Big Data Analysis with Python
(BIG-DATA-PYTHON.AJ1)
/ ISBN: 978-1-64459-315-8
This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)
Big Data Analysis with Python
Get hands-on experience of big data analysis with Python with the comprehensive course and lab. The lab provides hands-on learning in analyzing data with the use of python, beginning up with the basics to mastering different types of data. The course and lab deal with python data science stack, statistical visualizations, working with big data frameworks, handling missing values and correlation analysis, exploratory data analysis, reproducibility in big data analysis, and many more.
Lessons
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9+ Lessons
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20+ Exercises
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50+ Quizzes
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65+ Flashcards
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65+ Glossary of terms
TestPrep
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30+ Pre Assessment Questions
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30+ Post Assessment Questions
LiveLab
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48+ LiveLab
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12+ Video tutorials
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20+ Minutes
- About
- Introduction
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Summary
- Introduction
- Types of Graphs and When to Use Them
- Components of a Graph
- Seaborn
- Which Tool Should Be Used?
- Types of Graphs
- Pandas DataFrames and Grouped Data
- Changing Plot Design: Modifying Graph Components
- Exporting Graphs
- Summary
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
- Introduction
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Summary
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
- Introduction
- Defining a Business Problem
- Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
- Structured Approach to the Data Science Project Life Cycle
- Summary
- Introduction
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Summary
- Introduction
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
- Summary
Hands on Activities (Live Labs)
- Interacting with the Python Shell
- Calculating the Square
- Grouping a DataFrame
- Applying a Function to a Column
- Subsetting a DataFrame
- Slicing and Subsetting
- Reading Data from a CSV File
- Viewing the Standard Deviation
- Calculating the Median Value
- Calculating the Mean Value
- Plotting an Analytical Graph
- Creating a Graph
- Creating a Graph for a Mathematical Function
- Creating a Line Graph Using Seaborn
- Creating a Line Graph Using pandas
- Creating a Line Graph Using matplotlib
- Detecting Outliers
- Displaying Histograms
- Using a Box Plot
- Constructing a Scatterplot
- Plotting a Line Graph with Styles and Color
- Configuring a Title and Labels for Axis Objects
- Designing a Complete Plot
- Exporting a Graph to a File on a Disk
- Performing DataFrame Operations in Spark
- Accessing Data with Spark
- Parsing Text in Spark
- Creating a DataFrame Using a CSV File
- Creating a DataFrame from an Existing RDD
- Specifying the Schema of a DataFrame
- Removing a Column from a DataFrame
- Renaming a Column in a DataFrame
- Adding a Column to a DataFrame
- Creating a KDE Plot
- Creating a Linear Model Plot
- Creating a Bar Chart
- Filtering Data
- Counting Missing Values
- Handling NaN Values
- Using the Backward and Forward Filling Methods
- Calculating Correlation Coefficient
- Generating the Feature Importance of the Target Variable
- Identifying the Target Variable
- Plotting a Heatmap
- Generating a Normal Distribution Plot
- Performing Data Reproducibility
- Preprocessing Missing Values with High Reproducibility
- Normalizating the Data
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