Data Manipulation Techniques in Python Practice Exam

Data Manipulation Techniques in Python Practice Exam

About the Data Manipulation Techniques in Python Exam

Data manipulation techniques in Python involve using libraries like Pandas and NumPy to clean, transform, and analyze data efficiently. Common techniques include filtering, sorting, merging, reshaping, and aggregating data. Python also allows for handling missing values, removing duplicates, and applying functions to modify datasets. These techniques are essential for preparing data for analysis, making Python a powerful tool in data science and machine learning workflows.

Skills Required

• Basic Python Knowledge – Understanding of variables, loops, and functions.
• Familiarity with Pandas and NumPy – Basic knowledge of data structures like DataFrames and Series for manipulating data.
• Basic Data Analysis Concepts – Understanding of data types, data structures, and how data is represented.
• Mathematical Concepts – Familiarity with basic statistics and operations like mean, median, and standard deviation.
• Problem-Solving Skills – Ability to approach data-related problems and transform data into meaningful insights.

Knowledge Gained 

After completing a course on Data Manipulation Techniques in Python, you will gain:

• Proficiency in using Pandas and NumPy for data manipulation, including filtering, sorting, and aggregating data.
• Skills to clean and preprocess data, such as handling missing values, removing duplicates, and converting data types.
• Expertise in reshaping and merging datasets to analyze data from multiple sources.
• Ability to apply functions to manipulate data efficiently and create new features.
• A deeper understanding of data analysis workflows and preparing data for machine learning models.
• Practical experience in handling real-world data and transforming it into a usable format for analysis and reporting.

Who should take the Exam?

• Data analysts and data scientists who need to manipulate and analyze large datasets.
• Business intelligence professionals looking to clean and transform data for reporting and decision-making.
• Software developers working with data and seeking to enhance their data manipulation skills.
• Machine learning enthusiasts preparing data for model training and optimization.
• Students and job seekers aiming for roles in data analytics, data science, or business intelligence.
• Anyone interested in improving their ability to work with structured and unstructured data using Python.

Course Outline

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