• Basic understanding of Excel's interface;
• Familiarity with Excel's basic functions (SUM, AVERAGE, etc.);
• Ability to navigate workbooks and organize data (optional but helpful);
• Understanding of basic mathematical operations;
• Knowledge of high school-level statistics (probability, distributions).

Objective: Establish a strong foundation in Excel for data management.

• Day 1: Excel Introduction and Key Functions 
  • Interface overview: Ribbon, Quick Access Toolbar, workbook navigation.
  • Functions: SUM, AVERAGE, MIN, MAX, COUNT.
  • Shortcuts for efficiency.
• Day 2: Formatting and Organizing Data 
  • Number and text formatting.
  • Sorting, filtering, creating tables.
• Day 3: Basic Data Validation 
  • Drop-down lists, error messages, and basic rules.
  • Hands-on: Validate and organize a dataset.

Objective: Learn to clean and prepare data for analysis in Excel.

• Day 1: Advanced Cleaning Techniques 
  • Text functions: TRIM, CONCATENATE, LEFT, RIGHT.
  • Flash Fill and Find & Replace.
• Day 2: Logical Functions 
  • IF, AND, OR, IFS, and nested formulas.
• Day 3: Lookup Functions 
  • VLOOKUP, HLOOKUP, INDEX, MATCH.
  • Hands-on: Solve real-world lookup problems.

Objective: Explore data analysis tools and visualization techniques in Excel.

• Day 1: PivotTables and PivotCharts (2 hours)
  • Create, customize, refresh PivotTables.
  • Add slicers and timelines.
• Day 2: Conditional Formatting (2 hours)
  • Highlight trends, outliers, thresholds.
• Day 3: Chart Creation (2 hours)
  • Bar, line, scatter, and combo charts.
  • Hands-on: Visualize trends in a dataset.

Objective: Understand foundational statistical concepts and apply them in data analysis.

• Day 1: Introduction to Descriptive Statistics 
  • Mean, median, mode, variance, standard deviation.
  • Calculate statistics in Excel.
• Day 2: Probability Basics 
  • Probability rules, distributions (normal, binomial).
  • Basic calculations using Excel.
• Day 3: Statistical Judgments 
  • Statistical significance, margins of error, confidence intervals.
  • Real-world case studies.

Objective: Transition to SPSS for statistical analysis.

• Day 1: SPSS Interface and Basics 
  • Overview of SPSS: Data View and Variable View.
  • Importing Excel/CSV data into SPSS.
• Day 2: Variable Management in SPSS 
  • Defining variable types, labels, handling missing values.
• Day 3: Descriptive Statistics in SPSS 
  • Frequencies, mean, median, and mode.
  • Hands-on: Analyze a small dataset

Objective: Introduce Python for data cleaning and analysis.

• Day 1: Python Basics (2 hours)
  • Setting up Python.
  • Introduction to pandas: Reading Excel/CSV files.
• Day 2: Python Data Cleaning (2 hours)
  • Handling missing values, duplicates, outliers.
• Day 3: Data Transformation in Python (2 hours)
  • Creating new columns, filtering, sorting data.
  • Hands-on: Clean a messy dataset using pandas

Objective: Build statistical skills and enhance Python proficiency.

• Day 1: Inferential Statistics 
  • Hypothesis testing: T-tests, ANOVA, Chi-Square tests.
• Day 2: Python Data Analysis 
  • Descriptive statistics using pandas.
  • Data visualization with matplotlib and seaborn.
• Day 3: Regression and Correlation 
  • Pearson correlation and multiple regression analysis in SPSS and Python.

Objective: Transition from Excel to Power BI for advanced data visualization.

• Day 1: Power BI Basics 
  • Interface overview, connecting to data sources.
  • Importing Excel data into Power BI.
• Day 2: Basic Visualizations 
  • Create tables, bar charts, slicers.
• Day 3: Power BI Dashboards 
  • Combine visuals into interactive dashboards.
  • Hands-on: Design a simple dashboard for a business dataset.

Objective: Master Power BI dashboards and statistical reporting.

• Day 1: Advanced Power BI 
  • Use DAX for calculated fields and measures.
  • Create relationships between tables.
• Day 2: Statistical Reporting 
  • Present statistical tests in SPSS.
  • Design professional reports.
• Day 3: Power BI Dashboard Design 
  • Build dynamic dashboards combining multiple data sources.

Objective: Apply skills from Excel, SPSS, Python, Power BI, and Mathematics to solve a real-world problem.

• Day 1: Project Setup (2 hours)
  • Define the problem and expected outcomes.
  • Prepare and clean data using Python and Excel.
• Day 2: Analysis and Visualization (2 hours)
  • Perform statistical tests in SPSS.
  • Create dashboards in Power BI.
• Day 3: Presentation and Feedback (2 hours)
  • Present findings and receive feedback.
  • Review key takeaways from the training.

1. Excel Expertise:
   • Mastery of advanced Excel features, functions, and dashboards.
   • Ready for Excel Expert Certification.
2. SPSS Proficiency:
   • Ability to perform complex statistical tests and create professional reports.
3. Python Basics:
   • Foundational skills in data cleaning, transformation, and visualization.
4. Power BI Skills:
   • Proficiency in building dynamic dashboards and analyzing business data.
5. Mathematics Statistics:
   • Strong understanding of descriptive and inferential statistics.
   • Ability to apply statistical judgments in decision-making.
6. Capstone Project:
   • A real-world project demonstrating integration of Excel, SPSS, Python, Power BI, and Mathematics Statistics.

Minimum criteria:

1. Course Attendance - 80%

2.Final Project / Exam - 20%