whizqueen:
I took this remote internship and I believe it would be beneficial to everyone interested in Data Science

Thanks for sharing.

saheedniyi22:
*Day 6 of Day 70 prebootcamp class*

Hello guys,

Get to participate and learn the fundamentals of machine learning/ deep learning by registering so that you can get an invite link to classroom here. https:///prebootcamp2020 . Ensure you select your preferable stream of learning.

All intuitive discussion and concept breakdown takes place via our slack channel *#machine-learning-stream-2020* *#deep-learning-stream-2020.* Get to engage in a discussion with other ML enthusiasts by registering to get your membership ID in few minutes. https:///aimembership

Ensure you tweet your experience at us by tagging us @DataScienceNIG, and using the following hashtags #70daysofML #70daysofDL

Cheers.

The links are not working.

BelieverDE:

The links are not working.

I would resend

You guys should go and learn[b] excel , SQL Pandas,, statistics,and maybe powebi or tabulea[/b].
If you really want to work in the field of data
Leave all these machine learning and Deep learning for now. Nigeria is not yet there

nwele2017:
You guys should go and learn[b] excel , SQL Pandas,, statistics,and maybe powebi or tabulea[/b].
If you really want to work in the field of data
Leave all these machine learning and Deep learning for now. Nigeria is not yet there

Then which one should we learn Sir??

TheGenius001:


Then which one should we learn Sir??

learn what i have listed
Excel ,pandas , SQL ,statistics ,tableau or power bi
That's only what you need to become a data analyst any where in the World.many people are running into Machine learning thinking that it is self heaven and sexiest job of 2020 but, they don't know there is high demand of Data analyst than a Data scientist. Even software engineers are in high demand than data scientist.

A standard firm can employ tens of software engineers, 5 to six data analysts and only one Data scientist.
Am not discouraging people from learning data science; i just want to be realistic , this is what is obtainable in the industry .it is just like playing the devils game.
There is high demand of Data analyst and software engineers than data science.
I will tell you that data science is like a bubble that will soon burst. people see machine learning like a black box that can solve any problem but in reality it's a lie, the problem machine learning algorithm can solve is limited.And also data science is not all about knowing algorithm.
There are more to data science than knowing algorithm, understanding the context of industry you are working and also communicating the result visually and orally is also important.
The sure and easiest way of becoming a data scientist is to become a data analyst or business intelligence analyst and by doing this you become acquainted with problem that is need to be solved.

nwele2017:

learn what i have listed
Excel ,pandas , SQL ,statistics ,tableau or power bi
That's only what you need to become a data analyst any where in the World.many people are running into Data science thinking that it is self heaven and sexiest job of 2020 but, they don't know there is high demand of Data analyst than a Data scientist. Even software engineers are in high demand than data scientist.

A standard firm can employ tens of software engineers, 5 to six data analysts and only one Data scientist.
Am not discouraging people from learning data science; i just want to be realistic , this is what is obtainable in the industry .it is just like playing the devils game.
There is high demand of Data analyst and software engineers than data science.
I will tell you that data science is like a bubble that will soon burst. people see data science like a black box that can solve any problem but in reality it's a lie, the problem data science algorithm can solve is limited.And also data science i not all about knowing algorithm.
There are more to data science than knowing algorithm, understanding the context of industry you are working and also communicating the result visually and orally is also important.
The sure and easiest way of becoming a data scientist is to become a data analyst or business intelligence analyst and by doing this you become acquainted with problem that is need to be solved.

I agree with your points sir. But I believe that a Data analyst can also stand in as a Data scientist. Although I stand to be corrected.

nwele2017:

learn what i have listed
Excel ,pandas , SQL ,statistics ,tableau or power bi
That's only what you need to become a data analyst any where in the World.many people are running into Data science thinking that it is self heaven and sexiest job of 2020 but, they don't know there is high demand of Data analyst than a Data scientist. Even software engineers are in high demand than data scientist.

A standard firm can employ tens of software engineers, 5 to six data analysts and only one Data scientist.
Am not discouraging people from learning data science; i just want to be realistic , this is what is obtainable in the industry .it is just like playing the devils game.
There is high demand of Data analyst and software engineers than data science.
I will tell you that data science is like a bubble that will soon burst. people see data science like a black box that can solve any problem but in reality it's a lie, the problem data science algorithm can solve is limited.And also data science i not all about knowing algorithm.
There are more to data science than knowing algorithm, understanding the context of industry you are working and also communicating the result visually and orally is also important.
The sure and easiest way of becoming a data scientist is to become a data analyst or business intelligence analyst and by doing this you become acquainted with problem that is need to be solved.

I can agree with you when you say that Data scientist doesn't really mean much in Nigeria, but don't relate it globally. Data science encompasses both analysis, big data and the machine learning you mentioned, it's like a unicorn role. The issue is that it involves a lot that when someone is doing just part of it, he/she will identify as a Data scientist. Machine learning like you mention is on a very high demand in a developed country. Of course it is artificial intelligence, and there is no field I can ever think of that is in demand more than AI and Robotics. Netflix, amazon, facebook, and microsoft recommendation engines, Google self driving cars, tesla, fraud detection, image recognition, and so many others are all ML and DL.

You wouldn't advise someone in US, Canada and co. to forget ML. You can even get it as a remote job. It is not even easy to learn ML, cos you need a good knowledge of statistics, algorithms and maths(linear algebra and calculus) to understand how model works. A machine learning engineer like they said is one that knows statistics more than a software engineer and knows algorithms more than a statistician.

For me, they should just delete the Data Science name cos it is a confused role. Unless you can do analysis, visualization, maths, stat, algorithms handle big data and good business knowledge, you are not a Data scientist, but if you do, you are the hottest in the market.

Blesser28:
Anyone here who usés vs code and juypter and conda together

Anaconda has the other two na

Oddy16:



I can agree with you when you say that Data scientist doesn't really mean much in Nigeria, but don't relate it globally. Data science encompasses both analysis, big data and the machine learning you mentioned, it's like a unicorn role. The issue is that it involves a lot that when someone is doing just part of it, he/she will identify as a Data scientist. Machine learning like you mention is on a very high demand in a developed country. Of course it is artificial intelligence, and there is no field I can ever think of that is in demand more than AI and Robotics. Netflix, amazon, facebook, and microsoft recommendation engines, Google self driving cars, tesla, fraud detection, image recognition, and so many others are all ML and DL.

You wouldn't advise someone in US, Canada and co. to forget ML. You can even get it as a remote job. It is not even easy to learn ML, cos you need a good knowledge of statistics, algorithms and maths(linear algebra and calculus) to understand how model works. A machine learning engineer like they said is one that knows statistics more than a software engineer and knows algorithms more than a statistician.

For me, they should just delete the Data Science name cos it is a confused role. Unless you can do analysis, visualization, maths, stat, algorithms handle big data and good business knowledge, you are not a Data scientist, but if you do, you are the hottest in the market.

Well said!
Truer words have never been spoken.

nwele2017:

learn what i have listed
Excel ,pandas , SQL ,statistics ,tableau or power bi
That's only what you need to become a data analyst any where in the World.many people are running into Machine learning thinking that it is self heaven and sexiest job of 2020 but, they don't know there is high demand of Data analyst than a Data scientist. Even software engineers are in high demand than data scientist.

A standard firm can employ tens of software engineers, 5 to six data analysts and only one Data scientist.
Am not discouraging people from learning data science; i just want to be realistic , this is what is obtainable in the industry .it is just like playing the devils game.
There is high demand of Data analyst and software engineers than data science.
I will tell you that data science is like a bubble that will soon burst. people see data science like a black box that can solve any problem but in reality it's a lie, the problem data science algorithm can solve is limited.And also data science i not all about knowing algorithm.
There are more to data science than knowing algorithm, understanding the context of industry you are working and also communicating the result visually and orally is also important.
The sure and easiest way of becoming a data scientist is to become a data analyst or business intelligence analyst and by doing this you become acquainted with problem that is need to be solved.

I beg to differ with your opinion.
When it comes to developing smart and innovative solutions machine learning and deep learning can never be ruled out as so many tech companies like google, amazon, Netflix, and Facebook depend heavily on it's algorithm.

If you did a little research, you would have seen that Artificial intelligence (ML and DL) is making waves in all areas of human endeavors including finance and healthcare.

Nigeria can never be fully prepared for any digital revolution but fortune favours those who tap from global trends.

nwele2017:

learn what i have listed
Excel ,pandas , SQL ,statistics ,tableau or power bi
That's only what you need to become a data analyst any where in the World.many people are running into Machine learning thinking that it is self heaven and sexiest job of 2020 but, they don't know there is high demand of Data analyst than a Data scientist. Even software engineers are in high demand than data scientist.

A standard firm can employ tens of software engineers, 5 to six data analysts and only one Data scientist.
Am not discouraging people from learning data science; i just want to be realistic , this is what is obtainable in the industry .it is just like playing the devils game.
There is high demand of Data analyst and software engineers than data science.
I will tell you that data science is like a bubble that will soon burst. people see data science like a black box that can solve any problem but in reality it's a lie, the problem data science algorithm can solve is limited.And also data science i not all about knowing algorithm.
There are more to data science than knowing algorithm, understanding the context of industry you are working and also communicating the result visually and orally is also important.
The sure and easiest way of becoming a data scientist is to become a data analyst or business intelligence analyst and by doing this you become acquainted with problem that is need to be solved.

Take this advice and you won't regret it. Couldn't have put it beta.

Abcruz:



I beg to differ with your opinion.
When it comes to developing smart and innovative solutions machine learning and deep learning can never be ruled out as so many tech companies like google, amazon, Netflix, and Facebook depend heavily on it's algorithm.

If you did a little research, you would have seen that Artificial intelligence (ML and DL) is making waves in all areas of human endeavors including finance and healthcare.

Nigeria can never be fully prepared for any digital revolution but fortune favours those who tap from global trends.

How many data science roles are there in d world? Yet thousands are rushing in to learn machine learning in weeks, hoping to work in amazon and co
. Being a data scientist , at its core, requires sound knowledge of advanced statistics, some advanced maths, some solid computer science. it's only those with these capabilities DAT are likely going to work as data scientists at big tech firms. how many running into machine learning have these.?

tplayer:

How many data science roles are there in d world? Yet thousands are rushing in to learn machine learning in weeks, hoping to work in amazon and co
. Being a data scientist , at its core, requires sound knowledge of advanced statistics, some advanced maths, some solid computer science. it's only those with these capabilities DAT are likely going to work as data scientists at big tech firms. how many running into machine learning have these.?

The bolded is not entirely true as I've personally created machine learning and deep learning models without any form of experience in advanced of maths, statistics nor computer science.

Applied knowledge of a skill is not only done on big tech companies. What happened to trading your skill to global clients through freelancing on Fiverr or Upwork? There're people offering data science skills over there and cashing out big so think out of the box bro.

Even the data analyst we speak about Nigeria companies does not fully recognise it's potential that's why many are still unemployed after acquiring the knowledge.

Truth be told guys. There are more offers for Data Analyst in Nigeria and in the world generally than Data Scientist . Real life business world is beyond importing data or running model and evaluating such model.
Like I said most businesses need insights to their problems. Very well. It’s very important you understand and implement simple or complex supervised learning most especially Regression models and also the KNN model when it comes to clustering. It gives you that edge. To my point. It’s easier to see offer as an analyst than data scientist because this field is still at infant stage in Nigeria.However these skills sets you above your fellow;
1. SQL: When I mean sql it’s not that SELECT * most peeps are doing. I’m talking advanced sql
2. Excel: in-fact deep in it
3. Tableau and power BI: Not drag and drop. You using Calculated fields and DAX syntax
4.Python or R
5.Pandas if you are on python
6. Machine learning. Please don’t stress yourself on deep learning. Just take that time and learn SQL well well.

Some may not know that both powerBI and Tableau can implement machine learning model.

Finally you all can try using pycaret. It makes your machine learning much simpler with just few lines of code.
In all learn all that’s linked to data. It’s very helpful.

Ejiod:
Truth be told guys. There are more offers for Data Analyst in Nigeria and in the world generally than Data Scientist . Real life business world is beyond importing data or running model and evaluating such model.
Like I said most businesses need insights to their problems. Very well. It’s very important you understand and implement simple or complex supervised learning most especially Regression models and also the KNN model when it comes to clustering. It gives you that edge. To my point. It’s easier to see offer as an analyst than data scientist because this field is still at infant stage in Nigeria.However these skills sets you above your fellow;
1. SQL: When I mean sql it’s not that SELECT * most peeps are doing. I’m talking advanced sql
2. Excel: in-fact deep in it
3. Tableau and power BI: Not drag and drop. You using Calculated fields and DAX syntax
4.Python or R
5.Pandas if you are on python
6. Machine learning. Please don’t stress yourself on deep learning. Just take that time and learn SQL well well.

Some may not know that both powerBI and Tableau can implement machine learning model.

Finally you all can try using pycaret. It makes your machine learning much simpler with just few lines of code.
In all learn all that’s linked to data. It’s very helpful.

Abcruz:



The bolded is not entirely true as I've personally created machine learning and deep learning models without any form of experience in advanced of maths, statistics nor computer science.

Applied knowledge of a skill is not only done on big tech companies. What happened to trading your skill to global clients through freelancing on Fiverr or Upwork? There're people offering data science skills over there and cashing out big so think out of the box bro.

Even the data analyst we speak about Nigeria companies does not fully recognise it's potential that's why many are still unemployed after acquiring the knowledge.

Not entirely true but still true, isn't it?. A thorough understanding of machine learning requires more than just a few weeks at a boot camp, bro. classification, k clustering, random forest etc require a solid background knowledge of those skills I mentioned. you might build some models here and there but tweaking them and build advanced models requires more than just a few clicks. And yes I agree u can believe models without those skills I mentioned, but at a lower level. Becoming an amazon or google type data scientist requires a solid background. in short, taking it to the next level require more than what many enthusiasts have. If u are coming from a non-stem background, u will need to put in more effort.

Ejiod:
Truth be told guys. There are more offers for Data Analyst in Nigeria and in the world generally than Data Scientist . Real life business world is beyond importing data or running model and evaluating such model.
Like I said most businesses need insights to their problems. Very well. It’s very important you understand and implement simple or complex supervised learning most especially Regression models and also the KNN model when it comes to clustering. It gives you that edge. To my point. It’s easier to see offer as an analyst than data scientist because this field is still at infant stage in Nigeria.However these skills sets you above your fellow;
1. SQL: When I mean sql it’s not that SELECT * most peeps are doing. I’m talking advanced sql
2. Excel: in-fact deep in it
3. Tableau and power BI: Not drag and drop. You using Calculated fields and DAX syntax
4.Python or R
5.Pandas if you are on python
6. Machine learning. Please don’t stress yourself on deep learning. Just take that time and learn SQL well well.

Some may not know that both powerBI and Tableau can implement machine learning model.

Finally you all can try using pycaret. It makes your machine learning much simpler with just few lines of code.
In all learn all that’s linked to data. It’s very helpful.

Good day sir, I've noticed you stress 'SQL' a lot in some of your posts,I just wanted to know, can't basic sql knowledge be augmented with a sound knowledge in pandas?
PS:forgive my naivety

hardytech:

wow finally, really appreciate your response, what do you plan on doing after the course?

pick some little project on it(maybe two projects) then take another course from another tutor i think i will go for the krill emenrenko data science master class
after that i will work pn another project then i will take andrew yen course to get a goo theriotical background and then project upon project till midd 2021 then i will focus on deep learning only das my plan i just want to explore for knowledge sake no necessary looking for a job in ds for now

Marveaux:

Good day sir, I've noticed you stress 'SQL' a lot in some of your posts,I just wanted to know, can't basic sql knowledge be augmented with a sound knowledge in pandas?
PS:forgive my naivety

Reason is that most businesses and corporate organisation store their data on database and they look for who will pull that data up.
Pandas is great but thats descriptive data analysis which many software these days can do.The advantage you have with pandas is data wrangling.

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Marveaux:

Good day sir, I've noticed you stress 'SQL' a lot in some of your posts,I just wanted to know, can't basic sql knowledge be augmented with a sound knowledge in pandas?
PS:forgive my naivety

SQL is not just about explorative analysis and data manipulation. pandas is quite good for that, Advanced sql is a lot deeper. Creating Views, temporary table, sub queries, index, stored procedure, the list is endless, up to testing queries and all. A good data analyst must be adept in SQL. I SUBMIT

tplayer:


Not entirely true but still true, isn't it?. A thorough understanding of machine learning requires more than just a few weeks at a boot camp, bro. classification, k clustering, random forest etc require a solid background knowledge of those skills I mentioned. you might build some models here and there but tweaking them and build advanced models requires more than just a few clicks. And yes I agree u can believe models without those skills I mentioned, but at a lower level. Becoming an amazon or google type data scientist requires a solid background. in short, taking it to the next level require more than what many enthusiasts have. If u are coming from a non-stem background, u will need to put in more effort.

Advance maths and statistics is mainly for academia's in the field of Artificial intelligence. Most of the high level algorithms needed to build and tune-up a model to optimum performance has already been developed and shipped as libraries in order to abstract the foundational complexities of advanced knowledge in data science.

You don't have to reinvent the wheel by coding k clultering, classification, nearest neighbor from scratch before building a state of the art model cos that has already been handled by libraries. You only need to know how it works.

A renowned fellow in Machine learning whose name is Jason Brownlee once said he studied computer science, acquired masters degree and PHD in machine learning only to discover that most of the academic aspect is not practical applicable just theory upon theory.

Data science is not Rocket science. Developers at google and other big tech companies were once baby programmers mastery comes by continuous practice it is not a days job. Even those at google will tell you they're still learning.

Abcruz:



Advance maths and statistics is mainly for academia's in the field of Artificial intelligence. Most of the high level algorithms needed to build and tune-up a model to optimum performance has already been developed and shipped as libraries in order to abstract the foundational complexities of advanced knowledge in data science.

You don't have to reinvent the wheel by coding k clultering, classification, nearest neighbor from scratch before building a state of the art model cos that has already been handled by libraries. You only need to know how it works.

A renowned fellow in Machine learning whose name is Jason Brownlee once said he studied computer science, acquired masters degree and PHD in machine learning only to discover that most of the academic aspect is not practical applicable just theory upon theory.

Data science is not Rocket science. Developers at google and other big tech companies were once baby programmers mastery comes by continuous practice it is not a days job. Even those at google will tell you they're still learning.

You only need to know how it works right? The how requires a fundamental understanding of what you are doing, why u are doing it, the limitations, how it works etc to really deploy it optimally. take "easy" regression, for example. You think its just about knowing what libraries to use? no need to understand probability, what R squared really means, multi-collinearity and how to avoid or fix it, studentizing, cook's distance etc. Breaking boundaries in data science requires a fundamental knowledge of a lot, even if not all, of those complexities.
Jason is as good as he is cos of his computer science degrees, make no mistake about it. Now, does theory hinder practical learning? Yes, but for d most part, it helps. And don't forget DAT data science, the part he's into, is just one aspect of computer science that covers lots of other areas he studied in school.
Again, can one do data science without knowing some fundamental complexities? Yes, of course. Thanks to libraries. Even Excel can do ML. But you need to learn the why's to be a don.

Abcruz:



Advance maths and statistics is mainly for academia's in the field of Artificial intelligence. Most of the high level algorithms needed to build and tune-up a model to optimum performance has already been developed and shipped as libraries in order to abstract the foundational complexities of advanced knowledge in data science.

You don't have to reinvent the wheel by coding k clultering, classification, nearest neighbor from scratch before building a state of the art model cos that has already been handled by libraries. You only need to know how it works.

A renowned fellow in Machine learning whose name is Jason Brownlee once said he studied computer science, acquired masters degree and PHD in machine learning only to discover that most of the academic aspect is not practical applicable just theory upon theory.

Data science is not Rocket science. Developers at google and other big tech companies were once baby programmers mastery comes by continuous practice it is not a days job. Even those at google will tell you they're still learning.

By d way, I agree its not rocket science but its not rookie science, either. Practice is key as you have alluded to.

Ejiod:
Truth be told guys. There are more offers for Data Analyst in Nigeria and in the world generally than Data Scientist . Real life business world is beyond importing data or running model and evaluating such model.
Like I said most businesses need insights to their problems. Very well. It’s very important you understand and implement simple or complex supervised learning most especially Regression models and also the KNN model when it comes to clustering. It gives you that edge. To my point. It’s easier to see offer as an analyst than data scientist because this field is still at infant stage in Nigeria.However these skills sets you above your fellow;
1. SQL: When I mean sql it’s not that SELECT * most peeps are doing. I’m talking advanced sql
2. Excel: in-fact deep in it
3. Tableau and power BI: Not drag and drop. You using Calculated fields and DAX syntax
4.Python or R
5.Pandas if you are on python
6. Machine learning. Please don’t stress yourself on deep learning. Just take that time and learn SQL well well.

Some may not know that both powerBI and Tableau can implement machine learning model.

Finally you all can try using pycaret. It makes your machine learning much simpler with just few lines of code.
In all learn all that’s linked to data. It’s very helpful.

I think I would listen and follow this, I think this issue started when I asked for review on andrie zero to mastery on machine learning and data science, but know I have to start with immediate opportunities around me.
please this is to everyone now, courses do get outdated all the time with new information and all, please for all the aspect sir ejiod has listed above can I get a list of courses I can start with, I would really appreciate because it would help alot of newbies like me to know the best list to start with, I know alot of people would ask me to go through the thread but I want everyone to know there lots of courses here, alot of information so I would appreciate like maybe a list we can be updating monthly to help newbies know what resources to learn from.

tplayer:


You only need to know how it works right? The how requires a fundamental understanding of what you are doing, why u are doing it, the limitations, how it works etc to really deploy it optimally. take "easy" regression, for example. You think its just about knowing what libraries to use? no need to understand probability, what R squared really means, multi-collinearity and how to avoid or fix it, studentizing, cook's distance etc. Breaking boundaries in data science requires a fundamental knowledge of a lot, even if not all, of those complexities.
Jason is as good as he is cos of his computer science degrees, make no mistake about it. Now, does theory hinder practical learning? Yes, but for d most part, it helps. And don't forget DAT data science, the part he's into, is just one aspect of computer science that covers lots of other areas he studied in school.
Again, can one do data science without knowing some fundamental complexities? Yes, of course. Thanks to libraries. Even Excel can do ML. But you need to learn the why's to be a don.

I agree with you!

hardytech:


I think I would listen and follow this, I think this issue started when I asked for review on andrie zero to mastery on machine learning and data science, but know I have to start with immediate opportunities around me.
please this is to everyone now, courses do get outdated all the time with new information and all, please for all the aspect sir ejiod has listed above can I get a list of courses I can start with, I would really appreciate because it would help alot of newbies like me to know the best list to start with, I know alot of people would ask me to go through the thread but I want everyone to know there lots of courses here, alot of information so I would appreciate like maybe a list we can be updating monthly to help newbies know what resources to learn from.

So true!

I've been learning Excel for 3 months now. The truth is, Excel is so broad and I get lost in it by lingering on it. I don't know whether to move to SQL or PowerBI (as suggested from the Microsoft edx Data Analyst Xseries courses)

Gurus in the house, please suggest a learning path for me to follow

After Excel, should what should I learn next. And what course would you suggest.

@Ejiod, Kirill Eremenko's course on Machine A-Z is no longer on freetutorialsus.com


Thanks all, in anticipation of your help always.

peterincredible:
pick some little project on it(maybe two projects) then take another course from another tutor i think i will go for the krill emenrenko data science master class
after that i will work pn another project then i will take andrew yen course to get a goo theriotical background and then project upon project till midd 2021 then i will focus on deep learning only das my plan i just want to explore for knowledge sake no necessary looking for a job in ds for now

great plan you have here bro

BelieverDE:


So true!

I've been learning Excel for 3 months now. The truth is, Excel is so broad and I get lost in it by lingering on it. I don't know whether to move to SQL or PowerBI (as suggested from the Microsoft edx Data Analyst Xseries courses)

Gurus in the house, please suggest a learning path for me to follow

After Excel, should what should I learn next. And what course would you suggest.

@Ejiod, Kirill Eremenko's course on Machine A-Z is no longer on freetutorialsus.com


Thanks all, in anticipation of your help always.

yes bro gone are those days when we shout lack of information, now there is alot out there and if not properly mentored you would get lost that is why I feel some of the big guys here should help create a learning path for data analyst since that's the most visible path for a newbie to start with, they can be updating it monthly if there is anything new to add, this would help create and change people lives because once you know what is current you can easily learn with the right material.

oga Ejiod has touched the path we need to focus on, now we need recommended courses on each part.

Zabiboy:
What did you start 20 days ago?? Re-phrase your question

no answer