Data Manipulation Techniques in Python Online Course
Data Manipulation Techniques in Python Online Course
Data science is a rapidly growing field, and this course is designed to help you build essential skills in Python for data science. Starting with a Python refresher (for beginners), the course covers key libraries like NumPy and Pandas, focusing on data manipulation, cleaning, and analysis. You’ll also learn how to visualize data and work with time series. By the end, you’ll be able to efficiently perform data science and analytics tasks in Python.
Who is this book for?
This course is open to students of all skill levels. Prior programming or statistical knowledge is not required to get started.
What you will learn
- Python fundamentals
- Pandas for data analysis
- Work with numerical data in Python
- Statistics and math with Python
- How to code in Jupyter Notebook
- How to install packages in Python
Course Table of Contents
Python Quick Refresher
- 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
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
Fundamental NumPy Properties
- Introduction to NumPy Arrays
- Creating NumPy Arrays
- Indexing NumPy Arrays
- Array Shape
- Iterating Over NumPy Arrays
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
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()
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
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
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
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
