10 projects for data analysis to help you get your first job.
Tags: productivity, projectbenatar, webdev, analytics
Published: 2023-09-02 12:36:35

To land a Job in a very technical field such as Data analysis you need to have a solid portfolio, This portfolio will be your identity Card that you will introduce yourself to the world through it.

So Here are 10 entry-level projects you can test your limits & skills through these Ideas, where I will also mention the difficulty of the project & The data Source which you can download to start with it right away

**Important note:** if you are still wondering what to study & where to go in this field, I will publish another article series to guide you to follow step by step that will help you master the essential skills, but for now the main focus is the portfolio creating step.

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## 1. EDA for the Iris Flower dataset:
DIFFICULTY: EASY

Data source: The Iris Flower Dataset — https://archive.ics.uci.edu/ml/datasets/iris

Tools: Python (Pandas, Matplotlib, Seaborn)

To start with this data, you can import the dataset into a Jupyter Notebook using Pandas. Then, you can perform exploratory data analysis using Matplotlib and Seaborn to create visualizations such as scatterplots, histograms, and boxplots.

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## 2. Sales Analysis:

DIFFICULTY: MEDIUM

Data source: Superstore Sales Dataset — https://www.kaggle.com/jr2ngb/superstore-data

Tools: SQL (MySQL), Tableau

To start with this data, you can import the dataset into a MySQL database and use SQL queries to perform data analysis. You can then connect Tableau to the database to create visualizations such as bar charts, line charts, and heat maps.

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## 3. Customer Segmentation:

DIFFICULTY: Advanced

Data source: Online Retail Dataset — https://www.kaggle.com/vijayuv/onlineretail

Tools: Python (Pandas, Scikit-Learn), Tableau

To start with this data, you can import the dataset into a Jupyter Notebook using Pandas. Then, you can use Scikit-Learn to perform clustering algorithms such as K-Means to segment the customers. You can then connect Tableau to the data to create visualizations such as scatterplots and heat maps.

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## 4. Social Media Analytics

DIFFICULTY: Medium

Data source: Twitter API

Tools: Python (Tweepy, Pandas, Matplotlib)

To start with this data, you can use the Tweepy library in Python to connect to the Twitter API and retrieve tweets. You can then use Pandas to clean and manipulate the data. Finally, you can use Matplotlib to create visualizations such as bar charts and word clouds.

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## 5. Stock Market Analysis

Data source: Yahoo Finance API

Tools: Python (Pandas, Matplotlib, Plotly)

To start with this data, you can retrieve stock market data from the Yahoo Finance API. You can then import the data into a Jupyter Notebook using Pandas and perform data analysis using Matplotlib and Plotly to create visualizations such as candlestick charts and line charts.

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## 6. Sports Analytics

Data source: NBA API

Tools: Python (Pandas, Matplotlib, Plotly)

To start with this data, you can use the NBA API to retrieve player and team statistics. You can then import the data into a Jupyter noNotebooksing Pandas and perform data analysis using Matplotlib and Plotly to create visualizations such as scatterplots and heat maps.

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## 7. Healthcare Analytics

Data source: Healthcare Dataset — https://www.kaggle.com/center-for-pain-and-the-brain/cpb-healthcare-dataset

Tools: Python (Pandas, Matplotlib, Seaborn)

To start with this data, you can import the dataset into a Jupyter Notebook using Pandas. You can then perform data analysis using Matplotlib and Seaborn to create visualizations such as bar charts and scatterplots.

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## 8. Fraud Detection

Data source: Credit Card Fraud Detection Dataset — https://www.kaggle.com/mlg-ulb/creditcardfraud

Tools: Python (Pandas, Scikit-Learn)

To start with this data, you can import the dataset into a Jupyter notebook using Pandas. You can then use Scikit-Learn to perform classification algorithms such as Logistic Regression to detect fraud.

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## 9. Website Analytics

Data source: Google Analytics API

Tools: Python (Pandas, Plotly)

To start with this data, you can use the Google Analytics API to retrieve website analytics data. You can then import the data into a Jupyter notebook using Pandas and perform data analysis using Plotly to create visualizations such as line charts and heat maps.

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## 10. Weather Analysis

Data source: Weather Dataset — https://www.kaggle.com/selfishgene/historical-hourly-weather-data

Tools: Python (Pandas, Matplotlib)

To start with this data, you can import the dataset into a Jupyter Notebook using Pandas. You can then perform data analysis using Matplotlib to create visualizations such as line charts and scatterplots.

these are 10 ideas that you can pick of them to boost your portfolio and go on hunting for your first entry-level job in Data Analysis, keep in mind to use different tools as much as your local market will need you to use or whatever your target market will need from you to fit in with your portfolio to fasten finding your first job.