The dilemma you face when preparing to enter the field of data analysis is not new. In job listings, employers often ask for the experience; but what should you do if you’re seeking your first position as a data analyst? In this case, your portfolio will come in handy. Include projects that demonstrate your skills and expertise. Even if you do not have previous experience, creating a portfolio with relevant projects will go a long way toward convincing employers that you are a good candidate.
Now that you have an idea of where to begin, let’s talk about how to begin with data analytics projects. As mentioned in this article, we have listed some data analytics project ideas that are suitable for beginners as well as intermediate and advanced data analysts. Before diving into these data analytics project ideas, let’s first take a look at what data analytics actually is.
Data has been the talk of the town for ages and it has become the new currency for organizations across different domains and sectors. Almost every strategic decision we make today is based on data. No matter what type of data is generated, whether from large-scale enterprises or an individual, it is imperative to analyze every aspect of data to draw crucial insights that can be used to improve the overall efficiency of a business or a system. The question is, how do we do it? Ultimately, that’s what data analytics is all about.
Data analytics are a crucial part of data science in today’s data-driven world. From a business perspective, companies can understand the significance of their actions with the help of data analytics. Data analysis, in short, refers to the process of analyzing massive amounts of unstructured data (does not have any pre-defined data model or schema) in order to extract meaningful insights. As a result, businesses are able to analyze the data more effectively to increase their business efficiency and performance. In addition to this, it assists organizations in gaining a deep understanding of the subject matter necessary for their growth. By analyzing data, companies can learn more about their customers, develop advertising campaigns, personalize their content, and streamline product development. There may be successful businesses created on a whim, but nearly every successful business decision is data-driven.
What is The Role of Data Analytics?
Data analytics plays the following roles:
- Uncover Hidden Insights: Data is collected and then analyzed based on business requirements to uncover hidden insights.
- Generating Reports: The report is generated based on the data and is sent to the respective teams and individuals for further consideration. It can be general reports (query report, data entry report, etc.), aggregate reports (irregular reports like complex bills, etc.), or dashboard reports.
- Perform Market Analysis: Market analysis is useful for understanding a company’s strengths and weaknesses.
- Enhance Business Requirements: Data analytics helps improve the customer experience and business requirements.
In response to the burgeoning demand for Data Analytics, many tools have been developed that provide various functionalities. There are several top tools in the data analytics market, either open-source or user-friendly, including Tableau, OpenRefine, Apache Spark, RapidMiner, KNIME, QlikView, Power BI, etc.
Having just read all this about Data Analysis, let’s now move on to data analytics project ideas.
Data Analytics Project Ideas
Are you passionate about Data Analytics and seek a solid grounding in this field? If so, have a portfolio of data analytics projects to show off.
Now, the challenge is finding projects for your data analytics portfolio, especially if you’re new to the data analytics field. First, you should decide what level of data analytics projects you are comfortable with, and then decide whether to get started with beginner, intermediate, or advanced projects.
- Beginner level: The data analytics project examples in this section will help those who are just getting started with data analytics. There are no heavy applications techniques or complex algorithms used in these projects, so you can move ahead smoothly.
- Intermediate level: At this level, project requirements call for working with large data clusters and a thorough understanding of both machine learning techniques and data mining principles. Therefore, the projects outlined in the intermediate section can be completed by those who are competent in these concepts.
- Advanced level: This section is intended for industry experts dealing with neural networks and high-dimensional data. Those who have the creativity and expertise to undertake such projects should consider the advanced data analytics project.
Let’s explore a few of the most useful data science projects that will assist you in building a strong portfolio and adding value to your resume as your career progresses.
Best Data Analytics Projects for Beginners
1. Color Detection Project
Color detection is the process of identifying any color in an image. Color detection is essential for the recognition of objects, and it is also included in many drawing and image editing applications. Most of us can’t differentiate between or even remember the names of colors since there can be up to 16 million colors based on RGB (Red, Green, Blue) values. Therefore, Color Detection is an excellent data analytics project for students, as they will be able to build an interactive application that will accurately identify the color in an image.
Source Code: Color Detection
2. Exploratory Data Analysis Projects (EDA)
A data analyst’s job would be incomplete without EDA (Exploratory Data Analysis). An EDA analyses the structure of data, allowing you to identify patterns and characteristics. Furthermore, they assist you in cleaning data, extracting important variables, identifying anomalies, and testing your underlying assumptions often using statistical graphics and other data visualization methods.
Programming languages such as R and Python are commonly used. They have a number of pre-existing algorithms you can use to carry out your work and speed up the process. Some data analysis techniques are easier to do with Python and others with R – so one should know what language to use so they can simplify their projects and needs. EDA can be performed with or without graphics. Although this process can be challenging and time-consuming, it is also extremely rewarding for a data analyst.
Source Code: Exploratory Data Analysis
3. Sentiment Analysis
It is a type of data analysis that measures the inclination of people’s opinions using computational linguistics, NLP (natural language processing), and text analysis. By undertaking a sentiment analysis project, you will be able to determine the viewers’ positive or negative polarizations based on their sentiments (emotions). Such extractions can assist you to discover what your viewers are thinking about a particular idea by looking at their comments and opinions posted/shared on websites, social media accounts, etc. Online communities use this type of analysis extensively to manage the reputation of a brand or perform competitor analysis using the R framework.
The aim of this data analytics project in R is to determine what viewers think and feel based on the words they use. Depending on the analysis, classes are either binary (positive or negative) or multiple (happy, confused, angry, sad, disgusted, etc.). Such an analysis lends itself well to public review sites and social media platforms, where people are likely to share their opinions publicly.
Source Code: Sentiment Analysis
4. Social Media Reputation Monitoring
It’s no secret that social media platforms play a crucial role in establishing a relationship between a brand and its customers. The image of a brand can be tarnished by a single comment about the product’s poor quality or service in a matter of minutes. So how to address such a concern?
By undertaking Social Media reputation monitoring projects, data generated on social media can be collected. Monitoring social media is the best way to discover what people are saying about products, competitors, industry, pandemic response, customer service wait times, basically anything the audience might be inclined to comment on. As a result, one can identify comments pertaining to their brand and identify possible ways to improve it. Using it, you can ensure that your brand is not being tarnished on the web. In case you find it, you can strategize and deal with it.
5. Fake News Detection
What do you think about the news you hear on social media? Most of it is fake, right? If so, how can you tell if it’s fake? Python is your best bet.
Practicing this Python project on detecting fake news will help you distinguish between real and false news easily. Python can be used to create this data analytics project, which can detect hoaxes or false news that are created to fulfill a political agenda. Social media and other online media are used to spread this news. The model is created using the Python language to detect the authenticity of news stories. It will be helpful if you get familiar with terms related to the project such as fake news, TDFIDFvectorizer, and PassiveAggressive classifier before proceeding.
Source Code: Fake News Detection
Intermediate Data Analytics Projects
There’s nothing more amazing than telling someone everything and being completely unjudged. What a top-notch experience, and that’s what chatbots are all about.
With the chatbot project, you will create a piece of software that can communicate and perform actions akin to that of a human.
It is difficult for businesses to handle the surge of customer queries and messages without the use of chatbots. Our lives are surrounded by powerful chatbots that use AI and Machine Learning techniques – from messaging applications to smart wearables. Designing a chatbot is based on three principles: Artificial Intelligence, Data Science, and Machine Learning. You can train chatbots utilizing recurrent neural networks as well as JSON datasets. Python is the most common programming language used.
Source Code: Chatbots
7. Handwritten digit recognition
Human handwritten digits are not perfect and can be made in many different ways, which makes it difficult for machines to recognize them. By taking handwritten digit recognition projects, you will be able to give machines the capability to recognize human handwritten digits. Using the image of a digit, handwritten digit recognition is able to identify the digit in the image.
An important project that uses neural networks is handwritten digit recognition utilizing MNIST datasets. With the help of an integrated GUI, a handwritten digit recognition system could not only detect scanned images of handwritten digits but might allow digits to be written on the screen for recognition.
Source Code: Handwritten digit recognition
8. Gender and Age detection
This interesting data analytics project can be built in Python, allowing it to predict age and gender from a single image. For this project, you must be familiar with computer vision (enabling computers to recognize digital images and videos as a human does) and its principles. You will use Deep Learning to determine an individual’s age and gender by looking at only one picture of their face.
There may be a predicted gender of ‘Male’ or ‘Female’. Factors like makeup, facial expressions, lighting, and obstructions make it very difficult to determine an individual’s exact age from a single image. Therefore, the predicted age may range from (0 – 2 years), (4 – 6 years), (8 – 12 years), (15 – 20 years), (25 – 32 years), (38 – 43 years), (48 – 53 years), (60 – 100 years).
Source Code: Gender and Age detection
9. Detection of Global Suicide Rates
Every year, there is a rapid increase in suicide attempts due to the deteriorating quality of mental health. In addition, the worldwide pandemic situation has further exacerbated mental suffering. Still, mental health specialists are doing their best to address this issue. In other words, you should take the time to develop such a project if you work in the health or social care domain. Data analytics project ideas like this can help you discover how many suicides occur worldwide.
Data from this global suicide rates project covers suicide rates in various countries, as well as information on age, year, gender, population, GDP, and more. Additionally, you can determine whether the overall suicide rate is increasing or decreasing, and which gender commits suicide more often. Using this analysis, you can determine suicide rates as a percentage.
Source Code: Detection of Global Suicide Rates
10. Real-time pollution density measurement
Pollution levels caused by different industries and urbanization have risen dramatically over the past few years.
By the end of this project, you will be able to make an automated system for measuring pollution density, which will trigger an alarm if pollution quality goes below a certain level. You can choose either water, environment, sound, radiation, or any other type of population according to your interest. To maintain your project’s efficiency, you should stick to a particular type of pollution as a beginner. In this type of project, you can include sub-ideas such as upcoming pollution attention, comparative study of pollution density before and after lockdown, etc.
Advanced-Data Analytics Projects
11. COVID19 Data Visualization Using Python
Having topical subject matter in a portfolio is always a plus, and the pandemic is no exception. Monitoring the evolution of COVID-19 cases is of the utmost importance for the authorities during the current Coronavirus pandemic to make informed policy decisions (e.g., lockdowns) and to share information with the general public so that appropriate public health measures can be taken.
In this project, we will use the COVID19 dataset containing data related to the numbers of confirmed cases, recovered cases, and deaths cases. As a result of this dataset, we will be able to answer these questions: Which countries have been most affected by the spread of the virus? How have COVID19 national lockdowns and self-isolation affected COVID19 transmission in different countries? How about providing a global heatmap that shows where cases have spiked and where there are few?
12. Most followed on Instagram
Over the last few years, social media has grown at an unimaginable pace, and more people have become influencers. It is important to understand popularity so that ordinary users may raise their popularity, and business users may choose better influencers.
With this project, you might be able to build an interactive bar chart that tracks and shows the most followed accounts over time. This dataset of the most-followed users on Instagram is an excellent resource for those interested in social media or celebrity, or brand culture. You may also check whether brands or celebrities are more effective at influencer marketing.
Source Code: Most followed on Instagram
13. Insurance Pricing Forecast
You can get insurance in many different forms, including motor, property, travel, and health. Small amounts of money known as premiums are collected periodically by insurance companies from an individual or an organization. These premiums are then used to pay the individual or organization for any losses that the insurance company covers. It is up to the insurance companies to decide how much premium to charge investors.
If insurance companies wind up overcharging their investors, it is only natural that those investors will prefer to buy insurance from their competitors. Insurance pricing forecast is an exciting big data analytics project solution that uses regression analysis to determine the best rates for insurance premiums.
Source Code: Medical Insurance Cost Prediction
14. Sales Forecasting
Product sales depend on a number of factors, including seasonality, location, reduced competition, advertisements, and promotions. To manage inventory effectively, it is very important for a store to know the expected sales of various products.
By taking this sales forecasting project, we will look for patterns in the data that affect sales in a particular store, using machine learning. The patterns can be used to determine which of the store’s products are popular. It helps in understanding the growth of a store as well. A person can estimate future revenue by anticipating how much product or service a sales unit (such as an individual salesperson, an entire sales team, etc.) will sell within the next week, month, quarter, or year.
Source Code: Sales Forecasting
Data Analytics Projects: Why are they so important?
Are you concerned about where you might get work without experience? Experience is necessary for a job, but experience can’t be acquired without a job. What should you do then?
If so, projects might be the answer, because you gain practical experience. Considering every aspect of human life, every region and industry, is becoming more data-driven, in fact, a heap of data analysis project scopes is lying around.
- Starting a project is the first step to exploring all the opportunities associated with data analysis. At an interview for a data analyst job, the quality of the data analyst project will determine your suitability.
- In modern times, enterprises seek data analysts familiar with a particular industry’s challenges and therefore find any projects associated with that industry on their portfolios. It is essential that your projects reflect how you have strengthened your Data Scientist skills.
- As you take on data analytics projects, you will acquire a deeper understanding of core concepts, you will acquire practical knowledge, and you will gain hands-on experience in data analysis.
- Each assignment in data science starts with evaluating data, so data analytics is a skill every data scientist must learn. This is a major reason why a practical, hands-on experience of data analytics projects is essential.
In light of the fact that data is the present and the future, it’s vital that you practice your skills on data analytics projects. As an example, you could take the trending data analytics projects mentioned above and create a similar project or one entirely new!
Hopefully, you will find some of these big data and analytics projects interesting enough to learn and practice. Taking on these newer projects will give you a chance to showcase your skills. Working with datasets that interest you is a good way to demonstrate your capabilities. It may seem at first that analytics projects must be complex, but this is not true. Starting with the beginner level and working your way up will let you build your portfolio of data analytics projects. Above all, being positive and making progress is the best approach.
Q. How do I start a data analytics project?
Sol: The following tips will help you get started on your data analytics project:
- Analyze the business issues: Identify the business objectives that need to be met.
- Pick a dataset that interests you: Working with datasets that interest you is a good way to demonstrate your capabilities.
- Perform analysis and modeling: Develop models to test your data and determine how they correlate with the objectives. Among the most common models are linear regressions, decision trees, and random forest models.
- Validating your idea: Finally, how can you tell if you’ve built the right project? It should have an impact. The project should be validated by others.
Q. Is data analytics a good career?
Sol: Yes, data analytics is a great career choice. The world’s most sought-after professionals are data analysts. Even at the entry-level, data analysts command high salaries and excellent benefits due to the enormous demand and the limited supply of people who can do this job well. As the internet age has progressed, data analysts have gained greater importance in industries including finance, marketing, and social media.
Q. Is coding required for data analytics?
Sol: The job of data analytics does involve some coding, but it does not require a deep understanding of software engineering or advanced coding. It is better to possess experience with analytical and data visualization software, as well as data management. The majority of data analyst jobs require strong mathematical and statistical skills, machine learning, decision analysis, software, analytical thinking, etc.