Technologies / Artificial Intelligence

Practical Application of AI in Business Data Analysis Using Python and Power BI

A three-day course — AI for business analysts: data preparation (Pandas, NumPy), building ML models (Scikit-learn), integration with Power BI, built-in AI features, interpreting results, case studies.

AI is not just for data scientists — a business analyst with Python and Power BI can build predictive models

You don’t need a PhD in statistics to use AI in business. Scikit-learn lets you build a sales prediction model in 20 lines of code. Power BI has built-in AI features (Key Influencers, Decomposition Tree, anomaly detection) that work without programming. Combining Python (data preparation, model building) with Power BI (visualization, dashboards) gives a business analyst tools that previously required a data science team.

A three-day workshop: from data preparation (Pandas, NumPy, EDA) and building ML models (Scikit-learn — regression, classification, clustering), through Python → Power BI integration (Python visuals, predictive dashboards) and built-in AI features (Key Influencers, Q&A, anomaly detection), to case studies (marketing, forecasting, financial risk).

Who is this training for?

For business analysts and BI specialists. Especially when:

You build reports in Power BI and want to add predictions — sales forecasts, customer segmentation, anomaly detection
You know Python at a basic level and want to learn to build ML models for business applications
Your boss asks about “AI in reporting” and you need concrete skills, not buzzwords

Three days: from data to a predictive dashboard

Day 1: Data and ML — AI/ML concepts (supervised, unsupervised), data preparation (Pandas, NumPy), handling missing data and outliers, exploratory data analysis (EDA), feature engineering.

Day 2: Models and Power BI — Scikit-learn (pipeline, cross-validation), regression (linear, logistic), decision trees and random forest, clustering (K-Means), Power BI integration (Python visuals, dashboards).

Day 3: AI in Power BI and case studies — built-in AI features (Q&A, Key Influencers, Decomposition Tree, anomaly detection), interpreting results, case studies (marketing, forecasting, financial risk, process optimization).

With over 2,500 trainings in our portfolio, EITT is a trusted partner in competency development. Contact us — we organize closed AI trainings for analysts, dedicated to BI and analytics teams.

Benefits

Prepare data for ML analysis — cleaning, transformation, feature engineering (Pandas, NumPy)
Build and evaluate ML models — regression, classification, clustering (Scikit-learn)
Integrate AI analysis results with Power BI — Python visuals, interactive dashboards
Use the built-in AI features in Power BI — Key Influencers, Decomposition Tree, Q&A
Interpret model results and communicate AI insights to different audiences
Apply AI to specific business cases — churn, forecasting, segmentation

Who is this training for?

Business analysts who want to use AI in their daily work with data

BI specialists building predictive dashboards in Power BI

Intermediate-level data analysts extending their skill set with ML

Strategic planning specialists looking for predictive tools

People from marketing, sales and finance departments who automate data analysis

Python programmers moving into business analytics and machine learning

Prerequisites

Basic knowledge of Python — variables, loops, functions, importing libraries
Experience working with data and analytics tools
Basic knowledge of Power BI — data import, visualizations, reports
Basic statistical concepts — mean, standard deviation, correlation

Training program

Key AI/ML concepts — supervised vs unsupervised, regression vs classification, overfitting
ML applications in business — an overview of use cases (churn, demand forecasting, segmentation)
Data preparation — Pandas (cleaning, transformation), NumPy (numerical operations)
Handling missing data and outliers — imputation techniques, IQR, z-score
Exploratory data analysis (EDA) — visualizing distributions, correlations, feature engineering

Scikit-learn — pipeline, train/test split, cross-validation
Regression — linear, logistic, metrics (R², accuracy, precision, recall)
Decision trees and random forest — interpretation, feature importance
Clustering — K-Means, customer segmentation
Importing ML results into Power BI — Python visuals, export to CSV/JSON
Creating interactive visualizations and predictive dashboards

Built-in AI features in Power BI — Q&A (NLP), Key Influencers, Decomposition Tree
AI Insights — anomaly detection, sentiment analysis, predictive analytics
Interpreting model results — how to communicate AI insights to business decision-makers
Case study: marketing data analysis — segmentation, CLV, attribution
Case study: sales prediction and demand forecasting
Case study: financial risk analysis and process optimization

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

Do I need to know Python before the training?

Yes — at a basic level (variables, loops, functions, imports). You do not need to know Pandas or Scikit-learn — that is what we teach. If you have never programmed in Python, start with an introductory course.

Do I need to know Power BI?

Yes — at a basic level (importing data, creating visualizations). The training teaches Python → Power BI integration and the built-in AI features, not the Power BI basics.

Does this training teach deep learning?

No — we focus on classic ML (Scikit-learn): regression, classification, clustering. These are models that a business analyst can build, interpret and deploy on their own. Deep learning (TensorFlow, PyTorch) is a separate topic for data scientists.

What is the format and duration of the training?

The training lasts 3 days (9:00 AM–4:00 PM) and is available online and on-site. Workshops with hands-on exercises on real business data.

Will I receive a certificate after the training?

Yes, every participant receives a personal certificate of completion issued by EITT.