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  • Writer's pictureKelly Adams

Frequently Asked Questions (FAQs)

Updated: Mar 20

Below are questions I'm asked frequently. I will be updating this post from time-to-time when new questions are asked. Some questions may have blog/LinkedIn posts linked that go into more detail. The questions are broken up into 4 different sections:

  1. General - any questions that didn't fit into the categories below

  2. Resources/Tools - any questions about resources I've used to learn data analytics (e.g. courses) or tools/skills I've learned (e.g. SQL, Tableau)

  3. Portfolio - any questions related to building a portfolio, how I've built mine, or questions about my projects

  4. Job Search - any questions about the job searching including interviews, networking, etc.

Within these sections the questions are in no particular order.


Note: There's a lot of overlap between this FAQs and my Data Analytics Resources article. The main difference is the resources article lists any resources I've found useful for my data analytics journey, it focuses on links. While this one includes resources but it gives more detailed answers to questions I've been asked frequently. These may include questions about what resources I've used to learn data analytics skills or interview advice.


Questions:

  1. General

    1. How did you become a data analyst?

    2. How can I become a data analyst?

    3. How long will it take to become a data analyst?

    4. How do you get experience in data analytics without a full time job?

  2. Resources/Tools

    1. What courses do you recommend?

    2. What did you think of the Google Course?

    3. What skills should a data analyst learn?

    4. What sites do you use to learn data analytics skills?

    5. Where/how do you practice your skills?

  3. Portfolio

    1. Where do you host your portfolio?

    2. Where do you recommend to host your portfolio?

    3. Do you have any recommendations for projects?

    4. How many projects should I do?

    5. Where can you find public/free datasets?

    6. How did you do [project]?

  4. Job Search

    1. Can you review/give feedback my resume?

    2. Did you use a template for your resume/can you send me your template?

    3. How did you get started on LinkedIn?

    4. What do you do during your job search?

    5. Do you have any advice for me?

    6. Do you have a job for me?

    7. Is your company hiring?

    8. Can you refer me?

 

General

How did you become a data analyst?

I did a lot. I wanted to do everything I could to get a job. I don't think all of it is necessary but here's what I did in my journey to become a data analyst:

  • Learned Excel, SQL and Tableau

  • Networked with other analysts

  • Practiced interviewing and learned how to demonstrate my analytical abilities

  • Became active on LinkedIn by posting and commenting

  • Built a portfolio showcasing my data skills

  • Gained real life experience by volunteering and finding part time work

  • Write blog posts about data analytics

But if I had to pick 3 things I found the most helpful it would be:

  • Practice interviewing

  • Build a portfolio

  • Get real life experience

There's a lot of ways to become a data analyst. This is my personal journey and everyone's is different. Not all of this is necessary but the top 3 I mentioned above definitely help.


If you want to read more about how I became a data analyst check out my article: How I Became a Data Analyst.


How can I become a data analyst?

There are a lot of different ways someone can become a data analyst. But here's a general path I would recommend.

  1. Spend time learning the following skills:

    1. Excel (or Google Sheets)

    2. SQL

    3. Data Visualization Tool (like Tableau, PowerBI or Google Looker)

  2. Create projects showcasing each of these skills and host it in a portfolio (see portfolio section for more details).

  3. Network with other data professionals both others switching careers and current data analysts. You can do this in person at networking events or through LinkedIn.

  4. Apply for data analyst (or similar) roles. If you are able to get a referral from someone already at the company.

  5. Once you have interviews practice your interview skills and technical skills (see the interview section for more details).

I don't have a specific timeline for how long it will take you. It depends on your prior knowledge and your personal circumstances.


If you want a more detailed guide check out my article: How to Become a Data Analyst (Ultimate Guide).


How long will it take to become a data analyst?

It may take you less time to become an analyst or more. It depends on a lot of factors. In my network I had someone who became a data analyst in 3 months (from learning the skills to applying) while another person it took 18 months. There's no specific timeline to become an analyst.


How do you get experience in data analytics without a full time job?

There are a few ways. But the main ones I recommend are:

  1. Volunteer your time for a non-profit or business

  2. Make your current role more data drive (more on how to do that in my blog post: How to Make Your Current Job More Data Driven)

  3. Build a portfolio and create projects that demonstrate your data analytics skills. Check out the portfolio section for more details.

If you need more ideas check out my article: How to Get Experience in Data Analytics without a Full-Time Job.

 

Resources/Tools

What courses do you recommend?

I don't have any courses I see as "must haves" but below are a few I've taken and enjoyed:

  • Google Data Analytics Course - overview of what data analysis is and what a data analyst does along with introducing important skills needed for a data analyst like Excel, SQL, Tableau and R. It's good as an introduction but it won't get you job ready alone.

  • Data with Danny Serious SQL Course - focuses on case studies to provide real world practice, it takes a while to set up the IDE but worth it for the case studies.

  • The Complete Python Bootcamp From Zero to Hero in Python - walks you through Python you learn the basics to more complicated ideas like object oriented programming. It also has assessments and mini-projects to complete to test your knowledge which I found the most useful.

  • Python for Data Science and Machine Learning Bootcamp - learn how to use common Python libraries used in data analytics/science like NumPy and Pandas. I'm still going through this program but have learned from this instructor before and would recommend him.

  • The Complete SQL Bootcamp: Go from Zero to Hero - learn PostgreSQL to perform data analysis. The setup takes a bit but once you do it you can analyze a dataset using SQL. It includes real world problems and a database.

  • Alteryx Bootcamp - learn the basics of Alteryx. The setup doesn't take too long and it's easy to go through. It has a lot of hands on examples.


I went into more detail on course platforms I've used in my article: Data Analytics Resources.


What did you think of the Google Course?

I did enjoy the course, it presented the information in easy-to-understand ways. Someone without a mathematical or technical background should be able to get through this course if they put the time in it. It gave a basic overview of what a data analyst is but it went through the common tools used by data analysts such as: SQL, Tableau and Spreadsheets. This program will not get you job ready. While it claims to do that, many people, myself included feel like that's not the case. You will probably need to supplement your learning on each of the skills (SQL, Tableau, and R) after this course. There's a lot more to getting a data analyst job than this program, especially if you're switching careers. A few things include: creating a portfolio; revising your resume; and networking.


If you are interested in my detailed review check out my article: Google Data Analytics Course Review.


What skills should a data analyst learn?

Below are the the main skills data analysts are asked to know for jobs. I'd recommend picking 2-3 skills and learning those first. After getting a basic/intermediate understanding then expand your knowledge. Before applying for a job you should be proficient (have a good working knowledge) in at least 2 of these tools. For example knowing SQL and Excel. Because every data analyst job is different there's no "right" set of tools to know. But the most popular two skills are: Excel and SQL.


Main Skills (in no particular order):

  • Microsoft Excel

  • SQL (Structured Query Language)

  • R

  • Python

  • Tableau

  • Power BI

Some companies may ask for specific knowledge in tools like: Azure, Google Web Analytics, AWS. The above tools are the most popular and frequently recommended to learn. These are by no means all tools a data analyst can learn and it should be seen as a starting point/beginners guide.


For a more in-depth article about what to learn for each skill check out my article: Learning Path for Data Analysts.


What sites do you use to learn data analytics skills?

I've used the following course platforms to learn data analytics skills like: SQL and Python.

  • Coursera - has many courses on a variety of topics, it has popular courses for data analytics like the Google Course or the IBM course. Along with specific courses for skills. There's a free option and a subscription based option which is $59/month.

  • Maven Analytics - they have everything from specific courses to learn skills to a Bootcamp and learning paths for different types of careers. Check out the "Learn" tab on their site for specifics. It is a subscription based service for $39/month.

  • freeCodeCamp - basic introduction to various skills and it's completely free

  • Udemy - has all types of courses that if you get them on sale they cost usually ($15-$25). I used this for learning specific skills like SQL or Python.

  • Datacamp - an all encompassing platform to learn common skills in data analytics. They have specific courses to "learning tracks" for jobs like Data Analyst or Data Scientist. The platform has a free version (first chapter of every course and select courses) and a paid version which is $25/month.

  • Codeacademy - this is tailored towards people interested in coding. It has a variety of coding languages but it does include courses on Python and SQL. There's a free version and a paid version which is subscription based. The pro version is $25/month.

Where/how do you practice your skills?

These sites are where I practice my skills. Kind of like practice problems you would do during math classes. Most of these are free but may have a paid version. Some focus on interview questions that may be asked during technical interviews. I use these sites for quick practice (I spend about 15-30 minutes a day on one of these site).

I practice my skills by completing projects. For each skill (e.g. SQL, Tableau, etc.) I have a portfolio project. If I already have a comprehensive portfolio project I worked on a mini-project to complete within a time frame. Like creating a dashboard in 5-6 hours.

 

Portfolio

Where do you host your portfolio?

I use Wix to host my website which houses my portfolio. My portfolio has links to all of my projects. I use it as a "landing page" for all of my projects. Since these projects use different programs to host (e.g. Github, Tableau). For each project I included the title, the tools I used and a brief description. I also link to my Github and Tableau Public page in case an employer is interested in a specific tool (coding ability or data visualization).


If you want to learn how I built my website check out my article: How I Created My Website.

Where do you recommend to host your portfolio?

Below are 5 free sites (or whose platforms have a free option) where you can host your data analytics portfolio:

  1. Carrd.co - You can create a simple landing page to host your portfolio. Great if you have projects that are on different sites like Github or Tableau Public.

  2. Github - Good for hosting coding files like SQL or Python. You can also create your own landing page using Github pages.

  3. Maven Analytics - An all-in-one platform to showcase your data dashboards/visualizations.

  4. Tableau Public - Host your Tableau dashboards on this site and look at other visualizations for inspiration.

  5. novyPro - A site to host your PowerBI dashboards/visualizations.

My biggest suggestion for hosting a portfolio is to pick one that works for you. For example, if you're more focused on data visualizations then use Tableau Public or novyPro. The most important part is to get started and showcasing your portfolio.


Do you have any recommendations for projects?

There are a few types of projects I'd recommend (from easy to more difficult) along with example projects I've done.

  • [Easy] Course Project - a guided portfolio project in a course (e.g. my Tick Tac Toe game using Python from a course I took on Udemy)

  • [Moderate] Challenge - use a given dataset from websites like Kaggle or Maven Analytics to analyze a dataset and/or create a dashboard (e.g. my Maven Magic Challenge)

  • [Moderate-Difficult] Passion Project - something related to your hobby (e.g. my weightlifting project)

  • [Difficult] Business Problem - Try to solve problem you've noticed with a business, using publicly available data, web scrapping (if allowed), or dummy data (if you're worried about legality and privacy issues). An example is my student performance dashboard for my tutoring job.

Note: While I labeled these as easy, moderate, challenging this can vary depending on your skill level and experience. Don't feel bad if the "easy" project is difficult for you. It just means you need more practice.


If you want specific examples of projects you can work on check out this article: 10 Project Ideas for Beginner Data Analysts. If you're still struggling to come up with a project idea check out my article: Struggling on Coming Up with a Data Analytics Project? Try This.


How many projects should I do?

I recommend having at least 3 projects in your portfolio. One for each skill you're trying to demonstrate. Here's a typical tech stack that is recommended: Excel, SQL and Power BI. In theory you would want to have a project for each. So your portfolio should have:

  1. For Excel, a project to clean and analyze a dataset to find trends and insights.

  2. In your SQL project, you could create queries to extract and analyze data from a database.

  3. And using Power BI  build a dashboard or report that visually represents data and insights.


Where can you find public/free datasets?

Below are sites I've found free and public datasets.

  • Datahub - This site covers a wide range of topics from climate change to entertainment, but it mainly focuses on economic and business data.

  • Dataset Search - You're able to use Google to search for datasets. It's great if you have a particular topic in mind.

  • Kaggle - It has variety of free datasets provided by users from everything to arts & entertainment to social science data.

  • Data Gov - Public data from the US government from everything from crime to healthcare.

  • Maven Analytics Data Playground - Datasets that are hand picked by Maven's instructors. These datasets can be more fun like analyzing the Harry Potter movies scripts to more business focused like analyzing sales of a pizza place.

  • Awesome Public Datasets - A list of topic focused public data sources that are high quality. These are collected from blogs, answers, and user responses.

  • Datacamp Datasets - These datasets are from a variety of fields from real estate to retail. All of the datasets have the data and packages needed.

  • NASA Data - Has open-data provided to the public from NASA. The dataset pages only hold the metadata and the actual data may be on another NASA site. There will be links to the data in these other locations.


How did you do [project]?

I get a lot of questions on specific projects like my Google Capstone Project or my Weightlifting Project. For all of my major projects I've written detailed articles on my process. Most likely your question has already been answered there.

Below are articles on all of my portfolio projects:

Each article has the following sections:

  • Introduction - Why am I doing this project? Is there any background information the reader needs to know?

  • Process - I go through how I analyzed the data. Through each tool I used and how I used them.

  • Final Project - What the final project looks like (image of the dashboard). Any other relevant links (Github, Tableau Public)

  • Overview of Data - Summarize the data (what I found through my analysis)

  • Insights - Sharing my suggestions/insights based on the data. For example on my weightlifting project sharing what rep range and sets are best suited to build muscle

  • What I Learned - Anything I learned throughout this process, it can be about specific tools are about the data analysis process in general

  • Conclusion - Share my final thoughts about the project along with resources I used

  • Resources - Include any resources I used

Each article is linked either in my Github readme file or the "published on" link in Tableau Public.

If you're interested in how I approach portfolio projects check out my article: My Process for Creating Portfolio Projects.

 

Job Search


Can you review/give feedback my resume?

No, because: (1) I don't have time to review the resumes of everyone that asks me; and (2) I don't feel comfortable giving feedback on resumes since I don't have any hiring experience. For certain connections/friends I do proofread resumes but this is to look for spelling/grammatical errors and making sure hyperlinks work.


Did you use a template for your resume/can you send me your template?

I did not use a template for my resume. Feel free to check out my resume for inspiration (located on my homepage). You can also check out Practical Advice for Perfecting the Data Resume (a digital download for $15) from Carly Taylor.


I do however have a template for my cover letter, check that out here.


How did you get started on LinkedIn?

I became more active on LinkedIn by: commenting on other posts; connecting/following other data creators and updating my LinkedIn profile. If you want specifics on this you can check out my article: 3 Simple Ways to Become Active on LinkedIn.


For posting I began with the LinkedIn Hard Mode Challenge created by Albert Bellamy. It's where you post original content for 30 days straight. After that I decided to continue creating content (because I enjoyed it). But this challenge is what got me started on posting on LinkedIn.


What do you do during your job search?

Below is what I worked on (almost) everyday while searching for a job:

  • Apply to jobs - This is obvious but I take a more focused approach. I research companies and look for job posts that align with my interest, skills, etc. Revise my resume using key words for the job post and (sometimes) write cover letters. Connect with people at the company (for specific tips on this see this post from Austin Belcak)

  • Network - Go on LinkedIn and engaging with content (like or comment). I have coffee chats with people in my network (at least 2 a week). Converse with people through LinkedIn messages.

  • Upskill - Practice my SQL skills through sites like Stratascratch or Hackerrank. Take courses to expand my knowledge on sites like Udemy.

  • Portfolio Project - Work on my portfolio project which focused on a specific skill (SQL, Tableau, etc). Write a detailed article about my project.

  • Practice my interview skills - I spent time practicing my answers using the STAR method to commonly asked interview questions.

How did you get more interviews?

A lot of my interviews came from referrals from people or recruiters contacting me through LinkedIn. Below are the some things I think helped:

  1. Posting on LinkedIn - I post 3x a week with a focus on data analytics (learning new skills, switching careers, job advice , etc.) this is how most of my new connections find me

  2. Networking - I prioritize building relationships with people instead of collecting as many connections as possible. I don't think there's a right or wrong way but this the way I prefer.

  3. Optimize my profile - I've edited my LinkedIn profile many times to make sure my About section was good and I had the right skills included (which makes it easier for recruiters to find you using the recruiting platform on Linked). For more advice check out: How to Build an Amazing LinkedIn Profile [15+ Proven Tips] by Austin Belack.

While I think posting helped me I don't think everyone needs to do it. Post if you want to but don't feel obligated. I do think everyone should be active by: commenting, building your network and updating your LinkedIn profile. I went into that in my article: 3 Simple Ways to Become Active on LinkedIn.

If you want more details on each of these check out my article: 4 Ways I Increased My Chances of Getting an Interview.


Do you have any advice for me?

I don't know your specific situation but here's some general advice I'd give for job searching. It's broken into three sections: (1) LinkedIn; (2) Interviews; and (3) Applications.


LinkedIn

  • Be active. Comment on other posts and reach out to others to make new connections.

  • Update your profile. Add specific bullet points to your work experience, focus on achievements; include skills related to data analytics (e.g. Tableau), and generally fill out as many of the sections as you can. For more advice check out: How to Build an Amazing LinkedIn Profile [15+ Proven Tips] by Austin Belack.


Interviews

  • Do your research beforehand on the company, job, interviewer.

  • Practice answering commonly asked interview questions, I like writing my answers down beforehand and using the STAR method (any similar method works). If you want common interview questions asked for data analyst roles check out my article: Common Interview Questions for Data Analysts.

  • Practice walking through your portfolio. Have answers to questions like, "how did you clean your data?". "what tool did you use?", "what insights did you find?".

  • Have a list of questions to ask at the end. I have a list of general questions I ask for every company and specific ones I based on my research.

  • Emphasize on what you can bring to the company. Mention specific skills and experiences you have.

  • Thank the interviewer for their time via email. You can mention your continued excitement for the role or specifics on what you discussed.

  • If you have a technical interview I suggest practicing specific problems and reviewing contents. If you need more advice check out my article: How to Prepare for a Data Analyst Technical Interview.

  • Want more interview tips? Check out this blog post: 15 Additional Interview Tips from Commenters.

  • What if you got a take home assessment? Take your time and be thorough. For more advice see my article: Tips for Business/Data Analyst Take Home Assessments.

Applications

  • I tailor my resume for each job. I make sure I have keywords in my resume (skills mentioned in the job post) and include specific projects/experience that are relevant. Depending on time/the job I also may write a cover letter, though not all the time.

  • If you can, get a referral from someone in the company (look at your network to see if anyone would be open to doing this).

If you're interested in how I prepare for interviews check out my article: How I Prepare for Interviews (Before, After, and During).


Do you have a job for me?

I don't. I am not a recruiter or a hiring manager. I don't have a job or internship opportunity.


Is your company hiring?

I don't know. I am not with my company's hiring team. Check out the career page for Golden Hearts Games.


Can you refer me?

Unless I know you personally I won't refer you.

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