# Big Data Analysis with Python: A Complete Course

Practice and refine your big data analytical skills with Python to distill complicated data into digestible and meaningful insights.

(BIG-DATA-PYTHON.AJ1) / ISBN : 978-1-64459-315-8## About This Course

This big data analysis with Python course online is your go-to training guide for mastering the art of handling and analyzing massive piles of data. You’ll experiment with Python libraries like Pandas, Seaborn, and Spark. Also, our course modules will help you visualize data, manage missing values, and perform in-depth statistical analysis, giving you hands-on experience. By the end, you’ll have the technical skills to tackle real-world challenges and make data-driven decisions.

## Skills You’ll Get

- Use Pandas and Spark for effective data handling
- Create insightful statistical visualizations using Seaborn and Matplotlib to communicate findings clearly
- Work with frameworks like Hadoop and Spark to manage large datasets
- Handle missing values and prepare data for analysis and accuracy
- Translate business problems into a measurable metric and actionable insight
- Maintain data analysis reproducibility with best practices using Jupyter Notebooks
- Dive deep into Spark DataFrames for advanced data manipulation and analysis
- Compile full analysis reports to present data findings professionally
- Execute SQL operations on Spark DataFrames for efficient data querying

### Interactive Lessons

9+ Interactive Lessons | 20+ Exercises | 50+ Quizzes | 65+ Flashcards | 65+ Glossary of terms

### Gamified TestPrep

30+ Pre Assessment Questions | 30+ Post Assessment Questions |

### Hands-On Labs

48+ LiveLab | 12+ Video tutorials | 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

## Any questions?

Check out the FAQs

Get quick answers to common questions about the Big Data Analytics in Python course.

Contact Us NowBig data consists of massive amounts of datasets that are analyzed to identify and reveal patterns, trends, and relationships. Big data analysis helps organizations to make decisions, improve their operations, and discover new opportunities to penetrate the market.

Python programming language is famous in the data science field due to its simplicity, improved readability, user-friendly libraries, and strong developer community support.

Polish your data visualization skills to present raw data in graphical formats and identify patterns and trends to make data-driven decisions. Eventually, it provides you with a competitive advantage in the market.

Yes, having basic knowledge of Python programming is beneficial to take this data analysis with Python course.

This course is ideal for data scientists, analysts, and anyone interested in improving their analytical skills using Python.

Yes, a basic understanding of Python programming languages is recommended to get the most out of this course.

The course covers tools and libraries like Pandas, Seaborn, Matplotlib, and Spark for data manipulation and visualization.

No, prior knowledge of machine learning is not required to enroll in this course.

The average salary of a big data analyst varies, but typically ranges from $80,000 to $120,000 per year, depending on experience, location, and industry.

By learning Python big data analysis and visualization, you’ll be able to handle large datasets, perform advanced analysis, and make smart, profitable decisions. In addition, you’ll be in a position to pursue high-paying jobs, promotions, and other career opportunities.