The TechEco Pathways project funded by Lux-Development, the Luxembourg Agency for Development Cooperation (LUXDEV) aims to contribute to sustainable economic growth in Kosovo by addressing critical skill gaps in the renewable energy and ICT sectors. 

GreenTech Pathways (part of the TechEco Pathways) addresses the shortage of skilled professionals in Kosovo’s renewable energy and ICT sectors. This program offers hands-on training sessions for 75 young graduates, followed by paid internships for 35 selected participants. The training curriculum integrates renewable energy technologies, digital tools, and data management systems to equip participants with industry-specific skills. Additionally, partnerships with private companies ensure students gain practical experience and build valuable networks for future employment.

This training is carried out with the support of the "Skills for sustainable jobs in Kosovo”, a project funded by the Grand Duchy of Luxembourg and implemented by the Ministry of Education, Science, Technology and Innovation, and LuxDev, the Luxembourg Development Cooperation Agency. 

• 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).
• Applicants must be citizens of the Republic of Kosovo
• Applicants must be 18 years or older
• Unemployed and not in education individuals are encouraged to apply

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%