Introduction

The fascinating world of data and AI has brought forth many scientific tools, algorithms, processes, and knowledge extraction systems to identify meaningful patterns from structured as well as unstructured data. The boom in data analytics in the last couple of years is only growing and will reach the next level with so many innovations in the artificial intelligence domain.

If Data Analytics is something you fancy and want to get a solid foundation on this topic then you must have a portfolio of data analytics projects to showcase. If you are wondering how to start with data analytics, we have here data analytics project ideas that are good for beginners as well as those who are in intermediate or higher levels. If you are a student then our ideas could also be used for data analytics projects for students.

Data Analytics Projects (Easy, Medium, Hard)

To get started with data analytics project topics, you would first need to understand what level you are comfortable in and then decide whether you want to get on with data analytics projects for beginners, intermediate, or higher levels. Let us take a look at what it entails to do a project in these 3 levels:

  • Beginner level – If you are someone who is just starting with data analytics, you must go through the data analytics project examples in the beginner section. These projects do not employ heavy application techniques and their simple algorithms would let you move forward smoothly.
  • Intermediate – Here medium to large data clusters are taken and need you to have a sound foundation of data mining projects along with machine learning techniques. If this is something you are well-versed with then you can work on the projects outlined in the intermediate section.
  • Expert – This section is for industry experts where neural networks and high-dimensional data are worked with. If you have the blend of creativity and expertise required for such projects, then the data analytics mini project in the advanced section is for you.
  1. Easy or Beginner level projects
  2. Intermediate Level Projects
  3. Advanced Level Project

1. Easy or Beginner level projects

  • Fake News Detection – If you know python then you could develop this data analytics project in python which can detect a hoax or false news that is generated to fulfill some political agenda. This news is propagated through social media channels and other online media. The model is built using the python language which can accurately detect the genuineness of a news item. You could use a PassiveAggressiveClassifier to build a TfidfVectorizer which can classify news into “fake” or “real”.
  • EDA or Exploratory Data Analysis Project – This is the first thing a data analyst needs to do as part of their job. In this project, we look into data to recognize and identify patterns. Using data modeling techniques you can provide a summary of the overall features of data analysis. EDA could be done with or without the help of graphics. You could also use univariate or bivariate quantities to perform EDA. IBM Analytics community is a valuable resource if you want to delve into an EDA project.
  • Sentiment Analysis – This analysis is used widely in online communities for reputation management of any brand or perform competitor analysis using the R framework. This data analytics project in r will try to understand the opinions and sentiments of viewers based on the words they use. In this classification, classes are either binary (positive or negative) or multiple (happy, angry, sad, confused, disgusted, etc.). You could use the “janeaustenR” package with a relevant dataset. Using general-purpose lexicons like bing, Loughran, and AFINN and performing an inner join you could build a word cloud for the final display of the data analytics project report.
  • Color Detection Project – This is a good data analytics projects for students where they can build an interactive app to detect the selected color from an image. Many of us can not recognize or remember the name of color since there can be around 16 million colors based on RGB values. 

2. Intermediate Level Projects

  • Chatbots – Chatbots are an extremely useful tool in businesses as the huge surge of customer queries and messages can be handled by chatbots without slowing down business. The three pillars of designing a chatbot are Artificial Intelligence, Data Science, and Machine Learning. Chatbots can be trained by using recurrent neural networks along with intent JSON datasets. The main implementation could be done in python. 
  • Handwritten digit recognition – The machine learning enthusiasts widely use the MNIST datasets of handwritten digits. You use convolutional neural networks and do the real-time prediction of digits drawn on a graphical user interface.
  • Gender and Age detection – You can build this interesting data analytics project in python which can predict gender and age by analyzing just one image. You would need to know about computer vision and its principles to do this project. 

3. Advanced Level Project

  • Movie recommendation system – The concept of recommending movies is complex and is based on the abstract click method. It requires a huge implementation of machine learning and accessing humungous datasets that include user’s movie browsing history, preferences, etc. You would need to use collaborative filtering to get a hang of user’s behavior and the R Framework along with the MovieLens dataset is a good fit for such projects. To channel through the datasets you could make use of surprise model selection and matrix factorization too. Brands like NetFlix use this method and is a lot of grueling work even for industry experts.
  • Credit Card Fraud Detection – Another data analytics project in r, this will need you to work with decision trees, gradient boosting classifier, logistic regression, and artificial neural networks.  By using the card transactions dataset, you can classify transactions on a credit card into fraudulent or genuine categories. 
  • Customer Segmentation – This is one of the most popular data analytics projects for companies as they need to create various groups of customers at the beginning of any of their campaigns. This project is an implementation of unsupervised learning and uses clustering to identify different segments of customers so that companies can target the customer base they need to. Customers are divided into groups based on age, gender, preferences, spending habits, etc. This is done to market to each group more effectively. You can use K-means clustering and visualize gender and age distributions. 

Conclusion

If a data analytics project felt out of reach earlier, hope these interesting projects can give you headway in this direction more smoothly. By working on these newer and unique projects you can exhibit your skills and gain confidence. You might feel in the beginning that analytics projects need to be complex but that is not the case. You must demonstrate your skills by working on the datasets that interest you. You could start with the beginner level and then move on to higher levels to build your data analytics project portfolio.

It is also important to find the right place to learn and become proficient in all these skills and languages. Jigsaw Academy, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. Jigsaw Academy offers an Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10 months and is conducted live online. Learners are offered a joint certificate by the Indian Institute of Management, Indore, and Jigsaw Academy.

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