₦airaland Forum

feckos:
Pls could you advise how to make payment on Coursera. I want to begin the Google Data Analytics course but payment has been a problem.

lovelybobo:
Option 1 : Get Dollar card from any bank. I am sure it will work. You can't make Dollar payment using Naira Debit cards anymore.

Option 2 : Look for how to get scholarship to enable you do courses in Coursera.

Option 3 : IF the certificate is not important to you, You are just interested in the knowledge and entire materials. You can private chat me and I share it with you.

Hinokami:
I started learning excel on the side before an internal opportunity within my organisation opened up. I was able to convince them plus my enthusiasm for upskilling gave me the job during the interview.

Since then I've learnt sql, power bi, tableau and python. I hate python but I have to learn it because I need it to venture into data engineering.

For projects ensure that you work on unique project. Not just capstone projects or the ones that everyone is doing making copy and paste easy. Work on projects you are able to talk about keenly.

Coursera, YouTube and Udacity have been my lifesavers
Excel
SQL
Visualisation tool
Python
In that order.

Also analytics isn't just about following the process of gathering data, cleaning it and propping out some visuals
Ask questions, know the data in depth to see if it can solve your problems. What insights can you glean from the data that can guide decision making of the stakeholders
Presentation skills, narrative visuals and data storytelling are important skills too.

Hope this helps

Omotaday:
Hi, please where can I learn figma for data viz as a beginner?

Kaycee54321:
Very true too.

I also feel that it's because of how the Nigerian employers try their possible best to not give a F*CK about the programming mumbo jumbo.

I remember vividly, my very first job, I prepped a long ass Word Doc on my processes, talked about how we should integrate Jupyter so we can work on code and bla bla bla

The CEO met me on Monday morning and told me that all he's interested in is, whether he's winning or losing.

If he's winning, why?
If he's losing, why?

I jeje began to use Excel pivots and sweet BI dashboards and everybody was happy.

KlausMichaelson:
Tell me to choose between learning:

Python Vs (Excel+Power Bi/Tableau)

I will choose Python a million times. Python has more rewarding to me as a Programmer, a Technical writer and a Data Scientist/Ml Engineer.

BeLookingIDIOT:
I've been reading similar posts like this on Reddit. It's the same thing abroad.

GreatKingg:
You chose python a million times because it aligns more with what you do (Data Scientist/MI Engineer). But an analyst will choose Excel, PowerBI and Tableau a million times over python... so you see, different strokes for different folks..

KlausMichaelson:
Good point. I am analyst as well, however, there is more to life than limiting oneself to not learning it. Life can be more fun with that knowledge. When you no longer find interest in spending time on your dashboard trying to create visuals like a designer, you will understand what I mean. The opportunity the language alone presents to you cannot be overemphasized.

I just hope you get the point. It's just a simple advice to anyone who is wearing that shoe.

Basoj:
Good evening house. I'm currently learning Python and Machine Learning. I plan to buy a laptop that will enhance my learning. My budget is between 200k and 220k. Someone told me to go for core i5 and above but the price of the new one is beyond my budget. My intention is to buy new one. Is it advisable to go for the UK used core i5 or buy new core i3. Your suggestions and advices will be highly appreciated. Thanks

Hinokami:
Please anyone can help me resolve this issue of installing ggplot2 on RStudio...

I use R version 3.6.3 which I believe is the latest version

I have tried installing with dependencies set to TRUE

I have scoured stackoverflow for answers but none have worked.

It keeps telling me these two errors

1 (as 'lib' is unspecified )... As seen on the top of the image

2. Installation of package ggplot2 had non zero exit status.

I'd appreciate it guys, anyone using RStudio who ever encountered this problem?

Hinokami:
I started learning excel on the side before an internal opportunity within my organisation opened up. I was able to convince them plus my enthusiasm for upskilling gave me the job during the interview.

Since then I've learnt sql, power bi, tableau and python. I hate python but I have to learn it because I need it to venture into data engineering.

For projects ensure that you work on unique project. Not just capstone projects or the ones that everyone is doing making copy and paste easy. Work on projects you are able to talk about keenly.

Coursera, YouTube and Udacity have been my lifesavers
Excel
SQL
Visualisation tool
Python
In that order.

Also analytics isn't just about following the process of gathering data, cleaning it and propping out some visuals
Ask questions, know the data in depth to see if it can solve your problems. What insights can you glean from the data that can guide decision making of the stakeholders
Presentation skills, narrative visuals and data storytelling are important skills too.

Hope this helps

KlausMichaelson:
Go for a fairly used one, core i5, from a trusted seller. You can always use Google Colab to run your codes if your system sucks (no GPU).

Hagiazomakai:
Let me continue @Ejiod,moreso Excel cant handle large datasets,so tell coders to go into Python straightup (learning the basics of it including functions, class,inheritance) and then delve into pandas and co.

The place of machine learning cannot be overemphasised...ML has covered the predictive intelligence of Power BI (though the visually appealing dashboards of Power BI cannot be downplayed,but I prefer the drag and drop of Tableau )

Data Science requires more skills in creative thing and bringing insights out of data with a line of codes rather than with a boring formula which Excel provides.

Many thanks

adaperry25:
Is AI taking over data analytics job?
This new released Microsoft Fabric is an all in one software package for analytics and data science

wisdomremz:
quote me with your email, i will send the pdf

Ejiod:
There are 2 or if not 3 field in data analysis
Descriptive/Explorative analysis
You need to master these skills
Excel,PowerBI,Tableau,Pandas,SQL,

Predictive analysis
This is where machine learning,deep learning and NLP comes in.This forms the real basics of AI.

Both are under data science. However its advisable to have those skills set under both so you have edge.If you are just doing machine learning without knowing any visualization tools or excel it's just you playing around.

wisdomremz:
just wanted to drop this
if you are a beginner and willing to start a career in data science without any skill, you should start with excel then sql..

if you prefer videos for training in details

you can get full trainings on excel and sql server from beginner to advance in details from YouTube below;

1) for excel- watch *excelisfun* YouTube channel cover all you need from beginner to advance and also using excel for data analysis


2) for sql server- watch *kudvenkat* youtube channel covers all about sql server from beginner to advance its quite long(winks)



with this you're 30% covered in data science..



I love video tutorial cos you watch Wat is taught been put in use and you can perform simultaneously, books can be used for references.

Ejiod:
Possibility is 15% but you your skillsets on data science can set u aside and boost it to 50%. You just need constant practice with data. Infact be grounded on SQL, TABLEAU or power BI.

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.

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

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.

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