Beginner Data Analysis Projects for Business Professionals
Data is only becoming more critical to business strategy, so business leaders need to develop the skills to utilize advanced data analytics in their decision-making processes. However, few business leaders have the data skills they need to excel in a data-driven business environment. It takes practice to become better at data analytics — and the following projects can provide business leaders the practice they need to build their skill.
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Before anyone can experiment with analytics, they need data to work with. Web scraping is a way of extracting a large amount of data from a website, so an analyst can begin manipulating that data to gain valuable insights about performance and strategy. Often, web scraping involves collecting information like reviews and product descriptions from e-commerce sites. While it is possible to acquire this data through manual processes, that method will take hours of effort. In contrast, a beginner can employ simple Python codes to execute the task in minutes, or less.
Business leaders with minimal coding experience can find YouTube tutorials on web scraping using Python. To make this task slightly more difficult, leaders might consider developing related areas of expertise, like HTML and XPath. Executives who are uninterested in scraping their organization’s website might scrape other data-rich web environments, like a review website, a job site, a forum site or a website filled with financial data.
With data in hand, a business leader is equipped to begin the arduous process of manipulating data into the right format for analysis. In its raw form, data is essentially unusable; it is always riddled with errors and inconsistencies, and if these problems are not addressed early in analysis through proper wrangling, they will result in major inaccuracies that negatively impact business decision-making.
Data wrangling takes practice, which is why this project is perfect for beginners. Using the data acquired in the web scraping project, professionals can experiment with different methods of cleaning and normalizing data, which involves identifying redundancies, typos, shifting categories, conflicting units and more. Data scientists who become particularly good at this process might compete in data wrangling competitions to test their skill.
Exploratory Data Analysis
Exploratory data analysis — often abbreviated to EDA — is related to data wrangling in that it helps data scientists understand and refine the datasets they are working with, but it should be considered a standalone process that deserves distinct practice. EDA involves summarizing the primary characteristics of a dataset, like identifying major patterns or trends that can form the basis of a scientist’s initial insights. There are two types of EDA to explore:
Univariate analysis, which investigates individual variables at a time, and
Multivariate analysis, which looks into the relationships between two or more variables.
EDA projects will require tinkering with Python, so business professionals should choose a trustworthy Python library that does not automate the EDA process. After loading data into an appropriate Python tool, executives will need to review the data and summarize it before converting it into a visualization. Performing this project again and again will improve almost every other skill within the realm of data analysis.
Playing with visuals is one of the most creative aspects of data science, and it is often the step that most data science beginners look forward to. Visualization is converting data into graphs and charts, which should organize information in such a way as to help those less familiar with data science intuit valuable insights. Data visualization is equal parts art and science; professionals need to know what types of visualizations work for which types of data, but they also need to understand how to use color and other visual elements to make their graphs and charts appealing and interesting. Beginners should always opt for simplicity in their visualizations, as visual flair can obscure the ultimate meaning of the data. As experience grows, so will the ability to add more exciting visual elements without causing confusion.
Business leaders can and should hire teams of data scientists and take advantage of the cutting-edge data analysis tools available — but they should also understand the processes involved in working with business data. Then, they can set reasonable expectations for their data teams and glean more practical insights from their own data analysis.
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