Data Science Projects

The issue with Data Science learners is that they spend a lot of time understanding all the theoretical concepts but shy away from implementation and solving problems for various reasons. As keen Data Science enthusiasts, it is extremely vital to..Read More

  • Updated on 28th May, 20
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To begin working with projects, it always helps understand a problem thoroughly. You can pick up a project that excites you. It is key in you whether you solve the problem or not. In the following Data Science projects for beginners blog, we will check out a variety of project examples that will help in keeping you informed about what goes into working with projects. You can also use this post to check out the interesting tips provided to help you create your own Data Science projects for resume.

We will be covering the following aspects:

  • Reasons Why You Should Consider Data Science Projects
  • Top Data Science Projects You Should Consider Learning
  • Easy Data Science Mini Projects
  • Intermediate Data Science Projects
  • Advanced Data Science Projects
  • Skills Needed for Best Data Science Projects

Check out our Data Science Project video tutorial on YouTube designed especially for beginners:

Let us begin this Data Science projects for beginners blog, by understanding why you should consider working on data science projects.

Reasons Why You Should Consider Data Science Projects

There are many reasons why one should consider working with Data Science projects in today’s world, be it bringing upon a change in handling data or learning concepts quicker or more. The following are some of the major reasons why you should consider engaging yourself with projects in this field of Data Science:

Foundation

Working with a project will ensure that you, as a learner can thoroughly implement theoretical concepts and understand the working of everything that is there to know. This can act as a foundation for you to implement projects actively in various domains.

Rapid Learning

As per a survey conducted in 2018, it is said that people who implement something practically, alongside learning, have an increased chance of over 60 percent toward project completion and goal achievement.

Job Offerings

Adding projects to the resume will be nothing but an advantage for learners to be potentially on the preference list for their candidature across multiple companies. It is believed that over 75 percent of the hiring managers prefer candidates with practical experience of working with tools and techniques when compared to people with just theory-based knowledge.

Solving Problems

One of the major achievements of Data Science is that it is used to solve problems in a swift way that the world had not seen before. With experience working in projects, you could see yourself solving a problem around you and leverage that to start your career or use it for potential employment opportunities across the world.

Objective-based Learning

In a field like Data Science, where most of the end goals are based on objectives, it gives you a sense of direction and clarity with concepts learned previously when working with projects, which can be leveraged and implemented in other fields in your life as well.

Also read: What is Data Science?

Next up on this Data Science projects for beginners blog, we can check out the top data science project ideas you can use!

Top Data Science Project Ideas You Should Consider Learning

Movie Recommendation System (Easy)

With the onset of movie streaming and video-on-demand services becoming more and more popular with passing days, it is very clear that online streaming platforms like Netflix and Amazon are making full use of Data Science to attract more viewers to their platforms and also help retain the existing customers.

Here, you are given recommendations based on your preferred genre and the shows or movies that you have previously watched and liked. This is done by making use of various concepts such as Deep Learning and Artificial Intelligence.

By making use of a dataset such as the MovieLens dataset, you can be on your way to build a good movie recommendation system. By tracking the similarity and patterns in viewing, collaborative filtering can be implemented to put forth a recommendation. Content-based filtering is also an important aspect where users’ history plays a crucial role in recommending movies effectively.

Customer Segmentation System (Easy)

Companies that involve working on a B2C-based model need customer segmentation always. Because with segmentation, the companies can spend their time and resources in targeting potential user base and also understanding the best customers for their product.

The process involves three simple steps:

  • Identifying the potential customer base for product sales
  • Implementing clustering algorithms to group similar customers together
  • Providing customized solutions to each of the clustered groups

Clustering techniques are championed here to identify the segments in which customers’ behavior can be traced to put forth a customized campaign. K-means clustering algorithm is a very widely used technique for clustering when working with an unlabeled dataset.

Segmentation based on age, annual income, country of origin, or even spending amount can easily be analyzed and understood, thereby helping the companies leverage the concepts of Data Science and turn them into something lucrative.

Also read: How to Learn Data Science?

Next category of Data Science projects in Python involves working with intermediate projects, check them out!

Sentiment Analysis Modeling (Intermediate)

Among the thousands of organizations out there, a majority of them make use of sentiment analysis to check and assess the attitude of their customers toward the products offered by them. This is one of the vital steps used to analyze gaps in business processing and to sense potential growth.

The sentiment can be analyzed as positive, neutral, or negative by analyzing the text and presenting the user with a scorecard based on the presence of certain words by mapping it from the dataset. The most popular way to analyze sentiment has been by making use of the data from Twitter. Users’ tweets can be used for analysis. This could be a very good Data Science project that you could use for learning and implementation.

Credit Card Fraud Detection Analysis (Intermediate)

Security has been the primary concern of the 21st century. With multiple ways of fraud coming into the limelight, the world of Data Science helps curb all of this. This project aims to quickly detect a fraudulent transaction on a credit card and possibly alert the user or block the card. One common situation is that a user swipes the card in a location in the USA and, in the next couple of minutes if it is traced to being used somewhere in Europe or Asia, then this would pose as a black flag, thereby rejecting the transaction.

This method uses classifiers built on a variety of Machine Learning algorithms to detect such an anomaly. The process of feature extraction, alongside training models and testing predictions, is very vital here. Increasing the level of accuracy in terms of detection can be beneficial overall.

Next up are advanced Data Science projects for final year that you should consider working on!

Amazon vs eBay (Advanced)

We have all been in a situation where we buy an item online only to see it priced cheaper elsewhere. With the help of Data Science, it becomes very easy to find the price difference for a product and eventually calculate the total spending.

Let’s say, we use a dataset with 3,500 products, and the cart value is somewhere around US$193,000 by picking up items from the most expensive seller. The best case is seen to be around US$149,000. Here, there is a whopping 23 percent (US$44,000 worth) saving! So, all it took was a comparison between the top e-commerce sites and their offerings for the same product.

Next up on this Data Science projects in Python blog, let us check out the skills that are needed to work on these data science project ideas.

Skills Needed for Data Science Projects

Since Data Science poses to be a convoluted field to a nascent learner, it is vital that you understand all the skills that play an invaluable role in the process of end-to-end completion of projects.

Data Cleaning

It is quite popular in the field of Data Science that most of the time spent in working on a project goes into cleaning data during the preprocessing stage. In fact, almost 80 percent of the time is spent on this. Working on your skills to master data cleaning will not only ensure you have a smooth path in the projects but also will make you more valuable in terms of being a potential candidate for employment.

If you are wondering where you can go to look for data, I would suggest you check out the Data.gov website. There is a topic-wise search option that will help you get your hands on this data and begin working. Quora can also be a great source for finding some messy data which is the need of the hour to sharpen your preprocessing skills.

After getting your hands on the data, you need a tool such as the Pandas library in Python to work with the data across DataFrames and drop a couple of columns if they do not add any value to the data. If you prefer programming with R, then the dplyr package can be considered.

Exploratory Data Analysis

One of the key reasons for the implementation of Data Science can be seen through Exploratory Data Analysis (EDA). This is a process that involves the generation of questions and trying to find answers to those questions by making use of visualizations. It is very vital to do so because it allows you to understand your data better and gives you a chance to make unintended discoveries as well.

One of the good sources where you can find EDA datasets is the IBM Analytics Community. After the dataset is obtained, various tools and libraries can be used to perform the analysis. Matplotlib is a widely used Python library which is used for EDA. On the other hand, ggplot2 is also a wonderful package to work with if you are an R user.

Interactive Data Visualization

The creation of beautiful-looking visualizations and dashboards is something that sets apart an exceptionally sound Data Scientist from a person who’s not very inclined to do this. The creation of visualizations helps immensely even when implemented for business-related outcomes.

Dash is one of the nicest libraries for Python that can be used to create stunning dashboards in a rapid manner. In the case of R, there is Shiny, which is a great tool that can be used for interactive visualizations.

Some of the important points that you need to keep in mind when creating dashboards:

  • Know the requirement from your audience
  • Creatively put across a story
  • Use good-looking visualizations
  • Keep it simple

Also Read: Introduction of Data Science

Machine Learning

If you are serious about building a portfolio in Data Science, Machine Learning is a must. Rather than delving into a complex Deep Learning project, it is suggested you keep it simple and work from the basics. When it comes to Machine Learning, it is always considered a good start to work with concepts such as linear regression and logistic regression.

Python offers learners with the Scikit-learn library, which is a pleasure to work with. It covers all of the concepts such as classification, regression, clustering, and preprocessing data and helps in model selection as well. For R users, the Caret package is a very well-rounded package that can be used to implement Machine Learning in R.

To have the most impact when you work with Machine Learning projects, make sure to work on a dataset that has a noticeable business impact. Everything from loan default detection to credit card fraud detection can be used when working with Machine Learning.

Communication

On a final note, one of the primary skills that you should be working on, alongside all the above-mentioned concepts is your communication skills. It is one of the key components that set apart a good Data Scientist from the great.

It would not be of much value if you could design the most efficient models or create the most detailed analytics but cannot convey the same to either a customer or a fellow teammate. It always helps if you practice this skill and add it to your portfolio.

Direct communication in the field of Data Science can come through collaboration by making use of Jupyter Notebooks or R Markdown files. Documenting all of these on your GitHub will help in a good manner when communicating with potential employers as well.

With this post, I hope I was clear regarding all the concepts involved in doing your first project on Data Science.

If you are looking to become an expert in Data Science and earn a course certificate in the same, check out the latest Data Science offerings from Intellipaat: Data Science Courses. You can thoroughly master all of the concepts in Data Science and implement them as well.

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