Duration 5 days – 35 hrs
Overview.
This training course is designed to equip participants with the skills to create insightful dashboards using open-source tools. The course emphasizes data processing techniques, including data cleaning and analysis, to transform raw data into actionable insights. Participants will gain hands-on experience in building dashboards that effectively communicate data stories and insights, fostering data-driven decision-making.
Objectives
- Understand the fundamentals of data processing, including cleaning and analysis, using open-source tools.
- Learn to work with large datasets and handle common data issues such as missing values and outliers.
- Develop skills to create dynamic and interactive dashboards.
- Gain proficiency in open-source tools like Python (Pandas, Matplotlib, Plotly).
- Develop skills to present cleaned data using Power BI for impactful visualization.
- Understand best practices for data visualization and storytelling to communicate insights effectively.
Audience
- Data analysts, business analysts, and professionals looking to upskill in data visualization and dashboarding.
- IT professionals and software developers transitioning to data roles.
- Students and enthusiasts interested in learning data processing and visualization using open-source tools.
Pre- requisites
- Basic understanding of data concepts and familiarity with datasets (spreadsheets or databases).
- Knowledge of any programming language is beneficial but not mandatory.
- A laptop with administrative rights for software installation.
Course Content
Day 1: Introduction to Data Processing
Overview of Data Processing
- Importance of data cleaning and analysis.
- Understanding data quality issues in real-world datasets.
- Introduction to open-source tools (Python) and Power BI.
- Overview of Python as a tool for data cleaning.
Data Cleaning Techniques
- Handling missing data.
- Dealing with duplicates and outliers.
- Formatting and restructuring data for analysis.
- Standardizing data (e.g., dates, text formats).
- Hands-on Activity: Cleaning sample datasets using Python (Pandas).
Day 2: Data Analysis and Data Visualization
Exploratory Data Analysis (EDA)
- Understanding data distributions.
- Generating summary statistics.
- Correlation analysis and trends.
- Visualizing patterns and insights with Python (Matplotlib, Seaborn).
- Hands-on: EDA using Python (Matplotlib, Seaborn)
Data Cleaning with Power Query in Power BI
- Introduction to Power Query: Overview and capabilities.
- Importing and transforming data with Power Query.
- Handling missing values and duplicates.
- Reshaping data: Pivoting and unpivoting.
- Merging and appending datasets.
- Hands-on: Cleaning and transforming datasets using Power Query.
Day 3: Data Visualization and Storytelling
Introduction to Power BI
- Overview of Power BI for data visualization.
- Connecting Power BI to cleaned datasets.
- Designing effective and accessible dashboards.
- Chart selection for different data types.
- Avoiding common visualization pitfalls.
- Hands-on: Building a basic dashboard in Power BI.
Interactive Dashboard Features
- Creating slicers and filters.
- Linking multiple visualizations for dynamic updates.
Data Storytelling
- Presenting insights effectively.
- Using narratives to complement visualizations.
- Aligning dashboard design with the target audience’s needs.
- Case studies: Examples of impactful dashboards and real-world applications.
- Capstone Project: Cleaning, analyzing, and visualizing data in Power BI.
