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Data Analysis and Visualization
YOUR PATHWAY TO SUCCESS
This 5-day course provides a practical introduction to the essential skills of data analysis and visualization. Participants will learn how to collect, clean, process, and analyze data using popular tools and techniques.
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Course Duration
5 Days
Course Details
This 5-day course provides a practical introduction to the essential skills of data analysis and visualization. Participants will learn how to collect, clean, process, and analyze data using popular tools and techniques. The course covers core concepts such as data manipulation, descriptive statistics, data visualization, and exploratory data analysis. Through real-world case studies and hands-on exercises using tools like Python with Pandas and Matplotlib/Seaborn, or R, learners will develop the ability to extract meaningful insights from data and communicate findings effectively through compelling visualizations.
This course emphasizes a hands-on approach to data analysis. Participants will work with real datasets, learning how to handle missing values, clean messy data, and transform data into a usable format. The course covers a range of visualization techniques, from basic charts to more advanced plots, enabling participants to create visualizations that effectively communicate data insights. Exploratory data analysis (EDA) techniques are a key focus, allowing participants to discover patterns, trends, and anomalies in data. By the end of this course, participants will be able to perform a complete data analysis workflow, from data collection to visualization and reporting.
By the end of this course, learners will be able to:
- Collect and clean data.
- Perform data manipulation and transformation.
- Calculate descriptive statistics.
- Create effective data visualizations.
- Conduct exploratory data analysis.
- Communicate data insights effectively.
- Data scientists, analysts, and business intelligence professionals.
- Individuals who want to gain practical data analysis skills.
- Anyone who needs to work with data and communicate findings.
Course Outline
5 days Course
- Introduction to Data Analysis and Data Collection:
- The data analysis process.
- Data sources and data collection methods.
- Introduction to data analysis tools (Python with Pandas, R).
- Practical exercise: Collecting data from different sources.
- Data Cleaning and Preprocessing:
- Handling missing values.
- Data transformation and normalization.
- Data cleaning techniques.
- Practical exercise: Cleaning and preparing a real-world dataset.
- Descriptive Statistics and Data Visualization:
- Calculating descriptive statistics: Mean, median, mode, variance, standard deviation.
- Creating data visualizations: Histograms, bar charts, scatter plots, box plots.
- Practical exercise: Exploring data using descriptive statistics and visualizations.
- Descriptive Statistics and Data Visualization:
- Exploratory Data Analysis (EDA):
- Identifying patterns and trends in data.
- Discovering anomalies and outliers.
- Data storytelling and communicating insights.
- Practical exercise: Conducting an EDA on a real-world dataset.
- Exploratory Data Analysis (EDA):
- Data Analysis Project and Reporting:
- Completing a data analysis project.
- Creating a data analysis report.
- Communicating findings effectively.
- Practical exercise: Working on a data analysis project and presenting the results.
- Data Analysis Project and Reporting:
