Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

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visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python Data Analysis and Visualization

# Introduction to raw files

Sometimes you get data in strange formats and you have to roll your own Python code to process the data. Fortunately, doing this is simple.

For this, we will assume that you have data in some type of text file. Each row of data corresponds to a row in your text file.

For example, you might have a file delimited by a pipe (|). It could look something like this:

```
James|22|M
Sarah|31|F
Mindy|25|F
```

Above we have 3 rows of data each with 3 values separated by a pipe (|) - name, age, and gender.


We can create this same file in Python to process using tempfile.

This lesson focuses on raw files. It gives a complete explanation about how to read data from raw files using Python.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Raw Files

Introduction to raw files #