Certified Data Analysis Training Course

Course Overview

This Certified Data Analysis Training Course is designed to equip participants with practical knowledge and hands-on skills in data analysis, visualization, and storytelling. It covers the core concepts of data handling, statistical analysis, and transforming insights into compelling data-driven narratives. Participants will gain proficiency in using analytical tools and methods to support data-driven decision-making in a variety of industries.

Why Select This Training Course?

By the end of this course, participants will be able to:

• Understand and apply foundational concepts in data analysis and statistics
• Perform exploratory data analysis and interpret key statistical measures
• Create meaningful data visualizations and dashboards
• Communicate insights clearly through data storytelling techniques
• Use tools like Excel, Python, Power BI or Tableau to analyze and present data

Who Should Attend?

This course is ideal for:

• Business analysts, marketing analysts, and financial analysts
• Data professionals seeking to enhance analytical skills
• Recent graduates with a background in business, mathematics, or computer science
• Mid-level managers and team leaders aiming to improve data-driven decision-making
• Anyone interested in transitioning into a data analysis role

Course Syllabus

Module 1: Introduction to Data Analysis

• What is data analysis? Importance and application in modern industries
• Types of data: Structured vs. Unstructured
• Introduction to data analytics lifecycle
• Overview of key tools: Excel, Python, SQL, Tableau/Power BI

Module 2: Data Collection and Cleaning

• Data sources and data types (quantitative vs qualitative)
• Data collection methods and techniques
• Data wrangling: cleaning, transforming, and preparing datasets
• Handling missing data and outliers
• Introduction to Python Pandas / Excel for data cleaning

Module 3: Data, Statistics, and Statistical Analysis

• Descriptive statistics: mean, median, mode, range, variance, standard deviation
• Inferential statistics: hypothesis testing, confidence intervals, p-values
• Correlation vs. causation
• Regression analysis (linear & logistic)
• ANOVA and Chi-square tests
• Hands-on with Python (SciPy, statsmodels) / Excel / R

Module 4: Exploratory Data Analysis (EDA)

• Introduction to EDA: objectives and approach
• Identifying patterns, trends, and anomalies
• Visual EDA using histograms, boxplots, scatter plots, heatmaps
• Feature selection and dimensionality reduction (basic PCA)

Module 5: Data Visualization

• Principles of effective data visualization
• Choosing the right chart: bar charts, line graphs, pie charts, scatter plots, etc.
• Dashboard design and user experience
• Hands-on with Tableau / Power BI / Python (Matplotlib, Seaborn)

Module 6: Data Storytelling

• The art of telling stories with data
• Understanding audience and context
• Structuring the narrative: beginning, insights, call to action
• Combining visuals and narrative for maximum impact
• Case studies and real-world examples

Module 7: Capstone Project

• End-to-end data analysis project:
  • Define the problem
  • Collect and clean data
  • Perform analysis and visualizations
  • Develop a data story and present findings
• Peer review and feedback session
• Tools Used (Depending on Track Chosen)
  • Excel/Google Sheets – Basic data handling and statistics
  • Python – Pandas, Matplotlib, Seaborn, SciPy
  • Tableau / Power BI – Dashboards and storytelling
  • SQL (Optional) – Data querying