Projects are a great way not only to learn but to showcase what you have learned. There are many data science online courses that boast of having a project-centric approach. Many of these courses would train you theoretically. However, you will only cement your learning from any data science online course when you take away the supporting wheels and dive straight into a project yourself.
It is true that finding a project which fits your skill set and at the same time has an attractive end product, is very difficult. Do not let your hopes and dreams of making big in the field of data science fizzle out by just a project. That is why to help you in the long and perilous path ahead of you, we have curated a list of some really unique projects.
But before that, for those of you who are uninitiated, let us discuss the career in data science and what it takes to become a data scientist.
A career in data science
Data scientists have many roles to play in organizations they work for. Since data science knowledge is required for any data-related jobs, you would be expected to have expertise in the area you applied to. That being said, in an organization, there is no hard and fast rule or role, and as a data scientist, you should be prepared for everything.
Your roles and responsibilities would include (but not limited to) doing business analytics, building data-centric solutions, deploying deep learning and machine learning models, creating a data mining and processing pipeline, working in tandem with the backend team to ensure your models are running smoothly, and integrating machine learning and deep learning algorithms with the pre-existing applications of the organization.
10 Data Science Project Ideas
Now, it is time to discuss project ideas on data science to build your portfolio.
1. Detection of fake news
We have seen first-hand the kind of destruction and anarchy fake news can cause in society. There are many sources through which the news procured should always be taken with a grain of salt. We could be using NLP (or natural language processing) library’s TfidfVectorizer to build and train a PassiveAggressiveClassifier, which would be doing all the work of detecting fake news.
2. Detection of credit card fraud:
Due to the increase in the number of credit card users, there is also an increase in the fraudulent transactions occurring daily. If you can implement this project, then you would be touching a million lives and helping out large credit card companies. In this project, you will be training a classifier to predict whether the transaction is fraudulent or not. You will be using logistic regression, decision trees, and neural networks towards this purpose. Whichever has the highest metrics should be chosen as your model of choice.
3. Segmentation of customers:
Segmentation is the process in which similar customers are clustered together in a group for focused marketing and advertising. This one could be a little tricky because you will be dealing with the unsupervised algorithms here like fuzzy clustering, density-based clustering, and model-based clustering. You should choose whichever works the best in the use case you have chosen.
4. Analysis of sentiments:
A sentiment is an emotion which is described through any form of communication. With the advent of social media, this project will only increase in value because you will quickly ascertain love, hate, sadness, and other emotions. You would again be working with natural language processing (or NLP). The algorithms you would be using are Naive Bayes, decision tree, and the package of Tidytext.
5. Recognizing emotions from the speech:
In this project, you would be dealing with identifying the sentiment which is being portrayed from the speech of any individual. You will be using CNNs, RNNs, NNs, Gaussian Mixture Models, and SVMs to aid you in your venture.
6. Making predictions:
With the help of data, you will be able to predict the future of the market. You could predict the approval of loans, HVAC needs, medical decisions, customers’ and employees’ risk, etc. You will be working mostly with data cleansing and preparation and using different models for each task at hand.
7. Analysis of time series data:
Time series data is perhaps the most valuable data which can be used to predict various things like hazards, stock pricing, etc. You will be using different modeling techniques (ARIMA, moving average, and exponential smoothing). Even using basic machine learning algorithms would suffice if your data is ready.
8. Analysis of regression:
Regression analysis becomes extremely important when we would like to predict the future using past data. Prediction of continuous values like prices is where regression shines like a diamond. You will be using decision trees, linear regression, etc., for this task.
9. System for recommendation:
There are basically two types of recommending systems that you can create. You can choose any one of them and make predictions on what the user might like depending on the things they already like.
10. Exploratory data analysis:
Actually, this is a step that you should be following in any project which you pick. However, specific datasets require you to analyze them and present your findings in a humanly readable fashion. Practicing EDA will help you in business analytics.
Need to take a course
Data science is a complicated discipline and requires thorough practice and learning. One can try to self-learn programming and computer languages but it is quite essential for you to do a course when it comes to emerging fields like data science.
Courses are not only a great way to learn the things you like, but the project-centric courses will even help you apply them in real life. It is true that most of the learning you would be able to do on your own with the help of books and other materials online. But, one key thing which you would lack would be the certification. The certificates you get upon the completion of any course is an easy display that you know what you are talking about.
When it comes to choosing a project, you should go with the one you find to be the most interesting. Even if you do not possess the necessary skills, by doing a project, you would learn them and remember them for the future. However, taking a data science online course for your project would ultimately help you get the essential certification and prep you for a job as well.
Also read: 8 Common Mistakes Amateur Data Scientists Are Always Doing