Last Updated on April 9, 2021
Are you preparing for data science? Then you are at the right place. Data science came into the market in the last 4-5 years. And lots of vacant seats are available for data science jobs. Data science has changed the way we work, use data, and our approach to the world. Many employees from different backgrounds have changed their career path to data science or Artificial intelligence.
It is not necessary to have CS or mathematics background. To get into the data science field, It requires nothing but little math, little science and lots of curiosity about data, learning new tools, and analyze data.
This is the project by which employers would know how curious you are to get into data science. To get hired by giant companies as a data scientist, it is mandatory to build a resume with data science projects.
If you have not learned data science and wanna learn, then you can go for Datacamp. There are many other online courses in the market. But in terms of data science, Datacamp is the best. Even if you don’t have any coding experience, you can go for it. Many people are not interested in MOOCs.
If you are one of those, then you can go for a data science book by Jake Vanderplas. For your convenience, I have given both links below. You can buy any one of them.
Learn Data science – Datacamp
data science book by Jake Vanderplas
Here I have listed 7 projects that will help you build a resume for data science jobs and the related dataset you will get, in a data science platform named Kaggle. Please note that the list is not in order.
1. Face detection
You are familiar with what this project is about. Face detection software is used in smartphones, unlocking the door, and many other places. This face detection technology is used in homes, organizations, etc.
A tool named OpenCV and algorithms like convolutional neural network and other facial recognition is used in this project.
2. Sentiment analysis
Sentiment analysis is widely used in organizations. Companies and Organization use them to know the sentiment behind posts which helps them to know the customer behaviour like why is the product not liked by the customers? What factors affect the sentiment of the customers? etc. and develop strategies. The tool named NLTK is used in this project.
3. Recommender system
When you watch a tv series on Netflix, you’d noticed that similar movies are shown to you below. How does Netflix recommending you those movies(based on your movies you are watching)? From shopping to video streaming, recommended system customize the content according to customer’s preferences. It now becomes very common.
Every company are now using the recommender system to leverage their sales. Python tool such as sklearn will be used while working on it.
4. Stock prediction
Stock prediction projects are good for those who want to work in the finance sector. Many stock prediction startups have been started in recent years. This project helps to find out the future price of various stocks.
Before you move ahead in this project, you have to familiar with some terms like predictive analysis, regression analysis, action, analysis, statistical modeling, etc.
5. Credit fraud detection
Credit cards are widely used in the present world. But it is not as safe as a debit card. In the credit card, there is no need for OTP while doing international transactions. If you want to go to the finance or banking sector, this project proves to be highly useful.
This project involves algorithms like ANN, Decision trees, Logistic regression, etc. These algorithms help to classify credit card transactions into genuine and fraudulent transactions.
6. Spam detection
Digital information is being shared across the world. So, it is very difficult to distinguish between fake news and real news. You may have to face such emails which create issues. So what big organizations like Google, Facebook does, they use spam detection feature which helps users to identify which information is genuine. This project requires the NLTK tool, naive Bayes classifier which helps to classify the information genuinity.
7. Object detection
I think every data science aspirant are familiar with object recognition. The difference between face detection and object detection is that face detection recognises face and object detection recognizes the whole object. Object detection requires a huge amount of data as compared to that of face detection. For image detection, you have to deal with the term DNNs(Deep neural network).
I would recommend you to build these projects using Python programming language. Perhaps, R is also a great programming language for ml projects. It takes less execution time than Python. But due to the lack of a library, it is not suitable to use it. You will get the related dataset on a website named Kaggle.
You can choose any of them and work on them. To be a data scientist, you have to choose which field you want to work for. For example, A data scientist who is passionate about the field of IT won’t necessarily excel in the field of healthcare if they are passionate about it.
I hope you like the article.
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