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As a newbie in the data world coming across this video is very helpful as it has helped me answer a question I had about the significance of industry related experience in generating data insights. But how can a newbie in data analytics build projects that are insights driven when most tutorials are just observations about the data as described in this video?

Honestly, i am subscribing to your channel, majorly for one thing....The patience and the time you took to respond to all questions, not forming over busy. I have been looking for videos on this and i stumbled on yours. Though, the video was very helpful, but the comments did justice to what i wanted. Now i know what to do. Thank you

Straight to the point and clearly explained

What about outliers in Data?

I recently started my data analytics course so that i can do freelancing. But I don't understand how can clients hire me as a data analyst .

What laptop would you recommend for someone learning data analytics?

I've been watching your videos regularly, and they've been very helpful-Jazakallah! I work as a Power BI developer and want to start consulting as a side hustle. I read the hourly rate is around £100/hr, and based on my experience, a typical project might take two weeks, totaling £7-8K. Do companies usually pay that much, or am I miscalculating or misunderstanding something?

CRYSTAL CLEAR

Based here in Antigua West Indies with ties to Nottingham this was very helpful❤

Short, concise and insightful. Thanks

Wow thanks for such valuable information I have been doing research on this business model and I really got attracted. How can one start such business with no knowledge in the technical side but has knowledge in marketing and how to sell. I have friends who are good with IT but how do I present this business idea to them and make them still need me even though I don't have any background knowledge with coding? I'm based in South Africa and I would really like to connect with other people so we can exchange knowledge and information.

Thanks ❤❤

Your videos are insightful. May Allah also make me a data analyst.

There is nothing that can't be done in Power BI without SQL knowledge. SQL is needed for very specific cases with complicated huge data models that require precision in optimization.

sql is universal instrument

Timestamps 00:04 - Data products must be repeatable and scalable to succeed. 00:43 - Scalability hinges on the repeatability of data product connections. 01:21 - Data variations across POS systems hinder scalability. 02:06 - Manual processes limit the scalability of data products. 02:50 - Manual processes limit scalability in data-driven businesses. 03:33 - Balancing scalability with team growth is crucial for SaaS success. 04:12 - Data access and repeatability are crucial for scalability. 04:56 - Service-based businesses require team growth to scale effectively.

Assalamu alaikum. how's the MSc program in Data Analytics at London Met

Thank you, will explained, I got answer to all my questions. you're articulative

Dear shorful, I am looking for data analysis internship Could you please help me 🙏

greet content

Informative content out there shorful. I could use your help with my job hunt in U.S. in data analytics.. thank you

These are really valuable content. Do you think sharing the work on TH-cam is an efficient way to reach more people?

Hey I’m an analyst (for about 5 years now) - great tips, especially about being proactive and taking ownership of a problem - I will try to take these on board!

In 100 years from now: can AI finally solve the bad data problem?

Fascinating perspective! I’ve been using Gemini and ChatGPT for my analysis, and with the right prompts, they’ve proven to be incredibly versatile for everything from data analysis to machine learning. It's surprising to see that companies are already leaning on these models for their data analysis and reporting. What does the future hold for data analysts then?

Bias amplification - great concept that I have learned. Thanks!

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Another reason of hiring an external data agency and contracting them to work on specific period and after the built still relying on same data agency to support and maintain is the financial model: opex vs capex. When done in-house, the comany needs to invest upfront on tools, licenses and hire permanent staff that are usually counted as capex or capital expense. But when contracted to a data agency, they still pay but count it as opex or operational expense. Maintenance and support are considered as BAU expense and contract can be terminated after certain period, i.e. 5 years which is usually tied with the review whether to decommission the applications kr reports. The company sometimes have budget for opex when revenue is forecasted, usually annually and that makes the cost manageable as it is spread across the period. Paying it upfront like in capex needs huge investment and it appears as a huge portion of the budget, making it difficult to convince the decision makers to do data analytics in-house. I know tbis because I have hired data analyts as contractors. Their rates are higher but has a definite end-date which makes it easier to calculate in the budget and count it as opex. In government organisations, it’s easier to get funding for opex than capex, making it favorable to hire external data agency.

HI Shorful, thank you for your effort. I want to enter data analytics career so what technical skills you recommend me? Also, how do stand our from the crowd. thank you

I mean make it easy and get that roi nobody in but I hate all of the subscriptions

Great stuff. I'm currently reading the book whilst I commute and it's been a great read 📔

Amazing 🎉 Congratulations on the book launch 🎉 Data culture: Input from senior leadership Teams knowing the value of data The Analyst in focus Case studies: when things work or not Processes to adopt for data implementation and integration A guide to grow into senior leadership role in Data

Amazing insight sir. I follow you on youtube and LinkedIn as well. The advice you give is really impressive and relevant

Good job brother ❤

The Data Analytics Project Process: 0:36 Define the business problem. 1:32 Plan how you are going to access data. 2:51 Look at the data and Data Preparation. 3:33 Do an exploratory data analysis 5:12 Run your analysis. 6:00 Present to the business. 7:32 Handover it over to the client. @Belayet Hossain

Spot on, thx for breaking it down.

Ma shaa Allah brother, I am from an computer science old school background and I understand every single thing you talked about even I am not a native English person. I used to playing with data and applying flitering approach according to a customized criteria and that was over 19 years ago and I am now in the web design industry but also I miss playing with more in depth technical skills in the backend coding but even I am struggling in living but I certainly I feel in the resposibility and the commitment towards the community around me and how to migrate some of the skills I know to learners everywhere. From Palestine, with all respect. Kind regards، Mohammad

Yes, Im a SocScience grad n ended up in DA 😂

Useful information!

How do you rate live coding (sql/python) rounds for data analyst/scientist roles? Are they really needed to make a decision or it's better to understand how would someone approach solving a problem?

Thanks

2 and a half hours total interview time for a data analyst position, not even a senior role?

Hi shorful, I always watch your videos ❤

Assalamu Alaikum Brother, I'm currently working as a Team Lead on Daraz & have 5+ years of experience in customer service & supply chain looking forward to land a role as a remote data analyst. I have completed a data analyst BootCamp & looking forward PL-300 certification to get an advantage to be a data analyst. Can you provide me a suggestion on this? Should I stick on this plan or do I need to restructure my plan?

Can you tell me how we make a resume which help to land a data analyst job.

That's good video to prepare ypourslef for a interview.

If anyone reads this, do not, I repeat DO NOT try and get into data analytics. You will fail. What people don't realize is that data science, at least at the entry level, is EXTREMELY easy. Ask yourself why everyone suddenly wants to transition to data from something completely unrelated. It's not because they suddenly got a "PaSsIon fOr DaTa". It's because the field is very easy to learn, and for a while, mid 2010s to 2021-ish, you could waltz into a data job with minimal effort. It's those same people who did that, now have courses to teach you how to supposedly break into data, while they parrot idiotic outdated advice and make millions at your expense. Find a different career path, and see if you can transition laterally into data. You will not break into data jobs unless you get a degree in analytics and convert an internship to a full time job.

Entry level data jobs are gone for good. That's just a fact. If you got into the field pre-2021, you now have 3 years of exp and you're the new entry level. For anyone else trying to break into the field, you're out of luck,. Find a new career path. Simple as that.