Practical Data Analysis and Visualization with Python

Practical Data Analysis and Visualization with Python

The main objective of this course is to make you feel comfortable analyzing, visualizing data and building machine learning models in python to solve various problems. 

This course does not require you to know math or statistics in anyway, as you will learn the logic behind every single model on an intuition level. Yawning students is not even in the list of last objectives. 

Throughout the course you will gain all the necessary tools and knowledge to build proper forecast models. And proper models can be accomplished only if you normalize data. In view of that, there is a dedicated class that will guide you on how to avoid Garbage-In, Garbage-Out and feed the right data, which most courses skip for some reason.

Sample Datasets Used in This Course

1. Weed Price
2. Chopstick size and pitching efficiency
3. Computer prices
4. Baby Growth
5. Unemployment Rate and Interest Rates
6. US Spending on Science and Suicide by Hanging
7. World Religions
8. Divorce Statistics by Gender
9. US Music Sales By Genre
10. Bank Statement
11. Customer Satisfaction Poll
12. Boston House Prices
13. Historical Speed Limits
14. Iris flower dataset
15. Handwritten digits dataset
16. NYSE Sales Volume for 2016 and 2017

Required Python Packages for This Course

1. Python 3.4 and above
2. NumPy
3. Pandas
4. Scipy
5. Scikit-learn
6. Matplotlib
7. Seaborn

Data Analytics, Visualization and Data Science for Everyone. Build Machine Learning Models to Solve Day-to-Day Problems

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What you will learn

• Understand the logic behind machine learning models without strain
• Build forecasting models with machine learning
• Analyze customer satisfaction

Rating: 3.8

Level: All Levels

Duration: 4.5 hours

Instructor: Bekzod Ruzmetov