SQL Data Analytics Project | Data Analyst Portfolio

SQL Data Analytics Project | Data Analyst Portfolio Project

Introduction:
Data analytics is an essential aspect of any business today. It helps organizations make informed decisions based on data-driven insights. In this article, we will discuss a SQL data analytics project that can be included in a data analyst’s portfolio. The project involves using SQL, Python, and Google Sheets to analyze data and gain valuable insights. Additionally, we will explore the process of removing hashtags (#) and mentions (@) from the dataset.

1. Project Objective:
   The objective of this SQL data analytics project is to analyze a dataset using SQL queries and Python scripts. The dataset contains social media posts from a fictional company. By analyzing the data, we aim to gain insights into customer behavior, sentiment analysis, and popular trends.

2. Dataset Description:
   The dataset is in CSV format and contains columns such as post_id, username, post_content, date_created, and hashtags. Each row represents a social media post made by a user. The dataset spans a period of six months and includes thousands of posts.

3. Importing the Dataset:
   To begin the project, we need to import the dataset into a SQL database. We can use Python to read the CSV file and insert the data into a SQL table. This can be achieved using the pandas and sqlalchemy libraries.

4. Exploratory Data Analysis:
   Once the dataset is imported, we can perform exploratory data analysis (EDA) to understand the data better. EDA involves examining the data’s structure, identifying missing values, and checking for outliers. We can also create visualizations to gain insights into trends and patterns.

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5. SQL Queries:
   After performing EDA, we can start querying the dataset using SQL. SQL queries allow us to retrieve specific data based on conditions or criteria. For example, we can find the top five users with the most posts, identify the most popular hashtags, or calculate the average sentiment score for each user.

6. Sentiment Analysis:
   Sentiment analysis is a crucial aspect of social media analytics. It helps determine the overall sentiment (positive, negative, or neutral) of a post based on its content. We can use Python’s Natural Language Toolkit (NLTK) library to perform sentiment analysis on the dataset. By analyzing sentiment, we can understand customer opinions and trends.

7. Removing Hashtags and Mentions:
   In social media data, hashtags and mentions are commonly used to categorize posts and identify users. However, for certain analyses, it may be necessary to remove these hashtags and mentions. We can use Python’s regular expressions (regex) to remove hashtags and mentions from the dataset. This process will ensure that the data is clean and ready for further analysis.

8. Data Visualization:
   Data visualization is a powerful way to communicate findings and insights. We can use Python libraries such as Matplotlib and Seaborn to create visually appealing charts and graphs. Visualizations can help stakeholders understand complex data and make informed decisions.

   

9. Reporting and Presentation:
   Finally, we can present the project findings and insights in a professional report or presentation. This will showcase our data analytics skills and demonstrate the value of the project. We can use tools like Google Sheets to create visually appealing reports with interactive elements.

Conclusion:
The SQL data analytics project discussed in this article provides a great opportunity for data analysts to showcase their skills and expertise. By leveraging SQL, Python, and Google Sheets, it is possible to gain valuable insights from social media data. The project covers various aspects such as data import, EDA, SQL queries, sentiment analysis, and data visualization. By removing hashtags and mentions, the dataset becomes cleaner and more suitable for analysis. Ultimately, this project will contribute to a well-rounded data analyst portfolio.

SQL Data Analytics Project | Data Analyst Portfolio Project using SQL, Python, Google Sheets

Source

1. SQL data analysis
2. Python data analytics
3. Google Sheets integration