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

Which parameter is used in the `merge()` function to assign the type of the join?

Which parameter is used in the <code>merge()</code> function to assign the type of the join?

The operation where the innermost column index is added as the innermost row index is known as ?

The operation where the innermost column index is added as the innermost row index is known as ?

How can outliers be identified in a data set?

How can outliers be identified in a data set?

Grouped values from dataframe `L1` and `L2`

```python
import numpy as np
import pandas as pd

df = pd.DataFrame({'L1':['A','A','B','B','C','C'],'L2':['C1','C2','C1','C2','C1','C2'],
    'val_1':np.arange(1,7,1),'val_2':np.arange(7,13,1)})

print(df.groupby(['L1','L2']))
```
What is the output of the above code snippet?

<pre><code class="language-python">import numpy as np
import pandas as pd

df = pd.DataFrame({'L1':['A','A','B','B','C','C'],'L2':['C1','C2','C1','C2','C1','C2'],
 'val_1':np.arange(1,7,1),'val_2':np.arange(7,13,1)})

print(df.groupby(['L1','L2']))
</code></pre>
What is the output of the above code snippet?

Data Wrangling Quiz

Data Wrangling Quiz

Let’s take a quiz on the data wrangling to check our understanding.

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

Quiz