Python for Data Analysis & Data Science

Hands-On Python Course for Data Analytics - Beginner to Advanced Level

4.60 (118 reviews)
Udemy
platform
English
language
IT Certification
category
instructor
445
students
14 hours
content
Mar 2024
last update
$69.99
regular price

What you will learn

Data Exploration & Manipulation using Python

Pandas Library

Data Analysis in Dataframe

Data Structures

Data Relationships

Functions

Working with Dates and Times Values

Series

Dictionaries

Tuples

Lists

.....and a lot more

Description

Python is the fastest growing Data Analytics Programming Languages. This course takes you from knowing nothing about Python to becoming an expert analyzing data in Python. You will also learn about standard Python which is relevant for anyone who needs to know Python for other purposes like Web Development, Software Development e.t.c.

Knowing Python is incredibly important if you are looking into a career in any data related field.

This course is designed to equip you with what you need to be successful learning Python:

  • Hands-on code along structure.

  • Work on multiple projects.

  • Lots of practice exercises and task which solidifies your knowledge of each lessons.

  • Quizzes on sections covered.

  • Replicate real life scenarios and coding in Jupyter Notebook.

IS THIS YOU ?

Looking to work with data personally or professionally?

Starting or transitioning into a career as a Data Analyst, Data Scientist, Business Analyst, Report Analyst, ETL Specialist, BI Consultant, Data Engineer, or any data related field? Then you need to learn Python.

Also, if you are going into the field of Web Application & Internet development, Artificial Intelligence, Cybersecurity, Web Testing; it is imperative that you learn Python.

Course Requirement or Prerequisites

This course does not require any prior knowledge or specific academic background. The only requirement is having a laptop or desktop computer. All applications necessary for learning the course would be downloaded free from the internet.


Content

Introduction

Course Introduction
Course Structure
Course Requirement/Prerequisite
Application Download & Installation
Install Python on Mac
Install Python on Windows
Adjusting Playback Rate
Using Jupyter Notebook
Python Import Files

Data Types & Variables

Section Introduction
Python as a Programming Language
Data Types
Variables
Section 1

Operators and Numbers in Python

Section Introduction
Operators
Using Numbers in Python
Section 3

Using Strings in Python

Section Introduction
String Data Type
String Methods
String Operators
Section 4

Slicing, Format Functions & Casting

Section Introduction
Slice
Format Function
Change Data Types Using Functions - Casting
Project 1 - Create a Bill Payment System
Section 5

List - Data Structure

Section Introduction
List
List Methods
Section 6

Control Flow

Section Introduction
IF Statement
Project 2 - Guessing Game - Part I
While Loops
Project 2 - Guessing Game - Part II
For Loops
Break & Continue Statement
Section 7

Tuple - Data Structure

Section Introduction
Tuples
Section 8

Dictionaries - Data Structure

Section Introduction
Dictionaries
Dictionary Methods
Create a List inside a Dictionary
Project 3 - Concert Tickets
Section 9

Functions

Section Introduction
Python's Built-In Functions
User Defined Functions
Variable Scope
Packing & Unpacking Data - ARGS
Packing & Unpacking Data - KWARGS
Section 10

Series

Section Introduction
Introduction to Series
Create Series from a List
Create Series from a Tuple
Create Series from a Dictionary
Create Series from a CSV Dataset
Head & Tail Methods on a Series
Count & Describe Methods on a Series
Sort_Values Method
Inplace Parameter
Sort_Index Method
Retrieve Records from a Series by Index Position
Retrieve Records from a Series by Index Label
Use Get_Method to Retrieve Records from a Series
Using Attributes on a Series
Section 11

Dataframe Part A

Section Introduction
Introduction to Dataframe
Create Dataframe from a List
Create Dataframe from a Dictionary of List
Create Dataframe from an Imported File
Retrieve Single Column from a Dataframe
Retrieve Multiple Columns from a Dataframe
Add a New Column to a Dataframe
Delete Column(s) from a Dataframe
Find the Sum of Null Values
Drop Rows with Missing Values
Replace Missing Value Using FILLNA Method
Broadcasting Operation
Count Unique Occurrences Using VALUE_COUNT Method
Sort Values in Dataframe Using SORT_VALUES Method
Sort Dataframe by Index Using SORT_INDEX Method
Remove and Replace Missing Values
Change Data Types Using ASTYPE Method
Section 12

Dataframe Part B

Optimizing Dataset
Refine Records By a Condition
Refine Records Using Multiple Conditions - AND Condition
Select Specific Columns After a Condition
Refine Dataframe Using Multiple Conditions - OR Condition
Use The ISIN Method to Retrieve Row Having Specific Values
Return Null and Not Null Values Using ISNULL AND NOTNULL Method
Return Values Within Range Using BETWEEN Method
Retrieve Duplicate Records Using DUPLICATED Method
Delete Duplicate Records Using DROP_DUPLICATES Method
UNIQUE and NUNIQUE Methods
Section 13

Dataframe Part C

Optimizing New Dataset
SET_IDEX and RESET_INDEX Method
Retrieve Rows by Index Label Using LOC[ ] Accessor
Retrieve Rows by Index Position Using ILOC[ ] Accessor
Return Specific Index Label Values
Change Values in a Cell
Change Values of Unique Groups
Change Label or Column Name Using RENAME Method
Delete Rows or Columns Using DROP Method
Retrieve Random Sample From a Dataframe
Retrieve Smallest or Largest Values
Rank Values Using The RANK Method
Create a Copy of Dataset

Manipulating Text Data in a Dataframe

Section Introduction
Optimizing Text Dataset
Change Text Case - Upper, Lower, Title, capitalize Method
Remove White Spaces - Lstrip, Rstrip, Strip Method
Replace Characters in a Column
Filtering Dataframe for Specific Values - CONTAINS Method
Split String Column - Part A
Split String Column - Part B
Section 15

Multi Index in a Dataframe

Section Introduction
Create Multi-Index
Sort Multi-Index
Retrieve Rows from Multi-Index
Stack & Unstack Method
Aggregate Values Using PIVOT_TABLE Method
Section 16

Groupby Object

Section Introduction
Groupby Object I
Groupby Object II
GET_GROUP method
Group by Multiple Columns
Use The AGG Method to pass Different Operations
For Loop & Groupby Object

Data Relationship

Section Introduction
Data Relationship
Normalization
Introduction to JOIN
Inner Join I
Inner Join II
Left Join
Right Join
Outer Join
Merge More than 2 Dataframes
Many to Many Data Relationship
Left_On & Right_On
Combine Dataframes Using PD.CONCAT
Section 18

Dates & Times Dataset

Section Introduction
Dates & Times
Pandas Timestamp Object
TO_DATETIME Method
PD.DATE_RANGE Method
PD.DATE_RANGE II
DT.Accessor
Format Datetime Objects with DT.STRFTIME Method
DT.STRFTIME Method
Section 19

Import & Export Datasets

Section Introduction
Import Dataset from URL
Export Dataset as Files from Pandas

Conclusion

Conclusion

Screenshots

Python for Data Analysis & Data Science - Screenshot_01Python for Data Analysis & Data Science - Screenshot_02Python for Data Analysis & Data Science - Screenshot_03Python for Data Analysis & Data Science - Screenshot_04

Reviews

Taiwo
September 7, 2021
This course is well explanatory to develop the python programming skills I need for data analysis so I highly recommend!

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3946846
udemy ID
3/29/2021
course created date
4/17/2021
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