How To Complete Learn To Fly 3 In 6 Days

[Diante Scharsch]

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As a data analytics instructor for over 2 years, I am often asked 2 questions: "what skills should I learn to become a data analyst?" and "what resources or courses are the best for learning those skills?". Here's the short answer for them:

1. The key skills to become a data analyst are: Excel, Power BI, Tableau, SQL, and Python.
2. In this article, I will be providing some of my favorite resources that I used on my data journey and use with my students. However, there are millions of courses online (free or paid) the specific course you decide to take does not matter instead focus on completing at least 2-3 projects utilizing each skill.

Warning: This guide to does not include any certifications. There has been a wave of misleading information that certifications are needed or required to transition to data roles. My guide in this article focuses on skill building, projects, and networking.

Disclaimer: This guide has been broken down into 100 days because that is how long I instruct a full Data Analytics course at technology schools and boot-camps. Feel free to modify the timeline as needed.

Three major advantages formal education provides over self-learning is structure, networking, and accountability. I'm providing the structure in this article but networking and accountability are on you.

1. Setup a LinkedIn and Twitter account before starting their tech learning journey. The profiles don't need anything fancy, just a clear photo and headline of who you are. I recommend using a headline/bio such as "Student Data Analyst". You will use these accounts during the next 100 Days to share your projects and thoughts along your journey. After you complete this guide, you will use these accounts to find a job!

• LinkedIn: set a daily connections goal with the main goal to reach 500 connections. I don't know what it is, but you unlock some next level LinkedIn at 500 connections. Search "data analyst", "business intelligence analyst", etc. to find connections. You can send a quick sentence explaining why you want to connect, but to be honest I don't always do that. I also recommend joining a tech or data group where you can post questions, ask for project feedback, and connect with people.
• Twitter: follow tech content creators and hashtags such as #blacktechtwitter, #blackindata, etc.
• Optional: There are also virtual tech communities on Facebook, Clubhouse, and Discord. You can find in-person networking events in your area on Meetup.

Whoever said "a goal without a plan is just a wish" had to be a Virgo. I think all goals require monthly, weekly, and daily planning but that's a whole other article. In order for you to learn the skills at the end of the 100 days (or even the end of the year), you need a plan.

1. Evaluate your current life schedule and identify when you have time for learning. I created this weekly template to help with this exercise. I recommend 10-12 hours per week in order to complete in 100 days.

During my data science boot-camp, we were given assignments in DataCamp to learn the topics and really used in-class time to work on projects and ask questions. I highly recommend DataCamp to anyone interested in any data career. The courses provide reading, lecture videos, and hands-on exercises to help learn and apply the skills to real world problems.

The Basic plan is free and offers the first chapter of each course and 6 full courses. The Premium plan is $300/yearly or $39/month and offers Tableau and PowerBI (cannot access with Basic plan). You can cancel at any time. I am not affiliated with DataCamp or paid for this message.

Do you have to be good in math to work in tech? No. Do you have to be "okay" in statistics to work in data? Yes. My data journey includes a Udacity Nanodegree, local boot-camp, and two master's degree programs and they all began with course(s) in statistics. If any of you are interested in data science, it is even more critical for you to learn and grasp concepts.

Most of you are probably familiar with Excel and may have even played with it for school or personal use. Excel can be a very powerful tool and is still used very commonly (if not daily) by data analysts.

Power BI is another Microsoft product so it will look and feel similar to Excel which hopefully help you ease into it. Power BI is a very common tool for data visualizations and can also be used for data cleaning.

1. Create a dashboard at least 4 visualization (always have a title) and utilizing slicer filters. Tell a complete story and/or answer business question(s).
2. Create a dashboard with a map with drill-down features.
3. Create a dashboard with time series analysis and cards to display KPIs.
4. Connect a web data source to the dashboard and visualize.
5. Use Query Editor to rename columns and join tables then create dashboard.

Tableau is another data visualization software with similar functionalities as Power BI. You can find debates of which one is better on Youtube and Twitter. I personally prefer Tableau but solely for aesthetic reasons, it feels more Apple/iOS like with its sleekness. And I can also download it on my Mac.

1. Company Database Querying Exercise - After answering the questions, I recommend uploading your SQL files to GitHub and sharing the link on LinkedIn.
2. Use MySQL's Table Data Import Wizard (video) to create a database from a series of csv files. You may need to tweak the files beforehand to make sure they have matching keys. Then complete a series of queries to answer business question(s) and BOOM project!

There are still tons of data analysts that never use Python or any other programming language to complete their work. This is an optional section truly but will help you stand out from other applicants or prep you for roles that really require it. If you're interested in finance or forecasting Python can be very beneficial as well.

freeCodeCamp has a full course on Python for beginners including installation, data types, variables, lists, tuples, functions, if statements, loops, and reading files. I will note this is a general Python course as Python can be used for tons of other fields outside of data analytics.

• If you have a preferred industry or domain (finance, healthcare, sports, etc.), I would recommend using data related to this for your projects. Doesn't have to be all but a good amount to tailor your resume when that time comes. ProjectPro provides project ideas for Power BI and Tableau broken down by industry.
• Connect with me on social media! Let me know if you have any questions and tag me when you share any part of this journey.

I started the class on Monday and wrote the exam on Friday afternoon. This meant I had roughly 4.5 days to watch 30+ hours of video lectures, understand all the concepts and master the math enough to pass a 3-hour comprehensive exam.

First though, why bother learning calculus at all? Beyond being a required course for my challenge, I think calculus has an unfair reputation as being either too hard or not useful enough to bother learning.

However, part of the criticism, is weighed against learning theory in general. A common misconception is that the best way to learn is simply to just go out in the real world and do things, and only learn theory when you absolutely need it.

Not everyone will be able to get through an entire course like calculus in 5 days, however this basic setup of waking up early and starting immediately, but having the evenings off is a great way to get more work done without feeling overwhelmed.

Each day I did approximately 10-11 hours of work, but because I had nights off, I could relax, watch movies, go to the gym or hang out with friends. When people talk about burnout, in my opinion, they are usually talking about missing those things, not the actual number of hours they are putting in.

The other reason I like using paid courses is because if the same instructor has multiple courses or lessons, you are less likely to miss or skip over any important skills, which is a down side of just relying on free tutorials.

The above image was rendered in KeyShot, not Blender, just to be clear. My focus at this stage is on integrating Blender into my existing toolset. This means that I will be trying to use it with KeyShot and Fusion 360 as opposed to replacing those tools.

On days 11-19, I spent a total of 12h 49min learning blender. On these days, I completed the battle axe project I was working through and I began working on a rigging course. Below are some images of the things I created in Blender during this period.

On day 20, I wanted to challenge myself to model another object. This model took me 4 hours and 16 minutes to complete. I later took the model into KeyShot for materials, lighting and rendering. For this project, I chose a Crankbrothers Eggbeater 11 Titanium mountain bike pedal. I have these pedals at home, which allowed me to take measurements. This helped with keeping things accurate. I also set up Blender to use millimeters as its unit of measure, which felt more comfortable to a traditional CAD-user like myself.

After the rigging course, I began working on a compositing course. Unfortunately, I ran into some issues I could not resolve and chose to abandon that project since compositing is not a high priority to me currently. This resulted in a couple of days wasted unfortunately.

For my day 50-day mini project, I chose to model a SmallRig Super Clamp. This was WAY more difficult than I expected given all the intersecting holes and threads. I was unable to keep the topology as clean as I wanted, but had to make some compromises in order to finish the model.

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