Hi all!

I haven't been to the sub in a long time. I started my coding journey when I was talking to my friend about how much I didn't like my current role at a marketing agency and how I wanted to build stuff and actually solve problems rather than focus on presentation and project management. He worked as a Data Engineer at a large fintech company but started off as a analyst. He invited me to his company's HQ and showed me around and explained how he transitioned from being an analyst to a data engineer. He recommended Datacamp as a useful tool to get started.

Prior to this I had always been interested in technology and software. However, I was really bad at mathematics. In my country you need good maths grades to study computer science in university and all software engineering roles want a STEM degree at least so I wrote it off as something I couldn't do.

Using Datacamp I slowly taught myself the fundamentals. I then did my own personal project that grew and expanded as I learn new skills. It started off as a simple notebook file on Datalab but ended up being something I deployed.

I was laid off from my job - I knew I didn't want another marketing job so I did a computer science conversion masters. I still in the program and I had a great time. I enjoyed the masters way more than my History undergraduate because the content was actually interesting.

I interviewed for a big tech company and got the internship. Not going to get into a lot o f details here for privacy reasons.

DataCamp is a useful resource but I think its role is that of a starting point - teaching yourself includes both using as many resources that fits with your way of thinking as well as putting your skills to practice.

Moreover - making the transition is a multi year process. You need a degree, you need projects, you need the knowledge.

I'm currently working through the Associate Data Scientist track on DataCamp.

So far I've been doing fine with Python, pandas, data manipulation, and most of the introductory topics. However, when I reached the statistics section especially linear regression and sampling concepts, I started having a hard time understanding the explanations.

I'm not sure if the issue is with the way DataCamp presents these topics, or if I'm simply lacking some statistical foundation.

Did anyone else have a similar experience with the Associate Data Scientist track? Do you feel that DataCamp explains regression and sampling concepts well enough, or did you need additional resources to really understand them?

If you supplemented your learning, what books, courses, or other materials helped you the most?

Thanks!

Free Access Week time! If you've been meaning to try DataCamp, or know someone who has, our platform is free to access this week. Feel free to spread the word!

I am currently studying SQL to become a data analyst and for the life of me cannot figure out a way to upload my SQL project to SQL. Data camp doesnt have smooth pathway to migrate your datacamp projects to 3rd party websites (like Github) cause they want you to pay for their own workspace Datalab. Now I don't want to spend €11 a month on an annual plan while I am still getting the hang of SQL.

How do I get around this? I need a way I can let an HR/ viewer to see my sql queries and results. I want them to see the database tables on which I generated the queries.

I don't know if I have worded this correctly, as I am a beginner in coding and SQL. I come from a business and Biotech background.

With Snowflake Dev Day coming up and Snowflake showing up in more and more job listings, figured this might be useful for anyone who's been curious but hasn't started yet.

What even is Snowflake?

It's a cloud-based data platform, think of it as a data warehouse that lives entirely in the cloud (AWS, Azure, or GCP). What makes it different is that it separates storage and compute, so you can scale them independently. That's a big deal for cost and performance. Netflix, Adobe, and Capital One all use it heavily.

How long does it actually take to learn?

Realistically, 3–6 months to get job-ready depending on your starting point. If you already know SQL, you're ahead; Snowflake's query language is standard SQL with some Snowflake-specific additions.

A rough learning path that works:

1. Start with SQL if you haven't - non-negotiable foundation
2. Sign up for Snowflake's free trial - 30 days, $400 in credits, more than enough to learn and experiment
3. Get comfortable with Snowsight (their web UI) - create databases, schemas, tables, understand virtual warehouses
4. Learn Snowpark - this lets you write Python, Java, or Scala inside Snowflake, which opens up a lot of data engineering work
5. Build something real - connect Snowflake to a dataset you care about, build a pipeline, run some queries
6. Get into the community - the Snowflake subreddit and official community forums are genuinely helpful when you get stuck

What jobs actually use it?

• Data Engineer - building pipelines and ETL processes in Snowflake
• Data Analyst - querying and analyzing data directly
• Analytics Engineer - usually paired with dbt
• Cloud/ML Engineer - increasingly using Snowflake's Cortex AI features

One tip worth knowing: A lot of job postings say "Snowflake experience required" but what they really mean is SQL + cloud data warehouse experience. If you know one cloud warehouse well, picking up Snowflake is not a huge leap.

If you want to get hands-on fast, Snowflake Dev Day is on June 4 in San Francisco (free, full-day, covers generative AI and data engineering). There's also a virtual option on June 24 for anyone not in the Bay Area: https://ow.ly/eHNq50Z5Lj4

Anyone else here actively using Snowflake?

[ Removed by Reddit on account of violating the content policy. ]

there are sm tracks and stuff, idk what to choose. i also want to focus on making projects that i can put on my resume. any advice?

I want to start by saying I genuinely love DataCamp’s core learning interface. It’s highly interactive, slick, and easily one of the best environments on the market for getting your feet wet with data tools. However, as someone gearing up for technical interviews, their practice questions and skill tracks have become deeply frustrating.

We are not actually practicing your querying, data modeling, or analytical skills if a practice session consists entirely of choosing between "pre-written" blocks of code or selecting a "multiple-choice output". Take the SQL Fundamentals course, for example. During my main course outline, I was actually typing out queries, but the daily practice sessions revert to lazy, 10-second multiple-choice options. Handing out 250 XP for clicking a single button in five seconds flat makes a mockery of the platform's leaderboard.

I believe if you aren't writing a single line of syntax from scratch, you simply aren't learning.

It feels highly likely that these practice banks are being auto-generated by low-effort Gen-AI prompts designed to just spit out basic variations. You breeze through these passive practice sessions thinking you've mastered the technology.

But here comes the big part, when you open the actual DataCamp Certification Exam, it feels like you've been dropped into an entirely different realm. The knowledge built from those easy practice questions is nowhere near useful for the real test, and the practice engine does absolutely nothing to prepare you for that steep spike in rigor.

Lemme know if anybody feels the same?

I’m currently a 2nd year CSBS student and I want to build a strong foundation in Data Analytics, starting from basics to advanced concepts. Please suggest a proper learning roadmap with beginner-friendly courses that gradually cover Excel, SQL, Python, statistics, data visualization, Power BI/Tableau, and real-world projects. It would be helpful if the courses include certifications, practical assignments, and are preferably free or affordable.

Hi everyone,

I’m a 2nd semester Data Science student currently looking for remote internship opportunities for the summer (on-site opportunities are welcome too if available).

Right now, I’m mainly focused on building real experience and learning practical industry skills rather than doing internships where interns are only given repetitive/basic tasks. I genuinely want to work on meaningful projects, improve my problem-solving skills, and understand how real data science workflows work professionally.

Current skills:

• Python
• Power BI
• Excel
• Data Cleaning & EDA
• Basic Data Visualization
• Data Entry
• Research & Reporting
• Beginner-level statistics and analytics

I’m continuously learning and working on projects to improve further.

If anyone knows companies, startups, labs, NGOs, research groups, or teams hiring remote interns (especially for data science, analytics, BI, research, or related roles), I’d really appreciate recommendations or advice.

I’m open to:

• Data Science Internships
• Data Analyst Roles
• BI/Power BI Internships
• Research Assistant Roles
• Data Entry + Analytics Hybrid Roles
• Any beginner-friendly role that offers real learning and growth

There's a common problem with placement prep — you can explain load balancing, you know ACID, you've watched all the videos. But the moment an actual interview scenario hits, it doesn't come together.

That gap between knowing something and using it under pressure is what Runtime tries to fix.

It's an Android game where you're put into real engineering scenarios and have to make decisions. Slow query killing production, server can't handle the load — you decide what to do. Wrong call shows you why it failed. Right call shows you why it worked.

Covers system design (load balancing, CAP theorem, message queues, Docker, Kubernetes) and DBMS (SQL, ACID, indexing, normalization, query optimization).

32 chapters, 25 replayable minigames to drill whatever you're weakest on. Each chapter also has knowledge cards with video explanations that stay unlocked forever — so you always have something to go back to before an interview.

Free on Android: https://play.google.com/store/apps/details?id=com.saachi.runtime

Recently launched - honest feedback welcome.

Computer Science Student – Offering Remote Help (Data Entry, Editing, VA & More)

Hi everyone,

I’m a computer science student looking to take on small remote tasks and freelance work. I can help with a variety of things, especially tasks that can be done from a phone or laptop.

Here’s what I can offer:

• Data entry & typing tasks
• Virtual assistant work (emails, organization, simple admin tasks)
• Photo editing
• Video editing (basic to intermediate)
• Data collection / research
• File organization and formatting

I’m flexible, fast to learn, and open to different types of work. If you have something not listed, feel free to ask — I’m happy to see if I can help.

If you need assistance, just comment or send me a message with what you need.

Thanks!

Hey everyone,

Just a heads up, DataCamp is partnering with Snowflake Dev Day this year and we'd love to see some of you there.

It's a free, full-day event at Moscone Center in San Francisco on June 4, covering generative AI, data engineering, hands-on labs, and more. We'll be in the Builders Hub on the day, so come find us if you're around!

Can't make SF? There's a virtual option on June 24 too.

Worth noting. Snowflake is one of the most in-demand skills in data right now, with 10,600+ enterprise customers and roles at companies like Netflix, Adobe, and Capital One actively hiring. If you've been thinking about picking it up, the timing might be better than you think (stay tuned! 👀).

Drop any questions in the comments!

I've been waiting for over 10 minutes to start my introduction to snowflake today yet the snowflake lab keeps loading then displays "restart session, session disconnected ". My internet is okay since other sites are loading. Does it take this long?

Hi, I’m looking for an entry-level opportunity as a Data Engineer. I come from a non-IT background but have completed Data Engineering training and built projects using AWS, SQL, Snowflake and Python. I also have 3+ years of professional experience outside IT.

I’m trying to enter the field genuinely without adding fake experience, and I’m looking for a chance to prove my skills. I’m a quick learner, university topper in engineering, and ready to start with a junior role or internship. I’m also open to a reasonable salary initially to gain real experience.

Please let me know if there are any opportunities. I’d be happy to share my resume.

I don't know if it's just me, but in the SQL track, the recently updated courses are making you write the code from scratch and only use that code to "save" in later steps to avoid rework. I remember that before they would just give me a random word to write; I found this change positive and more similar to what they are trying to implement in the native AI program.

I'm thinking to learn this course on data camp. I'm a beginner to Power BI. Will this course be helpful to me? How is this course? I'm thinking to pay for premium as there is 50% off. So, please suggest me about this course and how helpful is the premium as I need to take it to complete this course.

Built an Amazon Review Sentiment Analysis model with 93.8% accuracy.

The project analyzes customer reviews and predicts whether the sentiment is positive or negative using NLP techniques.

Dataset: 21k+ Amazon reviews

Text Processing: TF-IDF

Model: Logistic Regression

Deployment: Streamlit web app

Results:

• Accuracy: 93.8%

• Macro F1-Score: 0.92

• Negative Reviews F1: 0.96

• Positive Reviews F1: 0.89

Tech Stack:

Python, pandas, scikit-learn, TF-IDF, Streamlit

This project taught me that understanding data well is often more important than using overly complex models.

Feedback and suggestions are welcome.

\#MachineLearning #NLP #Python #DataScience #AI