Data Manipulation Techniques in Python Online Course

Data Manipulation Techniques in Python Online Course

This course introduces you to the fast-growing field of data science, starting with a Python refresher before diving into core libraries like NumPy and Pandas for data manipulation, cleaning, and analysis. You’ll explore mathematical applications, work with DataFrames and series, and practice data visualization on real datasets. The course concludes with time series analysis, equipping you to handle, transform, and analyze time-based data. By the end, you’ll be confident in using Python to perform essential data science and analytics tasks.

Who should take this course?

This course is ideal for students, data enthusiasts, analysts, and professionals who want to strengthen their ability to manipulate and transform data using Python. It’s well-suited for those with basic Python knowledge who are eager to work with libraries like Pandas and NumPy to clean, reshape, and prepare data for analysis. Whether you’re an aspiring data scientist, a researcher handling datasets, or a professional aiming to make data-driven decisions, this course will equip you with practical skills to perform efficient data manipulation in Python.

What you will learn

• A quick refresher to Python fundamentals
• Learn to use Pandas for data analysis
• Learn to work with numerical data in Python
• Learn statistics and math with Python
• Learn how to code in Jupyter Notebook
• Learn how to install packages in Python

Course Outline

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

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