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.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
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Test Prep
30+ Pre Assessment Questions |
30+ Post Assessment Questions |
Features
48+ LiveLab |
12+ Video tutorials |
20+ Minutes
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- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Preface
- About
Lessons 2:
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
Lessons 3:
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
Lessons 4:
Working with Big Data Frameworks
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
Lessons 5:
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
Lessons 6:
Handling Missing Values and Correlation Analysis
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
Lessons 7:
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
Lessons 8:
Reproducibility in Big Data Analysis
- Introduction
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Summary
Lessons 9:
Creating a Full Analysis Report
- Introduction
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
- Summary