Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

## Missing data
Until now, we have encountered various scenarios where `NaN` values were obtained and identified. This lesson focuses on removing or replacing these values based on the requirements. The `ffill()` method is already discussed [here](https://www.educative.io/courses/predictive-data-analysis-with-python/reindex-objects#forward-filling); it deals with `NaN` values resulting from reindexing. Some more additional functions on how to handle `NaN` values are discussed below.

* `isnull()` : This function returns an object with all instances of `NaN` values marked as `True` and the rest as `False`. This function is also used [here](https://www.educative.io/courses/predictive-data-analysis-with-python/series-in-pandas#more-functions-from-indexes) as an additional function for `Series`.


# Missing data
Until now, we have encountered various scenarios where `NaN` values were obtained and identified. This lesson focuses on removing or replacing these values based on the requirements. The `ffill()` method is already discussed [here](https://www.educative.io/courses/predictive-data-analysis-with-python/reindex-objects#forward-filling); it deals with `NaN` values resulting from reindexing. Some more additional functions on how to handle `NaN` values are discussed below.

* `isnull()` : This function returns an object with all instances of `NaN` values marked as `True` and the rest as `False`. This function is also used [here](https://www.educative.io/courses/predictive-data-analysis-with-python/series-in-pandas#more-functions-from-indexes) as an additional function for `Series`.


In this lesson, how to deal with missing or "NaN" values in pandas is explained.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

Dealing with Missing Data

Missing data