I need help to decide on approach to self learning data science or data analysis and the bad/good of moving into data science and🥺 please?

[Tinana]

So this year I started college and I’m doing economics I have no idea why I chose economics but so far I’m really enjoying it but everyone around me keeps telling me I can’t do anything with an econs degree. After my principal of economics midterms I was the only student that got an A and so my professor called me into his office and began to advice me to try taking python classes to help link my economics degree to data science. Ever since he said it I’ve been looking into online resources to begin learning data science but there are sooo many sources that it’s confusing where to start from 🥺 I have like 3 years and 4 months till I graduate and I’d like to make good use of the time to learn more about data science or data analysis and maybe venture into other tech courses 🥺 so that by the time i graduate i can make a decision on what I want to do. can I please get some advice on how to go about it and if my plan is good 🥺 and any sources you’d recommend me watching or reading on to be better. Can you please tell me the advantages of moving to data science from economics. I know it’s not going to be easy for me but I’m willing to try🥺

[Adam1978]

You pick script or programming language based on what you try to solve/create. Not the other way around. Don't be the one with a hammer seeing everything as nails. Python, perl, ruby, php, Javascript. Are all tools which can be used for similar things. Decide first what you want to use it for and picking a language might depend on a existing code base or community. So you don't need to write things from scratch.

[Tinana]

I have no coding experience at all or know what I would want to learn or should learn or what the languages mean or serve

[Adam1978]

If you had what would you use it for?

[Tinana]

I don’t know, I’m so new into this

[Adam1978]

Is there nothing you wish you could get the computer or phone do for you that isn't just out there ready for installation?

[psychkindle]

If SkillShiksha offers such a program, it probably gives big lessons on important parts of data science. This might involve studying data analysis, machine learning, statistical modeling and big data methods. It could also include lessons on how to show information in pictures or charts clearly called "data visualization". Students can also learn the programming languages often used in data science, like Python and R.

These programs often include hands-on projects and real world applications, which are very important. They help students use their theory knowledge in real life situations. In addition, classes might discuss moral issues in data science. They can also teach you how to talk about your findings clearly with people who are not technical experts.

SkillShiksha could make its program special by using real-world examples from the business world, giving customers a chance to learn from experts and work with companies on projects or internships. The program might also have parts about professional growth to get students ready for changing needs in the data science world.

In short, a Master's in Data Science from SkillShiksha or any good school should give you a balanced learning about data science. It combines theory with practice to get ready for many jobs linked to this fast-paced field. It's important to look into the details of SkillShiksha program. This will give you correct and current information.

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[DeepakVermaSub]

If you’re thinking about self‑learning data science / data analysis, here’s a breakdown of approaches, what’s good, what’s tough, plus my suggestions. If you tell me your background (math / programming / time available) I can give a more tailored plan.

Key Differences: Data Analysis vs Data Science

Before deciding which path to go deeper on, it helps to understand what each involves:

Feature Data Analysis Data Science
Focus Interpreting data, summarizing (“what happened?”), reporting, dashboards, insights. Building predictive models; extracting patterns; possibly working with large & unstructured data; sometimes building ML/AI systems.
Tools & Skills Excel, SQL, data visualization, basic stats. Good communication of insights. Stronger programming (Python, R), statistics + probability, ML algorithms, possibly big data, deployment, cloud infrastructure.
Complexity Moderate data wrangling; less focus on model deployment. More complex – data cleaning + model building + validation + possibly productionization.
Use cases Business reporting, operations, marketing analytics, dashboards. Predictive analytics, recommendation systems, anomaly detection, etc.

Sources: comparison guides show that data scientists need advanced statistical modelling, machine learning, deeper programming and sometimes domain / big data tools.
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Good Things (Pros) of Moving into Data Science / Data Analysis

Here are some advantages if you choose to learn and move into these fields:

High demand, good salaries: Data‑driven decision making is big now; firms want analysts, data scientists. Good supply/demand.
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Flexibility in roles / industries: Every industry needs data work: finance, healthcare, e‑commerce, marketing, government etc. You can apply data skills almost anywhere.

Learning opportunities: It pushes you to build quantitative reasoning, statistics, thinking about data, programming; these are useful even beyond the job.

Impact / insights: You can uncover useful business or scientific insights; influence decisions; maybe automate or improve processes.

Growth & evolving field: As AI, ML advance, there's scope to keep growing; staying relevant by learning new tools.

Difficulties / Challenges (Cons)

On the flip side, there are some things to watch out for:

Steep learning curve: Especially if you don’t already have math / stats / programming. Concepts like probability, linear algebra, machine learning, etc. require time and effort.

Messy real‑world data: Much of the time, data is incomplete, dirty, has missing values. Cleaning/preparing data often takes way more time than running fancy models.

Resource / tools: Some tools require computational resources (large RAM, cloud, etc.). ML models may need GPUs. If you're doing this from home with limited hardware, that can slow you.

Constant updating: The field evolves fast. Keeping up with new algorithms, tools, best practices, data privacy / ethics, etc.

Competition, expectations: Many people are learning similar skills; employers often expect some project / portfolio or domain knowledge; sometimes even advanced degrees for certain roles.

Ambiguities with roles: It’s not always clear in a job ad whether someone means “data analyst” or “data scientist,” what level of ML vs reporting vs cleaning vs deployment is expected. If mismatch, you may struggle.

Self‑Learning: What Works, What Doesn’t

If you plan to self‑learn, here are pros and cons, plus tips to make it effective:

What works well

Lots of good free / paid online resources (courses, MOOCs, tutorials).

You can learn at your own pace.

Being able to pick topics you need or want: e. g. start with SQL, data viz, then stats, then ML.

Building your portfolio / doing small projects is very doable and helps a lot.

Being able to combine learning with something practical (e. g. relating to your current job or domain).

What are the common pitfalls / cons

Hard to maintain discipline / motivation when you don’t have deadlines or structured accountability.
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Risk of knowledge gaps (not knowing which topics to learn in which order).
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Lack of feedback: you may do projects incorrectly or follow suboptimal methods without realizing.

Limited exposure to production level problems or messy, large datasets; often online courses are clean data, small.

Might spend too much time learning theory or irrelevant tools instead of what is practically useful.

How to Plan Your Self‑Learning Path (Suggested Roadmap)

Here’s a suggested way to go about it so that you avoid pitfalls and build useful, employable skills.

Assess where you are now

How comfortable are you with programming (Python / R)?

How strong is your math / stat background (probability, basic statistics)?

Do you have access to hardware / tools (laptop, internet, maybe cloud resources)?

Time you can dedicate per week.

Set a goal

What job / role are you aiming for? Data Analyst, Junior Data Scientist, etc.

In what domain (finance, healthcare, marketing, etc.) if you have preference.

What tools / outputs you want to master (e. g. dashboards, statistical models, ML).

Build foundational skills

Programming: Python (or R). Get comfortable with basics: data structures, control flow, functions.

Data manipulation: with libraries like Pandas, working with CSVs, cleaning data etc.

SQL: querying databases, joins, aggregations.

Statistics & probability: descriptive stats, distributions, hypothesis testing, confidence intervals.

Visualization and exploratory analysis

Tools like Matplotlib/Seaborn, or business viz tools like Power BI, Tableau.

Learn to communicate insights: plots, dashboards, stories.

Hands‑on projects

Find datasets (Kaggle etc.) and do mini‑projects: analysis + reporting. Show your process, what insights you found.

Try to replicate something real: e. g. analyze sales of a shop, sentiment analysis, etc.

Machine learning basics (if going into “Data Science”)

Understand regression, classification, clustering, evaluation metrics, overfitting/underfitting.

Use scikit‑learn, basic ML libraries.

Advanced / Production / Deployment (optional but useful)

If possible, learn how to deploy models, version control (Git), compute workflows.

Some work with cloud (e. g. AWS / GCP) or big data tools if required.

Portfolio + Real‑world experience

Build a few projects, publish on GitHub.

If possible, do internships, freelance, contribute to open‑source or data challenges.

Keep learning & adapting

Stay updated on new tools, methods (deep learning, GenAI, etc.).

Learn domain knowledge in the area you want to work in.

Which One Should You Pick? Data Analysis or Full Data Science?

Here are some guiding questions to help you decide:

Do you prefer more programming, models, algorithms vs more interpretation, business understanding, dashboards?

How much time do you have to invest? Data science (especially ML) takes more time.

What are the job prospects / demand in your location or domain? Sometimes “data analyst” roles are more available initially, then you move to data scientist.

Do you like math / statistics / machine learning? If you are interested, going the full way might be satisfying. If not, data analysis may still give you a solid, useful career.

My Suggestion (If It Were Me)

If I were you, with no strong background (just assumption), I'd probably start with data analysis first: master that, build some projects, understand data, stats, viz. Once comfortable, gradually expand into data science / machine learning parts.

You get confidence fast, you start delivering value, can decide whether the extra complexity of machine learning is worth the effort in your domain.

[Putinspenls1mm]

So many 🥺s

[Tinana]

😂😂😂 I’m trying to plead