NEW from Maven Analytics on Medium!

ICYMI: Learning Data with Maven Analytics is a Medium publication where our community can share their knowledge and find even MORE great content from Team Maven!

We’ve been hard at work with some pretty awesome things the last few weeks (a team Summit in Austin, our NEW immersive cohorts, some new guided projects, and did you catch our January Jumpstart?!), and we’ve got even more on the way!

Here’s some of the content you may have missed in the meantime:

New from Aaron Parry:

Aaron’s back with the highly anticipated third installment of his comprehensive Power BI Guide!

“Time to dig into Part III in our six-part series designed to help beginners master the essentials of Power BI. Fasten your seatbelts because data modeling is easily the most crucial aspect of Power BI usability and performance.

Part III dives into the crucial aspects of data modeling and covers how creating a proper data model can significantly influence the performance, usability, and flexibility of your Power BI reports and dashboards. Understanding the following concepts and components will not only help enhance your ability to create accurate calculations, impactful visuals, and insightful analysis but ultimately help you create optimized, flexible, and scalable data models.”

>> Read the Article <<

New from Margaret Efron:

“As a data analyst, I’ve found ChatGPT to be instrumental in leveling up my SQL skills. I use ChatGPT in many ways, including checking my SQL code for syntax errors, reviewing the error messages, adding comments, and more.

Below are five ways I used ChatGPT to improve my SQL skills in just this past week!”

>> Read the Article <<

New from Carlos da Costa:

“The best way to learn a skill is by applying it on hands-on projects, so today we will perform exploratory data analysis using SQL in a retail database to answer the following questions:

Customer Analysis:

• What is the distribution of customers by gender?
• What is the distribution of customers by age?
• Which city has the highest and lowest number of customers?

Purchasing Behaviour:

• What is the purchasing behavior by gender and category?
• What is the spending behavior by customer’s age?

Product Analysis:

• Top-selling product in each category?”

>> Read the Article <<

New from Jessica Preuss:

“My journey began at the crossroads of e-commerce, marketing finesse, and the nuanced artistry of journalism. Little did I know that this unique confluence would sculpt the stepping stones toward a profound understanding of data — a narrative woven with Excel sheets, Python scripts, and the silent poetry of SQL commands.

As I started learning about data analytics, I combined my prior working experience in e-commerce and marketing with what I learned during my studies in journalism and media. This diverse background forms the foundation for my expertise in data analytics.”

>> Read the Article <<

New from Dakota Brown:

“I have a confession to make:

I’m a marketer who has never taken a single data analytics course.

Learning something new is universally intimidating, and I’m certainly not the exception to that rule!

• I’ve avoided Google Analytics like the plague, which is laughable as a digital marketer who has managed multiple websites.

• Spreadsheets make me cringe and I arguably can’t even utilize the basic functions of Excel and Sheets; I mainly use them for lists and organization.

• I routinely use dashboards and analytics software that is automated for me, and I can tell you what I’m looking at and why…but the thought of pulling raw data and doing everything from scratch is overwhelming.

None of this is to say that I’m incompetent; I know that I’m good at what I do. But there is an element of overwhelm and an intimidation factor that exists, and now I’m choosing to overcome that barrier so that I can become even better.

So today I’m putting aside the imposter syndrome and diving in. I hope you’ll come along!”

>> Read the Article <<

New from Daniel Schlon:

“In “How Life Imitates Chess”, Gary Kasparov, a chess grandmaster, shares profound insights into strategy and decision-making.?

This blog post, the first in a two-part series, explores how these chess strategies can be applied to the field of data analytics, enriched with personal insights from my career as a data analyst.”

>> Read the Article <<

New from Naz Eylem:

“Data manipulation is the most crucial step of data analysis. You should manipulate your data to handle missing values, normalization, standardization, encoding categorical variables to fit machine learning algorithms, feature engineering, converting data types, and even visualization.

There is no one-size-fits-all solution. But you will encounter many cases that which you should encode categorical variables and create new features to visualize or use data in algorithms. I will share what I mostly use for manipulating data.”

>> Read the Article <<

New from Somesh Dev:

Data Science as a field might be growing with the advent of new technologies popping up, but the basics will remain.

SQL (structured query language) still serves as an important tool when it comes to retrieving and analyzing data. Here we outline the main syntax and functions that can cover almost any use case out there, as well as some other useful functions we might need for our next management meeting or job interview.”

>> Read the Article <<

New from Iwa Sanjaya

“In this article, we will discover how connecting tables helps you understand your data better. We’ll go through a case study, covering everything from assigning keys to visualizing data across multiple tables.”

>> Read the Article <<

Be sure to follow the publication and keep your eyes peeled; more great content is coming next week!

>> Learning Data <<

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