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

# Pandas
[Pandas](https://pandas.pydata.org/) is a Python package developed for data cleaning and manipulation. It provides extended data structures to hold different types of labeled and relational data, which makes it highly flexible and easy to use.

`Pandas` provides the following two objects on which all its operations are performed:

* `Series`: A 1-dimensional table object

* `Dataframe`: A 2-dimensional table object

The `Pandas` package can be imported in your program using the following line of code:

``` python
import pandas as pd
```

This command imports all of `pandas` features and functions into our program, which can be accessible with `pd` as its acronym.

## Features of `Pandas`

* Provides tools for reading and writing data in data structure objects and files of different formats like __CSV__ and __txt__.

* Provides powerful aggregation functions to manipulate data.

* Provides functionality to effectively deal with time-series data.

* Provides high flexibility in operations like __reshaping__, __merging__, __joining__, etc.
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In the next lesson, the pandas `Series` object is explained.

This lesson gives an introduction to the Pandas library of Python.

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

What is Pandas?

Pandas