Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Pandas - Analyze Your Data Efficiently in Python training are: Python basics: variables, lists, loops, functions.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Pandas - Analyze Your Data Efficiently in Python training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Pandas - Analyze Your Data Efficiently ... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Pandas - Analyze Your Data Efficiently in Python training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Pandas - Analyze Your Data Efficiently in Python training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Pandas - Analyze Your Data Efficiently in Python training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training program

Module 1Introduction to Pandas: Basic Series and DataFrames (import tools, essential methods)

Dive into Pandas from the morning by installing Anaconda and Jupyter Notebook, import your first library, create Series from lists and dictionaries, then DataFrames from real data like a sales CSV file. Explore indexes, select columns with loc and iloc, perform your first filters, and end with a practical exercise on an e-commerce dataset to export a cleaned report, gain immediate confidence.

Module 2Advanced DataFrame Manipulation: Cleaning and Transformation (apply, merge methods)

Master cleaning by handling missing values with dropna and fillna on a banking customers dataset, apply custom functions via apply and lambda, merge tables with merge and concat to recreate a complete history, manipulate dates with to_datetime, and carry out a concrete case on web logs to transform raw data into an actionable table, feel the power of Pandas live.

Module 3Analysis and Aggregation: GroupBy and Statistics (pivot tables, correlations)

Move to analysis by grouping sales by region with GroupBy and agg, calculate means, medians, and standard deviations on a marketing dataset, create dynamic pivot tables with pivot_table, explore correlations to detect hidden trends, test on an HR case with salaries and performance, produce quantified insights ready to present, transform your data into impactful business decisions.

Module 4Visualization and Final Project: Matplotlib Integration (charts, simple dashboards)

Conclude by visualizing your analyses with Pandas-Matplotlib plot, generate histograms, bars, and scatters on a health dataset, combine into an interactive dashboard via subplots, integrate Seaborn for pro charts, finalize a complete project on real Kaggle data with a deliverable report, leave with a concrete portfolio and job-ready skills to boost your data career.