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

# Topics covered in the course 

The following topics were covered in this course:

* The use of `NumPy` for numerical data

* The use of `Pandas` for data analysis

* How to manipulate the data according to our needs

* The use of `matplotlib` and `seaborn` for statistical plots

* The basic statistics required to analyze predictive insights from various kinds of data

* The use of `selenium` for data scraping

* Two real-world projects extracting predictive insights from real data

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In the next lesson, let’s look at what new prospects are viable for you after completing this course.


This lesson gives a quick overview of the concepts discussed in this course.

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

Summary

Topics covered in the course