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Dataviz with Matplotlib
This free introductory course to data visualization will provide you …
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Data Analyst Certified
This course provides comprehensive training in data science, covering topics …
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Machine Learning
Machine learning course: Learn AI foundations, algorithms, and techniques for …
What you'll learn
- Comprehensive understanding of machine learning algorithms and techniques
- Proficiency in building and training machine learning models
- Skills in evaluating and fine-tuning model performance
- Knowledge of feature engineering and data preprocessing techniques
- Ability to apply machine learning to real-world problems
- Experience with working on machine learning projects
- Understanding of model deployment and scalability considerations
- Familiarity with popular machine learning frameworks and libraries
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What you'll learn
Understanding of key statistical concepts and terminology
Proficiency in using statistical functions and methods in Python
Knowledge of probability distributions and hypothesis testing
Skills in data sampling and estimation
Ability to analyze and interpret statistical results
Familiarity with regression analysis and predictive modeling
Understanding of statistical visualization techniques
Practical experience in applying statistical analysis to real-world datasets
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Data Visualization
This course will teach students comprehensive data visualization skills using …
What you'll learn
Techniques for creating compelling data visualizations
Understanding of best practices in data visualization
Proficiency in using data visualization tools and software
Ability to select appropriate visualization types for different data scenarios
Skills in customizing aesthetics and design elements of visualizations
Knowledge of conveying data-driven narratives through visualizations
Understanding of data exploration and analysis through visual representations
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Python Data Expert
This course will provide an overview of Python data types, …
What you'll learn
1. Python Arithmetic
2. Basic Data Types
3. Variables
4. Lists
5. Tuples and Strings
6. Dictionaries and Sets
7. NumPy Arrays
8. Pandas DataFrames
9. Reading and Writing Data
10. Control Flow
11. Functions
12. List Comprehensions
13. Data Exploration and Cleaning
14. Working With Text Data
15. Preparing Numeric Data
16. Dealing With Dates
17. Merging Data
18. Frequency Tables
19. Pandas
20. NumPy
