Data Analysis can provide for a promising way to jumpstart your career, but the key to getting noticed by any potential employer is to have your data analytics projects presentable. An aspiring data analyst must work in different domains and obtain insights that can translate into your next prominent data analyst project idea!

In current times, enterprises look for data analysts aware of the challenges in a particular industry and therefore find any relevant projects in their resume. It can be an overwhelming task to decide on a project idea only to feel intimidated by its bulky codes and overused concept. This is precisely why we bring you an amalgamation of data analytics project ideas that would help you practice smart working with massive datasets. Let’s get started!

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Before you begin, you must understand the types of projects you’d be willing to work with:

Beginner: Projects in these levels can be pretty familiar and comfortable to work. For anyone starting in data analysis, such projects won’t require massive application techniques. Instead, with the help of simple algorithms, you can move forward easily.

Intermediate: This generally includes working with medium to large data clusters and requires a sound understanding of data mining principles. It may also require the application of Machine Learning techniques and is therefore recommended for seasoned data analysts.

Advanced or Expert: For industry veterans looking to build ambitious projects based on real-life data sets, such projects can prove to be gold. From neural networks to in-depth analysis of high-dimensional data, it requires the perfect blend of creativity, expertise, and insights for such projects.

Data Analytics Project Ideas – Beginner Level

1. Exploratory Data Analysis Projects (EDA)

A data analyst’s job remains incomplete without the Exploratory Data Analysis – the stage where the data gets looked into and patterns or findings are made. It provides a summary of the overall characteristics in data analysis and understanding it with data modeling techniques. What would have taken long, exhaustive sessions to find anomalies in numbers, exploratory data analysis is the perfect way to get it done.

EDA can generally be done in two ways: first, with the help of graphics or non-graphics, and second with univariate or bivariate quantities. For continuing with any data analytics projects, the IBM Analytics Community can prove to be an ample resource.

The topics that can come useful while building an EDA project are:

• Understanding the data and come up with a meaningful and relevant hypothesis
• Problem-solving with data visualizations or algorithms
• Spotting Data trends
• Understanding the relationship between variables and interacting with data visualizations in the form of plots.

A relevant field study can be the health industry where you can help in numerous ways, from understanding missing doctor’s appointment trends to lack of pieces of equipment.

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2. Sentiment Analysis

For data analysts, the objective of having a sentiment analysis project can be about understanding the positive or negative polarities of the viewers based on their sentiments. Such extractions can help to know the general viewpoint of your viewers about a particular idea, based on their opinions shared on websites, social media handles, etc. The various categories can be happy, angry, sad, curious, etc.

For professionals using the framework, R can also find the relevant dataset in the ‘janeaustenR’ package. As the difference is based on the word cloud, there can be clear distinctions between data groups and their corresponding sentiments. Such data analysis projects can be helpful in:

Online Reputation Management of any Brand – Social Media Monitoring

• Especially helpful in tracking and understanding the general perception of consumers over your brand
• Highlight key attention areas
• Any developments, like influencer campaign updates

Competitor Analysis

• Help you gain exclusive insights about the market and stay ahead of your competitors
• Collate information across various digital platforms
• Develop Business Intelligence

Data Analytics Project Ideas – Intermediate Level

3. Building Chatbots

Imperative for businesses online, chatbots have been trending for its many functionalities. They can be instrumental in automating customer service processes, as well as save time and resources. Laced with AI and Machine Learning techniques, powerful chatbots are all around us – from automated messages of messaging applications to smart wearables.

A chatbot is a smart program that simulates a real interaction with users via a chat interface. In this way, these bots react to any written or spoken queries and comprehend the conversation. As they’re self-aware, the more interaction they have, the more intelligent they get.

As a data analyst, the real challenge is to understand a chatbot’s performance quality, based on its comprehending potential of user requests as well as its ability to convey it clearly to users. As chatbots can be either domain-specific that requires chatbots to solve problems and open-domain where users can place an inquiry from any industry – there’s a lot of scope for the project.

Using Python and the Intents json dataset file, an analyst needs to look into the vast datasets and tricky languages with the help of multiple models. Such models can help enhance and improve customer support.

Data Analytics Project Ideas – Expert Level

4. Movie Recommendation System

One of the most basic methods to build user-customized services, building a stable movie recommendation system, may not come as easy as it sounds. Since the concept is based on an abstract click method, there would be massive implementations of Machine Learning. You’d require extensive access to large data sets of users’ movie browsing history, preferences, and more.

Methods like collaborative filtering may help in understanding user behavior. Therefore, to remove any system vulnerabilities, you can use frameworks like R and the dataset MovieLens. Matrix Factorization and Surprise Model Selection can also come handy to channel through the datasets.

Used by brands like Netflix, such data analytics projects can mean grueling work, even for industry experts.

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