I think coding is gone, it is too common, easy as in u could get a website to in minutes, there are no new idea think about something someone has done it before, u just have to know how to get the source, it is possible now to build a tech company without writing code

Data science is huge, and the name encompasses so many things, data analyst, product analyst, data engineers, most engineers etc, as the field progresses it is getting reserved for experts

I think I like the Ml part excluding deep learning which I think is just too difficult because of extreme maths,

I also think ml is very easy to automate, very we soon have ml as a service. At that point where anyone with data could use the service.

Especially that google killing start up, doing and investing in all emerging tech, as huge amounts of data, a lot of genius staffs, a lot google wannabe staffs, it really hard to do something on international scale.

What are ur thoughts? What can we could to capitalize on ml before it becomes a field for all like swe.


Do u think it is worth knowing the intricacies of how the model works or just knowing what each model does and fit others codes to ones data

Please do enlighten, I want to do something meaningful with ma life
I don't wanna waste time do something I don't what will happen to in the next 5 years

Hey grandlord

How u coping with stats and math?

Which aspect of ds are doing?

How difficult is it and the learning curve?

Is it fun to learn?

What made you get interested? Do have math or stat deg

What are potential benefits of ds

Till we have more expert on board, you are still the grandlord of ds on nairaland


You don't have to ans all

codeigniter:
Hey grandlord

How u coping with stats and math?

Which aspect of ds are doing?

How difficult is it and the learning curve?

Is it fun to learn?

What made you get interested? Do have math or stat deg

What are potential benefits of ds

Till we have more expert on board, you are still the grandlord of ds on nairaland


You don't have to ans all

Coding is not gone it is this new generation of programmers with their worthless frameworks and libraries that have killed it. Implement all the known data structures in c and c++ and write network libraries first and your view will change.

Maybe u are right, the programming market is oversaturated, I think ds is also heading to direction

codeigniter:
Maybe u are right, the programming market is oversaturated, I think ds is also heading to direction

What makes you think the programming field is saturated. According to an Upwork research, machine learning is the fastest emerging technology in the tech industry.

Job prospects in web development is estimated to grow by 13% over the next 10 years.

Web dev. and the technologies we have today are barely over a decade old.
Websites may be built in a few hours today but maintaining them is where the job lies. Running tests, writing scripts for security, updating databases and developing effective models for server side activities are what experts are mostly needed for, today. Not to mention mobile app development which has been incorporated into our very lifestyle and day to day activities.

DS as a field is an emerging market. The advent of Big data, internet of things, machine learning and artificial intelligence and their applications in top businesses, car manufacturing, robotics and automation, is clearly, the future.

codeigniter:
I think coding is gone, it is too common, easy as in u could get a website to in minutes, there are no new idea think about something someone has done it before, u just have to know how to get the source, it is possible now to build a tech company without writing code

Data science is huge, and the name encompasses so many things, data analyst, product analyst, data engineers, most engineers etc, as the field progresses it is getting reserved for experts

I think I like the Ml part excluding deep learning which I think is just too difficult because of extreme maths,

I also think ml is very easy to automate, very we soon have ml as a service. At that point where anyone with data could use the service.

Especially that google killing start up, doing and investing in all emerging tech, as huge amounts of data, a lot of genius staffs, a lot google wannabe staffs, it really hard to do something on international scale.

What are ur thoughts? What can we could to capitalize on ml before it becomes a field for all like swe.


Do u think it is worth knowing the intricacies of how the model works or just knowing what each model does and fit others codes to ones data

Please do enlighten, I want to do something meaningful with ma life
I don't wanna waste time do something I don't what will happen to in the next 5 years

Coding is not gone, It has never been.

codeigniter:
Hey grandlord

How u coping with stats and math?

Which aspect of ds are doing?

How difficult is it and the learning curve?

Is it fun to learn?

What made you get interested? Do have math or stat deg

What are potential benefits of ds

Till we have more expert on board, you are still the grandlord of ds on nairaland


You don't have to ans all

Bruv thanks for putting me on a pedestal I've not yet attained. You do well o. I'm a green horn in programming and I'm still in a dilemma as to whether to go into web dev or data analytics. You can help me choose. Las las I go do both. The notable name in the DS field in this section is Ejio.d, I think. You can go check out his thread on data science.

Data science has a higher bar of entry compared to say, web development. A data scientist in most cases must have a PhD in Maths, statistics or related disciplines to be considered for employment.

Data visualization analysis on the other hand can be mastered in say, a year. Though candidates with a Bsc in STEM fields have a better chance at employment compared to self-taught individuals.

Data visualization is all about the representation of raw data in a way that clients can make sense of it. This discipline utilizes softwares like Tableau, Powerbi, D3, advanced Excel as well as programming languages like python and R in the creation of interactive dashboards which present data in a way it can easily be analysed and inferred from.

DS and visualization has a steep learning curve. You must love the numbers. You live and die and communicate with numbers every day. So if you do love the numbers, it can be real fun. To build your portfolio, go on Kaggledotcom and solve data challenges and also enroll in data analytics competitions. Do a lot of data projects and build creative models and techniques.

I think a DS expertise can land you a six figure salary anually(dollars o) and Data scientists and analysts are sought after all over the world. With the advent of Big data, machine learning, and AI, data is the future. It ain't going away anytime soon. Boom!

cc: icode2

Grandlord:
Bruv thanks for putting me on a pedestal I've not yet attained. You do well o. I'm a green horn in programming and I'm still in a dilemma as to whether to go into web dev or data analytics. You can help me choose. Las las I go do both. The notable name in the DS field in this section is Ejio.d, I think. You can go check out his thread on data science.

Data science has a higher bar of entry compared to say, web development. A data scientist in most cases must have a PhD in Maths, statistics or related disciplines to be considered for employment.

Data visualization analysis on the other hand can be mastered in say, a year. Though candidates with a Bsc in STEM fields have a better chance at employment compared to self-taught individuals.

Data visualization is all about the representation of raw data in a way that clients can make sense of it. This discipline utilizes softwares like Tableau, Powerbi, D3, advanced Excel as well as programming languages like python and R in the creation of interactive dashboards which present data in a way it can easily be analysed and inferred from.

DS and visualization has a steep learning curve. You must love the numbers. You live and die and communicate with numbers every day. So if you do love the numbers, it can be real fun. To build your portfolio, go on Kaggledotcom and solve data challenges and also enroll in data analytics competitions. Do a lot of data projects and build creative models and techniques.

I think a DS expertise can land you a six figure salary anually(dollars o) and Data scientists and analysts are sought after all over the world. With the advent of Big data, machine learning, and AI, data is the future. It ain't going away anytime soon. Boom!

cc: icode2

Expertly written. Thanks

@bolded
After seeing that kind of figure, you'll still tell me not to go into the field because of the money.

iCode2:
Expertly written. Thanks

@bolded
After seeing that kind of figure, you'll still tell me not to go into the field because of the money.

What can I say? Money is the motivation.

I have written this before I saw ejio.d post on ds chronicle, I am impressed that Nigerian company are using data analyst

Anyways which ever field you choose from there will always be room for the best of the bests

Maybe we should all try to be damn good at anything maybe, coding or data usage or what ever field you desire

Nb I am currently studying pure and applied biology at lautech, I should have studied Cs or stats but no regret yet. There is this intersection between stat, cs and biology called computational biology or bioinformatics, and that is the reason I look in ds

University stuff is kind of old and stupid dey teach thing i don't want to learn, I learn more online and i am more willing to learn than I have ever done at sch, not that I don't have passion for course but there are too many different things done in a semester that aren't related and too many borrowed courses

If u can relate or also in University, do share ur xp

codeigniter:
I have written this before I saw ejio.d post on ds chronicle, I am impressed that Nigerian company are using data analyst

Anyways which ever field you choose from there will always be room for the best of the bests

Maybe we should all try to be damn good at anything maybe, coding or data usage or what ever field you desire

Nb I am currently studying pure and applied biology at lautech, I should have studied Cs or stats but no regret yet. There is this intersection between stat, cs and biology called computational biology or bioinformatics, and that is the reason I look in ds

University stuff is kind of old and stupid dey teach thing i don't want to learn, I learn more online and i am more willing to learn than I have ever done at sch, not that I don't have passion for course but there are too many different things done in a semester that aren't related and too many borrowed courses

If u can relate or also in University, do share ur xp

hey bro, am also regretting studying economics instead of CS. The thing dey pain me die. I just graduated recently and i think i will be focusing on programming since that's my initial goal. With regards to what you said about Nigerian schools teaching old stuff, it is actually true. Most of what are being taught in schools are old and outdated and that's why they keep producing below average graduates. Like grandlord, am having a tough time choosing between web development and data science. Web development is so so easy these days and rampant. Almost everbody wanna become a web developer. I tire sef

codeigniter:
I have written this before I saw ejio.d post on ds chronicle, I am impressed that Nigerian company are using data analyst

Anyways which ever field you choose from there will always be room for the best of the bests

Maybe we should all try to be damn good at anything maybe, coding or data usage or what ever field you desire

Nb I am currently studying pure and applied biology at lautech, I should have studied Cs or stats but no regret yet. There is this intersection between stat, cs and biology called computational biology or bioinformatics, and that is the reason I look in ds

University stuff is kind of old and stupid dey teach thing i don't want to learn, I learn more online and i am more willing to learn than I have ever done at sch, not that I don't have passion for course but there are too many different things done in a semester that aren't related and too many borrowed courses

If u can relate or also in University, do share ur xp

You are like me, I once posted on NL about my confusion on the field to enter. Right now I have found more love for Data Science and it's ML and AI prospects than ever, in fact attending lectures this days are very boring I always pray lecturers leave class very fast or don't show up at all, so that I can have my time to do what I like. Besides I am studying Computer Engineering but I am a great ML enthusiast. I recently started taking a free course on Statistics and Algebra on Udacity and I am enjoying it. You can build your Data Science and ML skills on Omenda with several experts and beginners working together to solve real life challenges.

ZackOF:
hey bro, am also regretting studying economics instead of CS. The thing dey pain me die. I just graduated recently and i think i will be focusing on programming since that's my initial goal. With regards to what you said about Nigerian schools teaching old stuff, it is actually true. Most of what are being taught in schools are old and outdated and that's why they keep producing below average graduates. Like grandlord, am having a tough time choosing between web development and data science. Web development is so so easy these days and rampant. Almost everbody wanna become a web developer. I tire sef

You have no reason to regret studying a course like economics, rather you should see it as an advantage to leverage on.

Regarding your background, I think data science should be your best bet. Dedicate like 6-12 months to it and you'll never regret combining data science with economics.

Your expertise in your primary field of study is an invaluable asset to you that'll make you stand out if eventually you venture into programming...

I'm a student of optometry with a great passion for DS and AI. I used to regret studying my course instead of computer science untill one of my lecturers explained what I just wrote above to me....

I hope you find it useful....

ibromodzi:


You have no reason to regret studying a course like economics, rather you should see it as an advantage to leverage on.

Regarding your background, I think data science should be your best bet. Dedicate like 6-12 months to it and you'll never regret combining data science with economics.

Your expertise in your primary field of study is an invaluable asset to you that'll make you stand out if eventually you venture into programming...

I'm a student of optometry with a great passion for DS and AI. I used to regret studying my course instead of computer science untill one of my lecturers explained what I just wrote above to me....

I hope you find it useful....

I must say bro, I really find it useful. Thanks for taking your time to put this down. I really appreciate it. I used to think studying economics was a bad Idea, but you really changed my mind and inspire me. Am going for my NYSC program this april and am planning on dedicating this period to learning DS. But what do you think about job prospects for DS in Nigeria?

ZackOF:
I must say bro, I really find it useful. Thanks for taking your time to put this down. I really appreciate it. I used to think studying economics was a bad Idea, but you really changed my mind and inspire me. Am going for my NYSC program this april and am planning on dedicating this period to learning DS. But what do you think about job prospects for DS in Nigeria?

I'm glad you are inspired....

You can check out this thread concerning the job prospects in Nigeria https://www.nairaland.com/5485400/chronicle-data-scientist-analyst

ibromodzi:


I'm glad you are inspired....

You can check out this thread concerning the job prospects in Nigeria https://www.nairaland.com/5485400/chronicle-data-scientist-analyst

Thanks

ZackOF:
Thanks

Please I can't find any ejio.d post.

Can you help me with a link?

tescoman90:



Please I can't find any ejio.d post.

Can you help me with a link?

Its Ejiod look at the link above your comment he is the OP

after series of research, I decided to chose DS over web development but I'm still confused on which path to focus on in DS
data analyst
AI
ML
DL
and so many of them,
can someone throw more lights on this to enable some of chose area to specialize on

I'm grateful I didn't make the wrong choice. A friend of mine introduced me to DS and i didn't delay by conducting research.

I came across ejiod post in my quest to know more about DS( I read more than half of his thread in the midnight. I completed it the same day).

Truth be told, I had wanted to study CS but found myself in Med lab.

Do I have any regret, I bet to say No.... Story for another day.

Currently, I'm taking a basic course in Python via edx (aside doing some other research work on YouTube and the rest).
If you're interested you can do that too.. Those guys are awesome.


I decided to settle for Python because it's more than fundamental to knowing DS, DA and many others (I will still learn R and some important programs too)


As much as I'm concerned, I'm taking the bull by its horn.

Apcexchange:
after series of research, I decided to chose DS over web development but I'm still confused on which path to focus on in DS
data analyst
AI
ML
DL
and so many of them,
can someone throw more lights on this to enable some of chose area to specialize on

I think this is more of a personal decision and the goal you intend to achieve after attaining a good level of competence in any of these subfields of data science. Do you want to learn the skill for the purpose of research or you are planning to seek for job with it?

While all these are subfields under data science, data analysis in my opinion can exist independently without going further in the pipeline of DS. I mean you can just build your skills around data analysis alone and seek proficiency in it and land yourself a job.

However, you can't learn about deep learning without first learning about ML (both DL and ML make up AI), whereas you may decide to start your DS career from ML without learning about data analysis (although you must be good in EDA to make a good ML engineer).

In addition, ML is still a type of data analysis known as predictive analytics whereas data analysis is only concerned about descriptive and inferential analytics. Every subfield of data science uses different approaches to gain insight from data and the approach you use all depends on your goal...

Make your findings and go for what best suits your need. If you just want to be a data analyst, go for it but if your goal is to become a data scientist, you have to combine all these subfields together....

Finally, I think I should share my journey so far in DS with you. I started learning python with the aim of using it for data analysis alone (learned the basics including OOP in less than two weeks), then I started mastering the libraries for analysis and after practicing with a number of data sets, I decided to go into ML because I'm this kind of person with an inquisitive mind. Currently I'm dealing DL and I hope to combine blockchain and AI in future for the purpose of research....

LostAddict:


You are like me, I once posted on NL about my confusion on the field to enter. Right now I have found more love for Data Science and it's ML and AI prospects than ever, in fact attending lectures this days are very boring I always pray lecturers leave class very fast or don't show up at all, so that I can have my time to do what I like. Besides I am studying Computer Engineering but I am a great ML enthusiast. I recently started taking a free course on Statistics and Algebra on Udacity and I am enjoying it. You can build your Data Science and ML skills on Omenda with several experts and beginners working together to solve real life challenges.

Computer Engineering, hug and enjoy that course my brother.

Ausrichie:

Computer Engineering, hug and enjoy that course my brother.

Why the course is very annoying, we haven't even started learning to program but those in Computer Science and Computer Science Education are already learning a third language. If not for my self taught skills I would still be floating around.

LostAddict:


Why the course is very annoying, we haven't even started learning to program but those in Computer Science and Computer Science Education are already learning a third language. If not for my self taught skills I would still be floating around.

Nigeria. Imagine you guys need it badly too, for embedded systems and Internet of Things.

ibromodzi:


I think this is more of a personal decision and the goal you intend to achieve after attaining a good level of competence in any of these subfields of data science. Do you want to learn the skill for the purpose of research or you are planning to seek for job with it?

While all these are subfields under data science, data analysis in my opinion can exist independently without going further in the pipeline of DS. I mean you can just build your skills around data analysis alone and seek proficiency in it and land yourself a job.

However, you can't learn about deep learning without first learning about ML (both DL and ML make up AI), whereas you may decide to start your DS career from ML without learning about data analysis (although you must be good in EDA to make a good ML engineer).

In addition, ML is still a type of data analysis known as predictive analytics whereas data analysis is only concerned about descriptive and inferential analytics. Every subfield of data science uses different approaches to gain insight from data and the approach you use all depends on your goal...

Make your findings and go for what best suits your need. If you just want to be a data analyst, go for it but if your goal is to become a data scientist, you have to combine all these subfields together....

Finally, I think I should share my journey so far in DS with you. I started learning python with the aim of using it for data analysis alone (learned the basics including OOP in less than two weeks), then I started mastering the libraries for analysis and after practicing with a number of data sets, I decided to go into ML because I'm this kind of person with an inquisitive mind. Currently I'm dealing DL and I hope to combine blockchain and AI in future for the purpose of research....

thank you so much, I guess you just cleared me on this.
I have to focus on data analysis cos I just want a job for now, maybe later in the future I might decide to further

so for data analysis, after learning Python, which other language should follow or is Python enough to make a good data analyst.

I have started learning Python already using my android phone , I have understood some basics already. where do I go from here??
thanks in anticipation

Apcexchange:


thank you so much, I guess you just cleared me on this.
I have to focus on data analysis cos I just want a job for now, maybe later in the future I might decide to further

so for data analysis, after learning Python, which other language should follow or is Python enough to make a good data analyst.

I have started learning Python already using my android phone , I have understood some basics already. where do I go from here??
thanks in anticipation

There are two things that make up data analysis i.e the theoretical knowledge (statistics) and the technical aspect (tools used for data analysis). If you already have a sound knowledge of statistics, you can go ahead and pick any data analysis tools that best suit your taste.

Python is just one of those tools and whether it is enough for your analysis or not depends on your proficiency and the task you are using it for. There are a number of tools used by data analyst and python is just one of them. Excel, Power BI, Tableu, SQL, R, etc.... are just few of these tools (you can use google to know more about them and what they are best used for).

As with your own case, since you have understood the basics of python, you just have to master the libraries used for data analysis. I'll try to list them for you.....
Pandas
Numpy
Matplot
Seaborn
Plotly
Researchpy
Statsmodel etc...

You can as well sign up for basic statistics course online and learn it along with python.
Good luck!

ibromodzi:


There are two things that make up data analysis i.e the theoretical knowledge (statistics) and the technical aspect (tools used for data analysis). If you already have a sound knowledge of statistics, you can go ahead and pick any data analysis tools that best suit your taste.

Python is just one of those tools and whether it is enough for your analysis or not depends on your proficiency and the task you are using it for. There are a number of tools used by data analyst and python is just one of them. Excel, Power BI, Tableu, SQL, R, etc.... are just few of these tools (you can use google to know more about them and what they are best used for).

As with your own case, since you have understood the basics of python, you just have to master the libraries used for data analysis. I'll try to list them for you.....
Pandas
Numpy
Matplot
Seaborn
Plotly
Researchpy
Statsmodel etc...

You can as well sign up for basic statistics course online and learn it along with python.
Good luck!

thank you so much, as for statistics knowledge I Don't think its an issue because I studied mathematics.
I guess I will continue with understanding python and maybe once I'm able to lay my hands on a PC, I can go into the libraries, thanks.

ibromodzi:


There are two things that make up data analysis i.e the theoretical knowledge (statistics) and the technical aspect (tools used for data analysis). If you already have a sound knowledge of statistics, you can go ahead and pick any data analysis tools that best suit your taste.

Python is just one of those tools and whether it is enough for your analysis or not depends on your proficiency and the task you are using it for. There are a number of tools used by data analyst and python is just one of them. Excel, Power BI, Tableu, SQL, R, etc.... are just few of these tools (you can use google to know more about them and what they are best used for).

As with your own case, since you have understood the basics of python, you just have to master the libraries used for data analysis. I'll try to list them for you.....
Pandas
Numpy
Matplot
Seaborn
Plotly
Researchpy
Statsmodel etc...

You can as well sign up for basic statistics course online and learn it along with python.
Good luck!

thanks

codeigniter:
I have written this before I saw ejio.d post on ds chronicle, I am impressed that Nigerian company are using data analyst

Anyways which ever field you choose from there will always be room for the best of the bests

Maybe we should all try to be damn good at anything maybe, coding or data usage or what ever field you desire

Nb I am currently studying pure and applied biology at lautech, I should have studied Cs or stats but no regret yet. There is this intersection between stat, cs and biology called computational biology or bioinformatics, and that is the reason I look in ds

University stuff is kind of old and stupid dey teach thing i don't want to learn, I learn more online and i am more willing to learn than I have ever done at sch, not that I don't have passion for course but there are too many different things done in a semester that aren't related and too many borrowed courses

If u can relate or also in University, do share ur xp

wow seems our stories are the same, am also studying pure and and applied biology at the same university (LAUTECH), what level are you bro?
am also thinking of going into data science but I think I will settle for web development as at now.