Data is everywhere and big data has become an exciting subject to explore. Big data carries insights, hidden trends, and patterns that cannot be seen unless the data is collected, cleaned, processed, and analyzed. No organization that intends to stay afloat can do so without harnessing the insights drawn from big data to make data-driven decisions and strategies. Owing to this, data analyst and data science skills are two of the most sought after big data skills in the United States. Big data skills have become valuable across various industries, for big and small businesses alike.
Beginners in the industry, particularly those that have opted for the self-study path, may feel overwhelmed trying to grasp big data concepts. Still, whether one undertakes to enroll for big data courses or study on their own, working on big data projects for beginners will help grasp the concepts and learn how to apply them to real-world situations faster.
Challenges that beginners are bound to encounter during a big data project
This article highlights some practical big data projects that beginners can work on to hone their big data skills and build hands-on experience. However, one is bound to experience some challenges in the course of carrying out big data projects.
- Too large data sets. Large data sets are not easy to work with. They require sophisticated storage and analysis tools that a beginner may not have at their disposal.
- Monitoring. Monitoring a project requires in-depth knowledge of big data tools and technologies. As a beginner, it may take you some time to find an effective solution for monitoring your projects in real-time thanks to lack of experience.
- Lack of working knowledge of scripting languages: Without a sound knowledge of scripting language, you may not be in a good position to use data analysis tools that utilize high-level scripting languages.
- Data security. Even with wide access to public databases, data breach whether intended or not has serious implications on the person doing the project.
- Tool selection. Tool selection becomes a challenge when in the middle of the project one realizes that inappropriate tools and technology were selected. This is bound to affect the progress of the project.
Top 10 Big Data Projects for Beginners
Whether your goal is to clean data, analyze data, or create powerful visualizations, the big data projects for beginners listed below are available and accessible at no cost for anyone who wants to practice. Most of them involve working with public data sets, the recommended starting point for beginners.
Data cleaning – Medicare Hospital Quality
The Medicare and Medicaid database is one of the biggest databases on quality of care that draws its data from the over 4,000 hospitals across the US with Medicare certification. This is a large database with sources of data spread across the region and makes a good option for practicing data cleaning.
Loan default prediction – SBA Loans Case Data Set
For banks and other financial institutions, being able to predict the probability of a client defaulting a loan. Using customer information in their databases, it is possible to apply machine learning to build predictive models that will predict a customer’s default rate.
This dataset available on the Kaggle website will help you do just that. It features data of loans issued out between 2007 and 2015. This data set includes credit scores, loan inquiries, and geographical information of loanees.
Prediction – World Trade Organization
For those with data science interest in the trade industry, the WTO website has some datasets available for practice. These allow you to perform various trade analyses, and discover trends and business insights and make predictions. Datasets are available for download therefore one needs to select the appropriate dataset depending on what one intends to practice.
Fake news detection – news.csv
This is an excellent big data project for beginners interested in applying machine learning and statistical analysis using Python language. In this age of social media, fake news is bound to penetrate and spread very fast through social media sites to gain political mileage among other reasons. Building a model that can tell fake from genuine news.
Text mining and analysis – Reddit Comments
Reddit and Twitter sites are good sources of data for forums and discussions where members post comments, images, links, and other information in reaction to a certain topic. Subreddit is a forum that is dedicated to a specific topic under the Reddit website. If you are focused on sentimental analysis on a specific domain, this is the dataset to make use of in your project. The dataset is available for download through BigQuery. If you are keen on analyzing a bigger dataset with a broad range of domains, check out Reddit’s ‘every comment’ dataset on the website. You can trim your dataset by marking it off between specific dates.
Text analysis – Enron Emails
Enron, the energy-trading and services company in Houston, Texas was involved in one of the biggest accounting scandals the world has ever witnessed. Its bankruptcy and later on its collapse, data scientists and big data professionals have access to its large, 500,000 emails dataset for their practice projects. For those intending to hone their text-related analysis skills, this is the dataset to go for.
Data processing and Visualization – Bureau of Labor Statistics
Visualization is one of the most critical skills for aspiring big data professionals. Visualization gives data its value. A visual presentation of analyzed data makes it possible to identify trends and patterns at a glance and draw insights from them.
The Bureau of Labor and Statistics website hosts various types of datasets from unemployment rates, consumer price index, occupational employment, to wages. These allow you to practice making great visualizations with different tools.
Data Visualization – United States Census Data
Another valuable source of datasets for your visualization practice project as a beginner is the Census Bureau site. This website contains US population data per state, city, and zip code. This dataset presents a good option for students who wish to focus on creating powerful visualizations without having to go through the manual data cleaning process.
Sentiment analysis – Amazon Product Reviews
Sentiment analysis, also known as opinion mining, is the process that is used to read the general feeling depicted in a data set It employs natural language processing and text analysis to gauge whether a specific dataset reads positive, neutral, or negative sentiments. Businesses use sentiment analysis to monitor their brands and product performance as well as understand customer needs by analyzing customer feedback.
For beginners who wish to launch a career in business intelligence or marketing, sentiment analysis presents a great project idea and the best dataset to use for practice is the Amazon Product review dataset. It is a huge dataset containing more than 142 million product reviews.
Time series analysis – FBI Crime Data
Time series analysis is another important aspect of data analytics. An interesting project to launch for beginners is the time series analysis project using the FBI Crime dataset. Here you will monitor changes of a certain variable over time and discover the relationships between these changes and those in other variables over the same period. This dataset contains crime rates in the United States over a 20-year period.
Conclusion
Completing your project is a great achievement in your quest to acquire big data, data science, and analytics skills. You will gain practical skills that you can apply to real-world situations. Adding your practice projects to your portfolio boosts your CV and employability significantly. They are a demonstration that you have not only mastered big data concepts but also familiarize yourself with big data tools.