Data Analysis – Essentials

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Meet your facilitator

Tomi Dayo-Isijola is a seasoned and experienced Technology, Operations & Business Support professional with over 17years working experience, with proven expertise and delivery in IT, Real Estate, Healthcare, Energy/Power-related service sectors in Nigeria. As a trainer he has successfully delivered various instructor-led training programs physically and virtually to a wide audience (700+) – including fresh graduates to senior/C-level executives for over a decade. He holds several certifications and credentials in IT/IM, project management, energy management, quality management, HSE, cloud solutions, consulting amongst others.

 

Course Outline

A practical training programme designed to develop data handling, analysis, visualisation and evidence-based decision-making skills.

1. Course overview

Course title: Data Analysis: Fundamentals, Tools and Practical Applications

Course description: This course equips participants with the knowledge and practical skills required to collect, organise, clean, analyse, interpret and present data for business decision-making. Participants will learn how to use spreadsheets, statistical techniques, visualisation tools and AI-assisted analytics to identify trends, measure performance, solve problems and communicate actionable insights.

Target audience

  • Beginners aspiring to become data analysts

  • Administrative and operations professionals

  • Business owners and entrepreneurs

  • Monitoring and evaluation officers

  • Finance, HR, sales and marketing professionals

  • Project managers and business analysts

  • Professionals seeking to improve reporting and decision-making

Recommended tools: Microsoft Excel, Power BI, Google Sheets and optional introductory SQL and Python.

Training methodology: Instructor-led sessions, demonstrations, guided exercises, real-world datasets, group assignments and a final data analysis project.

2. Course objectives

By the end of the training, participants should be able to:

  1. Explain the data analysis process and its business applications.

  2. Identify different types of data and suitable collection methods.

  3. Import, organise, clean and validate datasets.

  4. Apply spreadsheet formulas and functions to analyse data.

  5. Use descriptive statistics to summarise and interpret information.

  6. Create charts, graphs and interactive dashboards.

  7. Identify trends, patterns, anomalies and performance gaps.

  8. Develop reports and communicate evidence-based recommendations.

  9. Use business intelligence tools to support decision-making.

  10. Apply responsible data handling, privacy and data quality practices.

3. Detailed course modules

 

Module 1: Introduction to Data Analysis

  • Definition, purpose and importance of data analysis

  • Data, information, insights and intelligence

  • Types of data: structured and unstructured

  • Qualitative versus quantitative data

  • Descriptive, diagnostic, predictive and prescriptive analytics

  • Data analysis lifecycle

  • Business applications of data analytics

  • Roles and responsibilities of a data analyst

Practice & Review Session

 

Module 2: Data Collection and Data Sources

  • Primary and secondary data

  • Surveys, interviews and observations

  • Business databases, spreadsheets and system exports

  • Understanding variables, records and data fields

  • Sampling fundamentals

  • Data collection tools and forms

  • Data accuracy, completeness and reliability

  • Data privacy, consent and responsible data use

Practice & Review Session

Module 3: Data Management and Spreadsheet Fundamentals

  • Introduction to Microsoft Excel and Google Sheets

  • Data entry, formatting and organisation

  • Sorting and filtering datasets

  • Tables, ranges and named references

  • Basic formulas and functions

  • Relative and absolute cell references

  • Conditional formatting and data validation

  • Importing CSV and Excel files

Practice & Review Session

Module 4: Data Cleaning and Preparation

  • Identifying missing values and duplicate records

  • Correcting inconsistent formats and data types

  • Standardising names, dates and categories

  • Detecting errors and outliers

  • Text cleaning and data transformation

  • Combining and splitting columns

  • Data validation and quality checks

  • Introduction to Excel Power Query

Practice & Review Session

Module 5: Data Analysis and Statistical Fundamentals

  • Frequency distributions and percentage calculations

  • Mean, median and mode

  • Range, variance and standard deviation

  • Ratios, growth rates and percentage change

  • Cross-tabulation and comparison

  • Correlation and interpreting relationships

  • Trend and time-series analysis fundamentals

  • Introduction to sampling and statistical inference

  • Distinguishing correlation from causation

Practice & Review Session

 

Module 6: Data Visualisation and Reporting

  • Principles of effective data visualisation

  • Selecting appropriate charts and graphs

  • Bar, line, pie, scatter and distribution charts

  • PivotTables and PivotCharts

  • Highlighting trends, exceptions and comparisons

  • Designing clear management reports

  • Avoiding misleading visualisations

  • Writing findings and recommendations

Practice & Review Session

 

Module 7: Business Intelligence and Power BI

  • Introduction to business intelligence

  • Importing data into Power BI

  • Power Query transformations

  • Understanding tables and relationships

  • Introduction to data modelling

  • Creating measures and calculated columns

  • Introduction to DAX

  • Designing interactive dashboards

  • Filters, slicers and drill-down analysis

  • Publishing and sharing reports

Practice & Review Session

 

Module 8: Business Insights and Decision-Making

  • Translating business questions into analytical questions

  • Identifying key performance indicators (KPIs)

  • Root cause analysis and performance gaps

  • Segmenting customers, products or business units

  • Evaluating operational efficiency

  • Communicating findings to decision-makers

  • Developing actionable recommendations

  • Recognising analytical limitations and bias

Practice & Review Session

 

Module 9: AI-Assisted Data Analysis

  • Introduction to AI tools for analytics

  • Using AI to explain formulas and generate code

  • AI-assisted data exploration and summarisation

  • Generating analytical questions and hypotheses

  • Using AI to support chart selection and report writing

  • Verifying AI-generated findings and calculations

  • Confidentiality and responsible use of business data

Practice & Review Session

 

Module 10: Capstone Project and Assessment

  • Define a business problem

  • Prepare and clean the dataset

  • Conduct exploratory and descriptive analysis

  • Develop charts or a dashboard

  • Interpret results and identify insights

  • Present recommendations

  • Submit the final analytical report

Practice & Review Session

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