If you don’t have any coding experience or use Python before, try this topic.
Learn the foundational package for organising and numerical computing in Python.
Learn the most important data structure and manipulation tool for efficient data analysis in Python.
Visualise data in Python with Matplotlib and Seaborn.
Comprehend version control with your data projects.
Understand table relationships, interact with databases, and use aggregate functions.
Explore the valuable unlimited data assets in the world.
Learn how to efficiently explore your data and prepare to achieve the best outcome.
Diagnose and transform your raw data into useful assets for analytics.
Learn better design to structure your data and improve proformance.
The nitty-gritty to solve problems with computational algorithms.
The foundation behind machine learning algorithms.
Understand bias-variance trade-off in configuring your training sets.
Know how to extract useful information to optimise learning processes.
The importance of statistics allows you with better intuition to judge your data experiment.
Know the right way to create testable hypotheses, and be able to determine a model parameters.
Understand the rationale of labeled data and its applications.
Discover the patterns behind day-to-day data in the world.
Learn the third type of machine learning – the algorithm learning by itself.
Build models to predict future events of numerical values.
Generate if-then rules to predict future events.
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