The One and Only Data Science Project You Need
Summary
TLDRIn this video, Nate shares invaluable advice for aspiring data scientists seeking to create an impactful project. He emphasizes avoiding overused datasets like Titanic and Iris, and steering clear of Kaggle unless aiming for top rankings. Nate outlines key components for a successful project: utilizing real-time data, mastering modern tech like APIs and cloud databases, building robust models, and demonstrating project impact. He stresses the importance of understanding model decisions and underlying math, and suggests sharing insights through code, visuals, or even deploying applications for real-world validation. Nate's ultimate secret? One comprehensive project that covers all skills can serve as a foundation for future endeavors, impressing interviewers and solidifying a career in data science.
Takeaways
- 🚀 The ultimate data science project should help you gain full-stack data science experience and impress interviewers.
- 🙅 Avoid overused datasets like Titanic or Iris and common platforms like Kaggle unless you can rank in the top 10.
- 💡 Focus on real-world skills in coding, analytics, and modern technologies to become a fully independent data scientist.
- 📈 Work with real, updated data, preferably real-time streaming data, to demonstrate relevance and timeliness.
- 🔌 Learn to use APIs to collect real-time data, showcasing your ability to handle live data feeds.
- 💾 Utilize cloud databases to store and manage data efficiently, reflecting common industry practices.
- 🤖 Building models is crucial, but understanding the decision-making process behind them is even more important.
- 📊 Be prepared to explain your model choices, data cleaning processes, and validation tests during interviews.
- 🌟 A great data science project should make an impact and have validation from others, showing its value and interest to the community.
- 🛠️ Share your work through code repositories, visual insights, or by building an application to demonstrate practical application.
- 🔄 Once you've built an end-to-end data science infrastructure, you can reuse and adapt it for various projects with minor revisions.
Q & A
What is the primary advice given by Nate for someone looking to start a data science project?
-Nate suggests building a project that provides full stack data science experience and impresses interviewers, focusing on real-world skills and modern technologies.
What are the two things Nate advises to avoid when choosing a data science project?
-Nate advises to avoid projects involving analysis on the Titanic or Iris data sets as they are overdone, and to migrate away from Kaggle unless one can rank in the top 10.
What does Nate mean by 'full stack data science experience'?
-Full stack data science experience refers to having skills in both coding and analytics, as well as proficiency in using modern technologies and tools, making one a fully independent data scientist.
What are the four components of a good data science project according to the script?
-The four components are working with real data, using modern technologies like APIs and cloud databases, building models, and making an impact by getting validation.
Why is working with real-time streaming data important for a data science project?
-Working with real-time streaming data is important because it demonstrates the ability to work with relevant and timely data, as opposed to outdated datasets.
What are some examples of popular APIs that can be used for data analysis?
-Some popular APIs for data analysis include Twitter, Google Analytics, YouTube, Netflix, and Amazon.
What skills are essential when working with APIs in a data science project?
-Essential skills include setting up and configuring APIs, using libraries for making API calls, and working with data structures like JSON and dictionaries.
Why is it beneficial to store data collected from APIs in a cloud database?
-Storing data in a cloud database is beneficial because it allows for efficient management of regularly updated data, avoiding the need to re-pull and re-clean entire datasets.
What aspects of model building are most important to an interviewer according to the script?
-Interviewers are more interested in the thought process and decision-making behind model building rather than just the performance metrics of the model.
How can a data science project make an impact and get validation?
-A project can make an impact by sharing insights through visuals, graphs, blog articles, or by building an application that serves insights to users, demonstrating the value of the work.
What is the secret to mastering various data science skills as mentioned in the script?
-The secret is to build a single comprehensive data science project that covers all necessary components, which can then be iteratively improved and adapted for different analyses.
Outlines
🚀 Kickstarting Your Data Science Career
Nate introduces the video by emphasizing the importance of a comprehensive data science project to boost one's career. He advises avoiding overused datasets like Titanic and Iris, and steering clear of Kaggle unless you can rank in the top 10. Nate outlines the components of a strong project: working with real-time data, utilizing modern tech like APIs and cloud databases, model building, and creating an impact with validation. He stresses the need for full-stack data science skills and the ability to impress interviewers with real-world relevance.
🔍 Mastering Data Science with Modern Technologies
This paragraph delves into the specifics of working with real-time data and leveraging APIs to collect it. Nate explains the value of APIs in data analysis and the skills required to set them up, such as handling tokens and using Python libraries for API calls. He also discusses the importance of storing data in cloud databases to manage updates efficiently and the benefits of understanding cloud services like AWS and Google Cloud. Nate highlights the significance of building models and the critical thinking behind model selection, data cleaning, and validation, which are more important to interviewers than mere performance metrics.
🌟 Demonstrating Impact and Gaining Validation
Nate concludes the video by discussing how to demonstrate the impact of a data science project and gain validation. He suggests sharing code with data science communities, creating visually appealing graphs and insights in blog articles, and deploying applications with frameworks like Django or Flask on cloud platforms. Nate emphasizes that a great project should not only improve one's skills but also provide valuable insights to others, thereby showcasing the project's impact. He wraps up by encouraging viewers to build iteratively and improve their projects to make them valuable to others, which will impress interviewers and peers alike.
Mindmap
Keywords
💡Data Science Project
💡Full Stack Data Science
💡Real-time Data
💡APIs
💡Cloud Databases
💡Machine Learning Models
💡Model Validation
💡Thought Process
💡Making an Impact
💡Application Frameworks
Highlights
The one project to build for full stack data science experience and impressing interviewers.
Avoid overused datasets like Titanic and Iris for originality in projects.
As data scientists gain experience, they should move beyond Kaggle competitions.
Components of a good data science project include real-world skills and modern technology use.
Working with real, updated data is crucial for relevance in data science projects.
Utilizing APIs to collect real-time data demonstrates practical data science skills.
Popular APIs for data analysis include Twitter, Google Analytics, YouTube, Netflix, and Amazon.
Skills in setting up APIs, using Python libraries, and handling data structures like JSON are valuable.
Using cloud databases to store and manage real-time data updates efficiently.
Knowledge of cloud services like AWS and Google Cloud is a significant advantage.
Building and implementing models is fundamental, but understanding the decision-making process is more critical.
Interviewers prioritize the thought process behind model building over performance metrics.
Making an impact with a project involves validation from others and sharing insights.
Sharing code with data science communities and creating visual insights can validate a project's impact.
Learning application frameworks and deploying applications can demonstrate full stack capabilities.
Building a complete data science infrastructure allows for reusability and iterative improvement.
Mastering various components of data science can be achieved independently and then integrated.
The secret to effective data science project work is building a comprehensive end-to-end infrastructure.
Iterating and building valuable projects is key to standing out in data science interviews.
Transcripts
hey guys it's nate here with some advice
if you're trying to figure out your next
data science project
let's talk about the one and only
project that you need to build
that will help you gain full stack data
science experience
and impress interviewers on interviews
if your goal is to
jump start your career in data science
let's break down the components of
what a good data science project
includes and
exactly what an interviewer is looking
for and why they're looking for it
i'll also let you in on a secret about
this data science project and why i
think it's the best one out there and
the only one you need to actually do
so watch until the end to hear about
what this is so if you like content like
this
please subscribe to this channel now
let's get started
so one piece of advice before we start
talking about the components of a good
data science project
let me tell you about two things to stay
away from when you're trying to find a
project
number one avoid any analysis on the
titanic
or iris data set it's been done to death
and i don't care about your survival
classifier
number two as you gain more experience
you can start to
migrate away from kaggle so avoid kaggle
it's to me too commonplace too ordinary
everybody does it so unless you can rank
in the top 10 i just stay away from it
great so with that out of the way let's
start talking about the components of a
good
data science project again i'll break
down the components of a good project
and tell you what the interviewer is
looking for and
why they're actually looking for it but
basically as a summary
what an interviewer is looking for what
i'm looking for
is a data scientist with real world
skills
real world relevance skills in both
coding and analytics
but also in using modern technologies
and tools
this is going to get you closer to
becoming a full stack
or fully independent data scientist so
here's a quick breakdown on the
components of a good data science
project
so number one working with real data
number two
working with modern technologies like
apis and databases in the cloud
number three obviously building models
number four
making an impact getting validation and
i'll explain a little bit about
application frameworks towards the end
of this video
all right so now let's talk about each
component in detail so component number
one
working with real data specifically with
data that gets
updated in real time streaming data
working with real data that users
produce and working with data that is
produced in real time
helps prove to the interviewer that you
know how to work with relevant data and
timely data
you're not analyzing some data set that
was produced in 1912
like the titanic data set right you're
basically working with data that was
just produced
and data that's updated frequently so
having said that you're probably asking
well
how do i get a data set like this so
that's a perfect segue to component
number two
using modern technologies in industry so
how are you gonna get that real life
data set that is updated in real time
you can use
apis to collect that data almost all
apps and all platforms
use apis to basically pass information
back and forth learning how to use
configure
setup apis to get the data that you need
for your analysis
shows the interviewer that you have
relevant
keyword relevant data science skills to
be able to do your job effectively
some popular apis for example are
google analytics youtube netflix amazon
basically a good api for data analysis
will include
real time updates data and time stamps
for every record
geo locations are really nice to have
and obviously
numbers or text so you can actually do
an analysis
so for other api examples refer to the
links in the description so the skills
you're trying to learn when you're
working with
apis are these number one learn how to
set up and configure apis in your code
for example
dealing with api tokens number two learn
how to use libraries
like various python libraries that will
help you make
api calls and number three how to work
with
data structures like json and
dictionaries to help you collect and
save the data from the apis
all of these skills are skills that
you'd be using
on the job from day one as a data
scientist so as an
interviewer if i know that you have
these skills i would start seeing you
more as an
experienced data scientist than somebody
that's just starting off and this is
basically a leg
up and a bonus point to have on an
interview so now let's talk about the
second modern technology to work with
databases in the cloud so once you
collect your data from an api
and maybe after you clean the data a bit
you probably want to store it in a
database
why well number one because like i
mentioned before
the data that you're grabbing from an
api is
updated regularly so if you pull the
data again from the api you're going to
get new records
so instead of just pulling the entire
data set again and cleaning the entire
data set
all over again it would be nice to just
pull
the new records clean that and then
store that in the database
and so basically you'll just be storing
all of your clean data
in that database and adding new clean
records
every time you make an api call number
two every company uses databases
and many use cloud services like amazon
web services
aws and google cloud so having the
knowledge
on how to build a data pipeline with a
cloud provider
is a great skill set to have and it will
set you apart from other data scientists
again if i was interviewing you and you
have this experience
i'd be very impressed because i know
that you can hit the ground running
and make an impact from day one all
right so component number three
this gets us to the part of a data
science project that you thought was
probably the most important
building models so it's definitely
really important to learn how to build
and implement a model whether it's a
regression model or some sort of
ml machine learning model and that's
kind of why i told you to start with
kaggle
because i feel that kaggle will give you
the experience you need
in terms of building models so if you
just don't have a lot of experience
building models
kaggle is a great starting point but
while gaining experience building models
is important there's another
aspect that's even more important it's
understanding the decisions you make
and why you make them while building
your model so here are some questions
you would need to answer when
implementing your model you'll need to
be able to
eloquently explain your answers to these
questions
on an interview otherwise no matter how
good your model is
nobody's gonna be able to trust it so
here are some of the questions
number one why did you pick your model
why that model
what are you trying to accomplish with
this model that you couldn't do with
others
number two how did you clean your data
why did you clean it in that way
what type of validation test did you
perform on the data to prepare
for the model tell me about the
assumptions of your model
how did you validate those assumptions
how did you optimize your model
what were the trade-off decisions that
you made how did you implement your test
and control
tell me about the underlying math in
your model and how it works
what you don't see in this line of
questions is how your model performed
i don't really care about that as an
interviewer i care about your thought
process
and how you made decisions and i care
about if you
understand the underlying math of your
model so lastly
how do you know if you've built a great
data science project
your project should make an impact you
should have some validation from others
i understand that you're doing these
projects to gain more experience
and improve your skills but the job of a
data scientist
is to help others by turning data into
insights
into a recommendation that can make an
impact on the business
so how do you even know if your insights
and recommendations
are valuable if you're building in
isolation and not showing others
you need to show others your work and
build something that they would find
valuable so there are three ways to do
this
the easiest way the first way is to
share your code with others
that are part of data science
communities there are various subreddits
out there like data science and machine
learning
that would be happy to review and look
through your code
you can just put your code in a git repo
and share your project that way but
because you're just sharing code
it might not get the best engagement
from the community
so another way the second way is to
output your insights
in the form of visuals and graphs build
nice looking graphs that people want to
take a look at
share your graphs and write up your
insights in some sort of blog article
form you can share your articles on
various data science publications like
towards data science on medium or again
through various data science subreddits
and lastly
the hardest way is to learn an
application
framework like django or flas deploy
your application
using a cloud provider like aws or
google cloud
and serve your insights that way your
insights could be
an interactive dashboard that you built
using plotly that users can kind of
interact with
or it could be a simple api that users
can connect to
to grab your insights and
recommendations this is
obviously the hardest most involved way
to share your work
but it's worth it if you want to become
a full stack data scientist
and gain some software development
experience any interviewer
any data scientist would be super
impressed if you have this skill set
the main point in all this is just to
show that you built something valuable
and that people find it interesting show
the impact of your work
your teammates and the interviewer would
be really impressed
guaranteed all right so i ran through
all of the components here are the
components
for a good data science project again
working with real data
working with modern technologies like
apis and databases in the cloud building
models
and lastly making an impact and getting
validation
possibly from building an application so
you're probably thinking that this is a
lot of work
and it includes so many different skills
that it's gonna take you years to master
and the answer is yes it's supposed to
take you years to master
all of these skills to become a very
good data scientist
but the great part of these components
is that you can master
them independent of each other meaning
that you can learn
all about databases and get good with
that and then switch over to
apis and master apis and then so on and
so forth
so after a while you just would
basically master them all and so
now we come full circle from the intro
what is the secret to all of this
the secret is you don't need to do
multiple
projects to master these skills this is
basically one
big data science project you're building
a data science
infrastructure from end to end and
learning the entire data science process
so once you build the entire
infrastructure end-to-end like
connecting
and grabbing data from an api to
cleaning data to then storing it on a
database to building a model to then
having a visual as an output you can use
the exact same
framework and infrastructure to do other
analyses
the only thing you probably need to do
is just slightly refactor and revise
your code
so for example if you want to analyze a
new data set
using another api you can just use the
same code just revise it slightly to
connect to another api
and pull new data in you can use similar
code and techniques to clean your data
and push it into
a new database table but it's a database
that you already have running in the
cloud
so there's no more setup or
configuration that's really needed
so really once you have that
infrastructure set up you can just do
various other projects
learn various other models using the
exact same
framework with just simple revisions so
my advice is just to keep iterating keep
improving
and keep building to build something
that others would find
valuable so that's it for me i hope this
becomes your next data science project
is going to be the only data science
project you're ever gonna really need to
build
and it's definitely a project that would
impress interviewers on your next data
science interview alright so please
leave a comment
if you have any questions subscribe to
this channel if you like content like
this
until next time see you guys at the next
video
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