Data Analytics Program
- Algė Masiulytė
- Danielius Tuchta
- Jurgita Tamolytė
Link to the Data Analytics Program for the Danske Bank employees: https://turingcollege.atlassian.net/wiki/x/JwBMNw
UZT learners can learn more about their requirements by checking the UZT infopack
Program Structure (learners starting in 2024-04 and beyond)*
Percentages represent an estimated time commitment required compared to the full program. Keep in mind that the real time spent can strongly differ based on the knowledge background of different learners.
Module 1: Introduction to core Data Analysis tools | ||
3% | Sprint 1 | Structured approach to data analysis |
4% | Sprint 2 | Analyst toolkit. Spreadsheets/Excel |
5% | Sprint 3 | Analyst toolkit. SQL and databases |
5% | Sprint 4 | Analyst toolkit. Advanced SQL and databases |
Module 2: Communicating Analysis Results | ||
6% | Sprint 1 | Visualisation tools (Tableau/Power BI) |
6% | Sprint 2 | Presentations & Soft skills |
Module 3: Main Analysis Types | ||
4% | Sprint 1 | Cohorts, Retention, Churn |
3% | Sprint 2 | Funnels |
4% | Sprint 3 | CLV (Customer Lifetime Value), Customer segmentation, RFM (recency, frequency & monetary value) |
5% | Sprint 4 | Statistical Inference & A/B Testing |
Module 4: Python for Data Analytics | ||
4% | Sprint 1 | First Steps Into Programming |
5% | Sprint 2 | Data Processing with Pandas |
5% | Sprint 3 | Data Visualization with Python |
5% | Sprint 4 | Machine Learning |
Specialization modules (optional) | ||
5% | Specialisation | Financial Analyst |
5% | Specialisation | Risk analyst |
6% | Specialisation | Product analyst |
6% | Specialisation | Marketing analyst |
6% | Specialisation | Payments, Monetisation Analyst |
7% | Available after completing one of the specialisation modules | Capstone project |
Previous Program Structure (learners starting from 2023-01 to 2024-04)*
Percentages represent an estimated time commitment required compared to the full program. Keep in mind that the real time spent can strongly differ based on the knowledge background of different learners.
Module 1: Introduction to core Data Analysis tools | ||
3% | Sprint 1 | Structured approach to data analysis |
5% | Sprint 2 | Analyst toolkit. Spreadsheets |
7% | Sprint 3 | Analyst toolkit. SQL and databases |
8% | Sprint 4 | Analyst toolkit. Advanced SQL and databases |
Module 2: Communicating Analysis Results | ||
8% | Sprint 1 | Visualisation tools (Looker Studio/Tableau/Power BI) |
5% | Sprint 2 | Presentations & Soft skills |
Module 3: Main Analysis Types | ||
5% | Sprint 1 | Cohorts, Retention, Churn |
5% | Sprint 2 | Funnels, AB tests |
5% | Sprint 3 | CLV (Customer Lifetime Value), Customer segmentation, RFM (recency, frequency & monetary value) |
7% | Sprint 4 | Linear & logistic regression |
Specialisation modules (optional) | ||
5% | Specialisation | Financial Analyst |
5% | Specialisation | Risk analyst |
8% | Specialisation | Data analysis and visualisation with Python |
5% | Specialisation | Product analyst |
5% | Specialisation | Marketing analyst |
5% | Specialisation | Payments, Monetisation Analyst |
9% | Available after completing one of the specialisation modules | Capstone project |
*Turing College reserves the right to update and (or) amend the program curriculum and its structure as well as release new program versions.
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