The One and Only Data Science Project You Need

Summary

In 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

00:00

🚀 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.

05:01

🔍 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.

10:02

🌟 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.

Keywords

💡Data Science Project

A data science project is a systematic effort to analyze, interpret, and derive insights from data using scientific methods, processes, and algorithms. In the context of the video, the speaker emphasizes the importance of creating a project that showcases a full stack of data science skills, including working with real-time data, using modern technologies, building models, and making an impact. The video suggests that one comprehensive project that covers these areas can be more valuable than multiple smaller projects.

💡Full Stack Data Science

Full stack data science refers to the comprehensive set of skills required to handle all aspects of the data science pipeline, from data collection and preprocessing to modeling and deployment. The video suggests that a good data science project should help one gain experience across this full stack, impressing interviewers by demonstrating the ability to work independently and effectively with data from start to finish.

💡Real-time Data

Real-time data is information that is captured and processed as it is generated, without significant delays. The video stresses the importance of working with real-time data to demonstrate the ability to handle current and relevant information. This is contrasted with outdated datasets like the Titanic dataset, which the speaker advises avoiding.

💡APIs

An API, or Application Programming Interface, is a set of rules and protocols for building and interacting with software applications. In the video, APIs are highlighted as a modern technology for collecting real-time data, emphasizing the need for data scientists to know how to configure and use APIs to gather the data required for analysis.

💡Cloud Databases

Cloud databases are database management systems hosted on cloud computing infrastructure, allowing for scalable storage and access to data over the internet. The video mentions the use of cloud databases as a way to store and manage real-time data, which is a skill that can set data scientists apart and demonstrate their ability to work with modern data infrastructure.

💡Machine Learning Models

Machine learning models are algorithms that enable computers to learn from and make predictions or decisions based on data. The video discusses the importance of building and understanding machine learning models within a data science project, as it showcases the ability to derive insights and make data-driven decisions.

💡Model Validation

Model validation is the process of evaluating a machine learning model to ensure it is generalizable and performs well on unseen data. The video emphasizes the importance of being able to explain the decisions made during model building and the validation process, as this demonstrates a deep understanding of the model's capabilities and limitations.

💡Thought Process

The thought process in data science refers to the reasoning and logic behind the choices made during the data analysis and modeling process. The video highlights that interviewers are more interested in a candidate's thought process and understanding of the underlying math of their models than just the performance metrics of the model.

💡Making an Impact

Making an impact in data science means creating insights and recommendations that have a tangible effect on business decisions or outcomes. The video suggests that a good project should not only involve building models but also demonstrate the value of these models by showing how they can be used to make a difference.

💡Application Frameworks

Application frameworks are software libraries that provide a structure for developing applications. The video mentions learning an application framework as a way to deploy data science insights, which can involve creating interactive dashboards or APIs. This skill is seen as an advanced capability that can greatly impress interviewers by showing a candidate's ability to turn data insights into actionable tools.

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

twitter

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

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