Certified Data Science with Python Foundation Voeg toe aan favorieten

About Xebia Academy
We are Xebia Academy! A Leading Global Training Company in Agile, Cloud, Data and AI, DevOps, Leadership, Low Code, Microsoft, Product Management, Scrum, Security and Software Development. Learn from the best trainers and let us help you take your digital transformation to the next level.

Do more with data!

Learn the best practice in for effective data science and machine learning with this practical Python training. Explore how Python can help you take the next step in your Data & AI career, guided by Xebia Data Science experts in this 3-days course. Earn the Data Science with Python Foundation certification after the training.

This training is for you if...

• You have some basic experience with Python (or any other programming language).
• You want to get certified in data science.
• You are eager to get more out of your data and build your own predictive models.
• You would like to apply best practices to your data science projects.
• You hope to better communicate and collaborate with your data science colleagues.

This training is not for you if...

• You have never worked with Python or any other programming language before (check out the Python for Data Analysts training).
• You are already an experienced data scientist who is looking to further develop their skills (check out the Advanced Data Science with Python training or other specialized topics).
• You believe data science is a hype and cannot add value (check out the Certified Analytics Translation training).
• You are not interested in practical applications, only the maths behind the algorithms.

What will you learn during the Python Foundation training?Program

The program consists of both theory and hands-on exercises.

Day 1

• Working with Jupyter notebooks in a sustainable way.
• How to do numerical computations and linear algebra with NumPy.
• Visualizing data with Matplotlib, Seaborn, and other packages.
• Transforming and munging data with pandas.

Day 2

• Machine Learning concepts:
  
  * Why and when to use ML.
  
  * Types of learning tasks and ML approaches.
• ML Theory:
  
  * Optimization with gradient drop.
  
  * Under/Overfitting, Generalisation & Regularisation.
• Introduction to Scikit-Learn.
  
  * Train and evaluate a Scikit-Learn estimator.
  
  * Interpret a Scikit-Learn model.
• Overview of ML algorithms.
  
  * Advantages and disadvantages of the most common ML models.
  
  * How to choose a suitable ML model.

Day 3

• Scikit-learn Pipelines.
  
  * Pre-processing with Scikit-learn transformers.
  
  * Cross-validation and hyperparameter search.
• Hackathon.
• Data Science with Python Foundation exam.

You will learn

Machine learning with scikit-learn

• Machine Learning models.
• Data Transformations.
• Data Estimators.
• How to combine these into pipelines.
• How to automate everything in a grid search.
• How to write building blocks.

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, 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).

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.
• Learn to apply best practices, such as pipe and lambda, to prevent bugs.

Data Science Trainers

This Data Science training is brought to you in collaboration with Xebia Data. Xebia Data is part of Xebia, just like Xebia Academy. Xebia Data works with experts in their field who are always on the lookout for the most innovative ways to get the most out of data. Your trainer is a data guru who enjoys sharing his or her experiences to help you work with the latest tools.

Data Science with Python Foundation Certification

The exam and Data Science with Python Foundation certificate are included in the course. The exam can be taken directly after the training or at a moment of your choice. If you get at least 50% of the 50 multiple-choice questions right, you will pass the exam and receive your certificate. More information about the exam can be found here.

Prepare in advance with this Certified Data Science with Python Foundation sample exam here. Please select “Data Science with Python Foundation (2020)” from the dropdown menu.

Data Science Learning Journey

Your Data Science Learning Journey starts with the basics, like the Certified Analytics Translation, or this course. Continue your journey with the Advanced Data Science with Python course or three days of Deep Learning. If you want to proceed to Expert level, read more about our Data Science with Spark training.

Yes, I want to improve my Data Science projects!

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 there's a need to prepare. See you soon!

What else should I know?

• Virtual or in-person training: This training can be delivered both in-person and online. When hosting the in-person training, we provide lunch, snacks, and drinks to the participants. Accordingly, you'll get a discount for virtual training.
• The Data Science with Python Foundation certificate is included in the price of the training.
• The training requires a laptop. The hands-on labs are run in an online environment, eliminating the need to install software. We distribute all course materials for running locally as well.
• Literature and a delicious lunch are included in the price.
• Travel & accommodation expenses are not included.