5 Easy Projects for Beginner Data Analysts

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Projects are solid ways to get hands-on experience as a beginner data analyst. Besides the knowledge, you can add completed projects to your data analyst portfolio to increase your chances of landing a job faster, especially with little or no corporate work experience.

Thus, this article will discuss five types of data analytics projects (with feasible ideas) you can work on as a budding data analyst. Interestingly, while you take on these projects, you can also get insights into key data analysis concepts that will help you do your job better. Let’s get right into it.

Projects for Beginner Data Analysts

Projects for beginner data analysts come in varying forms, but the end goal is the same — to improve your expertise. Here are my top picks:

1. Data Cleaning

Data cleaning (also called data scrubbing) is the process of removing incorrect, irrelevant, or duplicate data, identifying any loopholes in the data, and ensuring consistent data formatting.

Similarly, handling data cleaning projects involves extracting data (from the web, different files, or any other source) and compiling it into a usable format. However, when looking for data-cleaning projects as a beginner, consider starting with something easy to enable you to grasp the tricks in data cleaning gradually.

Here are some data-cleaning project ideas to get you started.

• Remove duplicate data: For this project idea, find a company’s dashboard for specific datasets. It could be product catalog or delivery information, etc. Then, identify and remove duplicates (any information repeated more than once) to avoid irregularities.
• Data correction: Part of data cleaning involves identifying errors. You can work on a data correction project to help you get better at data cleaning. Select diverse data sets with little or no curation from sites like data.gov and data.world. Then identify and correct spelling or value errors in each dataset.
• Reformatting data: Reformatting data entails changing data from one file type to another for easy processing. On that note, you can gather several files in different formats and convert them to a specific file type suitable for the required analysis.

2. Exploratory Data Analysis

Exploratory data analysis (EDA) involves analyzing and visualizing data to discover patterns, relationships, characteristics, and trends that may not be immediately apparent. Likewise, handling exploratory data analysis projects can help you understand and structure the data to enable you to generate hypotheses for further investigation.

Although exploring data is one of the most time-consuming tasks for a data analyst — never mind a beginner — it can also be the most rewarding. Fortunately, languages like R and Python are great fits for exploratory data analysis, as they have already-existing algorithms that simplify the process. Here are some project ideas for EDA:

• Analyze sales data: Given that businesses are leveraging the expertise of data analysts nowadays, it is advisable to know how to analyze sales data, as it is a core part of their operations. Luckily, practicing this project idea is a good way to start. Select a retail store, then research them to identify trends, customer behavior, and product popularity across specific demographics.
• Analyze surveys: Another way to practice data exploration is by analyzing surveys. For instance, you can search the web for health-related surveys. Collect necessary data and explore them to gain insights about specific health conditions, including causative agents, risk factors, and demographics of survey participants.
• Customer segmentation analysis: This project idea will prepare you specifically for the business world. The reason is it involves analyzing businesses’ customer data, segmenting them, and using visualization tools to explore the characteristics of each segment.

3. Sentiment-based Data Analysis

As the title suggests, sentiment-based projects focus on measuring the inclination of people’s opinions and choices through their words. For example, you can identify specific emotions like happiness, excitement, sadness, and anger by handling sentiment-based data analysis projects.

Moreover, sentiment-based projects will help you gain hands-on experience in machine learning techniques and natural language processing, which are in-demand data analytics skills. Let’s look at some project ideas you can try.

• Social media sentiment analysis: Select a topic, product, service, or policy. Then fetch data about them from social media platforms such as Twitter, Instagram, or Facebook to understand public dispositions toward it. You could use natural language processing techniques to analyze text data and then use visualization tools to explore patterns and trends in the data.
• Product review sentiment analysis: Collect reviews of a particular product from online marketplaces like Amazon or eBay and analyze the sentiment of the reviews. The analysis should reveal customers’ common issues with the product, which could inform product development or marketing strategies.
• Customer service sentiment analysis: This project involves analyzing customer service interactions — via emails, text messages, chatbots, or phone calls — to understand customer satisfaction levels. The analysis could reveal customers’ common issues with a product or service, necessitating customer service training or product development.
• Brand reputation sentiment analysis: In this project, you can collect mentions of a specific brand on social media, websites, and other online sources and analyze the sentiment to understand the brand’s reputation. The analysis could reveal areas where the brand is excelling or struggling. Ultimately, you may also generate insights that could contribute to better marketing and branding strategies.

4. Data Extraction

Extracting data is a crucial step in data analysis that involves collecting or retrieving structured or unstructured data from various sources, including databases, websites, social media platforms, and files. You can perform it using software tools and some of the best programming languages for data analysis — such as Python, SQL, or R.

Below are some examples of data extraction project ideas that you can work on as a beginner data analyst:

• Web scraping: Extracting data from websites can provide valuable information for businesses and researchers. For example, you can extract product information, customer reviews, and pricing data from e-commerce websites using Python libraries such as Scrapy.
• Social media monitoring: Social media platforms are a rich data source that you can use to understand customer sentiment, brand image, and marketing effectiveness. Thus, you can use social media monitoring tools like Hootsuite or Sprout Social to extract and analyze data — that can be used to facilitate business operations — from platforms like Twitter, Facebook, and Instagram.
• Customer feedback analysis: Many businesses collect customer feedback through surveys, online reviews, and customer service interactions. Likewise, you can use natural language processing (NLP) techniques to extract insights from unstructured data sources like customer comments or reviews. You can also analyze the insights extracted further to categorize feedback as positive, negative, or neutral.

5. Finance-based Projects

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Financial institutions typically have access to large amounts of data, such as transaction records, stock prices, and investment portfolios. Thus, as a beginner data analyst, you can leverage this to practice extracting and analyzing financial data to provide insights that can necessitate certain financial decisions.

Here are a few project ideas to try your hands at.

• Analyze loan feasibility: You can use predictive analytics to determine the approval odds of loans. To begin this project, research what factors insurance companies consider when reviewing loan applications and create several personas with varying credit scores and financial assets.

Afterward, develop an algorithm to help you assess essential factors and rank them to predict each persona’s approval probability.

• Analyze stock prices: There are tons of stock price data to help you complete this project. To get started, select a particular stock market index and use statistical methods to analyze trends and patterns peculiar to it. You can also use regression analysis — an advanced technique — to predict future stock prices.
• Expense analysis: Gather expense data for a particular organization (could be a non-profit, small, medium, or large-scale business). Afterward, categorize each expense by type (e.g., salaries, rent, and miscellaneous). Then analyze trends over time, identify areas where costs could be reduced, or compare expenses to industry standards.
• Financial statement analysis: Use financial statement data (such as income and cash flow statements and balance sheets) to analyze the financial health of a particular company or industry.

This can involve calculating financial ratios (such as profitability or liquidity ratios), comparing the company’s financial performance to competitors or industry benchmarks, and identifying areas where it could improve its financial performance.

Start Working on Exciting Projects to Build Your Portfolio

The purpose of handling data analytics projects is to show prospective employers you have the necessary skills and experience to thrive in your dream position. Hence, strive to build a portfolio of projects to demonstrate your proficiency across different project types.

Furthermore, look for the best places to host your portfolio as a data analyst and leverage them. This will increase your visibility, and industry-related network, amongst other benefits.

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