Data Analytics

Module 1: Data Foundations (Understanding Your Data)

1. Define data, its various types (structured, unstructured, semi-structured), and their importance
2. Explore the Data Lifecycle (acquisition, storage, processing, analysis, visualization)
3. Discuss data quality concepts (accuracy, completeness, consistency) and data cleaning techniques
4. Introduce essential data analysis tools and technologies (Excel, Power Query, SQL, Python, etc.)

Activities:

1. Interactive exercises on data classification and lifecycle stages
2. Hands-on labs practicing data cleaning methods in a common tool (Excel/Python)
3. Group discussions on data quality challenges and best practices

Module 2: Data Sources: Where Does Your Information Come From?

1. Define data, its various types (structured, unstructured, semi-structured), and their importance
2. Explore the Data Lifecycle (acquisition, storage, processing, analysis, visualization)
3. Discuss data quality concepts (accuracy, completeness, consistency) and data cleaning techniques
4. Introduce essential data analysis tools and technologies (Excel, Power Query, SQL, Python, etc.)

Activities:

1. Interactive exercises on data classification and lifecycle stages
2. Hands-on labs practicing data cleaning methods in a common tool (Excel/Python)
3. Group discussions on data quality challenges and best practices

Module 3: Data Exploration & Analysis (Uncovering Insights)

1. Introduce Exploratory Data Analysis (EDA) techniques for understanding data characteristics (descriptive statistics, visualizations)
2. Cover data wrangling methods for preparing data for analysis (data transformation, feature engineering)
3. Discuss common data analysis techniques (hypothesis testing, correlation analysis)

Activities:

1. Interactive workshops on calculating descriptive statistics using a chosen tool (Excel/Power Query/Python)
2. Hands-on labs practicing data wrangling techniques and data analysis methods
3. Group projects conducting basic EDA and analysis on a provided dataset

Module 4: Data Visualization (Communicating Your Findings – Part 1)

1. Explain the importance of data visualization in communicating insights effectively
2. Introduce visual perception principles and best practices for creating clear and compelling visualizations
3. Cover common data visualization techniques (bar charts, histograms, scatter plots, line charts) and their use cases

Activities:

1. Interactive exercises on applying visual perception principles to data visualizations
2. Hands-on workshops on creating various data visualizations using a chosen tool (Excel/Tableau/Power BI)
3. Group discussions on selecting the right visualizations for different data types and analysis goals

Module 5: Data Visualization (Communicating Your Findings – Part 2)

1. Introduce advanced data visualization techniques (heatmaps, box plots, pie charts, network graphs) and their applications using Power BI
2. Discuss interactive dashboards and storytelling techniques for presenting data insights
3. Explore data visualization best practices for accessibility and ethical considerations

Activities:

1. Hands-on labs on creating advanced data visualizations using a chosen tool
2. Group projects on designing interactive dashboards to communicate data-driven stories
3. Case studies analyzing effective and ineffective data visualizations from real-world examples

Module 6: Data Storytelling (The Power of Narrative)

1. Explain the art of data storytelling: crafting a narrative using data to engage the audience and influence decisions
2. Discuss the key elements of a compelling data story (context, evidence, insights, recommendations)
3. Cover effective communication techniques for presenting data insights clearly and concisely

Activities:

1. Interactive exercises on identifying the elements of a strong data story
2. Group projects on developing data stories from provided datasets
3. Peer-review sessions on refining data storytelling techniques

Module 7: Data for Decision Making (The Power of Insights)

1. Explain how data analysis helps organizations make informed and data-driven decisions
2. Discuss real-world examples of data impacting business outcomes (marketing campaigns, product development, customer service)
3. Introduce key performance indicators (KPIs) and their role in measuring data-driven success
4. Explore potential challenges and biases in data analysis and how to mitigate them

Activities:

1. Case studies analyzing how companies have used data to achieve business goals
2. Group projects on identifying potential data-driven solutions to a business challenge
3. Interactive exercises on identifying potential biases in data and decision-making

Module 8: Harnessing Automation for Business Success

1. Explain the benefits of automation in data analysis tasks (data collection, cleaning, reporting)
2. Discuss different data automation tools and technologies (e.g., ETL/ELT tools, Python scripts)
3. Explore best practices for implementing data automation solutions within organizations
4. Identify potential challenges and limitations of data automation

Activities:

1. Case studies analyzing how companies have used automation to improve data analysis efficiency
2. Hands-on tutorials on basic data automation techniques using a chosen tool
3. Group discussions on identifying tasks for automation within a specific business scenario

Module 9: Understanding the Customer Data Journey & Decision Methodology

1. Explain the concept of the customer data journey and its touchpoints
2. Discuss various methods for collecting customer data at different stages of the journey
3. Analyze customer decision-making processes and how data can be used to influence them
4. Explore strategies for personalization and targeted marketing based on customer data insights

Activities:

1. Interactive exercises on mapping the customer data journey for a specific product or service
2. Group projects on developing customer personas based on data analysis
3. Case studies analyzing how companies have used data to personalize customer experiences

Module 10: Building a Robust Data Culture

1. Define a data-driven culture and its core characteristics (data literacy, collaboration, open communication)
2. Discuss the importance of fostering a culture of data-driven decision-making across the organization
3. Explore strategies for promoting data literacy and encouraging data exploration among employees
4. Identify potential challenges in building a data culture and how to overcome them

Activities:

1. Interactive exercises on identifying key principles of a data-driven culture
2. Group discussions on developing strategies for promoting data literacy within an organization
3. Role-playing exercises on communicating data insights to teams with varying levels of data expertise