Introduction to Python for Data Analysis

Introduction to Python for Data Analysis

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1. Introduction to Python 1/2

Here, we will face challenges related to data types in python. In this section, we will learn the Python syntax necessary for each person who wants to work with this language. After the section, we will undoubtedly be able to cope with simple Python tasks.

First Step in Programming

Deal with Variables

Practice Math Skills

Deal with Percents

How to Attract One User?

Boolean Numbers

Work with Strings

Get Familiar with Lists

How to Append New Element?

2. Introduction to Python 2/2

This section will teach you simple Python functions. We will be able to work with if/else statements and loops. Such skills are necessary for everyone who wants to start programming and doing fascinating Python tasks.

3. Explore Dataset

This section is the most complicated than the previous one, but it is gripping. Here, we will manage the most commonly used pandas function to help we do essential operations with data. Also, we will learn the basics of the NumPy library.

Operations with the File

How to Explore the Data

Max, min, mean, median

Dive Deeper into Grouping Data

Introduction to NumPy

Deal with Several Conditions

Visualization: First Steps

Dive Deeper into Visualization

4. Becoming an Analyst

This section allows you to become a real analyst. Let's go!

Examine Dataset

Costly Customers

Organic Traffic

How Much Do We Earn

Is Our Project Profitable? ROI 1/2

Зміст курсу

Introduction to Python for Data Analysis

Introduction to Python for Data Analysis

1. Introduction to Python 1/2

First Step in Programming Deal with Variables Practice Math Skills Challenge Deal with Percents How to Attract One User?Challenge Learn Numbers Boolean Numbers Challenge Work with Strings Get Familiar with Lists How to Append New Element?

2. Introduction to Python 2/2

If/Else Challenge If/Elif/Else Challenge For Loop While Loop Challenge

3. Explore Dataset

Data Study Operations with the File How to Explore the Data Sorting Data Max, min, mean, median Challenge Group Data Dive Deeper into Grouping Data Group Data 2.0 Introduction to NumPy Pivot Tables Recall Loops .loc Function Deal with Several Conditions Challenge Visualization: First Steps Dive Deeper into Visualization

4. Becoming an Analyst

Data Study Examine Dataset Challenge СAC 1/2 CAC 2/2 Costly Customers Organic Traffic How Much Do We Earn Is Our Project Profitable? ROI 1/2 ROI 2/2 Visualize ROI

Work with Strings

Strings are the standard way to store text. We can use it for storing users' emails, names, etc. You can keep it in different ways.

The first way is:


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string = 'Strings are easy'
print(string)

The second way is:


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string = "Strings are easy"
print(string)

The third way is:


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string = '''
Strings are easy
'''
print(string)

The fourth way is:


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string = """
Strings are easy
"""
print(string)

The first two ways are the most common, and the third and the fourth are used for storing several lines of code.

If you want to put a quotation mark inside the quotes, you can put an ordinary quotation mark inside a double one or vice versa.


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string_1 = "I've never learned Python"
string_2 = 'I"ve never learned Python'
print(string_1)
print(string_2)

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