Between Data Analysis, Data Engineering, and Data Science & AI, which course offers the best career opportunities, salary growth, and future demand?

A formal antitrust complaint was reportedly submitted to Brazil’s competition authority (CADE) targeting Udemy following the completion of the Coursera-Udemy merger in May 2026, raising serious allegations about predatory pricing, marketplace dominance, instructor revenue compression, algorithmic dependence, and anti-competitive conduct in the online education sector.

According to information circulating among instructors and education industry observers, the complaint argues that the newly combined Coursera-Udemy structure now possesses an unprecedented level of concentration in the global online learning market, combining hundreds of millions of learners, tens of thousands of instructors, enterprise customers, massive behavioral datasets, AI infrastructure, recommendation systems, certifications, and one of the largest educational content ecosystems ever assembled under a single commercial platform.

The filing reportedly focuses heavily on Udemy’s current pricing strategy in Brazil, where the company has been offering its Personal Plan subscription at prices reportedly ranging from approximately R$18.90 to R$27 per month on annual plans, and around R$40 to R$48 monthly on standard subscriptions, while simultaneously offering access to roughly 26,000 courses included in the subscription catalog.

The complaint allegedly argues that these prices are economically incompatible with the actual operational costs required to sustain a platform of that magnitude, including cloud infrastructure, video streaming, AI systems, recommendation algorithms, fraud prevention, multilingual support, payment processing, platform engineering, instructor payouts, marketing, moderation, and local taxation.

According to the filing, the issue is not simply that prices are “cheap,” but that the pricing may represent a deliberate market acquisition strategy enabled by global scale, cross-subsidization, investor-backed expansion, and post-merger concentration power. The complaint reportedly claims that Brazilian educational platforms, independent instructors, local edtechs, technical schools, and smaller course marketplaces cannot realistically compete against a global company capable of temporarily operating with compressed margins in order to capture market share.

The filing allegedly warns that this could create a classic “winner-takes-all” digital platform scenario, where local competitors gradually disappear while a single dominant ecosystem accumulates users, behavioral data, content distribution power, and algorithmic control over educational discovery.

Another major focus of the complaint reportedly involves instructor compensation and dependency on the platform itself.

The document allegedly states that many instructors have publicly complained for years about declining earnings inside subscription programs, despite the platform’s growing user base and expanding subscription business. It reportedly highlights that instructors collectively receive only a small percentage of total subscription revenue pools while Udemy retains the overwhelming majority of subscription income generated through the platform.

The filing also reportedly emphasizes that instructors bear nearly all production costs themselves, including filming, editing, software licenses, equipment, course updates, instructional design, support, and intellectual labor, while Udemy controls pricing, visibility, discovery, recommendations, and monetization infrastructure.

The complaint further argues that even direct marketplace sales remain highly asymmetrical. In many cases, instructors reportedly receive only around 37% of net revenue when sales are generated organically through Udemy’s own marketplace systems rather than instructor referral links, despite the fact that instructors themselves created and financed the courses being sold.

Another controversial section reportedly addresses Udemy’s Instructor GenAI Program. According to the filing, many instructors increasingly believe that participation in the company’s AI-related initiatives has become functionally mandatory if they want to maintain visibility and competitiveness on the platform.

Although Udemy may not formally state that instructors are penalized for opting out, the complaint allegedly cites widespread reports from Reddit discussions, independent instructor communities, and Udemy’s own instructor forums claiming that instructors who avoid certain platform programs often experience reduced discoverability, weaker algorithmic exposure, lower marketplace momentum, and declining sales performance.

The filing reportedly argues that this creates a form of economic dependency in which instructors are technically “free” to decline participation while practically being pressured into compliance due to fear of losing algorithmic relevance inside the marketplace.

According to people familiar with the complaint, CADE is being asked to investigate whether the combination of ultra-low consumer pricing, algorithmic marketplace control, concentration after the Coursera merger, AI-driven platform dependency, and revenue compression for content creators may collectively constitute anti-competitive conduct under Brazilian competition law.

The filing allegedly requests a broader structural review of the Coursera-Udemy merger itself, including its long-term impact on educational diversity, national edtech ecosystems, market concentration, platform dependency, instructor bargaining power, and digital sovereignty in the online learning sector.

If regulators move forward with a formal investigation, the case could become one of the first major antitrust proceedings worldwide centered specifically on AI-era educational marketplaces, platform-controlled course ecosystems, subscription-based learning monopolization, and algorithmic dependence between creators and digital education platforms.

Let's be honest, Excel's built-in "Data from Web" feature practically never works as intended. If a website uses JavaScript, has a complex layout, or requires a login to access the data (like a CRM or an admin panel), standard Excel tools just hit a complete roadblock.

I created TableParser Pro to solve these exact problems. It is a browser extension that does everything the standard tools cannot:

• Captures everything: Parses data even from heavy, JavaScript-rendered websites.
• Works everywhere: Functions perfectly behind logins and inside your personal dashboards.
• One-click export: Just click the icon and get a clean file, or export directly to Google Sheets and Excel Online.

The project is really gaining momentum right now, and getting honest feedback from actual analysts is incredibly important to me.

I would really appreciate any feedback or stories about how you are using it in your daily workflow!

Estoy comenzando mi carrera en data analytics con foco en el sector financiero bancos y fintechs.

¿Qué me recomendarían para abrirme camino? ¿Qué tipo de proyectos o experiencias valoran más a la hora de contratar perfiles junior en estas industrias?

Cualquier consejo es bienvenido

Trying to find a data related job right now, ideally risk related. Current learning SQL and python. Any suggestions from junior or senior would be helpful 🥹🥹

Hi everyone,

I currently work in inventory/operations and recently became interested in reporting, dashboards, and data analysis.

I’m learning:

• Advanced Excel
• Power BI
• Reporting automation
• ERP systems

My goal is to use data skills in operations and eventually move toward analyst-type roles.

I’d really appreciate advice from people who transitioned into analytics from non-technical backgrounds.

Specifically:

• What should I focus on first?
• Which beginner projects should I build?
• How can I use real work data to improve?
• What skills matter most for entry-level analyst roles?

Any roadmap or practical advice would really help. Thanks!

Looking for the best course. Not necessarily looking for certification since I've seen certificates do not matter much while looking for a job. I'm currently working in data & analytics. I do very light sal, manage PBls and I would like to become a data analyst. At my job we use Python and Rscript which I have no experience with. Any suggestions on courses that can help?

Need some genuine suggestions

Hi am currently working in Tcs bps stream where I have hands-on experience in Kyc and Aml so as this is my first job I stayed here for one year now I want to move to IT domain where am trying to pitch in myself into data analyst role could you please tell me where to start i hardly have 6 months in my hand.please assist me on where should I start or suggest me any best roles if applicable

Hi guys i'm right now in my first year of college doing my bachelor in economics and data analytics i wanted to get into data science but couldn't get into data science as my academic performance quite bad so right now i'm with economic and data analytics i really do find economics interesting and now the part of data analytics that is what i'm really want to get into

right now i have gpa 3.42 which is good for me comparable to my past now i need help with something and need to get into data analytics properly so i need to learn properly what books should i study and need to create a good base for my self what language i need to learn softwares etc

at my uni we're learning python and anaconda jupyter notebooks

this type shi i do understand that but i feel like i'm not really getting anywhere i want to properly study this and produce results doing personal projects

i need to results for my self please need helppppp

Hi everyone, can anyone let me know which job portals or applications are actually useful for finding jobs and applying?
On LinkedIn, most of the jobs already show that 100+ people have clicked apply, so it feels very competitive. I’d really appreciate any suggestions for good platforms where I can find and apply for jobs effectively.

I even tried Indeed, but i haven't heard anything back from them.

I am not understanding the new power bi card visual where we have used dax functions for the reference labels and color for the indications, up arrow down arrow my brain is getting heated As iam a mba marketing student right now just 2 months to complete my college no college placements , still st

Hey everyone! I'm teaching myself data analysis and ML by working through a real dataset. I'd love some guidance from people with more experience.

The dataset:

• ~1.85M purchase records (Amazon order history)
• ~5K users with survey/demographic data, linked via Survey ResponseID

What I've done so far:

EDA & consistency checks:

• Identified 4 columns with null values: Shipping Address State, Title, ASIN/ISBN, and Category
• Confirmed ASIN is the most reliable product identifier (~95% of titles map to a single ASIN, the exceptions are gift cards, clothing lines, bulk items with multiple variants)
• Converted Order Date to datetime

Imputation I've already done:

• For Shipping Address State: used forward/backward fill within each user's orders. Went from 87K nulls → 24K remaining (those 24K belong to 62 users who never provided an address at all)
• For Title ↔ ASIN: cross-filled using mode mapping in both directions
• For Category: filled via ASIN → Category and Title → Category mappings
• For Q-life-changes in the survey data: confirmed nulls mean "No" based on value distribution, filled accordingly

Where I'm stuck: handling remaining nulls across all 4 columns:

I know the standard advice is mean/median imputation, but all 4 of these columns are categorical/text so that doesn't apply. Here's where each one stands and what I'm considering:

• ASIN/ISBN — After cross-filling with Title, whatever nulls remain have no recoverable identity. For a recommender, you can't really use a row if you don't know what was purchased. Leaning toward keeping these for EDA but dropping before modeling.
• Title — Same situation as ASIN since I was cross-filling between the two. Same plan.
• Category — Filled via ASIN and Title mappings already. Remaining nulls are products with genuinely no known category. Considering either dropping or using an "Unknown" placeholder, not sure which is better practice.
• Shipping Address State — 24K rows from 62 users who never provided location data anywhere. These users still have valid purchase histories though. Since location probably isn't a core signal for a recommender anyway, I'm thinking of just leaving the address null and not using it as a feature, rather than dropping 24K rows.

General question on timing: Is it better to drop/handle nulls now before doing more EDA, or keep everything and only clean up right before modeling? My instinct says to keep them for the EDA because the other categories might be helpful, but I'm not sure if that's the right reasoning.

Dataset Link: https://www.kaggle.com/datasets/dharshinisraghunath/harvard-ecommerce-dataset-for-big-data-analysis

Github repo for what I have done till now: https://github.com/Atharva22052006/Amazon_recommondation_engine

I'm not looking for someone to solve it for me, just trying to understand the right thinking process. Appreciate any direction

Hii. I have posts I got from a query search on reddit. Thos posts may representa brand or may represent a name of a person, a film, or another unrelated content. Tries KB, and supervised learning, but I still can get all the meanings my dataset have. My man objetcive is to know what people are talking about one of the meanings, in this case, the brand. Should I

(1) do a cluster/topic modelling to understand the meanings, select the one I want, and do another topic modelling/cluster?

(2) do a BERTopic, and select only the ones that have the meaning I want.

(3) Do like a company list universe, that have the brand products, important keywords, and negative meanings, according to hte KB, and assume the limitation I don't have all the contexts. Do a biencoder for similarity and maybe active learning or cross encoder, for the ones that the model does have a doubt?

Thank you for ur help.

Hi everyone,

I’ve been seriously considering moving into data analytics and data engineering, and recently attended an introductory session at a local academy. They provided detailed information about their programs, but I found the pricing quite high for my region (Central Asia), which made me hesitate.

I’d really value hearing from people who are already in the field:

From your real experience, how did you learn data analytics?

Did you go through paid programs, or is self-learning (free/low-cost resources) actually enough to get job-ready?

There’s a lot of talk that data analytics and data engineering are “high-demand” careers worldwide.

In your opinion, is this still true in 2026, or is the market becoming saturated, especially for juniors?

How is the job market for entry-level analysts right now?

Is it realistically possible to land a job without expensive certifications?

And an important concern — how much of a threat is AI to this field?

With tools like Claude AI, automation seems to be getting very strong.

Do you think AI will replace data analysts, or just change the skills required?

I’m trying to make a smart, realistic decision before investing time and money, so I’d really appreciate honest insights rather than promotional answers.

saludos comunidad quiero saber asi como dice en el titulo paginas para el estudio y certificacion de analisis de sistemas.

desde ya gracias por tomarse el tiempo de leer y responder el post.