r/learndatascience

Hey everyone,

I’m trying to understand the difference between Data Science and Data Analysis because both sound very similar to me. From what I understand, both work with data, but I’m confused about how their actual work is different in real life.

Can someone explain in simple words what makes a Data Analyst different from a Data Scientist? I also want to know which field is better for beginners, which one needs more coding and math skills, and which has better career opportunities in the future.

I’d appreciate easy explanations or real-life examples because I’m completely new to this field.

Thanks!

What would be a preferred course to take between data science and Ai, data analysis and data engineering. Which one is more marketable and has for job opportunities both in freelance and job space

Hi I am a CSE student starting to learn data science by taking a 60 days challenge to complete it.

I already know some libraries like numpy, pandas, seaborn and matplotlib. And I will be revising them before moving deeper into ML, scikit learn, projects and deep learning.

For the next 60 days I will be posting what i studied, concepts learned, what I built and the whole progress.

The Goal is to learn, stay consistent and become good at data science.

If anyone else is doing something similar then feel free to share some knowledge, advice or resources.

Day 1 starts Today 🔥

Today was my first day of 60 day Data Science Challenge.

What I did today:

1. Revised Numpy basics

2. Understanding arrays and their properties & functions

3. Array slicing

4. Vectorized operations

5. Fancy indexing and Boolean Masking

6. Broadcasting of arrays

7. Revised Pandas

8. Series and Dataframe

9. Dataframe properties and functions like loc, iloc, etc

10. Filtering of Data with conditions

11. Data cleaning and handling missing values

12. map and replace functions on dataframe

13. Sorting and Ranking

14. melt and pivot functions to reshape dataframe

15. groupby and merge function

16. Reading and writing files in pandas

This was an effective day as i revised Numpy and Pandas libraries and got refreshed with it. Since I have already studied these libraries earlier, it was easy to revise them.

Looking forward to start data visualization with matplotlib and seaborn which i have studied earlier and will revise them tomorrow.

So yaa thats all for now and meet you Tomorrow.

GOODBYE 👋🏻

so i am 22 rn (soon to be 23), and a graduated in data science from a tier 3 clg since 2024, couldn't find a job, not good at communicating, i really want a job, i love this field its just i couldn't focus on one thing because of this job market too some says become a data analyst but there are so many scams going on, for data science fresher they are asking for exp, started to learn data eng but now im feeling like im too late

parents are also concerned about me, Live in a small city, how do i reclaim my life, so does anyone have any advice for me?

hi, i just finished my second year of university and was looking to get a new laptop for data science, computer science, and machine learning stuff

my budget for a new laptop is $2k max, thanks in advance for the recommendations

So I've this friend and she needs help with a data science course or roadmap which to cover first what to do next. YouTube vids and playlists are fine but must be structured and I want someone to send me the resources as per roadmap. Any pirated lecture link will work as well.

Thanks ;)

I’ve noticed a lot of people learning data science and cybersecurity don’t really get how data security works in real SaaS environments, even though it shows up everywhere in modern companies.

In practice, most data today lives in tools like Google Drive, Slack, Salesforce, etc. The main risks aren’t just hackers breaking in” it’s things like:

Files being overshared internally or externally

Old access permissions never being revoked

Contractors or employees still having access after leaving

Sensitive data quietly spreading through integrations and exports

This is where concepts like:

DLP (Data Loss Prevention)

SSPM (SaaS Security Posture Management)

SaaS security governance

actually come in but they’re often explained in a very abstract way.

I’m trying to break this down in a more practical way for learners:

how data actually moves, where it leaks, and how companies realistically control it

Hi everyone, I need some honest advice and I think this community would give me a straight answer.

I have a Bachelor's degree in Education and I trained as a teacher but I genuinely hate it. It never felt right for me.

Over the past few years I have been teaching myself new skills — virtual assistance, data entry, workflow automation using tools like Zapier, Make and Airtable. And honestly? I love it. For the first time I actually enjoy what I'm learning and I can see myself building a career in it.

Now I'm seriously considering going back to school to do a degree in Data Science or Data Analysis. But I'm torn because:

I'm already 30 and starting over feels scary

I don't know if a degree is necessary or if self learning and certifications are enough

I've heard data science is very math heavy — I have a math background from my education degree so that could help

I'm based in Kenya so opportunities here may be different from what people in the US or UK experience

Has anyone made a similar career switch? Was going back to school worth it or would you recommend online certifications instead? Would love to hear from people who have actually done this.

When I was a student, I often needed very simple machine learning explanations before exams not a full course, not heavy math from the first minute, just someone explaining the intuition clearly.

That’s why I started making short beginner-friendly ML videos.

The idea is to explain topics in a simple visual way first.

I’m not trying to replace proper courses or textbooks. I’m trying to make the “okay, what is actually happening here?” part easier to understand.

For people learning ML does this kind of simple explanation actually help, or do you prefer more technical depth from the start?

I shared one video here, but I’m mainly looking for honest feedback on the format and clarity.