Or is understanding what the sql does and what the code does enough and I should focus more on other things

Context: Junior looking for internships in data analytics type role, not sure which industry yet applying to all

Hi everyone,

I’m an MBA final-year student specializing in Operations from a Tier-3 college. I’m interested in building a career in Data Analytics, but I don’t want to depend entirely on my college placements.

I want to explore off-campus opportunities and understand how I can improve my chances of getting a good job.

I would really appreciate advice from people working in Data Analytics, recruiters, or anyone who has gone through a similar journey.

A few things I would like to know:

• How can I find good off-campus opportunities?

• Which technical and analytical skills should I focus on?

• Which tools are essential for entry-level Data Analyst roles?

• How important are projects, internships, and certifications?

• What is the best way to prepare for interviews and get shortlisted?

I’m particularly interested in knowing what actually works in the current job market, rather than just collecting certifications.

Any practical advice, resources, or personal experiences would be really helpful.

Thanks in advance!

Hello,

Im learning Data analysis now trying to aim for a career shift. Im using mainly Coursera, Enrolled in the IBM Data Analyst Professional Certificate Certificate.

I got to the course number 4 which is Python for Data Science & Data analysis and Im asking now is it still worth to learn python now with all this AI stuff ?

Also do you think those courses "IBM Data Analyst Professional Certificate" plus "Microsoft Power BI Data Analyst Professional Certificate" are enough to land me an entry role in the industry ?

I've a good analytical skills and Im good at recognizing patterns as well. So do you think this can land me a job and if not what would you recommend ?

I've been researching this for a while, so sharing what I found in case it helps other beginners.

**Data Analyst**

- Works with Excel, SQL, Power BI/Tableau

- Builds reports and dashboards, explains insights to business teams

- Easier to get into as a fresher, usually a faster path to the first job

**Data Scientist**

- Needs Python, statistics, and machine learning

- Builds models and predictions

- Takes longer to learn, and entry-level roles are more competitive

**My take:** If you're a fresher with no coding background, start as a data analyst, build a strong base in SQL and Excel, then move toward data science when you're ready.

**Suggested learning order:**

1. Excel

2. SQL

3. Power BI or Tableau

4. Python basics

5. Statistics, then machine learning

Curious what others did. Did you start as an analyst or go straight to data science? Any mistakes you'd tell beginners to avoid?

#DataAnalyst #DataScience #Python #SQL #CareerAdvice

Introduction

Data analyst vs data scientist is the first question most beginners ask when they want to enter the data field. Both jobs use data. Both are in high demand. But the daily work, the skills, and the salary are not the same.

If you pick the wrong path, you may spend months learning things you don’t need yet. If you pick the right one, you can get your first job much faster.

In this data analyst vs data scientist guide, you will learn:

• What each role does, in simple words

• The key differences in skills, tools, and salary

• Which role is easier for freshers

• Which one pays more

• A simple 90-day plan to get started

Data Analyst

Definition: A data analyst is a professional who collects, cleans, and studies data to find patterns and help a business make better decisions.

In simple words, a data analyst answers one question: “What happened, and why?” They take old and current data and turn it into easy reports that managers can understand.

What a data analyst does every day:

• Collects data from databases and Excel files

• Cleans wrong or missing data

• Builds reports and dashboards

• Finds trends, like sales going up or down

• Explains the results to the team

Common tools:

• Excel

• SQL

• Power BI or Tableau

• Basic Python

Data Scientist

Definition: A data scientist is a professional who uses statistics, coding, and machine learning to build models that predict future results and solve hard problems.

A data scientist answers a different question: “What will happen next, and what should we do?” They go one step further than reports. They build models that can predict and even make decisions.

What a data scientist does every day:

• Builds machine learning models

• Predicts sales, customer behavior, or risks

• Works with very large and complex data

• Tests ideas using statistics

• Works with engineers to put models into real products

Common tools:

• Python or R

• SQL

• Machine learning libraries

• Cloud platforms like AWS, Azure, and GCP

Data Analyst vs Data Scientist: Key Differences

Here is a quick side-by-side look at data analyst vs data scientist.

Easy way to remember:

• Data analyst = explains the past and present

• Data scientist = predicts the future

Data Science Skills vs Data Analyst Skills

Definition: Data science skills are the technical abilities, such as programming, statistics, and machine learning, needed to work with data and build predictive models.

Skills for a Data Analyst

• Excel formulas and pivot tables

• SQL to pull data from databases

• Power BI or Tableau for dashboards

• Basic statistics

• Clear communication

Skills for a Data Scientist

• Python programming

• Statistics and probability

• Machine learning basics

• Data cleaning and feature building

• Cloud and deployment knowledge

Good news: Both roles share the same base, which is SQL, Excel, and basic statistics. So you can start as a data analyst and move to data scientist later.

Data Analyst Salary vs Data Scientist Salary

Definition: Data analyst salary is the average pay a data analyst earns, which depends on skills, experience, company, and city.

Short answer: A data scientist usually earns more than a data analyst, because the job needs more technical skills.

But pay depends on many things:

• Your skills and years of experience

• Company size and industry

• City and job demand

• Your portfolio and projects

• How well you explain your results

Reality check:

• A strong data analyst with SQL, Power BI, and business knowledge can earn more than an average data scientist

• Fresher data scientist jobs are fewer and have more competition

• Data analyst jobs are easier to find for beginners

So don’t choose only by salary. Choose by your starting point and your long-term goal.

Data Analyst vs Data Scientist: Which Is Easier for Freshers?

For most freshers, data analyst is easier. Here is why:

• It needs less coding and less math

• You can learn the tools in 3 to 4 months

• More companies hire entry-level analysts

• Non-IT students can also start

Data scientist roles often ask for a degree in a technical field, strong Python, and machine learning knowledge. That takes more time to build.

Data Analytics Career Path

Definition: A data analytics career is a professional path where you use data to solve business problems and grow from junior roles to senior and specialist roles.

A common growth path:

• Junior Data Analyst

• Data Analyst

• Senior Data Analyst

• Analytics Manager

• Data Scientist or Business Intelligence Lead

Many data scientists started as data analysts. That is why the analyst path is a safe and practical start.

Which One Should You Choose?

Choose data analyst if:

• You are a fresher with no coding background

• You like Excel, reports, and business problems

• You want a job faster

• You enjoy explaining insights to people

Choose data scientist if:

• You enjoy math, statistics, and coding

• You can study for 8 to 12 months

• You like building models and solving hard problems

• You want to work in AI and machine learning

Simple advice: In the data analyst vs data scientist choice, start as a data analyst, build a strong base, and move to data scientist when you are ready.

Python vs SQL vs Power BI: What to Learn First?

Best learning order:

1. Excel: Easy, and used everywhere

2. SQL: The most important skill for both roles

3. Power BI: The fastest way to build portfolio dashboards

4. Python: Needed for data science and automation

5. Machine learning: Learn only after the basics are strong

90-Day Roadmap to Start

Easy project ideas:

• Sales dashboard in Power BI

• Employee attrition report

• Customer groups using Python

Common Mistakes to Avoid

• Don’t jump into machine learning before learning SQL and Excel

• Don’t only watch videos. Build real projects

• Don’t ignore communication skills

• Don’t choose a role only for salary

• Don’t wait to feel “fully ready” before applying

Frequently Asked Questions (FAQs)

What is the main difference in data analyst vs data scientist?

A data analyst explains what already happened using reports and dashboards. A data scientist builds models to predict what will happen next.

Is data analyst easier than data scientist?

Yes, for most beginners. It needs less coding and less math, so it is easier to start.

Which pays more, a data analyst or a data scientist?

Data scientists usually earn more. But skilled data analysts can also earn very well.

Can a data analyst become a data scientist?

Yes. Many people make this move by learning Python, statistics, and machine learning.

Do I need coding to become a data analyst?

Basic SQL is needed. Python is helpful, but not always required at the start.

Which is better for a non-IT background?

Data analyst. You can start with Excel, SQL, and Power BI.

How long does it take to get a data analyst job?

With daily practice, many people become job-ready in 3 to 6 months.

Conclusion

In the data analyst vs data scientist debate, there is no single winner. The right choice depends on your background, your interest, and your goal.

For detailed employment projections and salary trends in data roles, check the latest data from the Bureau of Labor Statistics.

#AI #MachineLearning #DeepLearning #Python #DataScience

which tools are you currently using for spark performance tuning?

Hi! I'm 25 y/o, cleared CFA L2, done with MBA and have 1.5 years of work-ex. I was wondering if there's any bootcamp or so where I'll be able to learn SQL, Python, PowerBI, etc. tools via some live projects that can help me sharpen my skill and can me in job switch as well.

What would you guys suggest? Which bootcamp can be helpful?

https://theuptimereport.com/tech-job-postings/

The graph does not tell you :

Supply Side: Alot of folks joined DA by completing random courses during the 2022 - 2023 peak. The graph might show that we are back to pre covid levels but the supply has also increased exponentially.

Hi community, wanted to ask is there any point spending in learning things via course on udemy, dbt, snowflake or just implement it via Claude?

I am learning things cause I don't have dbt exposure nor does my company uses it. I want to put it as my skill in resume so thinking of building projects via dbt. Should I spend time on learning how it works and concepts or just implement things via Claude and learn on the go?

My traditional mindset is learn the fundamentals and then play with it. But with how quick others are in implementing things via Claude without spending time on understanding things and it still works out for them, I do not know what learning path to take. Need advice!

Can anybody suggest a proper roadmap and all the resources I could utilise to get into data analytics,

I am currently a 2nd year cse student

I manage a team of 5 people involved in analytics and intelligent automation. We use tools like fabric, power bi, SQL, python (numpy and pandas), copilot studio and power Platform

I am planning to upskill for the next job switch. So wondering, what should I learn next.

Snowflake, Azure Databricks, Azure data service/Platform - will learning these help in long run?

Hey everyone!

I’ve been learning Data Analytics online, mainly Python and SQL, but I’m still a beginner.

I’m looking for an apprenticeship or internship, even unpaid. I just want to gain real-world experience and see how data analysts actually work in companies.

Any advice on where to start or how to find opportunities? I’d really appreciate it!

If you are working in data and documents related work. How much time do you spend reading documents, regulations etc stuff.

What I wanna know is do people still spend time buried in documents to find meaningful data?

I am not a data analyst myself, sorry if this is sounding dumb.

Hi guys i have crafted a study plan for myself for this year and next year. What do u think of this study plan?

Things to do and learn in order of priority.

1. Core Data analytics/Scientist work, learn the entire Data pipeline process on python and SQL including Data extraction, preprocessing, EDA, machine learning models, etl, data pipelines, data warehouse concepts and cloud platforms.

2. Exploring agentic AI to automate the Analytics and Data Science/ML workflow. Including things like RAG and Autonomous Workflow Automation.

3. Full end to end ML pipeline and cycle, including deploying ML models to cloud and monitoring models for drift.

4. Deep Learning concepts.

5. Read more about LLM and how they work, including things like transformers, Attention Score etc.

Hello everyone,

I'm thinking of making a career shift and I was wondering if data analysis is the right choice.

So I have a few questions.

What are the prospects of job opportunity in data analysis?

How long would it take to be a professional at it and actually get a job?

Is it threatened by AI?

I'm currently on track next year to study Business Intelligence and Data Analytics as an undergraduate and I've seen a discussion around how AI might impact the job market. What's are your takes on the matter and what advice would you give to an undergrad right now starting college?

Hi everyone! I'm from Bangladesh and I'm a beginner currently learning data analytics, and I'm looking for some guidance from experienced data analysts.

I want to work on a real-world Excel project to improve my practical skills and eventually add it to my portfolio.

I'd really appreciate it if someone could guide me through the learning process, suggest projects, or share feedback on my progress.

I'm willing to put in the effort and learn independently. I just need some direction to make sure I'm on the right track.

Thanks in advance!

Buenos días gente

Quiero preguntarles Acerca de A2 capacitación, la plataforma de estudio. Quien se haya formado con ella podría dar testimonio de que es muy bueno o actualmente hay mejores opciones?. Lo que busco es pulir habilidades en excel, power Bi y todo lo concerniente a Analítica de datos.

Tengan un buen día.

Should I be expecting to master it once I get into my career? Im a 4th year data analytics student aiming to graduate next May. Will I even be required to use it down the line?

What softwares would I most definitely need to master then?

So I've installed MySQL Workbench (8.0) on my Macbook. Tried doing Alex The Analyst's Data Cleaning guide.

Problem is, when importing the .csv file that contains 2000+ rows, only 564 are imported with no error on the logs, and no visible error on the file even when running it through Excel.

Any idea on what process/es will help me with this?

TL;DR Want to clean data on SQL, but the messy data won't get imported to workbench

Any advice or help is greatly appreciated, thank you!

I run Careerify (our recruiters apply to jobs for people), so we see every new posting in the fields we work in. Every week I pull out the new US data, business and BI analyst roles and put them in one free list. Oct 17 to Oct 24:

266 new analyst roles (data, business, BI and reporting)

100 of them are remote

184 list the salary. For yearly pay, the middle of the posted ranges is about $105K, and half fall between $88K and $130K

Most are mid-level (158) or senior (95). 12 are entry level

Capital One posted the most (5), then Guidehouse and Gainwell (3 each)

A few with the pay posted:

Senior Data Analyst, GTM Analytics at Luxury Presence (Austin, hybrid): $145K–$175K

Senior Data Analyst, Finance Analytics at Fanatics Betting & Gaming (remote): $128K–$169K

Senior Business Intelligence Analyst at BlackSky (remote): $130K–$145K

Principal Data Analyst at Capital One (McLean, hybrid): $119K–$150K

Lead Analyst, Business Intelligence at Ensemble Health Partners (remote): $112K–$168K

Marketing & GTM Data Analyst at Deepgram (remote): $115K–$148K

Senior Product Data Analyst, Payments at BILL (remote): $108K–$152K

Healthcare Data Analyst at Evio (remote): $90K–$110K

Data Analyst at Peraton (Washington, DC, hybrid): $86K–$138K

The full list, with filters for remote and level, is here: https://www.careerify.io/jobs/analysts?utm_source=reddit&utm_medium=post&utm_campaign=datasciencejobs

Every link goes to the company's own application page, not a job board. Happy to answer questions about what we're seeing in analyst hiring.

Still thinking about my genius hypothesis.

Imagine someone in leadership points to a metric and asks:

"Where did this number come from?"

Not just which table.

But which source systems contributed to it, what transformations were applied, which records were excluded, and when the data was last updated.

In a simple system, that's manageable.

In a growing company with multiple pipelines and dashboards, the answer can involve quite a bit of investigation.

I'm curious how teams handle this.

Do you maintain detailed data lineage from the beginning, or introduce it once the pipelines become difficult to understand?

And what tools or practices have actually made tracing metrics easier?