Launch your job-ready portfolio with these top-notch data analyst projects for freshers.
If you’re a fresher and planning to launch your career as a data analyst, you’ve probably faced a common challenge. Job postings often require some prior data analysis experience, leaving you in a challenging situation: How do you gain experience when it’s your very first data analyst role?
This is where the portfolio & certifications becomes your secret weapon. The projects you feature in your portfolio act as tangible proof of your skills to hiring managers and interviewers. Even if these projects aren’t from previous data analytics positions, they serve as concrete evidence of your data analysis capabilities, setting you apart from the competition. By populating your portfolio with suitable projects, you can significantly boost your chances of being seen as the ideal candidate for the job, even if you lack prior work experience.
This post covers best projects ideas with free certification which you can add in your resume highlighting your data analyst skills, especially for the freshers who’ve just started their career. You’ll also discover real world portfolio examples with a list of accessible public datasets that you can utilize to finish these projects.
Before mentioning the list of projects, let’s first discuss the required technical skills to become a data analyst.
- Programming Language – Python/ R
- SQL
- Alteryx
- Excel
- Data Visualization Tools- Tableau/Power BI
If you are confused on the programming language, then my recommendation would be to go with Python as its beginner friendly and versatile language. Python can be used in all 5 data analyst projects for freshers recommended in this post. You can access free “Data Analysis with Python” certification course for from Freecodecamp using this link.
Alright!
Now, lets quickly talk about the 5 interesting data analyst projects which will enhance your portfolio. We will also list the free dataset sources so that you can go ahead and work on the respective project instantly.
Customer Churn Analysis
About the project: This is one of the beginner-friendly data analyst projects for freshers. This project will help you in analyzing the churn rate to further understand reason behind the customers who are leaving a business or unsubscribing a plan. You need to dig dipper into dataset, analyze its key features, find factors that impact the churn rate most and then provide actionable recommendations it. You can include the steps like collecting customer data, clean it and conduct Exploratory Data Analysis (EDA) to uncover any hidden patterns or insights. This project will help your client in enhancing customer service based on the suggestions you provided.
You can add the creative charts and graphs that highlight churn rates based on customer demographics, and the impact of various factors on customer attrition. Optionally, you can also create a predictive model if you are want to showcase your Machine Learning knowledge. Make sure that to add storytelling in your peoject to effectively communicate a comprehensive presentation which showcase your ability to provide valuable information for business improvement.
Tools used in the project: You can start with Python to gather dataset, clean it & start with go ahead with further analysis using Pandas & Numpy. If you are comfortable with Matplotlib/ Seaborn libraries, then you can build visualizations within Python itself. Or else, once data looks good, you can extract the data in csv/xlsx format to further build your dashboards in data visualization tools like Tableau or Power BI.
Dataset links: You can find the links of Free Kaggle dataset & Tableau Public dashboard below
Github Repository with Project Details
FIFA 21 Analysis
About the project: If you are a FIFA fan, then this project would be a perfect choice for you. In this project, you need to analyze FIFA 21 video game data to identify patterns and provide valuable insights with respect to the factors like player attributes, team performance, and other aspects of the game.
Tools used in the project: You can use Python to perform exploratory data analysis using libraries like Pandas, NumPy, Matplotlib, Seaborn, and Plotly. You can choose to use any other tool based on your familiarity with that tool. You can also try to combine multiple tools to achieve comprehensive results. Try to avoid personal bias like favorite player or regional country in this data analysis project
You can find the links to Free dataset link below:
Real Estate Market Analysis
About the project: This project will help you in analyzing the real estate market data to gain insights with respect to potential buyers, sellers, or investors for the market.
Tools used in the project: You can start data analysis on this dataset by utilizing Python for data collection, cleansing, and initial analysis. You can also refer the Kaggle Projects created by other people to take inspiration and draw further insights. Optionally, you can also try to build a simple predictive model to estimate property prices based on features like location, size, and amenities using scikit-learn library.
Here you can find the link to Free Kaggle dataset: Real Estate Kaggle Dataset
Sentiment Analysis for Trading – Impactful data analyst projects for freshers
About the project: This is one of the most impactful data analyst projects for freshers. In this project, you need to investigate how sentiment data correlates with actual market movements, like stock prices or trading volumes. Further, you can analyze social media trends, news factors, and other textual data to gauge the sentiment of market participants and gain meaningful insights that can support trading decisions.
Tools used in the project: This project involves a range of tools to be used from APIs to advance ML libraries. Data collection involves web scraping tools like BeautifulSoup and Tweepy for accessing data sources, while data preprocessing leverages Python libraries such as NLTK and spaCy for text handling. Sentiment analysis is executed using tools like TextBlob and sentiment analysis models from the Hugging Face Transformers library. Data visualization is achieved with libraries like Matplotlib and Seaborn, whereas market impact analysis and sentiment indicators employ Python libraries such as NumPy and Pandas. Backtesting for trading strategies is facilitated by trading libraries like Backtrader or Zipline, and risk analysis is carried out using financial risk analysis libraries. Findings are summarized and presented through tools like Jupyter Notebook, PowerPoint, or Google Slides for effective communication and recommendation sharing.
Here is the link to Sentiment Analysis Kaggle dataset for Stock Markets: Sentiment Analysis dataset
Customer Personality Analysis
This is the last but certainly not the least recommended data analyst projects for freshers.
About the project: If you found the customer churn analysis project interesting, then here’s another one for you. In this project, you need to analyze the customer data to gain insights into several factors including customer personalities, behaviors, and general preferences, with the main focus on identifying the trends and recommended suggestions to serve them better.
Tools used in the project: As this dataset is available on Kaggle, you can use Python to perform data analysis. Additionally, you can use Tableau or Power BI to create dynamic and interactive dashboards. Optionally, here as well you can use machine learning libraries to perform advanced analyses, and provide actionable recommendations.
Here is the link to Kaggle dataset for this project: Customer Personality Analysis Kaggle dataset
If you are still confused on which dataset to pickup first, then my recommendation would be to go with something which interests you. You will find free datasets available online from multiple great resources like Kaggle. You can also choose to go with something you are doing on a daily basis. Many people analyze their daily YouTube or Instagram watch time to improve productivity.
Whatever project you decided to work upon, always remember to take actions as early as possible.
If you are struggling to get a job opportunity then you can refer this post to find Remote Data Analyst Job opportunities.
Hope it helps 🙂
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