محتوى التدريب
Introduction to Data Analysis
- - What is data analysis?
- - Data analysis process (steps).
- - Different tools and techniques used in data analysis.
- - Introduction to Python programming basics: variables, data types, operators, control flow.
- - Hands-on practice with Python basics through exercises and mini-projects.
- - Introduction to Jupyter Notebooks and their usage for data analysis.
Data Wrangling with Python
- - Introduction to pandas library for data manipulation.
- - Reading and writing data from different sources (CSV, Excel, JSON).
- - Data cleaning techniques: handling missing values, outliers, data types.
- - Data transformation and feature engineering.
- - Advanced data manipulation with pandas: grouping, aggregating, merging datasets.
- - Hands-on practice with data wrangling tasks on real-world datasets.
Data Exploration and Visualization
- - Introduction to exploratory data analysis (EDA).
- - Descriptive statistics and data visualization basics.
- - Creating visualizations with matplotlib and seaborn libraries.
- - Visualizing different data types (numerical, categorical, time series).
- - Advanced data visualization techniques: customization, storytelling with visuals.
- - Hands-on practice with creating various data visualizations for different analysis goals.
Introduction to SQL
- - Understanding relational databases and SQL query language basics.
- - Writing basic SELECT queries to retrieve data from tables.
- - Using WHERE clauses for filtering data and JOINs for combining tables.
- - Introduction to data aggregation functions (COUNT, SUM, AVG).
- - Advanced SQL queries: using subqueries, GROUP BY, ORDER BY, and HAVING clauses.
- - Hands-on practice with querying sample databases and analyzing real-world data.
Data Analysis with Tableau
- - Introduction to Tableau interface and functionalities.
- - Connecting to different data sources (CSV, Excel, SQL databases).
- - Creating basic visualizations (bar charts, line charts, pie charts).
- - Working with filters, sorting, and calculations.
- - Creating interactive dashboards with multiple visualizations.
- - Sharing and exporting dashboards for communication.
- - Hands-on practice with building dashboards for various data analysis scenarios.
Project and Portfolio
- - Choosing a final project based on personal interests and learning goals.
- - Defining the project scope, data sources, and analysis goals.
- - Planning and outlining the project execution steps.
- - Working on the chosen project: data collection, cleaning, exploration, analysis, and visualization.
- - Building a portfolio showcasing the completed project and highlighting learned skills.