blazepascal:


Stop comparing yourself to the ladies.

We don't have the same privileges. They have all the chances to live and experience a better life.

They might never go hungry. They might drive the latest version of car, they might live in the best apartment in the city, use the latest technology, fly first class.

But you as a man will have to suffer to become someone or experience these things. Working should be your moto.

Suffer today so you can have a better life tomorrow, don't compare yourself with your sisters, don't hate the females in your city for living a life more than you.

We are different, a man must suffer to become someone of importance. That's why you must understand what is taking your time and little money and cut off with them.

You identify your wastrel in your life. In this journey of life, a man's worst enemy is idle time.

Let this message scare you, wake up and stop comparing yourself with the females. A broke man is nothing even to his family. A broke girls is something to her family members.

Don't be delusional, we are different and our chances are not the same to smile or be respected. A broke guy is not respected, he has no value to his community, they laugh at you when you walk by, the beautiful ladies call you loser. Get up.

Get up! Be scared of your future.



#BLAZE

seanwilliam:

Pls send link to the thread

Shorty1980:
At 43 ,I regret getting married

JahBwoy:
Ejiod my guy.. I remember us conversing via mail some 2 years back on my old moniker. I had just got my first job as an intern and didn't know where I was headed in IT. I tried web dev initially..
HTML and CSS was easy. JS was confusing . I was making progress but I did not know If I should master vanilla JS or not. Too many technologies in that space to confuse a beginner.
I have always been average with numbers so I felt that limitation will come to the fore sooner or later so I was hesitant diving straight. But was very good with manipulating Excel.

Then .. boom I get my first real Job in Technical Support. I knew that was what I wanted lol. I have expanded into Cloud Computing so much now. Yes I still write codes to automate stuffs. But now it's Powershell scripts to interact with Azure.
I will be resuming my third Job after this whole SARS issue is over, in a cloud focused role and salary is just over 200k. Not bad for someone with 2 years experience and almost zero certifications .
I still have a thing for Data analysis wahlahi. And will later write a Microsoft Certification to certify my Excel skills. Will delve into python too as I work more with Linux and AWS.

It's all good. Any path you wanna taken just start doing something.
I hope to become more established in my field so that I can draw up a thread to guide people too. Una well done

ibromodzi:


From your write up, I can deduce that two things are missing in your data analytics armament. First is the theoretical knowledge of data analysis (most guys joke with this always) and the second one is the domain knowledge needed to navigate through any data you are working on. You can't make any meaningful conclusion about datasets without these two.

Now, back to the question. Honestly, a simple google search would have given you the answers to your questions, but I'll still try to break it down according to my own little understanding of data science.

Any data collected in its raw form has no meaning until it is processed according to what the company requires so as to utilize the data solely for decision making purpose (making conclusion), which in turn helps the company to grow. Now, there are two important things here,
1. The company's requirement (i.e why was the data collected in the first place?) - Let me give you a couple of real life examples here:
a. The government wants to know whether a newly developed drug will be effective in the treatment of Covid-19. So they do this by conducting a round of clinical trials on patients willing to participate. Remember why the clinical trials were conducted? To test the effectiveness of the drug.
b. A financial institution (bank for instance) wants to develop an advanced system for detecting fraudulent transactions among their customers. They do this by getting a large record of transactions that have taken place in the past. Again, you remember why they needed the record of past transactions? I hope you are getting the gist by now?

2. Now, let's continue with the second important thing in data analysis process which is the goal/objective of obtaining the data in the first place - to make decisions or conclusion about the data.

Going back to the examples I gave above, the government can conclude whether or not the drug is effective based on the outcome of the clinical trial, the financial institution can spot fraudulent transactions based on the model built with the data collected, a country can project their future population based on the available data and make appropriate policies to address any resulting economic/social challenges, Netflix can recommend movies for you based on your history, this makes you stay glued to them, which eventually generates more revenue for them, YouTube suggests videos for you based on your past activities, you are tempted to watch more which means more money for them.

So what's data analysis in real life all about? Three basic things;
1. Why collecting the data?
2. What conclusion (actionable insights) are we making from the analysis?
3. How does the conclusion affect the growth or otherwise of the company?

The takeaways...

When carrying out any analysis, ask yourself or the client these questions;
1. What is the objective of the analysis?
2. Do we have research questions we are trying to answer?
3. Do we have any research hypothesis (which eventually leads to conclusion) ?

The above are what your visualizations and statistical analysis tell you. The more experienced you are, the more accurate you are likely to get the steps.

I hope you find this useful and grow with it.

wisemania:


We need to be careful when dishing advice.

We get it! You are a bookworm, you can't help it, we understand, but be careful with your advice. Ejiod already explained the process of becoming a data scientist.

How?
By FIRST becoming a data analyst. You do not need to understand the intricacies of advanced statistics (or ML) to become a data analyst. What you need is

1. basic mathematics (statistics),

2. basic intuition of how ML algos work(they've been written in packages already for your usage). Kiril eremenko did a great job on that on udemy. Just get the course for free and learn the intuition with how and when to use it.

3. Excel,PowerBI(because it's cheaper for companies to integrate as most companies rely on Microsoft products and it's simply advanced excel) and or Tableau (or any other visualization tools). Tableau is a bit expensive though

4. SQL (T-Sql/MySQL/P-SQL) they are all 90% the same. Learn MySQL and the rest will become a breeze!

5. A programming language (Python/R/Julia, etc). You don't need to know all the functions of the language, just learn the basics and the necessary libraries for now (trust me, there'll be plenty of time to dive deeper into these languages later!)

After you're pretty solid on the above, start applying for jobs (junior data analyst or Business Intelligence Analyst).

Or if you want a higher pay,
Immerse yourself in MSSQL (Data warehouse tools)
I.e
Learn


T-SQL (Microsoft customized SQL)

SSRS (pretty deprecated, but many companies still use it for reporting. To be frank, modern visuals like PBI, Tableau are taking over.dont spend too much of your time here)

SSIS -makes your life easier, as it reduces the amount of dml you need write. It's very easy.

SSAS - same.


Spend your time learning the above and you'll begin to earn a lot.

Because if you spend your time going though the Forever learning path, you're gonna be a jack of all trades yet a master of none.

If you need to feed, get a job with excel,powerBI/tableau,SQL,Python/R
then learn the remain (Statistics, ML-DL) on the job as required.

Else, you go HUNGRY


This is barely my 3rd month into data analysis and I've gotten some interviews already... No time to waste, as you need to survive...

My 2cents

tplayer:

1- Excel(and relatives/natives like pivot table, power query(you can skip the M language in it), learn to create dashboards with excel(because what a lot of business people know is excel and excel is everywhere; power bi is d shiny kid getting lots of love now, and for good reasons), learn formulas and array formulas. Once u understand how cell referencing works, excel becomes easy.
1.5- power pivot (or just move to 2)
2-power bi(please learn data modelling here, please please learn how to use d dax language to create calculated columns, measures, time intelligence so you can stand out. power query and power pivot are still here too.
Note that tableau can replace power bi too. These 2 are d most popular.
3. - involve urself in d statistical aspect of what you are learning. Using tools without knowing d statistics is not complete. after all data science, forger d sexy name, is basically statistics, some pure mathematics, cimputer science, and common sense. Please read all you can about probability, distributions, Central limit theorem, experimental design, ANOVA, manova, correlation, regression, Exploratory data analysis(e.g make sure you know all about d box plot, not just how to plot it), parametric and non parametric test, post-hoc tests. etc. Baba if you try all d things here, u are already a small boss.
4- Python or R, you choice (to implement 3, and even parts of 1 and 2. They are d languages of data.

5-Now, advance statistics and calculus( For MACHINE LEARNING!!!!!! which can be implemented using python or R.) You can start machine learning by using clicks and libraries before you go under d hood. machine learning no be beans sha o and its not just simple regression. But its learnable.

6. this has no number really. insert SQL anywhere after 2.


7- PRACTICE. PRACTICE. PRACTICE!!!

If all you want is mainly extraction, cleaning, munging, visualisation of data, and creating a shiny(apologies to R guys) dashboard, A simple knowledge of statistics may do it. Excel, Power bi and SQL will do.

To become a real data scientist aka machine expert, statistics, maths, must come in.

please, I put dis together in a hurry. others here should please chime in.

Jahzrockballer:
Omo. Ah, my brother! You don come to the right place, Nairaland, where we dey drop knowledge like pure water for your thirsting career quest. Ehen, make I yarn you the truth as e be. The best career, na the one wey you sabi do wey go put food for your table and enough jara for your pocket. If you think say I go just come here dey pour grammar like Wole Soyinka, you go wait tire. No be joke oh!

Make I tell you one secret: for this 21st century wey we dey so, tech skills na the key to unlock doors wey dey lead to success. E no be like that time wey our forefathers dey hunt bush meat for jungle. If you never dey sharpen your tech skills, my brother, you go soon become oyibo for your village. The world don change oh! No be only "Iyawo mi" dem dey wire for village, dem dey wire Bitcoin too.

So, if you wan shine for this life, make you hustle to learn tech skills. No be only programming oh! Anything wey get to do with tech, whether na web design, cybersecurity, digital marketing, or even video editing, just pick one wey sweet your belle and run with am. Na so dem dey say, "The sky na your stepping stone." But if you like, carry stepping ladder climb the sky, na your own wahala be that.

Leveraging freelancing na another game changer for career. See, no be only your papa office you fit work, you fit use your talent sell am to the highest bidder for freelancing platforms like Upwork, Fiverr, and the rest of them. Just make sure say you no go fall victim to those clients wey dey like to pay with expo. Know your worth and collect your money complete before you finish work. No dey form, "I be charitable organization."

But wait oh, make I tell you something funny. For this Naija, e get some people wey go dey call themselves "Yahoo Yahoo boys" and "G-boys" and dey think say na career. Abeg, if you see any of them, just bone face, commot eye, commot ear. No follow them do meeting, unless you want make EFCC use your name swear for court. Hustle legit, my brother. Yahoo no be career, na express ticket to Kirikiri prison.

So, make you find your passion, build your tech skills, leverage freelancing, and hustle legit. I dey tell you, the world na your village; conquer am! If you like, take this advice, if you like, leave am. Na only you fit write your own success story. No forget say, "Person wey wan fly like eagle, no suppose follow pigeons dey waka."

Wetin I talk? E do, abi I suppose add salt and Maggi join? Make I drop my pen here before I begin preach. May the hustle be with you, my guy!


https://chat.openai.com/share/9229ee49-cb71-4273-868d-b3a16ac658c8