Welcome to the Data Analytics Projects repo! This repo is designed to provide a comprehensive analysis of each projects done using a combination of tools and techniques in the field of data analytics. The goal is to extract valuable insights, uncover patterns, and present findings through visualizations.
Projects Highlights
- sql_and_powerbi_analysis_on_sales_data_set
- r_language_analysis_biking_data_set
- python_analysis_moroccan_population_data_set
- Sql_nosql_analysis_amazon_Dataset
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Data Cleaning and Preprocessing: Initial steps involve cleaning and preprocessing the raw dataset to ensure data quality and consistency.
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Exploratory Data Analysis (EDA): Dive into the dataset using statistical and visual methods to understand its characteristics, identify trends, and uncover potential correlations.
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Feature Engineering: Enhance the dataset by creating new features or transforming existing ones, contributing to the overall quality of the analysis.
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SQL Queries: Leverage SQL queries to extract targeted information from the dataset, enabling a more in-depth analysis and facilitating integration with relational databases.
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Python Scripts: Utilize Python for data manipulation, analysis, and, if applicable, machine learning modeling. Scripts are included to automate and streamline various aspects of the analysis.
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Power BI Visualizations: Create interactive and insightful visualizations using Power BI, allowing for a dynamic exploration of the data and the communication of key findings.
- Data Cleaning and Preprocessing:
Identify and handle missing values, outliers, and inconsistencies in the dataset. Standardize data formats and units for consistency.
- Exploratory Data Analysis (EDA):
Generate descriptive statistics, including mean, median, and standard deviation. Create visualizations such as histograms, scatter plots, and heatmaps to understand data distribution and relationships.
- Feature Engineering:
Create new features based on domain knowledge or insights gained during EDA. Transform variables to better fit the assumptions of the analysis.
- SQL Queries for Data Extraction:
Use SQL queries to extract relevant subsets of data for specific analysis purposes. Join tables if necessary for a more comprehensive view of the data.
- Python Analysis and Modeling:
Apply statistical analysis techniques to identify patterns and trends. If applicable, develop machine learning models to make predictions or classifications. Power BI Visualizations:
- Import the cleaned and processed data into Power BI.
Create interactive dashboards and reports to present key findings visually. Getting Started To get started with the project, please refer to the provided documentation and follow the steps outlined in the Setup section. Ensure you have the necessary prerequisites installed and access to the dataset.
Contributions in progress