applying for a Data Analyst role when you're ready!
That being said, it's a good idea to start with the basics: Microsoft Excel, Google Sheets - these will be your everyday programs that store your data in a table in a row & column format. Almost everybody you meet will use this as a starting foundation for storing data before working with any sort of database.
From there, you can start getting into more intermediate skills such as: SQL, Tableau, and Power BI - these are powerful query and data visualization tools that will help you transform your data into dashboards that can be viewed by the average user. Databases such as Oracle, SQL Server, Apache Hive, and others can also be great to familiarize yourself with. Like many others here have stated, these are popular tools in the industry that will help you go far if you take the time to develop the skillset to use them.
There's plenty of learning material out there on YouTube, LinkedIn Learning, Udemy, and more if you wish to learn these skills at your own pace!
Hope this helps and best of luck! 😊
There's definitely nothing wrong with learning at your own pace and applying for a Data Analyst role when you're ready!
That being said, it's a good idea to start with the basics: Microsoft Excel, Google Sheets - these will be your everyday programs that store your data in a table in a row & column format. Almost everybody you meet will use this as a starting foundation for storing data before working with any sort of database.
From there, you can start getting into more intermediate skills such as: SQL, Tableau, and Power BI - these are powerful query and data visualization tools that will help you transform your data into dashboards that can be viewed by the average user. Databases such as Oracle, SQL Server, Apache Hive, and others can also be great to familiarize yourself with. Like many others here have stated, these are popular tools in the industry that will help you go far if you take the time to develop the skillset to use them.
There's plenty of learning material out there on YouTube, LinkedIn Learning, Udemy, and more if you wish to learn these skills at your own pace!
Hope this helps and best of luck! 😊
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Joshua’s Answer
Don’t try to learn every single thing before applying, that’ll just slow you down. Focus on the main stuff first: Excel, SQL, one dashboard tool like Power BI or Tableau, and a little basic stats. The fastest way to improve is to learn a skill, use it in a small project, and keep going. Once you can clean data, make charts, and explain what the data means, you can start applying for jobs even if you’re still learning more. Basically, don’t wait until you feel perfect, just get the basics down, build a few projects, and apply.
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Kimberly’s Answer
You do not need to learn every program a data analyst might use before applying. Focus on becoming job-ready in the core stack: Excel, SQL, one visualization tool such as Tableau or Power BI, and Python at a practical beginner-to-intermediate level for data cleaning and analysis. The faster path is to learn these in parallel with projects, not in isolation, because employers care more about whether you can solve business problems with data than whether you have touched every tool once. I’d recommend a simple sequence: first build confidence in Excel and SQL, then learn dashboarding, then add Python for data wrangling and basic analysis. At the same time, create 3 to 5 portfolio projects using real or public datasets, and make each one show a clear business question, your analysis, and your recommendation. Once you can query data, clean it, visualize it, and explain what it means, you should start applying even if you still feel imperfect, because applications, interviews, and project work will teach you faster than endless studying. To speed up your training, use a structured weekly plan, practice every day, limit your tool list, and focus on repetition, case-style projects, and explaining your findings clearly, since communication is a major part of the role. A strong practical portfolio plus targeted applications will usually move you forward faster than waiting until you feel like you know everything.
Updated
Durganand’s Answer
Feeling overwhelmed by the many tools available is normal, but don't try to learn them all before applying for jobs. Focus on mastering a core set of tools and solving business problems with data.
1. Start Building Projects
- Shift from just learning to actively working on projects.
- Choose a core stack: Excel, SQL, and a visualization tool like Tableau or Power BI.
- Once you know the basics of SQL and Excel, start building a portfolio instead of taking more courses. You'll learn faster by solving real problems.
2. Follow a Step-by-Step Plan
- Master SQL: Focus on querying, filtering, joining tables, and window functions.
- Learn a BI Tool: Choose Tableau or Power BI, connect a data source, clean the data, and create an interactive dashboard.
- Build 2-3 Projects: Use datasets from Kaggle or Google Cloud, clean and analyze the data, and create a dashboard. Write about the business insights you found.
3. When and How to Start Applying
- Apply once you have two solid projects on GitHub or a personal website.
- Don’t wait to feel "100% ready"—apply while continuing to learn.
- Highlight the business outcomes of your projects on your resume.
To speed things up, you can:
- Design your first project around an industry you like.
- Create a weekly study schedule to stay focused.
- Review core SQL concepts for interviews.
1. Start Building Projects
- Shift from just learning to actively working on projects.
- Choose a core stack: Excel, SQL, and a visualization tool like Tableau or Power BI.
- Once you know the basics of SQL and Excel, start building a portfolio instead of taking more courses. You'll learn faster by solving real problems.
2. Follow a Step-by-Step Plan
- Master SQL: Focus on querying, filtering, joining tables, and window functions.
- Learn a BI Tool: Choose Tableau or Power BI, connect a data source, clean the data, and create an interactive dashboard.
- Build 2-3 Projects: Use datasets from Kaggle or Google Cloud, clean and analyze the data, and create a dashboard. Write about the business insights you found.
3. When and How to Start Applying
- Apply once you have two solid projects on GitHub or a personal website.
- Don’t wait to feel "100% ready"—apply while continuing to learn.
- Highlight the business outcomes of your projects on your resume.
To speed things up, you can:
- Design your first project around an industry you like.
- Create a weekly study schedule to stay focused.
- Review core SQL concepts for interviews.
Updated
Carissa’s Answer
All of the technical suggestions here are great. I'd add one more thing, start using AI as a mentor and thought partner.
A lot of people get stuck wondering what they should learn next. Tell AI what skills you already have and ask it what other skills would make you a stronger Data Analyst. Tell it about industries, business problems, or topics that interest you and ask how data analytics is used in those areas. Ask it to create a real-world problem for you to solve using your current skills and then challenge you to learn a few new ones along the way.
This approach helps you in several ways. First, you learn how to work effectively with AI, which is quickly becoming an important skill in almost every job. Second, you get better at asking questions and finding information, which is one of the most important parts of being a data analyst. Third, you discover new tools and techniques that may not be covered in your courses. Most importantly, you build real projects that you can talk about in interviews.
For example, have you ever wondered why certain products are placed on particular shelves in a grocery store? Why some products are displayed next to each other? Or why one brand gets much more shelf space than another? Those are all data and analytics questions. Companies use data to decide how products are arranged because those decisions can influence what people buy.
Pick a topic you genuinely find interesting and use AI to help you explore the data behind it. You'll learn faster because you're solving a problem you care about instead of just working through another tutorial.
Also, don't feel like you need to learn every tool before applying for jobs. You never will. Focus on the fundamentals, build a few projects that demonstrate your skills, and start applying. The combination of learning, building, and getting real-world experience will help you grow much faster than trying to master everything before taking the next step.
A lot of people get stuck wondering what they should learn next. Tell AI what skills you already have and ask it what other skills would make you a stronger Data Analyst. Tell it about industries, business problems, or topics that interest you and ask how data analytics is used in those areas. Ask it to create a real-world problem for you to solve using your current skills and then challenge you to learn a few new ones along the way.
This approach helps you in several ways. First, you learn how to work effectively with AI, which is quickly becoming an important skill in almost every job. Second, you get better at asking questions and finding information, which is one of the most important parts of being a data analyst. Third, you discover new tools and techniques that may not be covered in your courses. Most importantly, you build real projects that you can talk about in interviews.
For example, have you ever wondered why certain products are placed on particular shelves in a grocery store? Why some products are displayed next to each other? Or why one brand gets much more shelf space than another? Those are all data and analytics questions. Companies use data to decide how products are arranged because those decisions can influence what people buy.
Pick a topic you genuinely find interesting and use AI to help you explore the data behind it. You'll learn faster because you're solving a problem you care about instead of just working through another tutorial.
Also, don't feel like you need to learn every tool before applying for jobs. You never will. Focus on the fundamentals, build a few projects that demonstrate your skills, and start applying. The combination of learning, building, and getting real-world experience will help you grow much faster than trying to master everything before taking the next step.
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Chase’s Answer
Hi Jimmy - It is incredibly common to feel stuck or overwhelmed by the massive number of tools in data analytics, but you absolutely should not try to learn every single program before you start applying for jobs. The most effective way to speed up your training is to shift your focus from memorizing tools to building a portfolio of practical, real-world projects. You only need to master a core foundation consisting of Excel for quick data manipulation, SQL for querying databases, and a visualization tool like Tableau or Power BI to present your findings. Instead of taking endless courses, pick a dataset about a topic you love, use these core tools to clean the data and find trends, and host your work on a platform like GitHub to prove your skills to hiring managers. Applying for entry-level roles or internships while you are still learning is actually the best strategy because hands-on workplace experience will teach you faster than any individual tutorial ever could.
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Dana’s Answer
Hi Jimmy!
As with any career goal in Information Technology, building a foundation is key. Seeking a 2 year degree or 4 year degree can build that foundation if that is in your reach financially. Additionally, acquiring key industry certifications & course work that can adapt to multiple areas. If you find a specific area you want to focus on, then you can pursue education aspects related to the specific area.
Programming courses, as mentioned by others, SQL is still widely utilized in many businesses, MS Office Suite, Power BI, Tableau & similar reporting apps which are mainstream now. I'm sure you have discovered there are many applications & platforms to learn!
Create a short term plan (2-5 yrs) & then a long term plan (5-10 yrs) which will be your roadmap to building your educational foundation, and your career journey. The industry is always changing so it is important to include continuing education coursework to keep your skillset on the cutting edge!
Best of Luck!
As with any career goal in Information Technology, building a foundation is key. Seeking a 2 year degree or 4 year degree can build that foundation if that is in your reach financially. Additionally, acquiring key industry certifications & course work that can adapt to multiple areas. If you find a specific area you want to focus on, then you can pursue education aspects related to the specific area.
Programming courses, as mentioned by others, SQL is still widely utilized in many businesses, MS Office Suite, Power BI, Tableau & similar reporting apps which are mainstream now. I'm sure you have discovered there are many applications & platforms to learn!
Create a short term plan (2-5 yrs) & then a long term plan (5-10 yrs) which will be your roadmap to building your educational foundation, and your career journey. The industry is always changing so it is important to include continuing education coursework to keep your skillset on the cutting edge!
Best of Luck!
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Olusegun’s Answer
Begin with the basics: Excel, SQL, and Python. Once you've got those down, start applying for entry-level jobs. Don't worry about feeling completely ready—just go for it. Best of luck!
Updated
Anuj’s Answer
To speed up your training, don't try to learn everything before applying. Focus on the core stack: Excel, SQL, and Power BI or Tableau.
Once you know the basics, follow this fast-track strategy:
Build Projects Instantly: Don't just watch tutorials. Find raw data on Kaggle, clean it using SQL, and build a dashboard.
Create a Portfolio: Host 2–3 strong projects on GitHub or Maven Analytics.
Apply Early: Start applying once your portfolio is ready. Real interviews will show you exactly what skills you are missing.
Once you know the basics, follow this fast-track strategy:
Build Projects Instantly: Don't just watch tutorials. Find raw data on Kaggle, clean it using SQL, and build a dashboard.
Create a Portfolio: Host 2–3 strong projects on GitHub or Maven Analytics.
Apply Early: Start applying once your portfolio is ready. Real interviews will show you exactly what skills you are missing.
Updated
Nirmal’s Answer
A key skill in data analysis/data science is figuring out the right questions to ask of the data. Some of this comes with practice/experience, but as you explore this area, it's good to think of the "so what" aspect.. i.e. if you did a particular analysis, what would the impact be, who would care and/or find this information useful? That lens will help you figure out what are the right questions to go after and ultimately lead to impact!
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Lavanya’s Answer
Hi Jimmy,
I am self trained ETL Developer started ny career as a Data Engineer.
SQL is the foundation for any Data role.
Learn any ETL tool(SSIS/Talend) and BI tool (Tableau/ Power BI)
OR
Cloud Platforms like Snowflake , IICS are valuable.
As mentioned SQL is the foundation for any of these tools.
Unix/Python scripting are preferred but not mandatory to shine as a data Engineer.
All the best!
I am self trained ETL Developer started ny career as a Data Engineer.
SQL is the foundation for any Data role.
Learn any ETL tool(SSIS/Talend) and BI tool (Tableau/ Power BI)
OR
Cloud Platforms like Snowflake , IICS are valuable.
As mentioned SQL is the foundation for any of these tools.
Unix/Python scripting are preferred but not mandatory to shine as a data Engineer.
All the best!
Updated
Yamuna’s Answer
Start by concentrating on the essential skills for a Data Analyst, like Excel, SQL, and a visualization tool such as Power BI or Tableau. As you learn, try using these skills in small projects or case studies. This hands-on practice will boost your abilities and your confidence. Once you have a good grasp of the basics, you can start applying for jobs while still improving your skills. Keep going, and you'll see great results!
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Alana’s Answer
As a data analyst, it's important to learn Python and SQL. Adding a visualization tool like Tableau or Looker can be helpful too. Basic Excel skills are also necessary. I suggest starting with a GitHub portfolio to display your self-taught skills. This will help show what you can do. Good luck!
Updated
Lewis’s Answer
Hi Jimmy,
Start by concentrating on the basics like fundamental statistics, Excel, and SQL. If there are other programs you're not familiar with, learn those too. Don't try to learn everything at once, as it can be tiring and may cause burnout.
Start by concentrating on the basics like fundamental statistics, Excel, and SQL. If there are other programs you're not familiar with, learn those too. Don't try to learn everything at once, as it can be tiring and may cause burnout.
Updated
Muhammad’s Answer
Data analyst jobs might be taken over by AI in the future. To stay relevant, learn about machine learning and AI to see how they can automate data analysis. Focus on Agentic AI, which is especially useful. There are many free resources available, but if possible, invest in formal training in Agentic AI. This will make you more attractive to employers when you're job hunting. After your training, work on a project to gain practical experience. You don't need to spend four years studying to achieve this.
Updated
Alec’s Answer
Don't learn the skills in isolation; work from a real project to see how they fit together. Pick a core stack: SQL for transformation, Power BI for visualization. Build 4-5 projects on messy public datasets and host them on GitHub or a personal site.
For practice and resources: Kaggle for datasets, DataLemur for interview questions, Mode for SQL tutorials.
Once the fundamentals are solid, learn to integrate AI like Claude into the workflow because the market is heading towards agentic data analytics.
Hope this helps!
For practice and resources: Kaggle for datasets, DataLemur for interview questions, Mode for SQL tutorials.
Once the fundamentals are solid, learn to integrate AI like Claude into the workflow because the market is heading towards agentic data analytics.
Hope this helps!
Updated
Ankit’s Answer
Hi Jimmy,
I'm a data analyst with over 13 years of experience, working with some of the biggest companies. When I was in college, I often felt overwhelmed by all the tools and technologies out there and wasn't sure which ones to learn.
To make things easier, I recommend starting with a simple tool. Power BI is a great choice because it's fun to use for visualizations and is widely recognized in the industry, making those skills very valuable. You can find an open dataset online and start visualizing. I began by analyzing video game sales and rock bands because I enjoy those topics. Once you find something that interests you, you can explore more complex areas like SQL and Python.
Eventually, you will start building your skills and get great career opportunities. Companies in NY area are not looking for people who are experts in everything but people who show willingness to learn.
I'm a data analyst with over 13 years of experience, working with some of the biggest companies. When I was in college, I often felt overwhelmed by all the tools and technologies out there and wasn't sure which ones to learn.
To make things easier, I recommend starting with a simple tool. Power BI is a great choice because it's fun to use for visualizations and is widely recognized in the industry, making those skills very valuable. You can find an open dataset online and start visualizing. I began by analyzing video game sales and rock bands because I enjoy those topics. Once you find something that interests you, you can explore more complex areas like SQL and Python.
Eventually, you will start building your skills and get great career opportunities. Companies in NY area are not looking for people who are experts in everything but people who show willingness to learn.
Updated
Sharadha’s Answer
Do not wait to learn every tool before applying. For entry-level data analyst roles, employers usually care more about whether you can use a practical core stack well and show it in projects than whether you know every program on a job description.
To speed up training, stop learning passively and switch to a project-based cycle:
Learn one skill for a few days.
Use it immediately in a mini-project.
Write 3 to 5 business insights from the output.
Add it to a portfolio.
To speed up training, stop learning passively and switch to a project-based cycle:
Learn one skill for a few days.
Use it immediately in a mini-project.
Write 3 to 5 business insights from the output.
Add it to a portfolio.
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Gunavardhan’s Answer
Begin with the basics: Excel, SQL, and a visualization tool like Tableau or Power BI. These three skills meet most entry-level job requirements. Spend 30 minutes daily practicing SQL on free sites like SQLBolt or HackerRank. As you learn, create 2-3 small projects using real data from Kaggle to build a portfolio for employers. After 2-3 months of focused learning, start applying for jobs even if you don't feel completely prepared. Interviews will help you identify what you need to improve. Remember, working on projects and applying early is more effective than watching endless tutorials. Learn, build, and apply—this is the quickest way to become a Data Analyst.
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Surya’s Answer
Udemy is a fantastic place to start learning about being a data analyst and how to grow in this field. It has lots of courses that show the important parts of the job. I started as a data analyst at an insurance company, and it really helped me develop useful technical skills for my career. Now, many courses also use AI to help you learn faster and understand better. I suggest trying Udemy’s free courses to build a strong base and get on the right track. I hope this helps you!
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Chris’s Answer
Don't wait until you're "ready" to apply. There's no finish line. Once you have SQL basics and 1-2 projects, start applying. Interviews will tell you what to study next.
Add Python to your stack. It's becoming essential for data analysts/scientists, especially for automation, advanced analysis, and working with AI tools. Start with pandas and matplotlib once SQL feels comfortable.
Pick a direction. Business, finance, product, marketing analytics. They use different tools and datasets. Picking a lane helps you stand out against generalists.
Learn to communicate findings, not just find them. The technical skills get you interviews. What gets you hired is telling a clear story with data to people who don't know SQL. Write up your projects like you're presenting to a manager.
Post your projects on LinkedIn as you go. It creates visibility with recruiters long before you're actively job hunting.
The goal isn't to be fully trained, it's to be employable enough to keep learning on the job.
Add Python to your stack. It's becoming essential for data analysts/scientists, especially for automation, advanced analysis, and working with AI tools. Start with pandas and matplotlib once SQL feels comfortable.
Pick a direction. Business, finance, product, marketing analytics. They use different tools and datasets. Picking a lane helps you stand out against generalists.
Learn to communicate findings, not just find them. The technical skills get you interviews. What gets you hired is telling a clear story with data to people who don't know SQL. Write up your projects like you're presenting to a manager.
Post your projects on LinkedIn as you go. It creates visibility with recruiters long before you're actively job hunting.
The goal isn't to be fully trained, it's to be employable enough to keep learning on the job.