Welcome to this learning path crafted to transform you from a curious enthusiast to a proficient data scientist. This path includes courses that equip you with the foundational knowledge, tools, and techniques to extract actionable insights from raw data using Python. Ready to start? Let's dive in!
222 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Verified skills you'll gain
ADVANCED
Data Querying and Retrieval
INTERMEDIATE
Exploratory Data Analysis and Visualization
DEVELOPING
Data Cleaning and Preprocessing
INTERMEDIATE
Machine Learning and Predictive Modeling
Tools you'll use
Numpy
Pandas
Python
Scikit-learn
Seaborn
Trusted by learners working at top companies
1
5 lessons
22 practices
Basics of Numpy and Pandas with Titanic Dataset
This entry-level course offers a deep dive into the fundamental functionalities of Python libraries, Numpy and Pandas, applicable to data science. It covers a wide range of topics, from numerical computations to data manipulation using authentic datasets.
An in-depth advanced course dedicated to mastering data visualization techniques using Python, Matplotlib, and Seaborn. You will get to work with a real-world Titanic dataset and explore critical aspects of data representation and interpretation.
This data-heavy course focuses on trends and patterns in air travel history. Using complex visualizations and time series analysis, you will learn about growth trajectories and seasonal fluctuations in the air travel sector.
Intro to Data Cleaning and Preprocessing with Titanic
This comprehensive course specializes in data cleaning and preprocessing techniques in Python, preparing you to apply these techniques in predictive modeling. The course covers a range of relevant topics, from missing data handling to feature engineering.
Intended for those interested in Machine Learning, this advanced course delves deeper into the extensive functionalities of Numpy and Pandas. The course covers complex operations, large-scale data manipulation, and cross-disciplinary applications.
An overview course into supervised machine learning techniques, focusing particularly on linear and logistic regression. By working with real-world datasets, you will implement both models to predict outputs and analyze the most predictive features.
This advanced course explores unsupervised machine learning, emphasizing dimensionality reduction and clustering methods. Using the Iris dataset, you will apply different methods and interpret the practical implications of the clusters identified.
