Master important data manipulation techniques for data science in Python by learning Python, NumPy, and Pandas
About This Video
In Detail
Data science is quickly becoming one of the most promising careers in the twenty-first century. It is automated, …
Master important data manipulation techniques for data science in Python by learning Python, NumPy, and Pandas
About This Video
In Detail
Data science is quickly becoming one of the most promising careers in the twenty-first century. It is automated, program-driven, and analytical. As a result, it’s no surprise that the demand for data scientists has been expanding in the job market over the last few years.
We will begin with a quick refresher on Python fundamentals for beginners in this course. This is optional; if you’re already familiar with Python, skip to the next chapter.
Data science will be the topic of the next three sections. We will start with the essential Python libraries for data science, then go on to the fundamental NumPy properties, and lastly begin with mathematics and how to use it in data science.
You will learn about Python Pandas DataFrames and series after learning about data science. Following that, we will get down to business and begin data cleaning. Following that, we will learn how to use Python to visualize data and do data analysis on some sample datasets. Finally, we will cover the Time series in Python and learn how to work with and convert datasets to Time series.
By the end of this course, you will be able to execute data manipulation for data science in Python with ease.
Audience
This course is open to students of all skill levels, and you will be able to succeed even if you have no prior programming or statistical knowledge.
Chapter 1 : Python Quick Refresher (Optional)
Welcome to the course!
Introduction to Python
Setting up Python
What is Jupyter?
Anaconda Installation: Windows, Mac, and Ubuntu
How to Implement Python in Jupyter?
Managing Directories in Jupyter Notebook
Input/Output
Working with Different Datatypes
Variables
Arithmetic Operators
Comparison Operators
Logical Operators
Conditional Statements
Loops
Sequences: Lists
Sequences: Dictionaries
Sequences: Tuples
Functions: Built-in Functions
Functions: User-Defined Functions
Chapter 2 : Essential Python Libraries for Data Science
Installing Libraries
Importing Libraries
Pandas Library for Data Science
NumPy Library for Data Science
Pandas versus NumPy
Matplotlib Library for Data Science
Seaborn Library for Data Science
Chapter 3 : Fundamental NumPy Properties
Introduction to NumPy Arrays
Creating NumPy Arrays
Indexing NumPy Arrays
Array Shape
Iterating Over NumPy Arrays
Chapter 4 : Mathematics for Data Science
Basic NumPy Arrays: zeros()
Basic NumPy Arrays: ones()
Basic NumPy Arrays: full()
Adding a Scalar
Subtracting a Scalar
Multiplying by a Scalar
Dividing by a Scalar
Raise to a Power
Transpose
Element-Wise Addition
Element-Wise Subtraction
Element-Wise Multiplication
Element-Wise Division
Matrix Multiplication
Statistics
Chapter 5 : Python Pandas DataFrames and Series
What is a Python Pandas DataFrame?
What is a Python Pandas Series?
DataFrame versus Series
Creating a DataFrame Using Lists
Creating a DataFrame Using a Dictionary
Loading CSV Data into Python
Changing the Index Column
Inplace
Examining the DataFrame: Head and Tail
Statistical Summary of the DataFrame
Slicing Rows Using Bracket Operators
Indexing Columns Using Bracket Operators
Boolean List
Filtering Rows
Filtering rows using ‘’ and ‘|’ Operators
Filtering Data Using loc()
Filtering Data Using iloc()
Adding and Deleting Rows and Columns
Sorting Values
Exporting and Saving Pandas DataFrames
Concatenating DataFrames
Groupby()
Chapter 6 : Data Cleaning
Introduction to Data Cleaning
Quality of Data
Examples of Anomalies
Median-based Anomaly Detection
Mean-Based Anomaly Detection
Z-Score-Based Anomaly Detection
Interquartile Range for Anomaly Detection
Dealing with Missing Values
Regular Expressions
Feature Scaling
Chapter 7 : Data Visualization using Python
Introduction
Setting Up Matplotlib
Plotting Line Plots using Matplotlib
Title, Labels, and Legend
Plotting Histograms
Plotting Bar Charts
Plotting Pie Charts
Plotting Scatter Plots
Plotting Log Plots
Plotting Polar Plots
Handling Dates
Creating Multiple Subplots in One Figure
Chapter 8 : Exploratory Data Analysis
Introduction
What is Exploratory Data Analysis?
Univariate Analysis
Univariate Analysis: Continuous Data
Univariate Analysis: Categorical Data
Bivariate Analysis: Continuous and Continuous
Bivariate Analysis: Categorical and Categorical
Bivariate Analysis: Continuous and Categorical
Detecting Outliers
Categorical Variable Transformation
Chapter 9 : Time Series in Python
Introduction to Time Series
Getting Stock Data Using yfinance
Converting a Dataset into Time Series
Working with Time Series
Time Series Data Visualization with Python