Welcome to the Business Analytics Using Python repository! This repository contains a collection of projects and resources aimed at providing comprehensive insights into various aspects of business analytics using Python. Whether you're a beginner or an experienced data scientist, you'll find valuable resources to enhance your understanding and skills.
- Big Data Analysis Using Hive
- Data Cleaning and Preprocessing
- Data Importing and Exporting
- Data Integration and Reshaping
- Data Quality Assurance
- Dealing with Time Series Data
- EDA and Visualization Using Tableau
- Forecasting Using ARIMA
- Regression Analysis on Real-Time Data
- Sentiment Analysis
This project demonstrates the use of Apache Hive for big data analysis. Hive is a data warehouse software that facilitates reading, writing, and managing large datasets residing in distributed storage using SQL.
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Big_data_analysis_using_Hive - Contents: Scripts, sample datasets, and queries used for analysis
Data cleaning and preprocessing are crucial steps in ensuring the quality and accuracy of your analysis. This project covers various techniques and methods for cleaning and preprocessing data using Python.
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Data_Cleaning_and_Preprocessing - Contents: Jupyter notebooks, scripts, and datasets showcasing different data cleaning techniques
Learn how to efficiently import and export data in various formats using Python. This project includes examples of working with CSV, Excel, SQL databases, and more.
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Data_Importing_and_Exporting - Contents: Scripts and notebooks demonstrating data import and export processes
Data integration and reshaping involve combining data from different sources and transforming it into a suitable format for analysis. This project covers methods for merging, joining, and reshaping datasets.
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Data_Integration_and_Reshaping - Contents: Example scripts and notebooks for integrating and reshaping data
Ensuring data quality is essential for reliable analysis. This project provides techniques and best practices for maintaining data quality, including validation, error detection, and correction methods.
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Data_Quality_Assurance - Contents: Scripts and notebooks focused on data quality checks and assurance
Time series data analysis involves analyzing data points collected or recorded at specific time intervals. This project covers various techniques for handling and analyzing time series data.
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Dealing_with_Time_Series_Data - Contents: Notebooks and scripts for time series analysis, including decomposition, smoothing, and forecasting
Exploratory Data Analysis (EDA) and visualization are key steps in understanding your data. This project uses Tableau to create interactive and informative visualizations for EDA.
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EDA_and_visualization_using_Tableau - Contents: Tableau workbooks and datasets used for creating visualizations
Autoregressive Integrated Moving Average (ARIMA) is a popular statistical method for time series forecasting. This project demonstrates how to use ARIMA for forecasting future data points.
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Forecasting_using_ARIMA - Contents: Notebooks and scripts for ARIMA modeling and forecasting
Regression analysis is used to understand relationships between variables. This project involves performing regression analysis on real-time data to make predictions and infer trends.
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Regression_Analysis_on_Real-time_data - Contents: Scripts and notebooks for performing regression analysis on real-time datasets
Sentiment analysis involves extracting and quantifying sentiments from text data. This project demonstrates various techniques for sentiment analysis using Python.
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Sentiment_Analysis - Contents: Notebooks and scripts for performing sentiment analysis on text data
This repository is licensed under the MIT License. See the LICENSE file for more details.
If you have any questions or feedback, please reach out to us at desicoder14@gmail.com(mailto:desicoder14@gmail.com).
Happy Analyzing!