Professional Traineeship in Data Analytics

A practical 4-month analytics program covering sql, kpis, dashboards, and a/b testing to prepare learners for real data analysis roles.

Dlytica Academy, Balkumari, Kathmandu

4 Months

Online

NRS

Total Seat

Batch Start Date

Jan.19,2026

Duration

4 Months

Training Description

The Master in Data Analytics program equips learners with strong analytical foundations through hands-on training in SQL, KPI design, dashboard creation, A/B testing, and real business case studies. The curriculum is structured to make participants industry-ready within a short period, supported by live projects, lifetime access to recordings, one-on-one internship assistance, mock interviews, and resume support. This program is ideal for anyone aiming to start or advance their career in analytics and business intelligence.

Training Highlight

Internship and Job placement

Mentorship and grooming

Readables & ResourcesProvided

Real time Project Engagement

Project Based Training

Feasible timing

Training Syllabus

Python for data (pandas, NumPy, basic scripting).
SQL fundamentals (joins, aggregations, filters) on banking tables.
Analytics basics and storytelling with simple reports.
Intro to machine learning and data engineering concepts.
Track selection: Data Analytics, Data Science (AI/ML), or Data Engineering.

Month 2: Core ML (regression, classification, evaluation) on banking risk and default datasets.
Month 3: Advanced ML (tree-based models, tuning) for fraud detection and risk scoring.
Month 4: Capstone ML project + intro to deep learning, NLP, and LLM-based banking assistant concepts.

Month 2: Advanced SQL, Excel/Sheets analytics, and BI dashboards using banking KPIs.
Month 3: Building interactive dashboards (Superset/Power BI/Tableau) and business reports.
Month 4: Final analytics project (e.g., churn & revenue analysis) plus presentation and internship-style tasks.

Month 2: Data modeling, ETL design, SQL optimization, and Git-based collaboration. Month 3: Data pipelines with Airflow/DBT/Spark and data quality checks. Month 4: Near real-time/streaming pipelines (Kafka concepts) and a production-style ETL capstone.