Data training and consulting
Data analytics and visualization
From collecting, preparing, and exploring data with SQL, Python, and R through to designing Tableau and Power BI dashboards. A course that gets working professionals to real insight and real decisions.
What are data analytics and visualization?
This course covers data collection, preparation, and exploratory data analysis (EDA) with SQL, Python, and R, through to designing dashboards in Tableau and Power BI, so that working professionals can draw insight from data and use it in decisions.
Built around real work
SQL, Python, and visualization tools are applied directly to your own business data, so you can use what you learn the following week.
End-to-end pipeline
Collection, storage, analysis, visualization, and insight are taught as one continuous flow rather than separate topics.
A finished portfolio
The final project produces a complete analysis on a real dataset that you can use when applying for a new role.
What you'll learn
The core material this course covers.
- SQL fundamentals for extracting, aggregating, and analyzing data
- Preparation and EDA in Python (pandas, NumPy)
- Visualization with Matplotlib, Seaborn, and Plotly
- Designing and publishing dashboards in Tableau and Power BI
- Machine learning basics (classification, regression, clustering)
- Building a data pipeline (collect, store, analyze, visualize)
Curriculum
What we cover
- 1
Stage 1: Collection and storage
Cover SQL and NoSQL basics, setting up a Python environment, web scraping, and data modeling, so you can obtain and structure the data you need.
- 2
Stage 2: Extraction and visualization
Prepare data and run EDA with pandas and NumPy, then turn it into clear charts and dashboards with Matplotlib, Seaborn, Tableau, and Plotly.
- 3
Stage 3: Statistics and machine learning
Cover inferential statistics and hypothesis testing, then build classification and regression models and learn how to evaluate and choose between them.
- 4
Stage 4: Advanced analysis
Extend your range with text mining, deep learning (CNN, RNN), and advanced predictive modeling.
- 5
Stage 5: Final project
Complete a full pipeline from problem definition through data collection, analysis, and visualization to insight, and submit it as your portfolio.
How teams put this to use
Public agriculture and food data analysis project
After collection and preparation, consumption trends were visualized in a Tableau dashboard and used as supporting material for policy proposals.
Power BI rollout for a corporate marketing team
A marketing KPI dashboard automated campaign performance reporting and sped up decisions.
Job-search portfolio from a data analytics bootcamp
Participants completed analysis and ML prediction models on finance, healthcare, and retail data and submitted them as job application portfolios.
Expected outcomes
What changes once your team puts this to work on site.
- Analyzing and visualizing your own data cuts reporting time and speeds up decisions.
- Bringing analysis in-house reduces spending on outside analytics work.
- You build the foundation for the Business Information Visualization certification.
- The whole team starts talking to each other in terms of data.
Data analytics and visualization — FAQ
Yes. The course starts from Python fundamentals and builds up step by step for people with no programming experience. If you have used Excel at work, that is enough to begin.
Both are covered in the hands-on sessions. Public institutions tend to lean toward Power BI and companies toward Tableau, so we adjust which one gets the deeper treatment to match where the participants work.
You choose a dataset in a domain you care about, or use one we provide, and run the project from planning through to presenting your results. Instructor feedback sessions help you bring the portfolio up to standard.
We'll design the training around your operation
Schedule, group size, and curriculum detail all get set with you.