So, you want to break into tech as a data professional, but you don't want to break the bank? I have collected all of the links, resources, and suggestions I have received on my journey of breaking into the field (starting from being a complete beginner!) and I am sharing all of them with you now.

If you would like to see me document my entire journey from start to present, check out my Tik Tok @Anniesanalytics, my LinkedIn, and My Website where you can find my entire portfolio. I also have a blog on my website, where I have gone more into detail about various topics along the way. If you would like to see the post that specifically contains the Roadmap I followed to become a data analyst/Tableau consultant- here it is!

This is a complicated question, with no easy answer. Everyone's situation is different, and I cannot tell you what is going to work for you.

First, you should make sure that you are actually interested in data- there are many non-coding niches within tech, make sure you pick the one that resonates most with you. Here are some articles that explore your options.

It is also worth your time to explore the difference between data science, and data analytics.

As someone who is still fairly new to this, I understand completely how overwhelming it can be to try and get into this whole "data analytics" thing. There are so many options out there. Everyone talks about how you can learn all of these concepts for free online... but that leaves a lot of questions unanswered. Which topics? Where do I find these free resources? Will I be able to get a job without an official degree?

I do not have the answers to all of these questions. But- as of writing this I have 14,000 Tik Tok followers. I am in numerous LinkedIn groups. I have been collecting a lot of data points on the advice I have heard everyone giving, and here is what I learned: If you want to break into data analytics, you must know how to work with spreadsheets at a high level, learn SQL (sometimes pronounced sequel), and know how to create visualizations (Tableau is the most common way to do this). After reading about 100 job descriptions, you should also probably learn Python- but you should start with spreadsheets and SQL and build up from there. Everything else that I talk about in this guide will be optional, and depending on which direction you go in the field some will be more important than others.

As for the getting a job without a degree question- I have been over this with hundreds of people at this point. The general consensus is that if you do a good job building your portfolio, have a good resume, and network, you can get a job without a degree. There are many people in the field whose highest "education level" is high school. That being said, obviously having a tech degree would be helpful. Additionally, if you do have a degree in another field, especially one such as a masters degree, it will probably help give you a competitive edge because it shows you have the capacity to learn. Your first job is just that- your first job! You can still break into the industry and build your way up from there.

In addition, I would like to let you know that if you are just starting out on this journey of switching careers and jumping into data analytics/science and feeling overwhelmed- you are not alone! There are a lot of us in the same position right now. Please feel free to join the Discord Server I have created. I had never heard of Discord before a few months ago- so do not worry if you haven't. It is basically just another type of social media- but you get into communities focused around one subject. In our Discord (I moderate it with several others who are actually data pros) we have multiple channels on all sorts of different topics. You could ask a question about pretty much any corner of data analytics and have your question answered by someone who is actually in the field within a few days! It is an amazing resource and community, and I am so glad my followers asked me to make it!

In this guide, I will go over the following topics. If you want to skip ahead to a specific one, click on it and you will be brought there! (Neat trick I learned)

• Courses- Free and Paid
• Spreadsheets
• SQL
• Tableau
• Python
• R
• General Cheatsheets
• Resources For Building a Portfolio and Job Searching
  • Where to Find Data to Practice With
• Internet Resources for Learning More about Data and Being a Data Professional
• Books about Working With Data

Coming out of another field to try and break into tech is overwhelming. I am glad that I started with the Google Certificates course in Data Analytics, because it gave me a great foundation for starting out. Before you get started, you need to ask yourself honestly "what are my learning needs?". If you are someone who needs someone else to be giving you deadlines and keeping you on track, then a self-paced course may not be your best option. If you need to be able to talk to professors and work your way through questions, then one of the free/cheaper online courses where you work through a series of modules may not be your best option. If you, like me, are a self-directed learner who prefers to teach yourself and enjoys troubleshooting on your own, then the cheaper/free resources will be a good fit. Of course, your budget is important as well!

I was surprised to find out how many free courses there are out there. Here is a list of some of the platforms where you can find numerous free courses:

Free Code Camp: Their homepage has an impressive intro: "Learn to code — for free. Build projects. Earn certifications.... Since 2014, more than 40,000 freeCodeCamp.org graduates have gotten jobs at tech companies including (then they show the logos of Google, Microsoft, Spotify, and Amazon)"
Free Courses From Harvard:
EdX: They have several different courses, whichever flavor floats your boat. I am really interested in taking their computer science course, but I have not decided about it yet.
Khan Academy: I hear often in the comment sections of my videos from people who are taking Khan Academy courses and learning a lot from them.
Coursera: Coursera is not free, but if you apply for financial aid, give a good reason, and wait a few weeks, you can get it free.
COOP Careers: Wait, a FREE Bootcamp? Yep! If you are from a minority group, live in a few key regions, and meet the other requirements, you can apply for this free bootcamp that will help you break into tech- learn the skills you need (technical and interpersonal)- to land a new job.

EdX excel courses: Do you want to learn Excel, especially because I am saying it is definitely the most foundational skill and the one you should start with? They've got you.
AP Statistics from Khan Academy: I personally have not encountered a need for math so far on my journey to get into data analytics but people have told me that they took Khan Academy math courses and it was helpful? I think they might be in a related niche like computer science or software engineering or something but I do not know.
Welcome to SQL with Khan Academy:
Learn Python with Zero2Sudo:
Introduction to Python with Datacamp: Datacamp is not free, but as of writing this, this course is.
SQL for Datascience: with EdX
Practice SQL with SQL Server: This is a free course made by one of the girls in my Tik Tok network- Kenzie @Sql.pal
This Twitter user made a post about free courses for breaking into data science.

Elements of AI: ok, so this is not a beginner course. Do not start here. But it's free, so it's going in.

There are probably thousands of tutorials on the internet (for free). I have not looked at all of them. However, these few were suggested to me by people who tried and liked them, and they seem like they would be helpful for beginners. If you watch one, remember to give their video a like! Will take you a millisecond- but you actually contribute to their popularity and help them get paid by engaging positively like that. It is a little gift I try to give to every creator whose content I enjoy.
Learn SQL for beginners - Complete course: The title pretty much says it all. Might be a good one to pan through if maybe you want to get a peek into SQL and then decide if you want to jump in more? It is by user Kudvenkat
SQL Tutorial- Full database course for beginners:
Python for Everyone: Chuck Severance
Alex the Analyst has a lot of tutorials on his page about getting into data.

I am not going to give you a list of all of the programs and bootcamps out there. There are a TON of options for people who want the structure and support of a degree or bootcamp- but those cost tens of thousands of dollars and you'll have to do your own research if that is your path. This, is a list of the paid courses and platforms which I have come across. They still generally fit the theme of teaching yourself to break into tech. Most are under $100, but a few that seemed interesting or extra to helpful to me have made it onto the list as well.

Coursera: there are a ton of great courses on Coursera about breaking into tech, and for a lot of them you get a certificate and badge at the end. I was very impressed with the course I took on this platform. The subscription is $50/month. They offer a free trial- which was helpful to me when deciding if I wanted to jump into this or not! As mentioned before, you can apply for financial aid and get a course for free (people comment on my videos often that they applied and got accepted a few weeks later no problem).
Dataquest: This is another subscription platform for taking data science courses, specifically. Their subscription is $50/month.
Datacamp: has a ton of different courses on various topics within the data sphere. It seems like the normal price is $300/year for over 350 courses- but I see it is on sale right now for $139
Udemy: On the Udemy platform, you can take a lot of interesting courses about breaking into tech within the data analytics sphere. You can choose to pay for a subscription, or buy individual courses. It seems like the subscription to their top courses is $30/month. Individual courses seem to be around $90, but if you get them on sale they can be about $15 and you have lifetime access (not a subscription). I actually bought two of their courses on a sale, and I am saving them for when I am ready.
LinkedIn learning: You can build your skills with various training courses on LinkedIn Learning. The cool thing about this is that these can be easily shared right on your LinkedIn and it can lead to skill badges, I think. I have not done any yet. It looks to me like it is a subscription where the first 30 days are free, then it is $40/month. However, get connected with people on LinkedIn! I see links to take courses for free all the time.
Cloud Skills Boost With Google: This is a series of courses you can take for cloud skills specifically. Probably not the best place to start as a beginner, but good to know about. The first 30 days are free.

Courses
Google Data Analytics Certificate: This course is what I started with- it started this entire journey of breaking into tech for me. The first 7 days are free. I could have sworn I only paid $40/ month after that but when I looked at a Coursera subscription last night it said $50/month. Either way- this is a self paced course. It took me a month + a few of those 7 free days from the trial, so I only paid for one month. I absolutely loved this course- I thought it was a great introduction to the basics. It did not cover Python, is the only drawback. I feel like it gave me such good foundational knowledge on spreadsheets, SQL, Tableau, and R- and in addition, some concepts that are important to know as a data analyst, such as presentation skills. The course is designed so well- a great balance of readings, videos, and hands on learning activities and quizzes. When you finish you get a badge for LinkedIn, and access to a jobs platform. These jobs seem to be very basic- like mostly using spreadsheets and maybe some SQL. You also get connected with a huge community of people who also took this course.
Maven Analytics: Maven is a course platform that I have recently discovered, and I love them. They are a subscription for $39/month and it comes with a lot of very specific learn data analytics courses by people who have been helping people get into data analytics for many years. I Have tried a few of their courses- The into to Tableau Desktop course - this one I started when I was interviewing for a Tableau heavy position and realized I really needed to level up my skills before the second interview, because I did not feel like learning on my own was cutting it. The second one I will talk about later in this guide, but it is about Launching your Data Career. I love it, because all of that middle stuff between learning the skills and getting a job is HARD.
Data With Danny: This is like a hybrid course/apprenticeship- "Learn Data Analytics, Data Science & Machine Learning under guided mentorship and deliver challenging projects in a unique data apprenticeship". I don't know much about it, but it might be that hands on chat with your instructor type thing people are looking for. It is $50, but there is also a $20 student discount.
100 Days of Code: The Complete Python Pro Bootcamp for 2022: This is a Udemy course I have had recomended to me multiple times! Since I took the Google DA course, it did not cover Python. So I was looking for another course on Python. I actually bought this (impulse buy) because it was on sale for $15 last night. That's like one drink at a fancy bar. So I'll let you know when I take it what I think! Normally it's like $90.: Update! I have begun this course and I love it! However, I do not think it is the most relevant course for an entry level data analyst. I plan to finish it in the future, but I put it on the shelf for now. Learning Python has helped me with SQL a lot though!
Microsoft Certified: Data Analyst Associate with Power BI: I have heard a lot about power BI, so I also impulse bought this one for like $12 last night. I am interested in the fact that it says that you get a certificate that you can put on your LinkedIn when it is finished. I do not actually know what Power BI is yet... but it seems like something that is gaining popularity in the analytics space and could really help to know.
2022 Complete Python Bootcamp From Zero to Hero in Python: This seems like a more all encompassing option that the other Udemy courses: "Learn Python like a Professional Start from the basics and go all the way to creating your own applications and games". It is $140, or $25 on sale.
Python for Web Apps and Data: This strikes me as a good course for someone who wants to learn Python, and already knows SQL and stuff- and knows which areas of data they are looking to get into and really want to be job ready by the end. It is $550
Professional Certificate in Data Analyst: Ok, so this course is pretty expensive for this list. But, it seems like it is not only very comprehensive, but it is also an IBM course and I feel like it would look really great on a resume. This strikes me as more like a Bootcamp- like I feel like by the end of this you should be job ready for an entry level job.
Programming for Everybody (Getting Started with Python): This course by the U of Michigan has been suggested to me numerous times for learning Python. It is on Coursera- so that $50/mo subscription will cover the whole course.

The rest of what I have to share in here is for those of us who are taking a "do it myself" approach. These are all of the links that I have been directed to in my courses, by other people, or found myself that I think are worth sharing.

Spreadsheets seem to me to be the foundation of working with data. So imagine a big old START HERE sign on this section.
Do you need to just make sure you have the foundational skills, and want to learn the basics of working with Google Sheets? Here you go.

• Top 10 ways to clean your data
• Top 20 Sheets Functions
• Sort and filter data
• Edit and format a spreadsheet

How about the basics in Excel? Girl I got you:

• Excel Quickstarts
• Video Trainings
• Format a worksheet Quickstart guide
• Guidelines for Organizing Data
• Top 10 ways to clean your data

If you want to link a lot of concepts together and have someone show you how to do them, maybe a training is right for you:

• This is a video training of excel basics
• Google sheets training and help, for beginner, intermediate, or advanced users
• Excel for Analytics Playlist: This playlist was put together by someone who has worked in the analytics industry for years. He is always happy to help people who are new. This series he takes on what he calls "a fairly realistic request".

Who doesn't love a good cheat sheet? I found quite a few:

• Google sheets cheat sheet
• Excel keyboard shortcuts
• Google Sheets Keyboard Shortcuts
• Comprehensive List of Sheets functions
• Tips and tricks for working in sheets- these are like hidden secrets that make things so much easier!

There is some readings that I have found to be so useful, that I saved the entire thing. However, it would be impractical to just paste all of that information into here, and I cannot add a file for you to download. So my workaround is I have added a page on my website where I can have files which you can download. I am going to add each of the following information filled files there, and so just click this link to go to the page where I am going to link to all of the following files.
Go to where I have labeled "#1- Spreadsheets Files" to find these files.

• How to use VLOOKUP in Sheets/Excel
• Data cleaning functions in spreadsheets
• How to access your changelog of your spreadsheet
• Keyboard Shortcuts in Sheets cheatsheet
• Using Pivot Tables
• Common Errors in Spreadsheets and How to Fix Them
• SQL Functions learned in the Google Course (With examples)

It is so frustrating to come up with an error in your sheet, especially when you are trying to teach yourself this stuff and there is nobody to help you. There are actually people out there to help you on the internet! But in the meantime, this may help you problem solve:

• When a formula doesn’t work
• How to correct a #Value! error

Here are some links about information on how to make and format charts in spreadhsheets: Graphs in Google Sheets: Not only does this resource contain a detailed example of chart creation in spreadsheets, but it also provides you with downloadable sample data you can use to practice. As you have learned throughout this course, practicing these skills helps you learn more about the tools you are using. This example data is a great way to start!

Add and edit a chart or graph in Google Sheets: This article includes steps for creating, editing, and changing charts in Google Sheets with how-to videos. It also has a more in-depth guide to editing and customizing your chart after you have created it.


Create a Microsoft Excel chart from start to finish: This how-to guide from Microsoft’s support site includes instructions and a video tutorial for adding charts to Excel spreadsheets. This is a useful resource if you are working specifically with Excel spreadsheets. It also links to other useful articles about creating charts in Excel.

Microsoft Excel: Creating and modifying charts: This is an explanation of Excel charts with downloadable handouts. This resource is especially useful because it has downloadable content that you can save to reference later when you start creating charts in your own spreadsheets.

You may need some more information on how to do these somewhat more complicated functions:

• Import range (brings in values from another sheet)
• Sort data or range in tables excel
• Filter data/range excel
• Import a table from a Webpage

SUMIFS and COUNTIFS are just two examples of functions with multiple conditions. They help demonstrate how multiple conditions can be built into the basic syntax of a function. But, there are other functions with multiple conditions that you can use in your data analysis. There are a lot of resources available online to help you get started with these other functions:

• How to use the Excel IFS function: This resource includes an explanation and example of the IFS function in Excel. This is a great reference if you are interested in learning more about IFS. The example is a useful way to understand this function and how it can be used.
• VLOOKUP in Excel with multiple criteria: Similar to the previous resource, this resource goes into more detail about how to use VLOOKUP with multiple criteria. Being able to apply VLOOKUP with multiple criteria will be a useful skill, so check out this resource for more guidance on how you can start using it on your own spreadsheet data.
• INDEX and MATCH in Excel with multiple criteria: This resource explains how to use the INDEX and MATCH functions with multiple criteria. It also includes an example which helps demonstrate how these functions work with multiple criteria and actual data.
• Using IF with AND, OR, and NOT functions in Excel: This resource combines IF with AND, OR, and NOT functions to create more complex functions. By combining these functions, you can perform your tasks more efficiently and cover more criteria at once.

I will be honest with you, I copy and pasted the following right from my course I took:

Keyboard shortcuts in Excel: Earlier in this list, you were provided with a resource for keyboard shortcuts in Google Sheets. Similarly, this resource provides a list of keyboard shortcuts in Excel that will make performing regular spreadsheet tasks more efficient. This includes keyboard shortcuts for both desktop and mobile versions of Excel, so you can apply them no matter what platform you are working on.
222 Excel shortcuts: A compilation of shortcuts includes links to more detailed explanations about how to use them. This is a great way to quickly reference keyboard shortcuts. The list has been organized by functionality, so you can go directly to the sections that are most useful to you.
List of spreadsheet functions: This is a comprehensive list of Excel spreadsheet functions with links to more detailed explanations. This is a useful resource to save so that you can reference it often; that way, you’ll have access to functions and examples that you can apply to your work.
List of spreadsheet formulas: Similar to the previous resource, this comprehensive list of Excel spreadsheet formulas with links to more detailed explanations and can be saved and referenced any time you need to check out a formula for your analysis.
Essential Excel Skills for Analyzing Data: This blog post includes more advanced functionalities of some spreadsheet tools that you have previously learned about, like pivot tables and conditional formatting. These skills have been identified as particularly useful for data analysis. Each section includes a how-to video that will take you through the process of using these functions step-by-step, so that you can apply them to your own analysis.
Advanced Spreadsheet Skills: Mark Jhon C. Oxillo’s presentation starts with a basic overview of spreadsheets but also includes advanced functions and exercises to help you apply formulas to actual data in Excel.

If you have never heard of Boolean Operators, I guess you might think about skipping over this section. But! You actually should know about Boolean Operators and what each of them mean if you want to work with data. It is not a hard concept to understand, once it is explained to you.

SQL is, as I perceive it, the next most widely needed thing when working with data. Without any further ado, here is the free information I have to share:

Here is a list of all of the files that are either very helpful PDF's, or an informational reading that I thought was so helpful that I just had to save it. To get access to these files, just click this link, and then scroll to where it says "#2- SQL Resources" (I cannot add them to this page, wish I could!)

• CONCAT and variations of CONCAT in SQL
• 3 pages of SQL cheatsheets
• Intermediate Guide to SQL
• Temporary Tables
• Writing SQL Subfunctions
• SQL Best Practices

You can play fun online games where you get to practice your SQL skills! [Murder Mystery] (https://mystery.knightlab.com/) [SQL Island] (https://sql-island.informatik.uni-kl.de/)

What is SQL? Basics. This site also goes through tutorials on a lot of common SQL functions.
W3 schools has a SQL tutorial which not only provides you with a lot of awesome queries and explanations, but also the chance to try them yourself. You can follow the tutorial in order to learn about SQL from scratch
This article covers a lot of the basic functions in SQL, specifically for PostgreSQL. It gives explanations as well as code.
This is an article about common keywords used in SQL
SQL Server functions

Converting (casting) data
CAST and CONVERT: SQL Server reference documentation
MySQL CAST Functions and Operators: MySQL reference documentation
[How to: SQL Type Casting](How to: SQL Type Casting): Blog about type casting that has links to other SQL short guides Aliasing in SQL AND check out this

Column Aliasing: https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/sqlproc/p0aymxwsvbt5wcn1lncugwjtf758.htm

Joins are a tricky one. Kenzie @SQL.Pal did a whole (helpful) series about them if you want to see it in video form.

• SQL JOINs: This is a good basic explanation of JOINs with examples. If you need a quick reminder of what the different JOINs do, this is a great resource to bookmark and come back to later.
• Database JOINs - Introduction to JOIN Types and Concepts: This is a really thorough introduction to JOINs. Not only does this article explain what JOINs are and how to use them, but it also explains the various scenarios in more detail of when and why you would use the different JOINs. This is a great resource if you are interested in learning more about the logic behind JOINing.
• SQL JOIN Types Explained in Visuals: This resource has a visual representation of the different JOINs. This is a really useful way to think about JOINs if you are a visual learner, and it can be a really useful way to remember the different JOINs.
• SQL JOINs: Bringing Data Together One Join at a Time: Not only does this resource have a detailed explanation of JOINs with examples, but it also provides example data that you can use to follow along with their step-by-step guide. This is a useful way to practice JOINs with some real data.
• SQL JOIN: This is another resource that provides a clear explanation of JOINs and uses examples to demonstrate how they work. The examples also combine JOINs with aliasing. This is a great opportunity to see how JOINs can be combined with other SQL concepts that you have been learning about in this course.

This goes for SQL and all other things within the data analytics space:
Can’t get your code to work? Wondering “if….”? Try Stack OverFlow. The spot for problem solving.

It feels like it is totally switching gears when you suddenly start talking about visualizations. It really threw me off to go from "Ok, so the comma has to go HERE" to "What colors should I make the bars in this chart?".
So far, I only have a couple of text files/decision trees that I think are really helpful and want to share with you about data visualizations/Tableau. Click here to go to my website where you can download these files (for free, of course, no weird software downloads or anything, I just can't add them to my page here easily). Scroll to "#3- Visualization Files"

My Tableau CheatSheet: I took a Maven Analytics Course that I loved, and it gave me ALL the basics I could possibly need to know about Tableau. I made a cheatsheet of all of my notes of what I learned in the course. This video is a great starting place. It is divided into sections so you can skip to what you need if you want.

Explainations of how/why to use filters.

Here are some beautiful examples of all the ways you could go with your visualizations (called Vizzes). I know, I'm overwhelmed too.

• Are we alone?
• Viz of the Day
• Who’s talking in popular films?
• 2020 best vizzes
• Information is beautiful
• Data Studio good projects gallery
• I have cancer

One thing that can be intimidating when you are new to this stuff, is trying to decide which kind of graph or chart you should use to best visualize your data. Here are some resources that can help you along the planning stages.
This Explaination about different dashboard types.
This ebook provides you with questions that will help you when looking at your data and trying to figure out your relevant stakeholders and their needs- which guides how you design your dashboard
How should I visualize my data? This might help- it shows examples of each and helps you make decisions
Which chart or graph is right for you?

Ultimate cheat sheet on Tableau charts
How to choose which way to visualize your data

I am not going to lie, every time I use Tableau so far, it is a lot of me just clicking buttons and moving things around, hoping it will do what I want. Be smarter than me. Read these resources. They're free.

• Highlight tables appear like tables with conditional formatting. Review the steps to build a highlight table.
• Heat maps show intensity or concentrations in the data.
• Density maps show concentrations (like a population density map).
• Gantt charts show the duration of events or activities on a timeline.
• Symbol maps display a mark over a given longitude and latitude. Learn more from this example of a symbol map.
• Filled maps are maps with areas colored based on a measurement or dimension. Explore an example of a filled map.
• Circle views show comparative strength in data. Learn more from this example of a circle view.
• Box plots also known as box-and whiskers charts show the distribution of values along a chart axis.
• Bullet graphs compare a primary measure with another and can be used instead of dial gauge charts.
• Packed bubble charts display data in clustered circles.

The beauty of data visualization: In this video, David McCandless explains the need for design to not just be beautiful, but for it to be meaningful as well. Data visualization must be able to balance function and form for it to be relevant to your audience.
‘The McCandless Method’ of data presentation: At first glance, this blog appears to be written by a David McCandless fan, and it is. However, it contains very useful information and provides an in-depth look at the 5-step process that McCandless uses to present his data.
Information is beautiful: Founded by McCandless himself, this site serves as a hub of sample visualizations that make use of the McCandless method. Explore data from the news, science, the economy, and so much more and learn how to make visual decisions based on facts from all kinds of sources.
Beautiful daily news: In this McCandless collection, explore uplifting trends and statistics that are beautifully visualized for your creative enjoyment. A new chart is released every day so be sure to visit often to absorb the amazing things happening all over the world.
The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures: This is a comprehensive guide to data visualization, including chapters on basic data visualization principles and how to create useful data visualizations even when you find yourself in a tricky situation. This is a useful book to add to your data visualization library, and you can reference it over and over again.

This is an entire blog dedicated to data visualizaiton

I haven't learned Python yet, so I don't have much for you. But, I will add more when I do learn about it! Besides the courses I linked you to above, all I have to add is this tutorial for beginners.

I also have some information about R vs Python.
R versus Python, a comprehensive guide for data professionals: This article is written by a data professional with extensive experience using both languages and provides a detailed comparison.
R versus Python, an objective comparison: This article provides a comparison of the languages using examples of code use.
R versus Python: What’s the best language for data science?: This blog article provides RStudio’s perspective on the R vs. Python debate.

R is a little more niche than these others- I would say do not start with R! But, look into jobs you may want. If you see R listed consistently, I hear it is commonly used in research (which the health field does a lot of), then here's some free stuff!

First of all, I have actually a lot of files for you about R. They range from several long, informative readings which I still refer back to, to cheatsheets. You can find them here. Scroll to "#5- R Files"

• Output formats available in Markdown (and how-to)
• RMarkdown Cheatsheet
• ggplot
• Common problems when Visualizing in R
• R vocab
• Operators (this is a long one)
• Keeping your code readable
• Vectors dates and times (this is a long one)
• Vectors and Lists in R
• Ways to learn about programming
• Juptyr Notebooks
• R Functions Learned in the Google Course (with examples)
• Creating a new R MarkDown File Actity (hands on instructions)

This is a guide by R Studio on how to do basic things. They provide code and explanations right in this guide which is separated into different actions.
Getting Started in R: First steps- a very informative tutorial!
Advantages of RStudio- why would I use R?
Another resource on “Why R” for data analysis
Data transformation (and cleaning)

• gganimate information
• Writing Hamilton lyrics with tensorflow
• Dive into dpylr
• Data import in R
• Readxl
• R datasets package for practicing in R
• Ggplot2
• • and more on visualizing
• Putting it all together: ggplot and dpylr
• What is a Tibble, and how to use one
• Aesthetics of a plot
• Aesthetics Explained
• Save a plot
• More save a plot info
  Pivoting wide/long
• Pivoting: Consider this a starting point for tidying data through wide and long conversions. This web page is taken directly from tidyr package information at tidyverse.org. It explores the components of the pivot_longer and pivot_wider functions using specific details, examples, and definitions.
• CleanItUp 5: R-Ladies Sydney: Wide to Long to Wide to…PIVOT: This resource gives you additional details about the pivot_longer and pivot_wider functions. The examples provided use interesting datasets to illustrate how to convert data from wide to long and back to wide.
• Plotting multiple variables: This resource explains how to visualize wide and long data, with ggplot2 to help tidy it. The focus is on using pivot_longer to restructure data and make similar plots of a number of variables at once.

How to use RMarkdown
Reference Guide
Cheat Sheet
R Markdown, the Definitive Guide

Create an annotation layer: This guide explains how to add an annotation layer with ggplot2. It includes sample code and data visualizations with annotations created in ggplot2. How to annotate a plot in ggplot2: This resource includes explanations about how to add different kinds of annotations to your ggplot2 plots, and is a great reference if you need to quickly look up a specific kind of annotation. Annotations: Chapter eight of the online ggplot2 textbook is focused entirely on annotations. It provides in-depth explanations of the different types of annotations, how they are used, and detailed examples. [How to annotate a plot](How to annotate a plot): This R-Bloggers article includes explanations about how to annotate plots in ggplot2. It starts with basic concepts and covers more complicated information the further on you read. [Text Annotations](Text Annotations): This resource focuses specifically on adding text annotations and labels to ggplot2 visualizations.

RStudio: The best place to find help with R is in R itself! You can input ‘?’ or the help() command to search in R. You can also open the Help pane to find more R resources.
RStudio Blog: RStudio’s blog is a great place to find information about RStudio, including company news. You can read the most recent featured posts or use the search bar and the list of categories on the left side of the page to explore specific topics you might find interesting or to search for a specific post.
[Stack Overflow](Stack Overflow): The Stack Overflow blog posts opinions and advice from other coders. This is a great place to stay in touch with conversations happening in the community.
R-Bloggers: The R-Bloggers blog has useful tutorials and news articles posted by other R users in the community.
R-Bloggers' tutorials for learning R: This blog post from R-Bloggers compiles some basic R tutorials and also links to more advanced guides.

This is cool to look at if you are interested in R.

100+ Data Science, Machine Learning, AI, & Deep Learning Cheet Sheet PDF's The Complete Collection of Data Science Cheat Sheets – Part 1

The biggest surprise to me along this journey was that the hardest part, was job searching. Learning the tools was fun and really not that hard. But then when it came to all of the things I needed to do- such as figuring out how to build a portfolio, updating my resume, networking on LinkedIn.... figuring out how to do all of that was HARD and not what I signed up for! It totally burned me out. Towards the end of the process I found this course by Maven Analytics that I WISH I had found sooner. I took it to check it out and even after a month and a half of job searching and many edits, I made some adjustments to my resume and process. I 100% recommend it.

Portfolio and Resume Analysis with Data Science Hiring Managers: A video on Youtube with hiring managers about the things that you need to be including in your portfolio and resume.
Kaggle Career Con- TONS of good info

StrataScratch: StrataScratch is a data science interview platform that has over 900+ real interview questions from your favorite companies. New interview questions are released every month and cover SQL and python coding, statistics, probability, modeling, product sense, and system design.
An insider blog about interviewing and getting hired as a new data professional
How to build a compelling data science portfolio and resume: A hiring manager from Quora reviews actual resumes from data science candidates and gives candid feedback on areas of improvement. Learn what to include and omit from your resume and portfolio as well as formatting tips. This offers a great firsthand look into what hiring managers are seeking when reviewing your resume and portfolio.
Overview of the Data Science Interview Process: Hiring managers at Google discuss typical data science interviews, including the soft and hard skills you will want to prioritize. You will get a better sense of the interview process from both sides, and better prepare yourself for what to expect when interviewing for a data science role.
Live Breakdown of Common Data Science Interview Questions: Watch a mock interview to see how a Kaggle data scientist answers questions during a data science interview. The video also includes live coding! This video is great preparation for some of the most commonly asked data science interview questions.
Am I a Good Fit? Identifying Your Best Data Science Job Opportunities: Ever wonder where you will fit in for your future career? This chat with Jessica Kirkpatrick, an intelligence manager, gives you a great breakdown of the different types of categories within the data science job market, the different types of job opportunities you may notice, and how you can frame previous work and skills from another career to fit into the data science job market.
Real Stories from a Panel of Successful Career Switchers: Are you switching careers? Awesome! Learn from people who were in the same position as you and successfully switched their careers into data science. This panel discusses the different experiences in their careers and life that shifted them into the data science field.

4 Case Study Questions for Interviewing Data Analysts at a Startup

Open Data! Here are some places to find real data that you can get your hands on to practice with.

• Maven Analytics Data Playground: If you take nothing else out of this guide, take this. These datasets are specifically curated for beginner data analysts to build their portfolio.
• Tidy Tuesday
• Google cloud
• Google Research Datasets
• Kaggle
• Big Query
• Climate Data
• Climate/Weather
• FiveThirtyEight's Data Behind Their Stories
• BuzzFeed New's Datasets

Public health datasets
Global Health Observatory data: You can search for datasets from this page or explore featured data collections from the World Health Organization.

The Cancer Imaging Archive (TCIA) dataset: Just like the earlier dataset, this data is hosted by the Google Cloud Public Datasets and can be uploaded to BigQuery.
1000 Genomes: This is another dataset from the Google Cloud Public resources that can be uploaded to BigQuery.

Public climate datasets
National Climatic Data Center: The NCDC Quick Links page has a selection of datasets you can explore.
NOAA Public Dataset Gallery: The NOAA Public Dataset Gallery contains a searchable collection of public datasets.

Public social-political datasets
UNICEF State of the World’s Children: This dataset from UNICEF includes a collection of tables that can be downloaded.
CPS Labor Force Statistics: This page contains links to several available datasets that you can explore.
The Stanford Open Policing Project: This dataset can be downloaded as a .CSV file for your own use.

Want to do some good while practicing your skills? Here's your chance:

• Datakind
• Data For Good
• Statistics Without Borders
• Omdena
• Fruit Punch AI
• Catch A Fire

I follow all of these creators. I like their videos, and watch them, and share them- because I know that every creator who is just putting themselves out there on the internet for free to help people deserves engagement from people who enjoy their content 😌
Data Storyteller is my favorite Tik Tok page for analytics at the moment. She is a data scientist who is in product analytics, and she broke herself into tech a while ago from marketing. She shares so much great information about data analytics and breaking into data analytics.
Maven Analytics: Maven is a close second for my favorite Tik Tok related to analytics, and my favorite course platform. They are consistently putting out incredibly informative videos about exactly how to break into data analytics.
@sql.pal: Kenzie is another creator in the space of data analytics who are here to help and spread good vibes. She consistently puts out content about using and learning SQL, including an entire series about how to do JOINs. She is a sweet and positive creator, and she even hopped on to help moderate my Discord with me right away when I started out and has contributed some helpful things in different channels.
@zero2sudo: I just discovered his page recently and I love it. He is in software engineering, so not quite data analytics. However, he gives a lot of good tech tips in general on his page- and he has a whole playlist (free) on learning Python. As soon as I commented on one of his posts trying to chat with him he followed me and messaged me, and has been very friendly and helpful since then. If you have any troubles learning Python or questions in general, or just want to have people in your feed who are not only killing it in tech but also lifting while they climb, go check his page out.
@lucaswonderley: I only discovered him a day ago, but his bio is "Daily software development content". He seems to have interesting stuff on Python, so if you want to learn that, check him out.
@python.chick : Has a lot of content about getting into data science, learning Python, and what it's like to be in the field. She seems brilliant to me.
@charlotte_chz : Charlotte was working as a data analyst for several years and ended up making her way up to a very impressive salary. A few months after starting her Tik Tok this winter, she left her job to open her own business about breaking into tech, and become a full time bootcamp instructor for people who want to take a paid bootcamp to break into data analytics. She talks about transitioning into the field, and her bootcamp.

I have not taken/ watched any of these courses myself so I cannot compare them, but I can say they have been suggested to me by my followers! There are also tons and tons more videos on YouTube that I did not mention here. Learn SQL for beginners - complete course
SQL Tutorial - Full Database Course for Beginners
python for everyone chuck severance

Alex the Analyst: If you want to get into Data Analytics, definitely check out his page! He is so knowledgeable and helpful, and he taught himself! I have loved his videos and could not pick just one to suggest.

These are books that have been suggested to me:

• Storytelling with data: This book was suggested to me by someone who said that it was immensely helpful to them in learning how to use Tableau and create professional looking visualizations. It apparently even comes with practice examples where you can work on the skills you are learning. It's $44 new, but I found it for about $25 online used (I did not buy it)
• Understanding variation- the key to managing chaos: People on Amazon love this book. One review "This book is well written with great graphics (tables and charts) that illustrate main ideas and examples. It is moderate to difficult subject matter covered in such a way that makes for an easy read with necessary information to know for all decision-making professionals within an organization. The author provides real life examples where companies have analyzed data, completely missing or misinterpreting the clear signals the data was displaying causing the company unnecessary costs. Likewise, the author does a superb job at highlighting why behavior control charts are a mandatory and useful tool when analyzing data used for strategic decision-making. What is a behavior control chart you say? I recommend that you read this book". It is $47 but I found it for $8 used.
• Automate the Boring Stuff: Free! Online. "In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required". It is intriguing to me, and since it is free.99 I do think I will check it out at some point! I am interested in this idea of automating. I am definitely the type of person who goes "Ok, but how could I do that better?"