CareerPorte:
You just nailed it. That my point exactly. People learn and comprehend in different ways. Saying watching videos is not enough to become grounded is totally off the point. I learnt everything I know in Data Science through videos and online Bootcamp. I'm still a work in progress though, Nobody knows it all. But I can tell you for free that I have learnt enough to land me an entry-level data science role. I got a couple of invites for interview for data science jobs in the USA, I couldn't attend because they all want candidates residing in the US even though its remote. The idea was to convert remote workers to full-time after covid subsides.

I just want to enjoin everyone striving hard to learn data science not to give up. There a long road ahead, but appreciate yourself for what you have learnt so far. It is not easy to learn excel alone to mastery level not to talk of SQL and Machine Learning that requires a lot of complex algorithm for building your models. Have a plan, draw a learning schedule for your self and give yourself timelines. Don't try to learn everything at once. Take it one step at a time.

What seems difficult today, will seem like a stroll in the park to you in a couple of months from now with hard work, consistency and dedication. Download different data sets on Kaggle and try to make meaning out of them using what you have learnt so far, be it excel, PowerBI, SQL, TABLEAU or Machine learning.

Believe me, you have what it takes to become a full-fledged data scientist.

Don't be discouraged guys. We will all come back here one day to share our story of how we landed a great job through our skills.

KEEP GRINGING.

CareerPorte:
You just nailed it. That my point exactly. People learn and comprehend in different ways. Saying watching videos is not enough to become grounded is totally off the point. I learnt everything I know in Data Science through videos and online Bootcamp. I'm still a work in progress though, Nobody knows it all. But I can tell you for free that I have learnt enough to land me an entry-level data science role. I got a couple of invites for interview for data science jobs in the USA, I couldn't attend because they all want candidates residing in the US even though its remote. The idea was to convert remote workers to full-time after covid subsides.

I just want to enjoin everyone striving hard to learn data science not to give up. There a long road ahead, but appreciate yourself for what you have learnt so far. It is not easy to learn excel alone to mastery level not to talk of SQL and Machine Learning that requires a lot of complex algorithm for building your models. Have a plan, draw a learning schedule for your self and give yourself timelines. Don't try to learn everything at once. Take it one step at a time.

What seems difficult today, will seem like a stroll in the park to you in a couple of months from now with hard work, consistency and dedication. Download different data sets on Kaggle and try to make meaning out of them using what you have learnt so far, be it excel, PowerBI, SQL, TABLEAU or Machine learning.

Believe me, you have what it takes to become a full-fledged data scientist.

Don't be discouraged guys. We will all come back here one day to share our story of how we landed a great job through our skills.

KEEP GRINGING.

Singingbae:
Anyone to help me why this code is giving me error. I just want to encode a categorical column

CareerPorte:
10 Commandments of Data Science

1. Focus mainly on solving the problem and not on tools, technologies, and models.

2. Data will never be clean or easily available. Data gathering and cleaning will take 80% of your time and effort.

3. Don’t underestimate the power of Excel and SQL - they are still two of the most useful tools for data analysis.

4. Simple models such as Linear or Logistic Regression will be good enough for many problems. You don’t need neural networks to solve every problem.

5. Textbook solutions may not work for most of the practical problems. You will need to try new approaches and innovate as required.

6. Nobody can remember everything. On the job, you can always use Google, Stack Overflow etc.

7. Learn Data Visualisation and develop the ability to explain your key insights in simple terms - such skills will be very useful with non-technical and business stakeholders.

8. Learn PowerPoint and storytelling - people may not appreciate your great work if you can’t convince them with your story.

9. Data Science is evolving rapidly. Please learn continuously, else you may become obsolete soon.

10. Focus mainly on solving the problem and not on tools, technologies, and models.

CareerPorte:
10 Commandments of Data Science

1. Focus mainly on solving the problem and not on tools, technologies, and models.

2. Data will never be clean or easily available. Data gathering and cleaning will take 80% of your time and effort.

3. Don’t underestimate the power of Excel and SQL - they are still two of the most useful tools for data analysis.

4. Simple models such as Linear or Logistic Regression will be good enough for many problems. You don’t need neural networks to solve every problem.

5. Textbook solutions may not work for most of the practical problems. You will need to try new approaches and innovate as required.

6. Nobody can remember everything. On the job, you can always use Google, Stack Overflow etc.

7. Learn Data Visualisation and develop the ability to explain your key insights in simple terms - such skills will be very useful with non-technical and business stakeholders.

8. Learn PowerPoint and storytelling - people may not appreciate your great work if you can’t convince them with your story.

9. Data Science is evolving rapidly. Please learn continuously, else you may become obsolete soon.

10. Focus mainly on solving the problem and not on tools, technologies, and models.

CareerPorte:
10 Commandments of Data Science

1. Focus mainly on solving the problem and not on tools, technologies, and models.

2. Data will never be clean or easily available. Data gathering and cleaning will take 80% of your time and effort.

3. Don’t underestimate the power of Excel and SQL - they are still two of the most useful tools for data analysis.

4. Simple models such as Linear or Logistic Regression will be good enough for many problems. You don’t need neural networks to solve every problem.

5. Textbook solutions may not work for most of the practical problems. You will need to try new approaches and innovate as required.

6. Nobody can remember everything. On the job, you can always use Google, Stack Overflow etc.

7. Learn Data Visualisation and develop the ability to explain your key insights in simple terms - such skills will be very useful with non-technical and business stakeholders.

8. Learn PowerPoint and storytelling - people may not appreciate your great work if you can’t convince them with your story.

9. Data Science is evolving rapidly. Please learn continuously, else you may become obsolete soon.

10. Focus mainly on solving the problem and not on tools, technologies, and models.

Zabiboy:


I actually said that for those looking to monetize their skill in the short run...cz this data stuff is a long run stuff as regards opportunities in Nigeria but app/software/web dev. is already in a force.

DrinkWater10:

Jupyter notebook has refused to launch. I've used anaconda navigator it just loads but doesn't launch, anaconda prompt just brings up a prompt telling me to copy a link to my browser.
I need it to open directly in my browser like it should.
o use a Windows and Spyder launches but what I want is jupyter.
cc ejiod.

Ejiod:

Run your system by pressing Windows + R
Then type this
jupyter notebook
anaconda-navigator

The 1st opens jupyter notebook while the 2nd opens anaconda

DrinkWater10:

Thank you for responding.
When I do the first, it brings up a prompt telling me to copy a link to my browser.
The second step opens Anaconda Navigator but when I click on Jupyter notebook it just loads but doesn't open.

guysis:
Please guys, I need more ideas/processes/options on how to set up a platform to enable a school compute : (1) It's staff payroll (2) Students grade and constantly update it(3)an avenue to send these grade(results) ,other messages to their parent via email.

From what I have looked at so far, I would need to deploy ms Excel and Access.

Can this pass for a data analyst role?

Ejiod:
To avoid any complex stuff. Just go to your program files and delete it. Then reinstall a new anaconda

guysis:
Please guys, I need more ideas/processes/options on how to set up a platform to enable a school compute : (1) It's staff payroll (2) Students grade and constantly update it(3)an avenue to send these grade(results) ,other messages to their parent via email.

From what I have looked at so far, I would need to deploy ms Excel and Access.

Can this pass for a data analyst role?

rodhy:
Hi everyone, started reading this thread few weeks ago and mehn, I'm pumped�. I've learned Microsoft excel (started way before I discovered this thread), PowerBI and sql. I think I'm okay with the excel, I watched youtube videos(excelisfun) and took leila gharani course on udemy. Now, I'm not too confident in my powerbi, and sql(only watched coding with mosh 3hrs vid on YouTube). Can anyone please recommend tutorials on Powerbi and sql?

rodhy:
Hi everyone, started reading this thread few weeks ago and mehn, I'm pumped�. I've learned Microsoft excel (started way before I discovered this thread), PowerBI and sql. I think I'm okay with the excel, I watched youtube videos(excelisfun) and took leila gharani course on udemy. Now, I'm not too confident in my powerbi, and sql(only watched coding with mosh 3hrs vid on YouTube). Can anyone please recommend tutorials on Powerbi and sql?

randomShek:
For sql -

https://coursedrive.org/sql-mysql-for-data-analytics-and-business-intelligence/

Lemken:



for power Bi, I would suggest up and running with power Bi ( a nairalander told me this)
for SQL, I used Ms-sql and postgreSQL database design by krill eremenko

rodhy:


For the powerbi, Is "up and running'' on YouTube?

Lemken:


I actually don't know about YouTube, but below is a link to the the course

https://freetutsdownload.net/microsoft-power-bi-up-vs-running-with-power-bi-desktop-free-download/

rodhy:


For the powerbi, Is "up and running'' on YouTube?

ibromodzi:


That's the power of research. The more you read, the more information you have at your disposal.

Visual inspection of data distribution (histogram, box plot, q-q plot, p-p plot) is informal, unreliable and does not guarantee that the distribution is normal. Nevertheless, presenting your data visually gives your audience the ability to judge the distribution themselves.

There are many formal tests that can be used for normality aside Shapiro Wilk and K-S tests. However, the former is recommended by many researchers regardless of the sample size, owing to the fact that K-S test is sensitive to extreme values.

You can read more about Normality test here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3693611/

@ the emboldened.
I'm a final year student of optometry and vision science. No statistics/math. Although, I took some basic math/stat/CS courses in 100/200L

zeemahn:


Which school? I completed my internship in April. Currently waiting for nysc. I've been learning Data science with my free time.

ibromodzi:

Good to know sir, I rep Unilorin.