Dashboarding With Topics on Data Processing (Data Cleaning, Data Analysis)

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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.
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