Trying to build a data analytics project is often a step taken by a candidate to improve their resume to a great extent. Many candidates often are intimidated by the number of concepts in a project and the size of the codes. But, that does not mean your dream of becoming a data analyst is difficult or crushed altogether. This article has talked about some of the best data analytics projects for students that can help them showcase their contribution in real-time to prospective employers. Since mastering Python for data science is essential, most of these projects include Python as the source code.
Building data science projects can help students boost their confidence and tell employers that they are serious about a career in data science. Finding data analytics project ideas can be a more daunting task than implementing them. So, let’s list down the best data analytics projects for beginners, the steps to go through with them, and how to solve the major problems you may face while building a data analytics project.
The world of AI and data has brought forward some essential tools. These include scientific tools, processes, algorithms, and extraction systems to find patterns in both structured and unstructured data. Data analytics is one such tool that has only been exponentially growing in the last decade and will reach its next level only after significantly contributing to the Artificial Intelligence domain. If you want to build a career in data analytics and create a solid foundation before applying for jobs, you must engage in some data science projects.
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Fundamental Steps to Complete a Data Analytics Project
a. Understand the Business Issues
The first thing you need to do when starting a data analytics project is understand the needs of the business it is being designed for. You need to have a brief about the expectations in the industry and identify the key objectives of the business you’re trying to contribute to. The scope of the business, its goals, and the output they want should all be thoroughly understood before diving into the project.
b. Understand Your Data Set
Using several tools can help you organize your data more efficiently. Small datasets can be used handling Excel, but more advanced tools should be used for heavier data. Some best tools for preparing, cleaning, and exploring data include Python, R, SQL, and Tableau Prep. Mastering Python for data science can highly help in completing data analytics projects for beginners. These tools help in categorizing data and look for errors in the present data. These errors can be data that doesn’t seem logical or make sense, omitted data, and spelling errors. These errors have to be fixed to clean the data correctly.
c. Prepare the Data
After identifying the variable in the dataset and organizing them, the next step is cleaning them. Missing variables are to be identified and suitable input should be made in their place. Also, creating a broad category for data that doesn’t belong to any proper place and removing duplicate data also have to be done in this step. You can always enter the average data scores in place of missing data to process data efficiently without messing with the results.
d. Perform Exploratory Analysis and Modelling
This step will help you test the data you’ve collected and cleaned. You will need to build models for such testing and seek the answers to the objectives decided above. This step will include using statistical models and methods to determine the best approach for your data. Some of the most common models include decision trees, linear regressions, and random forest trees.
e. Validate Your Data
Once you’ve crafted the model, you’ll need to check whether the data you have is correct or not. You need to check if your model works properly and if the data needs more cleaning? You will also have to assess if the outcome is something you are looking for. In answering these questions, you may have to go back to previous steps repeatedly because this step involves a lot of trial and error.
f. Visualize and Present Your Findings
Once all the above steps are complete, the last step is data visualization. Data visualization is essential in communicating your findings. Communicating your results through visualizations is important because everybody cannot be tech-savvy. Tools like Tableau can help with data visualization in a very effective manner. You need to convey your findings through the data in the form of a story which will help clients better understand your findings.
These steps will help you in building a perfect data analytics project that can boost your resume and make you a perfect candidate for a data analyst job.
You may also like to read: What Is Data Science Process and Its Significance?
What Should You Include in Your Data Analytics Project?
Your data analytics project needs to show the employer that you can perform operations like:
- Scrapping data from the web
- Carrying out exploratory analysis
- Clean datasets effectively, especially if they are untidy
- Communicate your results with the help of visualizations
A data analytics project for students can also be done by using these qualities and presenting them as a mini project of their own. This will make you look more creative and innovative in front of any future employer. You need to keep the data science project nice and straightforward and explore a few tools along the way.
Data Analytics Project Ideas for Students
- Fake News Detection
Mastering Python for data science can be immensely helpful if you want to develop this data analytics project. This project can help detect false news and hoax that is spread or propagated through social media and other channels. This model can be built upon Python language and instantly detect a news item’s accuracy and genuineness. Find out more details on this project here, along with the dataset.
- EDA or Exploratory Data Analysis Project
This project helps to look into data and identify patterns. This is one of the first projects a data analyst needs to do. Data modeling techniques are used to provide a collective summary of the data analysis. EDA can be done both, with or without the help of graphics. Also, both univariate and bivariate quantities can be used to perform Exploratory Data Analysis.
- Sentiment Analysis
This analysis helps in maintaining the reputation of a brand and performs reputation management. It is widely used by many companies and is very common. R framework is used in this analysis to understand the sentiments and opinions of the customers and the target audience based on the comments they use. In this project, classes can be either binary, i.e. positive or negative, or multiple like sad, happy, angry, disgusted, and confused. You may find more details and datasets for this project here.
- Colour Detection Project
This is a very good data analytics project for students in which they can build an interactive app and use it to detect specific colors for any selected image. Recognizing all the colors is not possible for anyone because there are around more than 16 million colors based on RGB values. But you don’t need to feed it manually; this dataset with RGB values can do the job.
Chatbots have become an important part of all businesses as they resolve customer queries and questions and handle them effectively without slowing down the business. Chatbots can be designed by having knowledge of Artificial Intelligence (AI), Machine Learning (ML), and Data Science. The implementation of chatbots is widely done in Python, and they can be trained using recurrent neural networks. You may build your first chatbot using NLTK.
- Handwritten Digit Recognition
Enthusiasts in machine learning usually use MNIST datasets for identifying handwritten digits. Convolutional neural networks can be used to perform real-time prediction of digits written or drawn by users on the graphical user interface. It may help to first begin by loading these libraries and datasets.
- Gender and Age Detection
This interesting project of data analytics can be built in Python and can help in predicting the age and gender of any person after analyzing the image given to it. To do this project, the main principle you need to be familiar with is computer vision and its basic principles. Find more details on this project here to get a headstart.
- Movie Recommendation System
Abstract click method is used in building this project which is very complex but helps in recommending movies based on a customer’s interest and preference. Huge machine learning methods need to be implemented and a plethora of datasets have to be accessed and studied like the customer’s browser history, preferences and interests. Collaborative filtering is used in this project to get familiar with the user’s behavior. MovieLens dataset along with the R framework is the best fit for this project. This is one of the most advanced data science projects that entry-level data analysts or students can perform because it contains multiple factors such as matrix factorization and surprise model selection. This method is a very difficult task even for industry experts sometimes. Brands like Netflix and Amazon Prime use it. However, take your first step and try your hands on MovieLens dataset.
- Credit Card Fraud Detection
This is a data analytics project in R. Decision trees, logistic regression, artificial neural networks, and gradient boosting classifiers are some of the methods you need to work with to build this project. Card transaction datasets can be used to classify any transaction through a credit card as either fraudulent or genuine. This problem can be solved using statistical modeling or a machine learning based approach, find more details here.
- Customer Segmentation
One of the most popular projects in data analytics is customer segmentation. In this project, unsupervised learning is implemented and clustering is used to identify and divide customers into different segments to reach the target audience directly. Consumers are divided into groups based on gender, age, spending habits, preferences, etc. This is done to target each group individually and market the products more effectively. K-means clustering can be sued to visualize age and gender distributions. It helps companies in the segregation of customers into various groups before beginning any campaign. Refer to this project for a credit card customers segmentation example.
What Problems You Might Face in Doing a Data Analytics Project?
- The analytics project is not solving the business problem it was created for
According to a Gartner report, 80% of data science projects are bound to fail. The data analytics project often solves the wrong problem and leads to businesses not delivering the benefits.
How you can address it: Brainstorm to identify the roadblocks the business faces in completing its objectives and prioritize them based on these three factors – business impact, feasibility, and urgency.
- The analytics project doesn’t match the workflow of the user
This happens when you create a solution that works for everyone. This leads to ultimately solving the problem but not fulfilling the needs of the business. The second mistake can be building the users’ end without taking their input on what they want.
How you can address it: Identify your users from the start by conducting interviews, understanding usage scenarios, and building personas. Prioritize tasks and trim them according to the users’ needs. Design solutions to meet the users’ workflow that the model fulfills the users’ needs.
- The analytics project falls short on marketing
Even the best of data analytics solutions need a push for sales. Carefully planned efforts should be put into marketing in every individual project or it just ends up dying.
How you can address it: Follow a go-to-market approach. Plan launches, roadshows, and internal campaigns. Hand out cool giveaways and engage the customers. ROI must be quantified to help show value and secure budgets.
- The analytics project won’t scale
Companies often don’t plan for scaling and get too fixated only on the first version. Products need several tweaks and iteration to get the solution for the customers right. Many companies just don’t factor in this and miss out on the expectations of the users.
How you can address it: A broader roadmap should be set up while planning the first version itself. The budget and resources should be set aside by keeping this vision in mind that the expectations of customers undergo rapid revisions in a short span of time. Plan periodic upgrades keep the customers’ interest in the product or service for a long time.
- The analytics project lacks executive buy-in
An executive mandate is lacking in many companies, which makes the data analytics project fall flat. There can also be conflicting priorities within a company, which makes new projects highly vulnerable. If not performed properly, then projects relating to transformations stand little to no chance of success.
How can you address it: Innovations must be started and led from the top positions in the company to see its many benefits and build upon them. Old habits must be readily gotten rid of and new projects should be brought forward in which all the company leaders are invested.
FAQs- Frequently Asked Questions
Q1. Where can I find data analysis projects?
Data analytics projects can be found online on various data websites. This article also mentions the top 10 data analytics projects which you can try building to spruce up your resume and build upon your skills.
Q2. How do I start a data analytics project?
While building a data analytics project, start from scratch and think about the problem you’re trying to solve and start from there.
Q3. How would you describe a data analytics project?
A data analytics project involved a series of steps to solve a business problem using data and analyzing and studying that data. It uses programming languages, statistics, and data science to tackle the issue at hand.
Suppose you earlier thought that building data analytics projects were out of your reach. In that case, this article hopefully helps you provide a pathway to you building a data science project without any hassle. You can follow these steps and guide yourself to smoothly transition into a data analyst at the beginning of your career. Working on the projects mentioned above will give you new perspectives into the field and bring out unique ideas.
In the beginning, it may feel like a complex and challenging task, but you can demonstrate your actual skills in your resume if you can build any of these data analytics projects. Starting at beginner-level projects and then ascending to the higher-level ones is the best way to build data analytics projects.
You may also like to check out the following blog for more variety of projects: