Big data is big in every sense of the word and a key driver of business operations in this era. Enterprises rely on Big Data to discover market trends, customer behavior, and other information to make critical decisions and form growth and expansion strategies for the business. With its application in all industries, Big Data is a highly demanded skill.
For beginners pursuing AWS Big Data training or any other Big Data training, it is not enough to possess theoretical knowledge. Going the extra mile to work on Big Data projects will equip them with the practical skills required in real-world work environments.
There are a number of Big Data projects that beginners can work on to test their knowledge. In addition to gaining hands-on skills, one gets exposure to various Big Data technologies like Hadoop, MapReduce, Elastic MapReduce (EMR), MongoDB, Apache Spark, and others.
Applications of Big Data
- Social media analytics
- Customer analytics
- Patients admissions prediction in healthcare
- Risk assessment and fraud detection in the financial sector
- Security threat detection
- Pricing optimization
- Product development
- Network optimization in the telecommunications sector
8 Cool Big Data projects for beginners
As we have already seen, Big Data is useful in all industries. Therefore, getting a project to work on should not be a challenge. Working on projects offers beginners the chance to test and hone their skills in Big Data. Also, indicating the Big Data projects one has worked on in their CV gives them an added advantage during their job search.
1 Data cleansing
Given the huge volume of Big Data, it is important to first clean/scrub data to fix or eliminate wrong or inaccurate data to enable accurate analysis, well-informed decisions, and quality solutions. This process takes up the most time in a data scientist’s dealing with Big Data.
Some sources of datasets for data cleaning projects for a beginner include Data.gov, The World Bank, and /r/datasets. Select a dataset, for
- Chronic diseases data by region
- School system finances in the United States
- Educational statistics from any country
- Reddit submissions
Next, you will need to select the tools you will use. As a beginner, you may opt for
- Python or R programming language
- Pandas, Matplotlib, or Numpy libraries
Cleaning and transforming data involve the following steps
- Importing libraries and loading data into the libraries
- Joining multiple data sets and creating a data frame for exploration
- Decide which column will be useful in your project for data modeling based on its value
- Detecting missing values and inconsistent data entries
- Impute for missing values
- Data quality assurance
However, this is only a general guideline. Your project will determine the specific steps to take in cleaning your data.
2 Data classification – Iris data set
Data classification is the process of organizing data in various categories for it to be used or stored efficiently.
For beginners, the Iris dataset is a very good resource for classification projects. It is beginner-friendly and contains information on flower petals and petal sizes. It is organized into three classes and 50 instances for every class so that its data frame will have 4 columns and 150 rows.
Your project title would be something like:
“Building a machine learning classification model using Iris dataset”
3 Data classification – UCI Spambase Dataset
UCI Machine Learning Repository is a free-access dataset resource. Users are the main contributors to the datasets.
Another great data classification project for beginners is using the UCI Email spam dataset to classify spam and ham emails. This dataset contains more than 4500 emails with 57 meta-information. Your project would involve building models to filter out spam emails from non-spam emails.
4 Prediction – Web traffic time series forecasting
This project involves training datasets for the purpose of forecasting future traffic to Wikipedia pages. The dataset consists of about 145 thousand time series each representing a number of daily views for various Wikipedia articles between dates 1st July 2015, and 31st December 2016.
Your guidelines for this project may include
- Importing libraries and loading data into the libraries
- File structuring
- Missing values
- Data transformation
- Summary parameter extraction and visualization
- Individual observations using extreme parameters
- Forecasting using selected forecast technique
5 Prediction – Boston Housing Dataset
This project is intended to sharpen your linear regression skills. Linear regression models are used to discover or predict relationships between two variables one of which is the dependent variable and the other independent variables. The dependent variable is the one being predicted.
This is a pattern recognition project that involves predicting the value of houses using linear regression. The Boston Housing dataset was compiled by The U.S Census Service and contains information about different houses in Boston including the number of rooms and tax. This dataset can be accessed from the StatLib archive or Scikit-learn library. It consists of 506 rows and 13 feature variables in columns.
6 Sentiment Analysis – Twitter dataset
Sentiment analysis (opinion mining) uses text analytics to extract people’s opinions about a topic or product from various sources of data and then classify these opinions according to a range of sentiments. Sentiment analysis is usually done from various social media platforms and from online reviews on the internet.
Platforms like Facebook, Twitter, YouTube, and Reddit generate massive sets of Big Data that can be used to discover trends and public opinion. For beginners, the best source of sentiment analysis dataset is Twitter data.
Twitter presents a relatively easy-to-use platform to extract stream tweets. A stream tweets dataset consists of more than 31,000 tweets and may include meta-information such as hashtags, retweets, users, and user-locations.
Working with stream tweets datasets hones your social media data mining and data classification skills.
7 Data analysis and visualization – Uber pickups dataset
In data visualization, data is represented in graphs, charts, maps, and other visualization formats to discover patterns, relationships, trends, or outliers in data sets. Interactive visualization employs tools like dashboards to present data in an interactive mode. This way, users can draw insight into decision making.
The Uber pickups dataset consist of 4.5 million Uber pickups around New York City from April 2014 to September 2014 and 14 million Uber pickups from January 2015 to June 2015.
As they learn to analyze customer rides and visualize this data, beginners also learn to draw insights and communicate their findings. Also, they will get exposure using programming languages like Python and R and tools like Python’s Plotly graphic library and R’s Shiny package for building interactive visualization apps.
8 Customer segmentation – Mall customers dataset
Businesses undertake customer segmentation to deliver personalized products and services to their customers. This can only be made possible by collecting the data of customers that purchase their products or visit their stores and segmenting this data to gain insight on customer behavior and preference.
The mall customer dataset consists of metadata like age, gender, customer ID, income, region, and others. An example of a project for a beginner can be using this dataset to segment customers using unsupervised machine learning.
There is no better way that a beginner can hone Big Data analytics skills and build his/her portfolio than by working on projects. We have given just a few Big Data project ideas for beginners. There are many more projects one can work on depending on the skills they want to gain experience in. It is important, at this point, to mention that participating in Kaggle competitions is a great way to not only enhance your skills but also gauge yourself against other like-minded professionals in various industries.