Big Data Analysis with Python
(BIG-DATA-PYTHON.AJ1) / ISBN : 978-1-64459-315-8
Über diesen Kurs
Sammeln Sie mit dem umfassenden Kurs und Labor praktische Erfahrung in der Big Data-Analyse mit Python. Das Labor vermittelt praxisnahes Lernen in der Datenanalyse mit Python, beginnend mit den Grundlagen bis hin zur Beherrschung verschiedener Datentypen. Der Kurs und das Labor befassen sich mit dem Python-Data-Science-Stack, statistischen Visualisierungen, der Arbeit mit Big Data-Frameworks, dem Umgang mit fehlenden Werten und Korrelationsanalyse, explorativer Datenanalyse, Reproduzierbarkeit in der Big Data-Analyse und vielem mehr.
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Unterricht
9+ Unterricht | 20+ Übungen | 50+ Tests | 65+ Karteikarten | 65+ Glossar der Begriffe
Testvorbereitung
30+ Fragen vor der Beurteilung | 30+ Fragen nach der Bewertung |
Praktische Übungen
48+ LiveLab | 12+ Videoanleitungen | 20+ Minutes
Preface
- About
The Python Data Science Stack
- Introduction
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Summary
Statistical Visualizations
- 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
Working with Big Data Frameworks
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
Diving Deeper with Spark
- Introduction
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Summary
Handling Missing Values and Correlation Analysis
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
Exploratory Data Analysis
- 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
Reproducibility in Big Data Analysis
- Introduction
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Summary
Creating a Full Analysis Report
- Introduction
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
- Summary
The Python Data Science Stack
- 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
Statistical Visualizations
- 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
Working with Big Data Frameworks
- Performing DataFrame Operations in Spark
- Accessing Data with Spark
- Parsing Text in Spark
Diving Deeper with 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
Handling Missing Values and Correlation Analysis
- Filtering Data
- Counting Missing Values
- Handling NaN Values
- Using the Backward and Forward Filling Methods
- Calculating Correlation Coefficient
Exploratory Data Analysis
- Generating the Feature Importance of the Target Variable
- Identifying the Target Variable
- Plotting a Heatmap
- Generating a Normal Distribution Plot
Reproducibility in Big Data Analysis
- Performing Data Reproducibility
- Preprocessing Missing Values with High Reproducibility
- Normalizating the Data
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