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anonymusbosch · 2 months ago

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on wanting to do a million things

prompted by @bloodshack 's

i wanna learn SQL but i wanna learn haskell but i wanna learn statistics but i wanna start a degree in macroeconomics also sociology also library science but i wanna learn norwegian but i wanna learn mandarin but i wanna paint but i wanna do pottery but i wanna get better at woodworking but i wanna get better at cooking but i wanna bake one of those cakes that's just 11 crepes stacked on top of each other but i wanna watch more movies but i wanna listen to more podcast episodes but i need to rest but i need to exercise but i wanna play with my dog but i wanna go shopping but i need to go grocery shopping but i need to do the dishes but i need to do laundry but i need to buy a new x y and z but i need to save money but i wanna give all my money away to people who need it more but i wanna pivot my career to book editing but to do that i have to read more and i wanna read more nonfiction but i wanna read more novels but i wanna get better at meditating but i wanna volunteer but i wanna plan a party but i wanna go to law school. but what im gonna do is watch a dumbass youtube video and go to bed

I think I've been doing slightly better this year about Actually Doing Things. not great! but I do a lot and I've been "prototyping" ways to get closer to doing as much as is possible. and if I actually talk about it it's a bunch of very obvious statements but I'll try to make them a little more concrete

rule number one: experiment on yourself

there's no one approach that's right for everyone and there's not even one approach for me that works at all times. try things out. see what works. pay attention to what doesn't. try something else.

rule number two: ask what's stopping you and then take it seriously

example: I often want to do Everything in the evening at like 2 PM, but then get home and am tempted sorely by the couch, and then get stuck inertia'd and not doing much but being tired and kind of bored. why?

if I don't have plans, it's easy to leave work later than planned and hard to make myself do something by a specific time

i'm generally tiredish after work. 4 out of 5 times, that'll go away if I actually start Doing Something, but 1 out of 5 it's real and I will go hardcore sleepmode at 8 PM and just be Done

i use up a ton of my program management/executive function/Deciding Things brain at work and usually find it noticeably harder to string together "want to do Thing > make list of Things > decide on a Thing > do Thing" after I'm home. Even if I have a list of Things to Do, how does one decide! how does one start! and god forbid there's a Necessary thing. then it's all downhill

therefore, mitigations: have concrete time-specific plans in advance.

if I have an art class at 6:00 PM I need to leave work by 5:15 and NO LATER and I can't get sucked into "oh 10 more minutes to finish this" *one hour later*

that also means I have to have a fridge or freezer dinner ready and can't spend 45 minutes cooking "fuck it, what the hell did I put in the fridge, why don't we have soy sauce" evil meal that is not good

plans with friends: dinner! art night! music night! repair-your-clothes night! seeing a show! occasionally, Accountability Time where a friend comes over for We Are Doing Tasks with tea and snacks etc.

for some reason I'm way better about Actually Doing Things when the plan exists already. magically I overcome couch inertia even though I am the same amount of tired! and while I never learn the ability to decouch without plans I at least learn to make them

still working on:

a "prototype" for maybe next month is a weeklyish Study Session for a thing I want to learn about. I want to somehow make it employer-proof (I am accountable to some entity to being at place X at time Y) and haven't figured out a good way. Maybe I can leverage that the local library is open til 8 on wednesdays and somehow make it a Thing? maybe I'll try it!

oh god oh fuck the thing about plans is that if you want to have them you need to make them. christ. a lot of the time I can cover this with some combo of weekend planning + recurring events (things like weekly friend dinner/weekly class) + having cool friends who reach out proactively but it still requires active planning and it can fall thru the cracks

rule three: cool friends

they can take you to things

they can remind you that you can do whatever the fuck you please

i have a friend who is somehow Always doing cool classes and learning shit. and this reminds me that I can ... do that. and sometimes I do

you can take them to things!!

rule four: try to kill the anon hate in your head

obv this depends on your circumstance but sometimes it's worth it to me to look at constraints that "feel real" and check whether they're an active choice I made thoughtfully or, like, the specters of people I don't know judging my choices

time and money are obvious ones. recently was gently nudged towards looking at whether i could give myself more time to Do Things by cooking less. imaginary specters of judgmental twitterites: "it's illegal to spend money. if you get takeout you're the first up against the wall when the revoution comes. make all your lunches and dinners and hoard the money for Later. for Something. how dare you get lunch at the store. you bourgeois hoe. taking charity donations from the mouths of the poor cause you don't have your life together enough to cook artisanal bespoke dinners every night. fuck you." and obviously eating takeout 24/7 is not the answer, but realizing I was not making an active choice helped me try making the active choice instead. "how much do I actually want to balance cost, time, tastiness, and wastefulness of my food, given my amount of free time and my salary and the tradeoff against doing something else? can I approach it differently to do more quick cheap food + some takeout?" -> current prototype: substitute in 1 takeout dinner or restaurant-with-friends a week, 1 frozen type dinner, and then batch cook or sandwiches lunches w/ "permission" to get fast lunch at the store. we'll see how it goes!

i am really really bad at this and find it helpful to talk to other people who can help point out when I'm being haunted by ghosts about it.

rule five: what would it take? what's the next step?

this one i give a lot of credit to @adiantum-sporophyte in particular for, especially for prompting me with questions when I muse about the million-ideal-lives on car rides. what would it look like to do xyz? what's something I could do right now to move in that direction? what's the obstacle? like, actually ask the question and think through it. with a person talking to you! damn! maybe the obstacle to x is that I don't know if I'll like it or if I just like the idea of it. and I don't want to commit to x without knowing. Okay, so maybe an approach would be to find someone who does x and talk to them about how their life is, or maybe it's "spend 15 minutes looking up intro-to-x near me", or "actively schedule 1 instance of x", or something like that. Or maybe it's that I don't know what it takes to do x. Okay, how about on Tues after dinner Adiantum fixes a sweater at my apartment while I spend 20 min looking at prereqs for x. like, it's so basic to say "to do a thing, you could try figuring out how to do it" but I think the important thing here is the feedback/prompting to even recognize "hey, step back, if you don't know the next step then figuring out the next step is the next step"

rule six: habits

prototyping: exercise

I do a lot better when I exercise in the mornings. I do a lot better when I do PT exercises regularly. For a while I was doing PT with friend in the morning every morning before work (accountability! a friendly face to make it more pleasant!) but that didn't really solve - it's not the kind of exercise that makes me feel awake/active, it's like dumb little foot botherings. but: having the habit of morning exercise made it easier to swap out 2 of the 5 days for more intense exercise, and then to swap those 2 for a different more intense exercise when I needed a break. it's easier to build a low-effort version of the habit and then work in the higher-effort one than to just Decide to be the kind of person who gets up at ass o clock to do cardio or whatever

rule seven: set up the structure of your life to make it easy

this is also a "duh" thing but like. on so many levels it comes down to structure your life to make the choice more doable. this can be something like "i structure my life to make vegetarian cooking baseline and vegan cooking the majority by stocking the pantry with staples and spices from cuisines that work well that way" or "i chose an apartment that lets me commute by bike" or "i have my camping gear put away in a fashion that makes it easier to gather frequently and lowers the barrier to trips" or "i keep physical books around to prompt myself to read xyz" to "i don't use instagram or twitter or snapchat or facebook" to . idk.

and in terms of charitable giving: similar deal. I have an explicit budget at the beginning of the year (~10% of my before-tax income), I know in advance what charities I give to, and I know what timing I will use (basically, alerts for donation matching around specific fundraising times). Anything outside the Plan comes from my discretionary budget/fun money. That makes it less of a mental load (the choice is already made; I don't grapple with every donation request or every bleeding-heart trap because I have a very solid anchor on "I give to xyz, the money's set aside") and it's armor against impulsive-but-not-useful scrupulosity. I structure the rest of my spending/life to prioritize a set amount and it makes it easier to follow through

rule eight: if you can do it at work a tiny bit that counts for real life

(infrequently used)

"hi mr. manager I think it would be great if I could use enough SQL to make basic queries in the database so we don't have to go through the software team for common/basic questions. I'd like to take 1 hr on Friday to go through some basic tutorials and then 1 hr with Pat on Monday so he can walk me through an intro for our specific use case. I estimate this will help save the team a couple hours a week of waiting for answers from the other team." and then you have enough of a handle with baby's first SQL that you can add little bits and bobs as you exercise it. this is responsible for a medium amount of my knowledge of python and all 3 brain cells worth of SQL.

rule nine: life is an optimization problem

not in, like, "you need to optimize your skincare and career and exercise and social life and have everything all at once" that's not what optimization means. optimization is like, maximize something with respect to a set of constraints. i explicitly Do Not do skincare beyond "wash face" and "sunscreen" bc I want to optimize my life for like looking at weird plants in the mountains. explicitly choosing to put time and money elsewhere! can't have it all all at once. so fuck them pores. who give a shit. yeah i ate a lot of protein shakes instead of home cooked breakfasts this week bc i was prioritizing morning exercise. im looking at this beautiful bug and it doesn't know what fashion is or what my resume looks like. im holding a lizard. im not spending time on picking cool clothes or whatever bc i spent that time looking up lizard hotspots on purpose.

that's really long and probably mostly, like, not surprising? but i keep benefiting from ppl being like "hey have you considered Obvious Thing" framed very gently

#long post

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hillbillyoracle · 2 months ago

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A Case Study in DIY Divination: The SQL Oracle

My nesting partner is wanting to get more magically operant and in a conversation she realized she needed to get better at divination. I happen to know she's been trying to read tarot cards for years but it hasn't been clicking for her. I recommended instead of banging her head on that wall that she design her own system.

Given that she works all day everyday with SQL, I recommended she take the component parts of SQL and start to sus out possible meanings to use in a system. I also recommended she establish at least 36 components - it's a number that shows up in magic quite often plus it's around the number in the most common oracle decks.

She wound up deciding on about 40 SQL terms she liked and then we sat down to discuss. The first thing we noticed was that given that SQL is relational, it wasn't talking about anything in particular, it needed Objects to talk about. Originally, she learned toward coming up with a second deck to serve as the objects but I encouraged her to try doubling the cards and having one serve as the relation and one serve as the object - that way each term could pull double duty.

Initially, she was unsure of this so I ran her through some of the following scenarios and picked random terms as results to see if she could make meaning out of them:

i come to you asking about a health issue.

You are reading about an emotional issue you're going through.

Someone you know asks about an issue with their relationship.

At least one of these involves a follow up question and second draw.

These stood in for the most common querants - self, close friend, an acquaintance - and covered some of the most difficult topics to divine for - health, person decisions, and relationships. Finances would be another good one to throw in.

For each, she was getting so much information from the symbols that she struggled to put it into words - a very good sign especially for someone who isn't that experienced in other forms of divination. For most divination systems, the part people often get wrong is the information density of the symbols. Either they're too big and vague to get halfway relevant meaning out of or they're too specific to apply to a wide array of topics that might need read for. That the hard part was not in susing out the meaning but rather in the putting it into words was a sign that that balance was most likely being struck.

I'm really looking forward to her making her cards and practicing with them. I think it's so cool that she really does have an intuitive understanding of those terms that are so unlike what I use in tarot and geomancy and astrology - yet they work.

If you've hit a wall with tarot and similar, it's worth thinking about what symbols you already understand intimately and intuitively and building something around those meanings to work in instead.

Happy DIYing!

#witchblr #baby witch #divination #tarot

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presidentkamala · 9 months ago

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Ok more complaining abt work

More technically capable but genuinely sweet but also power hungry coworker keeps seeing other people (me) getting assigned to projects or working on things and then secretly tackles them herself IF she's interested and then makes no indication that she's working on it to the people assigned. So then we end up with these parallel solutions/reports and its like ????? I know you're "just trying to help" but the constant undercurrent is that we (I) am simply not to be trusted to be able to resolve anything and she needs to be the one to swoop in and save the day with her thing. It would be one thing if she was asked or if she was like hey can i join in? But no, us lesser mortals have to just be in a weird one-sided competition for things that just duplicate efforts!!!!!!!

Add to that the fact that she like WILLFULLY makes data architecture decisions that play specifically to HER strengths/certifications (she got our developers to provide the data UNSTRUCTURED so now we ALL need to get oracle drivers installed and need to learn SQL when she happily ALREADY has them installed and is used to SQL (none of us are data scientists or developers btw) so now she's the ONLY one w access to the data from the application) its like. Ok you clearly believe you are the ONLY one driving this unit or doing anything and unless anyone else is a coder we are basically useless. She keeps saying "i'll have to teach you SQL" like i haven't already gotten MEYE cert in it???? And then making off-hand remarks like "i need to build this so its easy for you to use it" like ms ma'am you are part of THIS team IM not your customer im your team member!!!!!!!!! OH AND WE HAVE ACTIAL DEVELOPERS FOR THE JOB YOU KEEP INSERTING YOURSELF INTO!!!!!!!!!!!!!

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redacted-s-journal · 10 months ago

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insatiable | 29/08/2024 | 22:48

yo,

not sure what to put on here. i was never the type to journal or write diaries as a kid. never quite understood the purpose of such things. i mean, i guess i can understand the practical reasons of it - whatever it may be - but it's one thing for the brain to know and for the heart to know.

so why am i doing this? i don't know, just thought it would be fun. it was kind of an impulsive decision. i can't remember what prompted me to start this either. this blog will serve as me writing down my thoughts i guess?

i guess don't really expect people to read these either (but that won't stop me from adding tags to this post! :D). maybe i'll look back at these entries one day. maybe i'll cringe, maybe i'll laugh. who knows.

but hey, if you're here and i have zero idea who you are, hello there.

y'know, i've always liked the idea of anonymous journals, like imagine finding a stranger's journal and reading about their lives, not knowing who they are. kinda sick.

whatever, i'm rambling. i'm mainly writing this right now because i have nothing else to do. i've already finished up with what i needed to do - assessments, email lecturers. i wanted to play some video games, but it doesn't feel fulfilling anymore. maybe i'll feel good in the moment, but it's not like i'm looking forward to doing it.

i just feel like i could be doing something more productive. i get i should set some time for myself where i can take a break from work/productivity for self-care or something. but i just can't shake off the thought that i could be doing something else, something more productive.

i was thinking of finalising a programming timeline for myself - basically just trying to learning all sorts of coding languages within a year. i'm already in a programming course, but i feel like i could be doing more, y'know? currently i'm learning SQL, HTML/CSS, and XAML/C#. but i could be doing more.

it kinda sucks, thinking like this. makes me feel like i can never do enough no matter how hard i try. but it's whatever. i just gotta push through it.

maybe i'm just burnt out.

yeah, that could be it.

but burnt out from what? existing? what's there to be burnt out about? i'm not the busiest person on earth.

i feel like i'm not doing anything - anything productive, that is, and it's killing me. i could be doing something else, i could be more productive, i could be more hardworking.

but why am i not? why am i still writing this?

whatever.

i'll sleep it off, see how i feel tomorrow. i'll try to play a game to take my mind off it.

haha, think this entry's a little too serious.

time to absolutely LOCK IN and QUIT feeling bad! just gotta STAND on BUSINESS WOOOOOO (this is hilarious btw)

Yours sincerely, [redacted]

#journal #my journal #journal entry #digital journal #diary #online diary #thoughts #thinking #diary entry #journaling #digital diary #my diary #dear diary

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lazar-codes · 2 years ago

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01/11/2023 || Day 104

Personal Chatter

I love that yesterday I was pumped and ready for November and preparing to be productive, and yet today I woke up feeling bad so productivity was low. That's ok, tmr will be better! I also had another ASL class today and the teacher brought us chocolate (and the better thing is that we already know the sign for chocolate before, so we could use that in a sentence today). We've learned about 500 words (do we remember them all? lol, nope), and it's been such a fun class, so I already signed up for the next unit of the class for January.

------------------------------------------------------------------------------

Programming

Even though I didn't feel great today, I did get some stuff done!

LeetCode

Decided to start off with an easy LeetCode question, so I went with Number of Good Pairs and 5 mins later I was done. I previously did the Top Interview 150 questions, but I want more practice with Arrays/Strings, so I'm just doing those types now. Will move onto sorting and binary search trees and linked lists later (once I feel smart again).

Hobby Tracker - Log # 1

Ok, I decided what my full-stack project will be. I want to create a web app in which a user can keep track of different media they want to consume; books, movies, TV shows, and video games. Essentially, I want something that will show me how many things I want to get to (i.e how many books I want to read), and what those things specifically are, and then show me how much of each media type I've completed. For example, I have a spreadsheet of video games I own and I have it show me the % of complete games. I want that, but for all books, movies, shows, and games I enter. Not only that, but for certain media, I want to be able to get a random title from my list. Say I have 20 books I want to read but don't know which to pick. Well, I can ask it to choose a book for me randomly, and I'll read it. That's the basic idea, anyways. Today I spent some time setting up a Trello board to keep track of features I want and functionality I need, and I also started designing the mobile version of the app using Figma. It'll probably be a while before I start coding, but the idea is to have the frontend be built with React, backend with NodeJs, and database be SQL. I'll also be using APIs to get the media info, such as title, genre, etc...This will be the biggest project in terms of scope I've worked on, but hopefully I'll learn a lot.

#codeblr #studyblr #100 days of code #programming #coding #lazar blogs

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ponycycle · 1 year ago

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What's your biggest hyperfocus and how did you discover it?

I had to think on this for a minute because I wasn't sure if it was true anymore. If it wasn't this then it would be something like MLP or motorcycles (it was tempting to say motorcycles!).

I think it's fair to still say personal computers, though. I'm not sure about when my first contact with them was, but I know a major development was when my dad bought our first PC, an IBM AT clone. (I think I still have most of the parts for it!) I would have been like, 7-9 years old at the time and I was fascinated with it. I ended up breaking it as a kid, because I was trying to figure out what all the DOS 4.0 commands did by running them... when I got to FDISK I rendered it unbootable by pressing buttons. A friend of my father's recovered the situation (I think he used Norton Utilities to recreate the partition table).

I can name pretty much every PC that we had as a family or I had personally:

-Aforementioned IBM AT clone (8088 with a Tatung Hercules monitor, DOS 4.0) -386SX that came from who knows where (Went straight from orange Hercules to VGA colour!!! Windows 3.1) -Tandy 1000HX (long term loan from a friend) -Cyrix 586 (dogshit computer - had fake onboard cache, a common scam at the time, crashed constantly. Windows 95) -468DX4 (think I built this from scrounged parts. Win95, slower than the other PC but way more stable) -Pentium II 233 (also built from scrounged parts. First PC I overclocked, gaining 33 mHz! So fast!!! Windows 2000... but later got repurposed as a Linux-based router) -AMD Duron 800 (built with NEW parts - parents gave me a budget to built a family computer. Windows ... 98? XP? Probably changed multiple times) -AMD Athlon XP 1600 (built with NEW parts - I truly don't remember where I got the money in highschool to put it together, but it was probably every penny I had) -AMD Athlon 64 X2 4400+ (admittedly I didn't remember this offhand... but I did have the physical CPU lying around to check. bought off the shelf very cheap as old stock for my parents to use. Windows Vista. Later upgraded to an Phenom X4, also for very cheap. This PC still lives running Windows 10 today!) -Intel Core 2 Duo Q6700 (built in a cute Shuttle XPC chassis. Eventually burned out a RAM slot because apparently it wasn't rated for 2.0V DIMMs. Windows 7) -Intel Core i5-2500K (I used this computer for YEARS. Like almost a decade, while being overclocked to 4.4 gHz from nearly the first day I had it. Windows 7/10) -AMD 5800X (Currently daily driver. Windows 10)

Not mentioning laptops because the list is already long and you get the point.

I actually did attempt to have a computer related career - in the mid 2000s I went to a community college to get a programming diploma, but I dropped out halfway. There was a moment, in a class teaching the Windows GDI API, where I realized that I had no desire to do that professionally. I did learn things about SQL and OS/400 that randomly came in handy a few times in my life. I did go back and successfully get a diploma in networking/tech support but I've never worked a day in that field.

Unprofessionally though, I was "that guy" for most of my life - friend of a friend or family would have a problem with their PC, and I would show up and help them out. I never got to the point where I would attempt to like, re-cap somebody's motherboard, but I could identify blown caps (and there was a time when there was a lot of those). As the role of PCs has changed, and the hardware has gotten better, I barely ever get to do this kind of thing these days. My parent's PC gathers dust in the corner because they can do pretty much do everything they need on their tablets, which they greatly prefer.

Today though... I used to spend a lot of time reading about developments in PC hardware, architectural improvements, but it doesn't matter as much to me anymore. I couldn't tell you what the current generation of Intel desktop CPUs use for a socket without looking it up. A lot of my interest used to be gaming related, and to this day the GPU industry hasn't fully recovered from the crypto boom. Nearly all of the games I'm interested in play well on console so I just play them there. I still fiddle with what I have now and then.

It is fun to think back on various challenges/experiences with it I've had over the years (figuring out IRQ/DMA management when that was still manual, Matsushita CD-ROM interfaces, trying to exorcise the polymorphic Natas virus from my shit). Who knows, maybe I'll get to curate a PC museum of all this shit someday haha.

#thank you very much for the question!!!

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gnh5blog · 2 years ago

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A TURN FROM B.Com OR BBA GRADUATE TO

DATA ANALYST

The business world is changing, and so are the opportunities within it. If you've finished your studies in Bachelor of Commerce (B.Com) or Bachelor of Business Administration (BBA), you might be wondering how to switch into the field of data analysis. Data analysts play an important role these days, finding useful information in data to help with decisions. In this blog post, we'll look at the steps you can take to smoothly change from a B.Com or BBA background to the exciting world of data analysis.

What You Already Know:

Even though it might feel like a big change, your studies in B.Com or BBA have given you useful skills. Your understanding of how businesses work, finances, and how organisations operate is a great base to start from.

Step 1: Building Strong Data Skills:

To make this change, you need to build a strong foundation in data skills. Begin by getting to know basic statistics, tools to show data visually, and programs to work with spreadsheets. These basic skills are like building blocks for learning about data.

I would like to suggest the best online platform where you can learn these skills. Lejhro bootcamp has courses that are easy to follow and won't cost too much.

Step 2: Learning Important Tools:

Data analysts use different tools to work with data. Learning how to use tools like Excel, SQL, and Python is really important. Excel is good for simple stuff, SQL helps you talk to databases, and Python is like a super tool that can do lots of things.

You can learn how to use these tools online too. Online bootcamp courses can help you get good at using them.

Step 3: Exploring Data Tricks:

Understanding how to work with data is the core of being a data analyst. Things like looking closely at data, testing ideas, figuring out relationships, and making models are all part of it. Don't worry, these sound fancy, but they're just different ways to use data.

Step 4: Making a Strong Collection:

A collection of things you've done, like projects, is called a portfolio. You can show this to others to prove what you can do. As you move from B.Com or BBA to data analysis, use your business knowledge to pick projects. For example, you could study how sales change, what customers do, or financial data.

Write down everything you do for these projects, like the problem, the steps you took, what tools you used, and what you found out. This collection will show others what you're capable of.

Step 5: Meeting People and Learning More:

Join online groups and communities where people talk about data analysis. This is a great way to meet other learners, professionals, and experts in the field. You can ask questions and talk about what you're learning.

LinkedIn is also a good place to meet people. Make a strong profile that shows your journey and what you can do. Follow data analysts and companies related to what you're interested in to stay up to date.

Step 6: Gaining Experience:

While you learn, it's also good to get some real experience. Look for internships, small jobs, or freelance work that lets you use your skills with real data. Even if the job isn't all about data, any experience with data is helpful.

Step 7: Updating Your Resume:

When you're ready to apply for data jobs, change your resume to show your journey. Talk about your B.Com or BBA studies, the skills you learned, the courses you took, your projects, and any experience you got. Explain how all of this makes you a great fit for a data job.

Using Lejhro Bootcamp:

When you're thinking about becoming a data analyst, think about using Lejhro Bootcamp. They have a special course just for people like you, who are switching from different fields. The Bootcamp teaches you practical things, has teachers who know what they're talking about, and helps you find a job later.

Moving from B.Com or BBA to a data analyst might seem big, but it's totally doable. With practice, learning, and real work, you can make the switch. Your knowledge about business mixed with data skills makes you a special candidate. So, get ready to learn, practice, and show the world what you can do in the world of data analysis!

***********

#data science #data analytics #data analysis

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skillpilothub · 2 days ago

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What You’ll Learn in a Data Science Bootcamp: A Syllabus Breakdown

At a time when companies are so dependent on information, it is not an exaggeration to say that the job of a data analyst is essential. Data analysts are vital whether they report to a retail company to understand their customer behaviours or a hospital to understand how to treat its patients better by making sense out of their data insights. So what can one do on those with little or no background in data? The following guide will help you, even starting with zero, on how to become a data analyst.

What Does a Data Analyst Do?

It is good to know what a data analyst does before getting straight to the steps. A data analyst gathers, analyses and interprets data in order to aid organizations undertake problem solving and make sound decisions.

Key Responsibilities Include:

Collection and cleaning up of data

operative Trends and pattern analysis

Report and dashboard creation

Presenting clear solutions to laypeople teams

Consider a data analyst as a translator, one who makes confusing numbers tell stories that other individuals can be able to act on.

Step 1: Understand the Role and Assess Your Interest

Everyone fond of the numbers is not suited to do the data analysis. It takes curiosity, attention to details, and communication abilities.

Problem:Most novices believe that it is more concerned with coding or math, but pay insufficient attention to the storytelling part and critical thinking.

Solution: Start by reading job descriptions or talking to professionals. Ask yourself:

Is it that I like solving puzzles?

Do I get along or am I comfortable with spreadsheets or numbers?

Is my preference to get the solution based on data?

Real-life example: Sarah, a customer support rep, saw trends in the field of complaints and began to monitor it in Excel. She did not realize it at the time, but she was already engaging in this kind of basic data analysis.

Step 2: Learn the Basics of Data and Analytics

You don’t need a degree in statistics to start, but you do need foundational knowledge.

Core Areas to Learn:

Spreadsheets (Excel or Google Sheets): These are often the first tools used for data analysis.

Statistics and Math: Understand averages, medians, probability, and standard deviation.

Data Visualization: Learn how to create charts and graphs that make data easy to understand.

Basic SQL (Structured Query Language): This helps you access and retrieve data from databases.

Antithesis: Some argue that you need to master advanced programming languages first. But in reality, many data analysts begin with spreadsheets and work their way up.

Step 3: Learn a Data Analysis Tool or Language

Once you’ve mastered the basics, it’s time to get comfortable with one or more tools used in the field.

Commonly Used Tools:

SQL: For querying databases

Python or R: For advanced analysis and automation

Tableau or Power BI: For creating dashboards and visual reports

Practical Tip: Don’t try to learn everything at once. Choose one tool based on the kind of job you’re aiming for. If you're interested in marketing analysis, Excel and SQL might be enough to start. If you’re leaning towards finance or research, Python may be more useful.

Step 4: Work on Real Projects

The theoretical study is amazing, but the practice is what leads to development.

Problem: Most learners are helpless upon completion of courses: they have experience only.

Solution: Run your own project. For example:

Open government data analysis

Follow your own spending and start trending

Compare the house prices locally based on the available information provided by the government

Real-life example: John, a teacher who was transformed into a data analyst, will have opportunities to find patterns and causes of absence by relying on school attendance data. He worked in Excel and later was able to include Tableau to add visualizations. It turned into a powerful resume item during job applications.

Step 5: Build a Portfolio

Employers would like to know what you are capable of. Portfolio demonstrates your abilities in practice and helps to prove that you are ready to be hired.

What to Include:

The description of the project in brief consists of the following:

Tool and approaches employed

Visual aids such as charts or dashboard

Your convictions and conclusions

You are able to share a portfolio on your personal blog, LinkedIn, or GitHub. It is all a matter of clarity and confidence with which you can deliver your work.

Step 6: Practice Communication Skills

Becoming a data analyst is not merely all about numbers. You should communicate your results to those who may not be conversant with data in any way.

Key Skills to Develop:

Clearly formulated writing

Creating great slide decks

Giving a secure presentation during meetings

Antithesis: Some others suppose that powerful technical proficiency is a guarantee on its own. Nevertheless, analysts that are somehow incompetent in communicating their results will not have much impact.

Step 7: Apply for Entry-Level Roles or Internships

With a few solid projects and basic tools under your belt, you’re ready to start applying. Look for roles like:

Junior Data Analyst

Reporting Analyst

Business Intelligence Intern

Tailor your resume to highlight practical skills and include links to your portfolio.

Final Thoughts

Turning into a data analyst is not a race. You do not require being a mathematical genius or a coding master to start. Curiosity, an ability to learn and patience to develop skills gradually are also needed.

Summary Checklist:

Understand the role

master fundamentals (spreadsheet, statistics, SQL)

Select any one analysis tool

Carry out real world projects

Create a portfolio

Practice communication

Take entry level jobs

It may seem overwhelming at first, but many successful analysts started just where you are—curious, uncertain, but ready to learn.

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champagne-socialiste · 5 days ago

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Most startups have no idea what they’re doing when it comes to making their first numbers hire. They overweight technical skills—SQL fluency, Accounting, tooling and systems—and miss the bigger picture entirely. They think they’re hiring someone to answer questions. But the real value of a great analyst, especially early on, is that they change what questions you ask in the first place.

This hire isn’t about execution. It’s about judgment. You’re looking for someone who can walk into a room of ambiguity and leave with clarity. Someone who knows how to navigate messy data, fuzzy metrics, and incomplete context and still get to something useful. That’s not about tooling. That’s about instinct. It’s about understanding how a business works, knowing what to measure, and being brave enough to say, “This number doesn’t matter. Here’s what does.”

I made this mistake early at Intercom. Some of my first hires were incredibly technical. They could query anything. They built beautiful charts. But they didn’t bring clarity. It wasn’t until later that we hired someone who could challenge assumptions and reframe the way we thought about performance. And have that conversation with the CEO. That hire changed everything.

And here’s the catch: what makes a great hire depends entirely on how your business works. There’s no such thing as a one-size-fits-all analyst. In a product-led company, you need someone who lives in databases. In enterprise up-market SaaS, you need someone with RevOps instincts. Someone who can talk pipeline health and win rates with a VP of Sales. In a marketplace or eComm business, you need someone who thrives on experimentation and iteration; conversion, pricing, and speed of feedback are everything. Hiring a generic “data person” is how you end up solving the wrong problems with beautiful dashboards. Fit matters.

Once you’ve nailed the context, the real differentiators aren’t found on a resume. Curiosity matters more than credentials. The best analysts don’t wait to be assigned projects—they’re already five “why’s” deep by the time you’ve finished asking your first question. They have the business judgment to separate signal from noise. They can tell you when a metric is lying. And they can communicate clearly—because if your Head of Sales doesn’t understand the insight, it might as well not exist.

There are also a few easy red flags. If someone needs perfect data to get started, they’re not ready for startup life. If they build dashboards and disappear, they’re not your person. And if they never ask about your customers, business model, or goals—they’re thinking like a technician, not a partner. And what you need at this stage is a partner.

I’ve hired for over 50 analytical roles in the last decade, and I’ve interviewed hundreds of candidates. I’ve tried it all: take-home tests, live case studies, fancy frameworks, behavioral questions. Most of it’s noise. Today, there’s only one question I really trust to give me signal.

I ask: “Walk me through an analysis you did, end to end.” Then I spell out what I mean. I want to understand where the idea came from, why they chose to work on it, how they structured the analysis, what blockers or aha moments they encountered, what the recommendation was, and what impact it had on the business.

It’s a mouthful. So I repeat it if needed and tell them to jot down the sub-questions. Any analyst worth hiring will light up and run with it. That’s when the conversation gets good. And that’s when your job begins— to listen analytically. Ask follow-ups. Don’t let them skip steps. Be genuinely curious. Try to learn something. It’s the best way to evaluate how they think.

As they talk, I’m listening for a few things. Can they tell the story to someone who wasn’t there? Were they intentional about choosing the project? Did they break down the problem with structure and clarity? Did they face blockers? And more importantly, did they push through them? Was there a real insight? Did it lead to a real decision? Did the business change as a result?

If they walk you through a project that’s still in flight and doesn’t have impact yet, ask for another one. If they can’t name one with clear business results, that’s your answer. Junior candidates might reasonably say the project was assigned. That’s fine. But for senior candidates, I want to see that they’re originating work—not just reacting to it.

The rest of the interview, I keep it simple. I’ll ask the same question again. Maybe with a twist. “Tell me about an analysis you came up with yourself.” Or, “Tell me about a time your recommendation didn’t pan out.” What matters is that we’re in the zone where the real work happens: context, methodology, insight, narrative, and impact. That’s the job. And this one question tests all of it.

When you get this hire right, it’s one of the most leveraged decisions you’ll ever make. Because the best analysts don’t just analyze. They provoke. They reframe. They change how the company sees itself. They don’t just give answers—they give you better questions.

Don’t botch the hire. Get it right, and your entire company will be sharper for it. Bobby Pinero

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howtousechatgpt · 17 days ago

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Is ChatGPT Easy to Use? Here’s What You Need to Know

Introduction: A Curious Beginning I still remember the first time I stumbled upon ChatGPT my heart raced at the thought of talking to an AI. I was a fresh-faced IT enthusiast, eager to explore how a “gpt chat” interface could transform my workflow. Yet, as excited as I was, I also felt a tinge of apprehension: Would I need to learn a new programming language? Would I have to navigate countless settings? Spoiler alert: Not at all. In this article, I’m going to walk you through my journey and show you why ChatGPT is as straightforward as chatting with a friend. By the end, you’ll know exactly “how to use ChatGPT” in your day-to-day IT endeavors whether you’re exploring the “chatgpt app” on your phone or logging into “ChatGPT online” from your laptop.

What Is ChatGPT, Anyway?

If you’ve heard of “chat openai,” “chat gbt ai,” or “chatgpt openai,” you already know that OpenAI built this tool to mimic human-like conversation. ChatGPT sometimes written as “Chat gpt”—is an AI-powered chatbot that understands natural language and responds with surprisingly coherent answers. With each new release remember buzz around “chatgpt 4”? OpenAI has refined its approach, making the bot smarter at understanding context, coding queries, creative brainstorming, and more.

GPT Chat: A shorthand term some people use, but it really means the same as ChatGPT just another way to search or tag the service.

ChatGPT Online vs. App: Although many refer to “chatgpt online,” you can also download the “chatgpt app” on iOS or Android for on-the-go access.

Free vs. Paid: There’s even a “chatgpt gratis” option for users who want to try without commitment, while premium plans unlock advanced features.

Getting Started: Signing Up for ChatGPT Online

1. Creating Your Account

First things first head over to the ChatGPT website. You’ll see a prompt to sign up or log in. If you’re wondering about “chat gpt free,” you’re in luck: OpenAI offers a free tier that anyone can access (though it has usage limits). Here’s how I did it:

Enter your email (or use Google/Microsoft single sign-on).

Verify your email with the link they send usually within seconds.

No complex setup, no plugin installations just a quick email verification and you’re ready to talk to your new AI buddy. Once you’re “ChatGPT online,” you’ll land on a simple chat window: type a question, press Enter, and watch GPT 4 respond.

Navigating the ChatGPT App

While “ChatGPT online” is perfect for desktop browsing, I quickly discovered the “chatgpt app” on my phone. Here’s what stood out:

Intuitive Interface: A text box at the bottom, a menu for adjusting settings, and conversation history links on the side.

Voice Input: On some versions, you can tap the microphone icon—no need to type every query.

Seamless Sync: Whatever you do on mobile shows up in your chat history on desktop.

For example, one night I was troubleshooting a server config while waiting for a train. Instead of squinting at the station’s Wi-Fi, I opened the “chat gpt free” app on my phone, asked how to tweak a Dockerfile, and got a working snippet in seconds. That moment convinced me: whether you’re using “chatgpt online” or the “chatgpt app,” the learning curve is minimal.

Key Features of ChatGPT 4

You might have seen “chatgpt 4” trending this iteration boasts numerous improvements over earlier versions. Here’s why it feels so effortless to use:

Better Context Understanding: Unlike older “gpt chat” bots, ChatGPT 4 remembers what you asked earlier in the same session. If you say, “Explain SQL joins,” and then ask, “How does that apply to Postgres?”, it knows you’re still talking about joins.

Multi-Turn Conversations: Complex troubleshooting often requires back-and-forth questions. I once spent 20 minutes configuring a Kubernetes cluster entirely through a multi-turn conversation.

Code Snippet Generation: Want Ruby on Rails boilerplate or a Python function? ChatGPT 4 can generate working code that requires only minor tweaks. Even if you make a mistake, simply pasting your error output back into the chat usually gets you an explanation.

These features mean that even non-developers say, a project manager looking to automate simple Excel tasks can learn “how to use ChatGPT” with just a few chats. And if you’re curious about “chat gbt ai” in data analytics, hop on and ask ChatGPT can translate your plain-English requests into practical scripts.

Tips for First-Time Users

I’ve coached colleagues on “how to use ChatGPT” in the last year, and these small tips always come in handy:

Be Specific: Instead of “Write a Python script,” try “Write a Python 3.9 script that reads a CSV file and prints row sums.” The more detail, the more precise the answer.

Ask Follow-Up Questions: Stuck on part of the response? Simply type, “Can you explain line 3 in more detail?” This keeps the flow natural—just like talking to a friend.

Use System Prompts: At the very start, you can say, “You are an IT mentor. Explain in beginner terms.” That “meta” instruction shapes the tone of every response.

Save Your Favorite Replies: If you stumble on a gem—say, a shell command sequence—star it or copy it to a personal notes file so you can reference it later.

When a coworker asked me how to connect a React frontend to a Flask API, I typed exactly that into the chat. Within seconds, I had boilerplate code, NPM install commands, and even a short security note: “Don’t forget to add CORS headers.” That level of assistance took just three minutes, demonstrating why “gpt chat” can feel like having a personal assistant.

Common Challenges and How to Overcome Them

No tool is perfect, and ChatGPT is no exception. Here are a few hiccups you might face and how to fix them:

Occasional Inaccuracies: Sometimes, ChatGPT can confidently state something that’s outdated or just plain wrong. My trick? Cross-check any critical output. If it’s a code snippet, run it; if it’s a conceptual explanation, ask follow-up questions like, “Is this still true for Python 3.11?”

Token Limits: On the “chatgpt gratis” tier, you might hit usage caps or get slower response times. If you encounter this, try simplifying your prompt or wait a few minutes for your quota to reset. If you need more, consider upgrading to a paid plan.

Overly Verbose Answers: ChatGPT sometimes loves to explain every little detail. If that happens, just say, “Can you give me a concise version?” and it will trim down its response.

Over time, you learn how to phrase questions so that ChatGPT delivers exactly what you need quickly—no fluff, just the essentials. Think of it as learning the “secret handshake” to get premium insights from your digital buddy.

Comparing Free and Premium Options

If you search “chat gpt free” or “chatgpt gratis,” you’ll see that OpenAI’s free plan offers basic access to ChatGPT 3.5. It’s great for light users students looking for homework help, writers brainstorming ideas, or aspiring IT pros tinkering with small scripts. Here’s a quick breakdown: FeatureFree Tier (ChatGPT 3.5)Paid Tier (ChatGPT 4)Response SpeedStandardFaster (priority access)Daily Usage LimitsLowerHigherAccess to Latest ModelChatGPT 3.5ChatGPT 4 (and beyond)Advanced Features (e.g., Code)LimitedFull accessChat History StorageShorter retentionLonger session memory

For someone just dipping toes into “chat openai,” the free tier is perfect. But if you’re an IT professional juggling multiple tasks and you want the speed and accuracy of “chatgpt 4” the upgrade is usually worth it. I switched to a paid plan within two weeks of experimenting because my productivity jumped tenfold.

Real-World Use Cases for IT Careers

As an IT blogger, I’ve seen ChatGPT bridge gaps in various IT roles. Here are some examples that might resonate with you:

Software Development: Generating boilerplate code, debugging error messages, or even explaining complex algorithms in simple terms. When I first learned Docker, ChatGPT walked me through building an image, step by step.

System Administration: Writing shell scripts, explaining how to configure servers, or outlining best security practices. One colleague used ChatGPT to set up an Nginx reverse proxy without fumbling through documentation.

Data Analysis: Crafting SQL queries, parsing data using Python pandas, or suggesting visualization libraries. I once asked, “How to use chatgpt for data cleaning?” and got a concise pandas script that saved hours of work.

Project Management: Drafting Jira tickets, summarizing technical requirements, or even generating risk-assessment templates. If you ever struggled to translate technical jargon into plain English for stakeholders, ChatGPT can be your translator.

In every scenario, I’ve found that the real magic isn’t just the AI’s knowledge, but how quickly it can prototype solutions. Instead of spending hours googling or sifting through Stack Overflow, you can ask a direct question and get an actionable answer in seconds.

Security and Privacy Considerations

Of course, when dealing with AI, it’s wise to think about security. Here’s what you need to know:

Data Retention: OpenAI may retain conversation data to improve their models. Don’t paste sensitive tokens, passwords, or proprietary code you can’t risk sharing.

Internal Policies: If you work for a company with strict data guidelines, check whether sending internal data to a third-party service complies with your policy.

Public Availability: Remember that anyone else could ask ChatGPT similar questions. If you need unique, private solutions, consult official documentation or consider an on-premises AI solution.

I routinely use ChatGPT for brainstorming and general code snippets, but for production credentials or internal proprietary logic, I keep those aspects offline. That balance lets me benefit from “chatgpt openai” guidance without compromising security.

Is ChatGPT Right for You?

At this point, you might be wondering, “Okay, but is it really easy enough for me?” Here’s my honest take:

Beginners who have never written a line of code can still ask ChatGPT to explain basic IT concepts no jargon needed.

Intermediate users can leverage the “chatgpt app” on mobile to troubleshoot on the go, turning commute time into learning time.

Advanced professionals will appreciate how ChatGPT 4 handles multi-step instructions and complex code logic.

If you’re seriously exploring a career in IT, learning “how to use ChatGPT” is almost like learning to use Google in 2005: essential. Sure, there’s a short learning curve to phrasing your prompts for maximum efficiency, but once you get the hang of it, it becomes second nature just like typing “ls -la” into a terminal.

Conclusion: Your Next Steps

So, is ChatGPT easy to use? Absolutely. Between the intuitive “chatgpt app,” the streamlined “chatgpt online” interface, and the powerful capabilities of “chatgpt 4,” most users find themselves up and running within minutes. If you haven’t already, head over to the ChatGPT website and create your free account. Experiment with a few prompts maybe ask it to explain “how to use chatgpt” and see how it fits into your daily routine.

Remember:

Start simple. Ask basic questions, then gradually dive deeper.

Don’t be afraid to iterate. If an answer isn’t quite right, refine your prompt.

Keep security in mind. Never share passwords or sensitive data.

Whether you’re writing your first “gpt chat” script, drafting project documentation, or just curious how “chat gbt ai” can spice up your presentations, ChatGPT is here to help. Give it a try, and in no time, you’ll wonder how you ever managed without your AI sidekick.

#Chat gpt #chat gpt free #chat gbt ai

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eduacations-blog · 1 month ago

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How to Start a Career in Data Analytics Without a Tech Background.

Let’s be honest—"data analytics" sounds like something only coders and spreadsheet wizards can do, right?

But here’s the truth: you don’t need to be a tech genius to start a career in data analytics. In fact, some of the best data analysts come from fields like marketing, finance, education, or even hospitality. What they all have in common? A curiosity to understand data and a willingness to learn.

This essay is for you if you've ever wondered, "I like solving problems, but I don't know where to start."

🧠 What Is Data Analytics, Really?

Think of data analytics as detective work with numbers. Businesses generate tons of data—sales numbers, website clicks, customer feedback—and they need someone to find the patterns, answer questions, and help them make better decisions.

A data analyst’s job is to: Collect and clean data Analyze trends Create reports and dashboards Help teams make smarter choices No complex coding needed to start—just clear thinking, basic tools, and some practice.

🌱 Step-by-Step Guide to Get Started Without a Tech Background

Start with the Mindset, Not the Tools First, believe this: you belong in data. Don’t let jargon intimidate you. You don’t need a computer science degree. What you do need is:

Curiosity Problem-solving skill A love for learning That’s your foundation.

Learn the Basics (One Step at a Time) Start small. You don’t need to jump into Python or SQL on Day 1.

Begin with:

Excel/Google Sheets – Learn formulas, pivot tables, basic charts.

Data literacy – Understand terms like KPI, metric, dashboard, etc.

Free courses – Try YouTube tutorials or beginner courses on platforms like Coursera, Udemy, or LinkedIn Learning.

📌 Tip: Try analyzing your own budget or workout data to practice.

Master Key Tools Slowly Once you’re comfortable, move on to the key tools data analysts use:

Tool Why Learn It? Excel Industry standard for data cleaning SQL Helps you pull data from databases Power BI / Tableau Used for creating visual reports and dashboards Python (optional) Helpful, but not required initially

Don’t try to learn everything at once—pick one and stick with it until you're comfortable.

Work on Real-Life Projects You don’t need a job to get experience. Use free datasets from sites like:

Kaggle.com Data.gov OurWorldinData.org

Start a project like: Analyzing COVID data in your region Creating a dashboard of your expenses Studying trends in Netflix shows or YouTube videos

Then write about your process on LinkedIn or Medium. It shows initiative and builds your portfolio.

Get Certified (Optional but Helpful) Certifications can give you a confidence boost and look great on a resume. Look into:

Google Data Analytics Certificate Microsoft Power BI Certification Coursera / Udemy beginner courses

They’re not required, but they help you stand out.

Apply for Entry-Level Roles (Even if You Don’t Tick Every Box) You don’t need to meet 100% of the job description to apply.

Look for roles like:

Junior Data Analyst Business Analyst Reporting Analyst Operations Analyst

Highlight your soft skills: communication, problem-solving, and attention to detail. Show how you’re learning the tools. That matters more than a perfect résumé.

💬 Real Talk: What Makes a Good Data Analyst (That Has Nothing to Do With Tech)? You ask “why” a lot

You enjoy making sense of chaos You’re patient with details You like telling stories with numbers Sound like you? Then you're already halfway there.

🎯 Final Words Starting a data analytics career without a tech background isn't just possible—it’s happening every day. With the right mindset, consistent effort, and a little curiosity, you can absolutely break in.

And remember: everyone was a beginner once.

If you’re ready to take the first step, our Data Analytics program at Ntech Global Solutions is built for career changers just like you. We teach you real-world skills, not just theory—so you can go from “I don’t know where to start” to “I got the job!”

#data analytics #data analyst course in delhi #data analyst training #code #python #programming #sql #excel #power bi #tableau

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sonadukane · 2 months ago

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Why Learning Data Science in 2025 is a Smart Career Move (Even Without a Tech Background)

Let’s get one thing straight: you don’t need to be a coder, a math genius, or have a tech degree to become a data scientist in 2025. In fact, many successful data professionals today came from totally different fields like marketing, HR, sales, finance, and even teaching.

And guess what? Data science is more accessible than ever.

If you're thinking about switching careers, upskilling, or just curious if this path is right for you, this blog will break it all down for you.

💡 Why Everyone's Talking About Data Science in 2025

Let’s look at a few numbers:

LinkedIn listed “Data Scientist” as one of the most in-demand jobs in 2025

IBM predicts 11.5 million new data science jobs by 2026

Average salary? Easily ₹8–20 LPA in India and over $100K+ abroad

But here’s the real kicker: companies no longer want just coders. They want problem-solvers, people who can think critically, communicate insights, and understand business needs. That’s where you can fit in.

🧠 “But I Don’t Have a Tech Background…”

Good news, you’re not alone, and you’re not late. Thousands of learners are breaking into data science from:

Marketing (to become data-driven marketers)

Finance (to move into FinTech or risk analytics)

HR (to do people analytics and workforce planning)

Healthcare (to transition into health data analysis)

Education (to explore edtech and learning analytics)

If you’re curious, willing to learn, and enjoy solving real-world problems, you’re already halfway there.

🛠️ What Skills You Actually Need to Start

Let’s demystify the skillset:

✅ Basic Python – You can learn this in a few weeks. No coding background? That’s fine. It’s like English for computers. ✅ Math (Just Enough) – You don’t need a PhD. Basic stats and logic are enough to start. ✅ Excel, SQL – You likely already use Excel. SQL is just asking questions to a database. ✅ Curiosity – The most important skill. If you love asking “why” or finding patterns, you’ve got the mindset.

🚀 The Best Way to Learn (Especially for Beginners)

Instead of wasting hours piecing together YouTube tutorials and blog posts, go for a structured course that takes you from zero to job-ready.

We recommend starting with the Intellipaat Data Science course – especially if you:

Have no tech background

Learn best through real-world projects

Need step-by-step guidance + mentorship

Want certification + career support

🎓 Watch the free course intro here

📈 Real Stories: From Non-Tech to Data Pro

Shruti, a marketing analyst, transitioned to a data scientist role after 6 months of learning part-time.

Anil, a mechanical engineer, cracked his first data analyst job in manufacturing.

Priya, a school teacher, now works in edtech, analyzing student performance data.

No CS degree. No prior coding. Just consistent learning and the right resources.

✅ So, Is 2025 the Year to Start?

Absolutely. With tools becoming no-code, AI assistants simplifying workflows, and businesses relying more on data—you have a golden opportunity.

Whether you're looking for a career switch, remote flexibility, or better pay, data science checks all the boxes.

🔗 Ready to Begin?

If you’re ready to stop overthinking and start learning, check out the Intellipaat Data Science Course. You’ll go from complete beginner to confident data professional—no tech degree needed.

Final Thought: You don’t need to be technical. You just need to be curious. And in 2025, curiosity pays really well.

#data science #data science projects #data science course #data science training #data science trends

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subb01 · 2 months ago

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How to Start a Career in Data Science with No Technical Background

If you’ve ever thought, “Data science sounds fascinating, but I don’t have a tech background,” you’re not alone — and you’re definitely not out of luck.

Here’s the truth: you don’t need to be a coder, a statistician, or a data engineer to start a career in data science. What you need is curiosity, consistency, and the right approach.

This blog will walk you through exactly how someone from a non-technical field — like marketing, finance, operations, education, or even arts — can break into the world of data science.

Step 1: Understand What Data Science Actually Is

Start by learning the basics of data science — what it means, how it's used, and the kind of problems it solves.

Think of data science as a combination of three core elements:

Math and Statistics – to make sense of data

Programming – to work with and process that data

Business Understanding – to know which problems are worth solving

The best part? You can learn all of this at your own pace, even if you’re starting from zero.

Step 2: Start with Tools You’re Familiar With

If you’ve used Excel or Google Sheets, you’ve already worked with data.

From there, you can gradually move to tools like:

SQL – to pull data from databases

Python – to manipulate, analyze, and visualize data

Power BI or Tableau – to create dashboards and visual stories

There are beginner-friendly platforms and tutorials available to help you learn these tools step-by-step.

Step 3: Focus on Real-World Applications

Don’t try to memorize formulas or force yourself to master every algorithm. Instead, focus on how data science is used in the real world:

In marketing to measure campaign performance

In HR to predict employee attrition

In finance to detect fraud

In supply chain to optimize delivery routes

Relating concepts to your current domain makes learning not only easier but more enjoyable.

Step 4: Work on Projects, Not Just Theory

Even if you’re still learning, try to work on mini-projects using publicly available datasets from Kaggle or government portals.

For example:

Analyze sales data and build a forecast model

Explore customer churn patterns for a telecom company

Create a dashboard showing COVID-19 trends

These projects will become part of your portfolio, making you stand out when applying for jobs.

Step 5: Keep Learning, Keep Growing

The field of data science evolves fast. Stay updated by:

Following data science communities on LinkedIn

Watching free courses and tutorials

Reading blogs and case studies

Connecting with mentors or peers online

Ready to Get Started?

If you're serious about breaking into data science, there's no better time than now — and no better way than starting with a free beginner-friendly course.

🎥 Check out this free YouTube course on Data Science that explains core concepts, tools, and techniques — all in simple, easy-to-follow language:

👉 Click here to watch the full course

You don’t need a tech degree — just a desire to learn and take the first step. Your data science journey starts today!

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hanasatoblogs · 2 months ago

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Snowflake vs Redshift vs BigQuery vs Databricks: A Detailed Comparison

In the world of cloud-based data warehousing and analytics, organizations are increasingly relying on advanced platforms to manage their massive datasets. Four of the most popular options available today are Snowflake, Amazon Redshift, Google BigQuery, and Databricks. Each offers unique features, benefits, and challenges for different types of organizations, depending on their size, industry, and data needs. In this article, we will explore these platforms in detail, comparing their performance, scalability, ease of use, and specific use cases to help you make an informed decision.

What Are Snowflake, Redshift, BigQuery, and Databricks?

Snowflake: A cloud-based data warehousing platform known for its unique architecture that separates storage from compute. It’s designed for high performance and ease of use, offering scalability without complex infrastructure management.

Amazon Redshift: Amazon’s managed data warehouse service that allows users to run complex queries on massive datasets. Redshift integrates tightly with AWS services and is optimized for speed and efficiency in the AWS ecosystem.

Google BigQuery: A fully managed and serverless data warehouse provided by Google Cloud. BigQuery is known for its scalable performance and cost-effectiveness, especially for large, analytic workloads that require SQL-based queries.

Databricks: More than just a data warehouse, Databricks is a unified data analytics platform built on Apache Spark. It focuses on big data processing and machine learning workflows, providing an environment for collaborative data science and engineering teams.

Snowflake Overview

Snowflake is built for cloud environments and uses a hybrid architecture that separates compute, storage, and services. This unique architecture allows for efficient scaling and the ability to run independent workloads simultaneously, making it an excellent choice for enterprises that need flexibility and high performance without managing infrastructure.

Key Features:

Data Sharing: Snowflake’s data sharing capabilities allow users to share data across different organizations without the need for data movement or transformation.

Zero Management: Snowflake handles most administrative tasks, such as scaling, optimization, and tuning, so teams can focus on analyzing data.

Multi-Cloud Support: Snowflake runs on AWS, Google Cloud, and Azure, giving users flexibility in choosing their cloud provider.

Real-World Use Case:

A global retail company uses Snowflake to aggregate sales data from various regions, optimizing its supply chain and inventory management processes. By leveraging Snowflake’s data sharing capabilities, the company shares real-time sales data with external partners, improving forecasting accuracy.

Amazon Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse solution in the cloud. It is optimized for high-performance querying and is closely integrated with other AWS services, such as S3, making it a top choice for organizations that already use the AWS ecosystem.

Key Features:

Columnar Storage: Redshift stores data in a columnar format, which makes querying large datasets more efficient by minimizing disk I/O.

Integration with AWS: Redshift works seamlessly with other AWS services, such as Amazon S3, Amazon EMR, and AWS Glue, to provide a comprehensive solution for data management.

Concurrency Scaling: Redshift automatically adds additional resources when needed to handle large numbers of concurrent queries.

Real-World Use Case:

A financial services company leverages Redshift for data analysis and reporting, analyzing millions of transactions daily. By integrating Redshift with AWS Glue, the company has built an automated ETL pipeline that loads new transaction data from Amazon S3 for analysis in near-real-time.

Google BigQuery Overview

BigQuery is a fully managed, serverless data warehouse that excels in handling large-scale, complex data analysis workloads. It allows users to run SQL queries on massive datasets without worrying about the underlying infrastructure. BigQuery is particularly known for its cost efficiency, as it charges based on the amount of data processed rather than the resources used.

Key Features:

Serverless Architecture: BigQuery automatically handles all infrastructure management, allowing users to focus purely on querying and analyzing data.

Real-Time Analytics: It supports real-time analytics, enabling businesses to make data-driven decisions quickly.

Cost Efficiency: With its pay-per-query model, BigQuery is highly cost-effective, especially for organizations with varying data processing needs.

Real-World Use Case:

A digital marketing agency uses BigQuery to analyze massive amounts of user behavior data from its advertising campaigns. By integrating BigQuery with Google Analytics and Google Ads, the agency is able to optimize its ad spend and refine targeting strategies.

Databricks Overview

Databricks is a unified analytics platform built on Apache Spark, making it ideal for data engineering, data science, and machine learning workflows. Unlike traditional data warehouses, Databricks combines data lakes, warehouses, and machine learning into a single platform, making it suitable for advanced analytics.

Key Features:

Unified Analytics Platform: Databricks combines data engineering, data science, and machine learning workflows into a single platform.

Built on Apache Spark: Databricks provides a fast, scalable environment for big data processing using Spark’s distributed computing capabilities.

Collaboration: Databricks provides collaborative notebooks that allow data scientists, analysts, and engineers to work together on the same project.

Real-World Use Case:

A healthcare provider uses Databricks to process patient data in real-time and apply machine learning models to predict patient outcomes. The platform enables collaboration between data scientists and engineers, allowing the team to deploy predictive models that improve patient care.