Certified Data Science with Python
5 February, 2024 – Virtual
Do you know how to perform basic data analysis in Python but want to learn more about machine learning? During this three-day data science deep dive, you will learn how to unlock Python’s potential for data analysis and machine learning. The Data Science with Python Foundation course covers training models with scikit-learn and best practices for transforming your data with pandas, with a perfect combination of theory and practice.
09:00 – 17:00
What will you learn?
After the training, you will be able to:
Perform exploratory data analysis on your datasets with pandas.
Train and evaluate machine learning models with scikit-learn.
Identify a suitable machine learning algorithm and metric for your data problem.
Prepare complex data for machine learning with scaling, encoding, and imputing techniques.
Apply best practices for data wrangling and model building.
Data Wrangling with Pandas
- Fetch descriptive summary statistics of your data with simple operations.
- Effectively select and filter parts of your data with loc.
- Retrieve advanced statistics with groupby aggregations.
- Extend your dataset by creating new columns with assign.
- Structure your code neatly by chaining methods.
Machine Learning with Scikit-Learn
- Use scikit-learn to train classification and regression models.
- Evaluate trained models with train/test set split and scikit-learn metrics.
- Use scikit-learn transformers for categorical variable encoding, scaling and missing values imputation.
- Pre-process complex data in scikit-learn with ColumnTransformer and Pipeline.
- Tune pre-processing and model hyperparameters with gridsearch.
Machine Learning Theory
- Identify the type of machine learning task (classification or regression, supervised or unsupervised, and others) .
- Differentiate between several machine learning algorithms (such as linear regression, decision tree, and support vector machine).
- Create models that generalize (underfitting and overfitting, train-test split, k-fold cross-validation).
- Understand how to evaluate your model’s effectiveness with various metrics (such as precision & recall, F1, root mean squared error, r2).
- Master exploratory data analysis with Pandas
- Introduction to machine learning (theory)
- Build your first machine learning model on a real dataset
Who is it for?
This training is perfect for data scientists who want to enhance their skills and gain practical knowledge using the most popular and essential data science tools. Whether you’re a beginner or have some prior experience with Python, this Foundation training will provide you with the necessary expertise to kickstart your venture into data science.
If you are already an experienced Data Scientist and want to develop your skills even further, check out our Advance Data Science with Python Training.
Basic knowledge of Python (or another programming language) is necessary to fully engage with the training.
If you are unsure about your Python proficiency, we recommend checking out the Python for Data Analysts training or contacting our sales representative.
Why should I
follow this training?
Dive into the world of Data Science and Machine Learning using Python.
The course combines theory and practice perfectly, making it a great introduction to the field.
Get best practices for data wrangling and model building.
should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.
See you soon!
For this training, you need a laptop on which you can install programs.
All literature and course materials are included in the price.
Meet the trainers
Meet James Hayward, a data science trainer at Xebia Academy. Get to know him here.
Meet Lucy Sheppard, trainer at Xebia Academy. Lucy is a data science trainer and teaches many Python courses as well as dbt Learn.
Lysanne van Beek
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