Programme objective
Frame a business question, choose an appropriate statistical method and turn findings into decisions.
Practical outcome
Clean and explore a dataset, analyse it with R and prepare charts with an explanatory final report.
Trainer and related programmes
Taught by Ramil Karimzade. Complementary programmes: Excel for business and Power BI.
Ramil Karimzade ↗
Data analyst, Business Intelligence specialist and corporate trainer
A data analyst with an academic background in statistics and business. Works on data analysis, business performance monitoring, management reporting and reporting automation across sectors. Specialises in Power BI, Excel, Power Query, DAX, data modelling and statistical analysis.
Work experience
2025–present | ENOR Group — Data Analyst, Strategy and Project Management. Analysis of sales, finance, credit portfolio and operations data; KPI and Power BI reporting; Balance Sheet and P&L automation; budgeting and forecasting; reporting improvements with Power Query and DAX.
Teaching plan and approach
Teaching follows data acquisition, cleaning, modelling, analysis and visualisation. Theory is reinforced through practical business exercises, datasets and an independent project. Participants learn both the tools and how to interpret results for decisions.
Training topics
Excel from beginner to advanced Power BI and interactive dashboards Power Query DAX and business metrics Data modelling Business and finance reporting automation KPIs and performance analysis Statistical analysis with R Presenting analytical findings
Learning objectives
Learn to draw evidence-based conclusions from business data using statistical methods, exploratory analysis and R.
Who this is for
Analysts, finance and operations specialists, data-driven managers and aspiring analysts.
Programme and topics
Business questions and data sources Data quality, cleaning and missing values Descriptive statistics and distributions Exploratory visualisation and outliers Sampling, confidence intervals and hypothesis testing Correlation and regression fundamentals Data processing and statistical analysis with R Interpretation, limitations and final project
Prerequisites
Basic Excel and mathematical concepts are recommended. Prior R knowledge is not required; entry level is checked before registration.
- Format
- Hybrid
- Duration
- Duration is agreed in the written proposal for the selected modules and group.
- Date / schedule
- Start date, teaching days and session times are agreed with the client before registration.
- Teaching language
- Azerbaijani; availability of English or Russian delivery is confirmed on enquiry.
- Training location
- Online access details or the in-person venue are provided before registration, according to the agreed format.
- Trainer
- Ramil Karimzade
- Participant limit
- Group size is agreed in the proposal to suit practical exercises and feedback requirements.
- Price
- Fixed package details are being prepared
- Tax note for the price
- Participation fees, payment stages and applicable tax terms are itemised in the written commercial proposal.