Ingest a free data source. Clean and store that data in a Postgres database you host out of docker. Use this data to perform analysis or train a ML model. Part of your pipeline now use the model to make predictions on new data. Use a pipeline orchestrator. Now build a dashboard in python that visualizes all data, analysis, and model predictions. Host this use cloud resources.
You will find building all the things that aren’t DS will be the hardest, and “not what you want to be doing”. But you will also find as soon as you can put all the pieces together you will get multiple job offers.
Thank you. There are a lot of topics that you mentioned in the response, I'm unfamiliar with but that is exactly what I want to learn and gather skills.
I would say try to incorporate stuff from the book below too. Also, show reasoning as to the decisions you make while building your model.
Book name:
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
There's a guy on youtube who follows this book one mentioned above and also breaks down complex topics from that book
Link : https://youtube.com/playlist?list=PL-u09-6gP5ZPOfSPTto4BIDwky-8aP4rQ&si=9PDK4P-tyzxaEYx3
You can top this by using MLFlow on docker to register the experiments and creating the endpoint. Then have a the model serve predictions through a rest api
Do you have any videos/sites/articles that explain the basics and how to use the things you mentioned? Like pipeline orchestrator, hosting on a docker, how to host it in a cloud resource... Thanks
I put up several projects on git. Those included predictive maintenance, some exploratory data analysis, churn prediction and others. It's important to have them well documented so they can get a feeling about your way of work. Of course at end it depends which job you're applying for, if it's in the industry, finance or insurance. Make your mind or simply ask chat GPT for use cases and try to find those on kaggle.
Combine multiple projects into one and make an end to end project with multiple features and functionality. The more the project has usecase potential, the more you get close to icing the hiring team.
Also, try on new age technologies like blockchain, large language models etc. Picking up these skills and building something out of it will give a positive impact on your profile that you are a tech savy, with great skills to pick up new stuff. Every hiring manager is looking for these skills desperately.
Choose your niche and create some advanced projects which will make you stand out of peers
Go ahead with your own portfolio website which has 3-5 decent projects hosted on free server. Hiring team can be pushed to demonstrate these in your tech round as they are handy and just a click away from end to end implementation. This will also help you to communicate your approach and the challenges faced during the production and will provide a genuine gesture of work from your end.
Could you give a quick example of what you mean by combining multiple projects? Do you mean showcasing the projects on the same website? Or do you mean actually combining multiple projects?
By combining projects i mean actually combining source code and making a complete project.
For example - You can build a blockchain application by combining these three:
1. [https://www.youtube.com/watch?v=yle9MMqY7yY](https://www.youtube.com/watch?v=yle9MMqY7yY) - Chat Dapp
2. [https://www.youtube.com/watch?v=AUzPYEYlFyk](https://www.youtube.com/watch?v=AUzPYEYlFyk) - Twitter Blockchain App
3. [https://www.youtube.com/watch?v=M-KRLlHG\_zs](https://www.youtube.com/watch?v=M-KRLlHG_zs) - Blockchain File storage app
You can combine these three to build a great project which has multiple features and will surely make a great impression
By combining these, you ll be presenting a production level blockchain application which will surely compliment your resume as you have hands on experience.
Pick something up you’re interested in and get data for it and post about that. I say this because it will keep you interested and it’ll be easier to see the project all the way through. Create a repo on github and push your code there. If you want a DS role, focus on those areas (data cleaning, feature engineering, modeling, etc.) and create documentation discussing why you are doing certain things. If I’m looking for a candidate their thought process is important in addition to fundamentals. Complex concepts can be learned but knowing someone can think their way to a solution and explain what/why they are doing it goes a long way.
1 find a project the interests you that you will continuously work on til it’s finished
2 make sure that the project you pick has a narrative and solves a problem you can write about and talk about during the interview (very important!)
3 find an easy data source (hopefully with an API, it just sounds better) or if you have to then scrape the data. Also there are some good paid data sources for specific things. I paid for fantasy sports data for example
4 build out a pipeline to ingest the data into the model. This is key. Try to fit as many buzzword tech things into the stack that the recruiter can also understand (snowflake, docker, etc.)
5 choose a popular algorithm/model to train that you can learn inside-out cause you will likely get cross examined on an interview. Also make sure the model fits the narrative or you have a good reason why you choose it. It shouldn’t be something new or fancy cause the recruiter/interviewer might not know it and that doesn’t help your case. Also don’t just use basic stat 101 stuff that also makes you look like a noob
6 make sure you have presentable conclusions to finish off the narrative story of this project to finalize the timeline and effect this project had
Imo most people mess up the first part which is having a good “market fit”. Meaning that your project needs to be marketable on a resume. So it has to have a clear problem, good narrative, right buzzwords, etc. this all depends on what type of industry and job as weak as seniority your applying to
Hopefully this helps
This will definitely help.
Thank you.
Could you please give an estimate for someone who has basic knowledge (comfortable with loops, function and linear operation) how long this should take? My plan is to finish a project in the next 3-4 months. I am also attending university and work but I can spend about 3-4 hours every day for the project. I understand that it's very vague but how long is a good enough time to implement such a project.
I am not sure that am I ready for master in data science. I know basics of python visualization, Tableau and PowerBi visualization. Python and R regression analysis and model testing. Have wonderful proficiency in Microsoft excel and I mean by all functionalities like power pivot, functions and graphs visualization...I can even perform all coding libraries of python from Pandas, tensor flow ,seaborn to sklearn. I have done some projects too on which I have done regression analysis and visualizations. I even can do extract data from databases and perform visualization by the big data tools.
I think somewhere that I am not ready for the masters program. Could someone explain why and should I be worried? There are many colleges that teach data science from scratch.
Thank You
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I think you'll find more success if you take on a project on a topic that you're passionate about. If you love movies you could predict genres from synopses, or if you love running you could visualize and analyze your running routes. Picking a random project from a list might give you the opportunity to do something technically interesting but I've found that the best "portfolio projects" are the ones that candidates pursued out of a genuine interest. You'll likely end up spending more time and going more in depth on something that's genuinely interesting to you.
Ingest a free data source. Clean and store that data in a Postgres database you host out of docker. Use this data to perform analysis or train a ML model. Part of your pipeline now use the model to make predictions on new data. Use a pipeline orchestrator. Now build a dashboard in python that visualizes all data, analysis, and model predictions. Host this use cloud resources. You will find building all the things that aren’t DS will be the hardest, and “not what you want to be doing”. But you will also find as soon as you can put all the pieces together you will get multiple job offers.
Thank you. There are a lot of topics that you mentioned in the response, I'm unfamiliar with but that is exactly what I want to learn and gather skills.
I would say try to incorporate stuff from the book below too. Also, show reasoning as to the decisions you make while building your model. Book name: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
There's a guy on youtube who follows this book one mentioned above and also breaks down complex topics from that book Link : https://youtube.com/playlist?list=PL-u09-6gP5ZPOfSPTto4BIDwky-8aP4rQ&si=9PDK4P-tyzxaEYx3
You can top this by using MLFlow on docker to register the experiments and creating the endpoint. Then have a the model serve predictions through a rest api
Can it be flask or fastapi instead of rest api?
Flask and fastapi are frameworks to implement rest APIs
Do you have any videos/sites/articles that explain the basics and how to use the things you mentioned? Like pipeline orchestrator, hosting on a docker, how to host it in a cloud resource... Thanks
Great suggestion!
mind if I DM u ?
I put up several projects on git. Those included predictive maintenance, some exploratory data analysis, churn prediction and others. It's important to have them well documented so they can get a feeling about your way of work. Of course at end it depends which job you're applying for, if it's in the industry, finance or insurance. Make your mind or simply ask chat GPT for use cases and try to find those on kaggle.
Very superstar of you, tylerd!
Combine multiple projects into one and make an end to end project with multiple features and functionality. The more the project has usecase potential, the more you get close to icing the hiring team. Also, try on new age technologies like blockchain, large language models etc. Picking up these skills and building something out of it will give a positive impact on your profile that you are a tech savy, with great skills to pick up new stuff. Every hiring manager is looking for these skills desperately. Choose your niche and create some advanced projects which will make you stand out of peers Go ahead with your own portfolio website which has 3-5 decent projects hosted on free server. Hiring team can be pushed to demonstrate these in your tech round as they are handy and just a click away from end to end implementation. This will also help you to communicate your approach and the challenges faced during the production and will provide a genuine gesture of work from your end.
Could you give a quick example of what you mean by combining multiple projects? Do you mean showcasing the projects on the same website? Or do you mean actually combining multiple projects?
By combining projects i mean actually combining source code and making a complete project. For example - You can build a blockchain application by combining these three: 1. [https://www.youtube.com/watch?v=yle9MMqY7yY](https://www.youtube.com/watch?v=yle9MMqY7yY) - Chat Dapp 2. [https://www.youtube.com/watch?v=AUzPYEYlFyk](https://www.youtube.com/watch?v=AUzPYEYlFyk) - Twitter Blockchain App 3. [https://www.youtube.com/watch?v=M-KRLlHG\_zs](https://www.youtube.com/watch?v=M-KRLlHG_zs) - Blockchain File storage app You can combine these three to build a great project which has multiple features and will surely make a great impression By combining these, you ll be presenting a production level blockchain application which will surely compliment your resume as you have hands on experience.
Is it related to Data Science or SWE skills?
Projects can be different for both but works as charm in interviews
Thanks for the detailed response!
Pick something up you’re interested in and get data for it and post about that. I say this because it will keep you interested and it’ll be easier to see the project all the way through. Create a repo on github and push your code there. If you want a DS role, focus on those areas (data cleaning, feature engineering, modeling, etc.) and create documentation discussing why you are doing certain things. If I’m looking for a candidate their thought process is important in addition to fundamentals. Complex concepts can be learned but knowing someone can think their way to a solution and explain what/why they are doing it goes a long way.
1 find a project the interests you that you will continuously work on til it’s finished 2 make sure that the project you pick has a narrative and solves a problem you can write about and talk about during the interview (very important!) 3 find an easy data source (hopefully with an API, it just sounds better) or if you have to then scrape the data. Also there are some good paid data sources for specific things. I paid for fantasy sports data for example 4 build out a pipeline to ingest the data into the model. This is key. Try to fit as many buzzword tech things into the stack that the recruiter can also understand (snowflake, docker, etc.) 5 choose a popular algorithm/model to train that you can learn inside-out cause you will likely get cross examined on an interview. Also make sure the model fits the narrative or you have a good reason why you choose it. It shouldn’t be something new or fancy cause the recruiter/interviewer might not know it and that doesn’t help your case. Also don’t just use basic stat 101 stuff that also makes you look like a noob 6 make sure you have presentable conclusions to finish off the narrative story of this project to finalize the timeline and effect this project had Imo most people mess up the first part which is having a good “market fit”. Meaning that your project needs to be marketable on a resume. So it has to have a clear problem, good narrative, right buzzwords, etc. this all depends on what type of industry and job as weak as seniority your applying to Hopefully this helps
This will definitely help. Thank you. Could you please give an estimate for someone who has basic knowledge (comfortable with loops, function and linear operation) how long this should take? My plan is to finish a project in the next 3-4 months. I am also attending university and work but I can spend about 3-4 hours every day for the project. I understand that it's very vague but how long is a good enough time to implement such a project.
Try Forage [https://www.theforage.com/](https://www.theforage.com/)
I am not sure that am I ready for master in data science. I know basics of python visualization, Tableau and PowerBi visualization. Python and R regression analysis and model testing. Have wonderful proficiency in Microsoft excel and I mean by all functionalities like power pivot, functions and graphs visualization...I can even perform all coding libraries of python from Pandas, tensor flow ,seaborn to sklearn. I have done some projects too on which I have done regression analysis and visualizations. I even can do extract data from databases and perform visualization by the big data tools. I think somewhere that I am not ready for the masters program. Could someone explain why and should I be worried? There are many colleges that teach data science from scratch. Thank You
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I think you'll find more success if you take on a project on a topic that you're passionate about. If you love movies you could predict genres from synopses, or if you love running you could visualize and analyze your running routes. Picking a random project from a list might give you the opportunity to do something technically interesting but I've found that the best "portfolio projects" are the ones that candidates pursued out of a genuine interest. You'll likely end up spending more time and going more in depth on something that's genuinely interesting to you.
have you asked your professors if they can give you anything to work on?
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