Data Analysis with Python
In a world overflowing with data, understanding how to interpret it is key. This course offers a deep dive into Python-based data analysis, making it a great fit for anyone keen to delve into data science. Python’s status as a top choice in data analysis stems from its easy-to-read style and powerful libraries.
The course zeroes in on Python libraries – pandas for data handling, matplotlib for charting, and seaborn for detailed visualizations. Key topics include cleaning data with pandas, creating charts with matplotlib, and making more advanced visuals with seaborn.
The course’s main aim is to help learners turn raw data into insightful charts and deep analysis using Python. By the end of the course, learners will have a full understanding of data analysis using Python and how to use its key libraries effectively.
The teaching style combines classroom learning with hands-on practice through a data science project. This blend ensures learners are well-prepared to tackle real-world data analysis tasks, balancing theoretical knowledge and practical skills.
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November 14, 2023
November 24, 2023
Language(s) of the training
Languages spoken by the coach(es)
- Understanding data science.
- Familiarization of data analysis tools in Python such as pandas, matplotlib and seaborn.
- Apply your data science skills to a real-life data set.
During this course, the learners will gain proficiency in Python data analysis techniques, be exposed to popular Python libraries: pandas, matplotlib, and seaborn. In addition, the learners will understand the process of cleaning and handling data using pandas. The learners will learn to create charts and visuals using matplotlib and detailed and advanced data visualizations with seaborn will be explored. The course aims to enable learners to transform raw data into meaningful charts and thorough analysis. By the end of the training, learners are expected to understand how to effectively use Python’s key libraries for data analysis. The training approach combines theory and practice, balancing classroom learning with a hands-on data science project. Students will gain practical experience to help them tackle real-world data analysis tasks.
Learning outcomes for the “Data Analysis with Python” course are clearly outlined to provide a comprehensive understanding of what learners can expect to gain upon completion. These outcomes are detailed in terms of the knowledge, skills, and competencies that can be achieved.
1. Master Python for Data Analysis: On successful completion of this course, learners will be able to proficiently utilize Python for data analysis tasks, leveraging its readability and versatility for various data science operations.
2. Use Python Libraries: Students will gain expertise in applying popular Python libraries, including pandas for data manipulation, matplotlib for data visualization, and seaborn for advanced data visualization tasks.
3. Perform Data Cleaning with Pandas: Learners will acquire the skill to cleanse and manipulate data using pandas, enhancing the quality of data for analysis.
4. Create Visualizations with Matplotlib: On course completion, participants will demonstrate the ability to create informative charts and graphs with matplotlib, translating data into clear and comprehensible visual formats.
5. Construct Advanced Visualizations with Seaborn: Students will exhibit proficiency in crafting detailed and complex data visualizations with seaborn, pushing the boundaries of traditional data depiction methods.
6. Transform Raw Data into Meaningful Analysis: By the end of the course, learners will transform raw data into meaningful charts and thorough analysis, thus unveiling hidden insights and trends within datasets.
7. Apply Python Libraries for Effective Data Analysis: Learners will be able to utilize Python’s key libraries efficiently for data analysis, ensuring a comprehensive understanding of these powerful tools.
8. Balance Theory with Practice: Students will demonstrate their ability to apply theoretical knowledge to a practical data science project, thus cementing their understanding through experiential learning.
9. Execute Real-world Data Analysis Tasks: After completion of the course, learners will be prepared to tackle real-world data analysis tasks, deploying their practical skills gained throughout the course.
Each of these outcomes utilizes clear and concise language, focusing on observable and measurable actions. The outcomes avoid overusing knowledge and comprehension-based verbs, instead incorporating outcomes based on application, analysis, and evaluation. Furthermore, the outcomes align with the course content and are neither overly broad nor too specific, ensuring a balance that is just right for a comprehensive data analysis course with Python.
This course has a total duration of 15 hours and takes place over 5 days
- 14-11-2023: 17:00 – 20:00
- 16-11-2023: 17:00 – 20:00
- 21-11-2023: 17:00 – 20:00
- 23-11-2023: 17:00 – 20:00
- 24-11-2023: 17:00 – 20:00
Format and Location
This course takes place ON-SITE
Terres Rouges building
14, porte de France
Basic knowledge in programming and Python
This training does not have any assessment or exams; a certificate of participation will be issued to participants.