Data analytics freelancing is a great way to earn a high salary and work from anywhere.

The problem?

Most people won't do what it takes to get started.

Here are 5 tips that worked for me:

1) Create your data portfolio

When you're just starting out, people need to SEE what you can do.

Your portfolio works online for you 24x7 so that recruiters and clients can see your work.

2) Forget about UpWork

Yes, there are some people that make freelancer sites work.

But I've never had any luck.

You have to start at the very bottom, earning a low salary on boring gigs.

3) Start local

Find business owners in your town.
• barbers
• bar owners
• storekeepers

Ask if they need any "Excel help". Most business people will get it.

Once you help them solve THEIR problem, they are MUCH more willing to give you (more interesting) problems to solve.

4) Overdeliver

Once you have your foot in the door, you have to knock the project out of the park.

Example:
Analyze their customer and sales data and produce some insights (top customers, sales channels, new opportunities, etc.)

This is the best way to build trust.

5) Scale from there

Once you have a few projects completed, start putting together a more complete package that you can sell.

Example:
Automated custom CRM analytics dashboards for $$$/month earning or saving the client $$$$/month.

Get a few of those and you're rocking.

5 Tips To Get Started In Data Analytics Freelancing – Even If You've Tried Before And Failed

5 mistakes to avoid as a beginner getting started with Python for digital marketing:

1. Not understanding the basics of programming.

It's important to have a solid understanding of fundamental concepts like variables, loops, and functions before diving into Python for digital marketing.


2. Not setting up a proper development environment.

This can lead to issues with compatibility and debugging.

Make sure to use the latest version of Python and a good IDE like PyCharm or VS Code.

3. Not using appropriate libraries and frameworks.

There are many libraries and frameworks specifically designed for digital marketing tasks, such as Pandas for data analysis and advertools for SEM and SEO.

Make sure to research and use the right ones for your project.

4. Not following best practices for coding and debugging.

This can lead to inefficient and buggy code.

Always write clean, organized, and well-documented code, and use debugging tools like print statements and the pdb module to find and fix errors.

5. Not keeping up with updates and changes in the Python ecosystem.

Python is constantly evolving, and new libraries, frameworks, and best practices are regularly introduced.

Make sure to stay up to date and adapt to these changes.


If you found this useful:

1. Follow me @NewPrediction
2. RT the 1st tweet to share with others
3. Check out the unrolled version here:

https://typefully.com/NewPrediction/Xjhqaz7

5 Beginner Mistakes to Avoid When Getting Started With Python for Digital Marketing

5 simple steps to writing an attention-grabbing headline for your next data analytics project:

Headlines are the most prominent part of a data analyst report or dashboard.


It’s right at the top, and it’s the thing that people see first.


Unfortunately, many data analysts skip the headline altogether.

But that’s a big mistake because it’s “prime real estate.”

Imagine you own a luxury storefront in New York City.


Would you:
- Cram garbage into every nook and cranny?
- Put cheap items next to expensive items?
- Distract shoppers from buying?


Of course not! But that’s what data analysts do every time they neglect these 5 steps:


Here are the 5 steps:

1) Create an early rough draft as soon as possible.
2) Put your conclusions right in the headline.
3) Use a sub-headline to provide extra context.
4) Add some formatting to drive your point home.
5) Create the final headline.


Let's break each one down...


1) Create an early rough draft as soon as possible.


After you’ve analyzed the data a bit, you’ll start noticing a theme, trend, or something interesting.


Jot down your initial headline ideas in a physical notebook or right in the analysis (Excel, Tableau, Jupyter, whatever)


2) Put your conclusions right in the headline.


Always choose a clear headline for your analysis instead of something clever.


Your coworkers and your boss are busy. Don’t make them think about what your analysis is about.


Just tell them straight up.


3) Use a sub-headline to provide extra context.


Sub-headlines are a great way to put additional background information into the headline without being too long or wordy.


4) Add some formatting to drive your point home.


Use color, bold, or italics to highlight key points.


💡Protip: match the colors of your report as a quick legend or reference for the end user.


5) Create the final headline.


Your final headline should be tight:
– no extra words.
– straight to the point.


That's it! Here's a before-and-after example from my online data analytics portfolio.


5 Steps to Writing Attention-Grabbing Headlines for Your Next Data Analytics Project

Data Portfolio Project Write-Ups 101:


Bad:
– Here's my project
– Here's how I built it
– Here's the tech I used

Good:
– Here's the problem
– Here's how I solved it
– Here's what I learned along the way

Others should finish reading and understand how you solve data problems.

Sure, having a data analytics portfolio is a great start.

The fact is, that most data analysts either:
1) don't bother creating one at all
2) slap something together and call it "done"

One of the major stumbling blocks is knowing how to actually write about each project.

The great thing is that once you know:
1) what managers are looking for
2) how to write about each of your projects.

Your portfolio really takes care of itself:
– Interviews become easier
– Your projects are online 24/7
– It's set-it-and-forget-it for years

On Saturday, I'm sharing the exact framework for you to create write-ups for your projects.

– Even if you don't know what to say
– Including an example from my own portfolio

2,300 data people will get it tomorrow morning.

Join the fun (for free!)
👉 https://startingwithdata

You can create an awesome online data analytics portfolio:

– even if you don't have good datasets to use
– even if you don't know what projects work
– even if you don't know where to host it
– even if you don't know what to write

Take the first step now:
https://startingwithdata.com

Data Portfolio Project Write-Ups 101

The Perfect Data Analytics Project:

1. Includes the skills that employers want to see
2. Applies to real-world business scenarios
3. Looks excellent in your data portfolio.

Checking off 1 or 2 of these is a good start.
Combining all three is a game-changer.

Here's how: 👇

How To Know What Employers Want to See


1. Find a data analyst job posting from your dream company
2. Make note of specific tools and tech required
3. Be sure to include these in the project


(btw, I expanded on this idea in my newsletter this week 👇)

https://newprediction.com/data-analytics-skills-employers-want-to-see/


How to Create Real-World Data Projects



1. Use data sets from Real World Fake Data (#rwfd)
2. Find volunteer opportunities
2. Use data sets from community projects



(btw, I shared 6 community projects websites last week 👇)
https://twitter.com/NewPrediction/status/1579609653054349315

How to Create Your Online Data Analytics Portfolio



1. Use a no-code website host (not GitHub or Kaggle)
2. Pick your best work and write about it
3. Share it with the world



(you can check out my step-by-step guide on this here 👇 )
https://newprediction.com/free-data-analytics-portfolio-hosting/

That's it!

TL;DR:
1. Focus on in-demand skills
2. Use real-world business scenarios
3. Update your data analytics portfolio

Follow these steps to escape the "data tutorial hamster wheel" and actually create something that gets attention.

3 Ingredients For The Perfect Data Analytics Project

Including Tableau projects in your data analytics portfolio sets you apart during interviews.

Fortunately, there are online communities to help you create attention-grabbing projects that look great in your portfolio.

Use these 6 websites to fast-track your portfolio today! ⤵

1) Iron Quest by @sarahlovesdata

Practice data sourcing, preparation, and visualization skills in a themed monthly challenge.

Also, be sure to check out the recent "Reasons to Enter Iron Viz 2023" post from Sarah!

👉 t.ly/iron-quest

2) Diversity in Data by @DiversityinData

Centered around diversity, equity & awareness by visualizing provided datasets each month.

September featured a number of datasets highlighting female representation in the economy, politics, and film.

👉 data.world/diversityindata

3) Back 2 Viz Basics by @ReadySetData

A bi-weekly project helping those who are newer in the community and just starting out with Tableau.

The current challenge lets your practice sourcing your own data.

👉 thetableaustudentguide.com/vizbasics

4) Makeover Monday by @VizWizBI & @TriMyData

A weekly social project where you can improve your data visualization skills, one chart at a time.

👉 makeovermonday.co.uk

5) Workout Wednesday at #WOW2022

Build your skills with a weekly challenge to re-create an interactive data visualization.

👉 workout-wednesday.com

6) Storytelling With Data Challenge by @storywithdata

Practice data visualization and storytelling skills by participating in monthly challenges and exercises.

👉 community.storytellingwithdata.com

That's it!

Use these community projects to create your Tableau portfolio.

1) Iron Quest
2) Diversity in Data
3) Back 2 Viz Basics
4) Makeover Monday
5) Workout Wednesday
6) Storytelling With Data Challenge

6 Online Communities That Aspiring Data Analysts Can Use To Create An Awesome Tableau Portfolio

There are two types of data analysts: action takers, and sideliners.⤵
#DataFam #AllAboutTheData #DataAnalytics

Data analysts that just sit on the sidelines don’t do well:

• Never getting started
• Bouncing from tutorial to tutorial
• Endlessly saving Twitter threads to Notion
• Stuck on the sideline, watching others take action

Action takers learn new skills. And – most importantly – apply them.

Tech skills:
• SQL
• Tableau
• Excel
• Python

Soft skills:
• Collaboration
• Critical thinking
• Communication
• Problem-solving

(btw, here are my recommendations for how to learn the 4 tech skills every data analyst must know) 👇

https://twitter.com/NewPrediction/status/1560271869022191619

(and here are my recommendations for mastering soft skills) 👇
https://twitter.com/NewPrediction/status/1561684644071866372

But there's one final skill that sets some action-taking data analysts apart from the rest.

Learn to write online:
• Sharing what you learn in public
• Building an awesome online portfolio

This is a life-changing recipe no matter where you are on your data analytics journey.

(btw, here's my recommendation for learning to write online) 👇
https://twtr.to/startwritingonline


There Are Only Two Types of Data Analysts

Top resources to become better at #DataAnalytics in 2022:

1 SQLBolt: level up your SQL skills
2 TableauPublic: create & publish data viz
3 AutomateExcel: finally learn Excel
4 LearnPython: Python practice for every level
5 StartWritingOnline: improve your writing in 7 days

1. SQL Bolt:

SQLBolt is a series of interactive lessons and exercises designed to help you quickly learn SQL right in your browser.

It's great for beginners!

twtr.to/sqlbolt 👇

2. Tableau Public:

Tableau Public is a free version of Tableau that allows you to visualize data on the web or on your computer and upload it to a public Tableau profile.

It's a great way to get practice with Tableau!

twtr.to/tableaupublic 👇

3. Automate Excel:

This interactive tutorial will teach you how to use formulas in Excel. It covers the basics of creating formulas, formula shortcuts, named ranges, and more.

twtr.to/automateexcel 👇

4. Learn Python:

Whether you are an experienced programmer or not, this website is intended for everyone who wishes to learn the Python programming language.

twtr.to/learnpython 👇

5. How To Start Writing Online:

If you've been thinking about improving your writing, I highly recommend How To Start Writing Online.

Everything you need to start writing, build an audience, and create new opportunities online.

twtr.to/startwritingonline 👇

Level Up Your Data Analytics Skills With These 5 Free Resources

In #DataAnalytics, it's impossible to memorize everything you need to know about #Tableau.

Fortunately, there are amazing cheat sheets so that you don't have to memorize the basics.

Here are 7 Tableau Cheatsheets for you to download now:⤵
#DataFam #AllAboutTheData

1) DataCamp

In this Tableau Cheat Sheet, you'll learn how to create your first visualization, top visualizations in Tableau, creating dashboards, stories, and more.

Download now: https://twtr.to/r6am

2) Intellipaat

This cheat sheet will provide you with all the basics of the Tableau desktop that you need to know to get started with Tableau

Download now: https://twtr.to/xqR-

3) Tableau Cheat Sheet

This cheat sheet includes some of the basic Tableau components like sheets, dashboards, and containers and also includes links to helpful videos.

Download now: https://twtr.to/B9Y7

4) Tableau Calculation Types (63-page PDF)

This MEGA cheat sheet covers Tableau calculations in an easy-to-understand way. Learn about calc types, common use cases, complex calculations, and more.

Download now: https://twtr.to/Y11s

5) Date Calculation Cheat Sheet from @flerlagekr 

This interactive Tableau cheat sheet includes 44 reusable date calculations, with filters to select the date calculation you're looking for.

Download now: https://twtr.to/WQgt

6) Tableau Keyboard Shortcut Cheat Sheet

Move around Tableau quickly with the keyboard by using this handy keyboard shortcut cheat sheet.

Download now: https://twtr.to/KYf9

7) Tableau Quick Reference Cheat Sheet

This quick reference guide covers the basics like each of the panels and cards in the Tableau UI, common tasks, and more. 

Download now: https://twtr.to/QQ3m