Certified Data Science with Python Foundation Training
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 GoDataDriven’s 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 specialised 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
Clients we've helped
What you'll 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)
- 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
- 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
- ML concepts:
- Why and when to use ML
- Types of Learning tasks & ML approaches
- ML Theory:
- Optimisation with gradient descent
- Under/Overfitting, Generalisation & Regularization
- Introduction to Scikit-Learn
- Training & evaluating a Scikit-Learn estimator
- Interpreting a Scikit-Learn model
- Overview of ML algorithms
- Pros & Cons of the most common ML models
- How to choose an appropriate ML model
- Scikit-learn Pipelines
- Preprocessing with Scikit-Learn transformers
- Cross-validation and hyperparameter searches
- Data Science with Python Foundation exam
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 the right machine learning algorithm and metric for your data problem.
- Prepare complex data for machine learning with techniques such as scaling, encoding, and imputing.
- Apply best practices for data wrangling and model building.
Data Science Learning Journey
Data Science with Python Foundation Certification
We have worked together with APMG International to offer you a recognized partner to get certified in Data Science.
The exam and Data Science with Python Foundation certificate are included in the course Data Science with Python Foundation. 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 Data Science with Python Foundation sample exam here. Please select “Data Science with Python Foundation (2021)” from the dropdown menu.
_ SKILL ASSESSMENT: PYTHON FOR DATA SCIENCE
Discover your knowledge level of Python for Data Science
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We believe that our courses empower people to be more effective with data and tech so they can better help colleagues and delight customers. Attending one of our training is also a great way to expand your network, increase your employability, and to command bigger salaries. If you already have a well-paying job, our prices are really affordable. Not everyone is so lucky though. So if you wish to attend one of our most popular courses, but require financial support, we'd love to hear from you.
James HaywardData Educator
James holds a Master’s degree in Artificial Intelligence from the University of Amsterdam (Cum Laude); a Master’s in Educational Leadership from UCL (Merit); and a Bachelor’s degree in Mathematics from the University of Manchester (First Class Honours). He is fluent in Python and its data science libraries, such as Pandas and Sci-kit Learn, and is proficient with the deep learning frameworks PyTorch and TensorFlow.
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.