Doing data science coding projects are the fastest way to get hired. Let's talk about the best data
science projects for beginners that will help you build your personal portfolio. I'm Samanvay
a data scientist living in Sydney, Australia. Reading data science theory can get boring
very fast. Doing data science projects on the other hand is the best way to apply the theory and
learn faster. So there are two types of projects. You can do coding projects and data science
projects. Coding projects will help you learn the ins and outs of python while data science
projects will help you learn the details of the different aspects of data science. Remember once
you do these projects you want to be showcasing it on Github which is essentially a public portfolio
that you can display on your resume and it gives the employer an insight into your practical coding
knowledge. Now let's talk a little bit more about the coding projects. Some examples of coding
projects include creating a weather app, building a web scraper to scrape data of your favourite
websites. You could even decide to code games like tic-tac-toe or rock paper scissors. Another
cool project I found was building a QR code encoder and decoder. So there are essentially
two approaches to doing this coding projects. The first is starting simple and increasing complexity
as you level up your knowledge of python. For example let's say you've just learned about
if else statements. So in this case you could build a simple application that takes a user
input in the command line and uses an if-else statement to print something based on the user
input. Now let's suppose that after the If else statement you learn about for loops so you can
add that extra layer of complexity to your initial program. In this case you could ask for a list of
user inputs and then you could take that list, use a for loop to run through each of the words
in that list and have a separate if else statement to action on each one of those words. So as
you can see you've taken something simple and you've made it a little bit more complicated.
This approach is best when you're incrementally learning python syntax. The second approach is to
start by building an application. This could be a web application or a home loan calculator and
visualiser. This approach involves learning the different bits that's required to build
the application sequentially. When I say to you go build me a weather app you might be left a little
bit confused because there's quite a few steps involved in building a weather app. So that's
exactly the point of it. This is the best when you know the basics of python programming and
you're ready to be thrown into the deep end and I've also got more detailed links in the blog post
I've got in the description below where I've got links to the most commonly used python projects
and tutorials to go along with it. So that could be a good place to start if you're looking for
your next coding project. Now let's talk about data science projects. The goal of data science
projects is to learn three things, firstly data analysis and exploration then data visualisation
and finally applying machine learning algorithms. You'll still be doing python programming in these
data science projects but these projects will give you more exposure to more data science
specific packages like NumPy, Pandas and Tensorflow. For your first data science
projects I'd recommend getting started with a Jupyter notebook. These are the easiest to get
set up with and the easiest to use right off the bat. I'd recommend getting an open source dataset
and exploring the different facets of that data. This process is called exploratory data analysis
or EDA. So in terms of where to find these open source datasets. First place you should look at
is Kaggle. So in Kaggle not only will you find different types of data sets but a lot of people
will also be uploading the Jupyter notebooks they've used for the exploratory data analysis.
So there's a bit of collaborative learning there. Other popular data sources for these open source
data sets is UCI machine learning repository, Google dataset search and Amazon Open Data
registry. Again I'll include a more detailed list in the blog post below. So once you've done the
EDA you should try to do some data visualisation using packages like Bokeh or Plotly. You can even
make an interactive dashboard that you host on a web server. So as a data person, it's truly
important to learn data visualisation because that ends up being the most effective way to presenting
insights that you get from your data sets so it's a killer skill to have. Alternatively you could
also look at building a dashboard of something personal to you such as your personal finances.
The final type of data science project you'll be doing is using machine learning algorithms
to solve a business problem. An example of a project is the digit recogniser. This is a project
where you're using computer vision techniques to recognise hand written numerical digits. Another
example of a project is predicting house prices in your local area or using a public data set. Then
once you've gotten a bit of practice doing this project you can come up with your own problems to
solve. An example of a project could be a travel destination recommender. So you could get a list
of photos and based on the ones you like, the recommender system would recommend you places that
you might like given your photo preferences and if you're a little bit more interested in doing these
projects alongside theory, then check out my three month roadmap for learning data science. That's a
wrap for this video if you enjoyed it then smash the like button and I'll see you next time.