Data science is a very vibrant and progressively growing field that you should consider entering. If you have started studying programming languages like Python or others that enable users to code data science solutions, you are on the right path.
Although that is true, it takes a little more than that because you need to gain experience and confidence in working independently. To reach that level of confidence and experience, you should practice constantly. If you would like to get started, here are five best open source data science projects to try at home
Uber Data Analysis Project
Uber Data Analysis Project can help you master the skill of using existing datasets to try and provide actionable business intelligence. Throughout this project, you will learn how to do this using data visualization. Knowing data visualization is one of the very few prerequisites of this project.
Another prerequisite is having a good command of the programming language R. You will use a dataset that has information about Uber Pickups and with a couple of libraries, the project will be a definite success. One of the greatest benefits of this project is understanding how to use data that might seem arbitrary to provide actionable business intelligence. Knowing the fundamentals of data visualization can help you apply them in other instances.
Mastering this skill can make you a great asset in teams using DevOps tools to develop SaaS dashboards for businesses. You will also be an asset to other companies that use data visualization to offer actionable business intelligence. You do not need much experience to undertake this project except for the prerequisites outlined above.
Detecting Fake News with Python Project
Detecting Fake News with Python Project is quite an interesting task to undertake because of the world you live in. There are talks of fake news everywhere, and a lot of people consume it, leading to catastrophic results at times. You can use this project to learn how to build a system that filters all fake news by identifying propaganda and other claims.
Going through this project by following each step on its webpage can help you identify how all the building blocks fit together. You will also understand how to develop tools with advanced analytical capabilities for other projects related to this one.
Once you have started being more skilled in this project, whether through regular classes or with the help of online tutors, you will start feeling much more confident when developing other tools of this kind. Your experience in programming this project will help you be more employable and build great projects.
Customer Segmentation using Machine Learning in R
Customer Segmentation using Machine Learning in R simplifies one of the most tedious marketing tasks businesses face. Segmenting customers is a crucial aspect of personalizing your customer’s journey. There is a lot that could go wrong when this task is done manually. Human error can wrongly classify customers, which means that doing this task will take longer and lead to confusion.
You can learn how to automate this process using the programming language R to code a machine learning model that does customer segmentation. Data collected on customers can be used to identify the demographic information which can be used to segment the targeted audience.
Completing this project will help you develop very efficient and accurate customer segmentation tools powered by Machine Learning. You can then service businesses that require customer segmentation tools such as eCommerce businesses and marketers. You might also consider developing your tool that can be available on the cloud as a SaaS product.
Exploratory Data Analysis
Kaggle’s Suicide Rates Overview 1985 to 2016 is a project you can easily undertake by yourself as it uses exploratory data analysis. Using data sets to find answers to complex questions is an invaluable skill. In this case, you will be using 4 data sets to try and determine the reasons leading up to suicide.
The data sets can then be used to identify common hallmarks of suicide that might be used to prevent it. Using the data sets, you will compare socio-economic information with suicide rates over the years all around the globe. You will use data sets from the United Nations, World Health Organization, World Bank, and another Kaggle data set.
The latter is called Suicide in the Twenty-First Century and it is a data set that has been made a Kaggle notebook. All of these data sets and information used in this project can help you make sense of global suicide trends and find probable ways of preventing it.
Data Science Movie Recommendation System project
Data Science Movie Recommendation System project can help you understand the fundamentals of using information gathered to identify personal preferences. In this project, you will learn how to use data to recognize patterns and associating them with people of the same demographics. That is called collaborative filtering and can be very successful in a lot of cases.
There is also another type of recommendation system you will be introduced to and it is called content-based filtering. The latter uses content historically viewed to find similar movies. Throughout this project, you will mainly focus on collaborative filtering, which recommends content viewed by someone else with similar demographics.
Mastering this skill will help you develop movie recommendation systems and other content recommenders that can be used in a variety of industries. Mostly, this skill can also be used in the marketing sector or at large eCommerce sites such as Amazon and eBay.
Undertaking a variety of projects will you attain more skill in data science and those skills will make yourself an invaluable asset. Above that, you will feel much more confident working independently with datasets and using them to create real-life projects.
Whenever you get some free time, take one of these projects to challenge yourself and enhance your skill. These projects are open-source and have detailed instructions while also detailing how to gain access to the needed datasets. Most of them are suitable for beginners, whereas some are more suited for intermediate to advanced programmers.