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

# Control structures in Python

## The if-else construct

**If-then statements** are a staple of any programming language. Basically, if you meet a certain condition, then something happens. In Python, `elif` stands for `else if`, meaning that if the previous conditions were not met, check that condition. `Else` is a catch-all condition for any remaining flows. Python follows the following syntax:
```python
if condition:
  statements
elif condition:
  statements
else:
  statements
```
Following is a code example:

def age_check(age):

    if age > 40:  # if age greater than 40, print "Older than 40"
        print("Older than 40")
    elif age > 30 and age <= 40: # if age greater than 30 and less than or equal to 40, print "Between 30 and 40"
        print("Between 30 and 40")
    else:  # if neither of the previous conditions are met, print "Other"
        print("Other")

print(age_check(41))

## The for construct

**Loops** allow you to iterate over an iterable. That's not a very helpful definition, so let's consider the most common use case, lists. A loop allows you to iterate over a list or other data types that also allow iteration.

You can contain your iterable in the `enumerate()` command to add a counter to your loop. This is useful if you want to loop over a list of values while still having access to the iterable index.

This lesson covers how the flow of execution transfers between lines of code depending on the scenarios. It also discusses some built-in functions for data structures studied in the previous lesson.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Control Flow and Built-in Functions

Control structures in Python #

The if-else construct #

The for construct