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Data analysis with Python and Java
Your data gains value when you can explore it and explain what it reveals. Use programming to structure processing tasks and make your analyses reproducible. Strengthen your ability to turn a business question into verifiable, understandable results.
- Duration
- 2 days 14 hours
- Code
- DATA008FR Code
Presentation
While Python is often regarded as the leading language for data science, Java remains a cornerstone of robust software architectures. This 2-day course offers an opportunity to explore and compare both ecosystems for data analysis. It is ideal for developers and analysts who want to choose the right tool for their project context.
The program follows an effective side-by-side approach. The first day focuses on Python's flexibility and its leading libraries, including Pandas and Matplotlib, for rapid exploration. The second day turns to the rigour of Java, exploring file processing and statistical calculations using streams and libraries such as OpenCSV or JFreeChart.
The emphasis is on practice and critical comparison. By addressing similar use cases in both languages, including file cleaning and indicator calculations, you will learn to identify the strengths of each approach: rapid prototyping in one, and performance and structure in the other.
Objectives
By the end of this course, you will be able to:
- understand the data analysis paradigms specific to Python and Java;
- work with complex data structures, including DataFrames, Collections and Streams;
- import, clean and transform heterogeneous datasets, including CSV, Excel and logs;
- create visualisations and interpret statistical results;
- automate complete data processing pipelines in both languages.
Program
Day 1: harnessing Python for exploratory data analysis
- Python's data ecosystem: getting started with NumPy and an introduction to DataFrames with Pandas.
- The data lifecycle: importing, cleaning (handling missing values) and transforming data (sorting and merging).
- Visualisation and statistics: creating charts, including line charts and histograms, with Matplotlib and performing descriptive calculations.
Hands-on exercises
- Clean a real customer dataset and visually analyse sales by region.
Day 2: building production-ready data processing with the Java ecosystem
- Java data tools: using Streams, Collections and libraries such as Apache Commons CSV and OpenCSV.
- File processing: reading, parsing and structured data transformation.
- Visualisation and comparison: an introduction to JFreeChart and comparative performance analysis of Python versus Java.
Hands-on exercises
- Process a large log file to extract KPIs and build a simple dashboard in Java.
Audience
This course is intended for technical professionals exploring new skills or changing roles, including:
- beginner or intermediate developers seeking to expand their skills into data;
- data analysts and engineers changing careers who want to compare technical stacks before choosing a technology;
- technical product owners who need to understand each language's constraints and benefits;
- computer science students seeking a dual skill set that is uncommon in the market.
Prerequisites
The following prerequisites apply:
- Technical skills: basic programming knowledge in either Python or Java.
- Theoretical knowledge: elementary statistics or experience working with data in Excel is recommended.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Comparative approach: go beyond learning to compare performance and syntax directly for the same tasks.
- Dual expertise: gain a clear understanding of two major ecosystems and strengthen your versatility.
- Practical relevance: workshops on logs and sales reflect real business challenges rather than purely academic exercises.
- Standard tools: use established libraries such as Pandas and OpenCSV that can be applied immediately in production.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description (such as Python, Java, Pandas, NumPy, Matplotlib and JFreeChart) belong to their respective owners. Their mention for educational purposes does not imply any commitment or partnership.