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

# CSV
*CSV* stands for comma separated values. These are the most common and widely used files with a pandas `DataFrame` object.
It is a kind of a text file that uses the extension **.csv** instead of **.txt**. Text is separated by *commas* `,` in this file, and when a `DatFrame` reads this file, values between commas become _cell_ values of the `DataFrame`.

The `pandas` package provides both *reading* and *writing* functions for *csv* files.

## Reading CSV files
The `read_csv(file_name)` function is used to read from a *csv* file. The `file_name` should be replaced with the name of your file. Make sure that the file is in the same directory as your program file or clearly define the path to that file, e.g., `folder/folder/file.csv`. If no path is defined with the filename, then the program looks for the file in the current directory.

In this lesson, we discuss how to read from and write to CSV files.

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 CSV Files

CSV