Processing big data in real time is challenging due to scalability, information
inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools
that can control this data avalanche for you. With this course, you’ll learn effective techniques
to aggregate data into useful dimensions for posterior analysis, extract statistical
measurements, and transform datasets into features for other systems. The course begins
with an introduction to data manipulation in Python using Pandas. You’ll then get familiar
with statistical analysis and plotting techniques. With multiple hands-on activities in store,
you’ll be able to analyze data that is distributed on several computers by using Dask. As you
progress, you’ll study how to aggregate data for plots when the entire data cannot be
accommodated into memory. You’ll also explore Hadoop (HDFS and YARN), which will help
you tackle larger datasets. The course further covers Spark and its interaction with other
tools. By the end of this course, you’ll be able to bootstrap your own Python environment,
process large files, and manipulate data to generate statistics, metrics, and graphs.
Tujuan
- Learning Objectives
- Use Python to read and transform data into different formats
- Generate basic statistics and metrics using data on the disk
- Work with computing tasks distributed over a cluster
- Convert data from various sources into storage or querying formats
- Prepare data for statistical analysis, visualization, and machine learning
- Present data in the form of effective visuals
Materi Pokok
- The Python Data Science Stack
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Statistical Visualizations
- Types of Graphs and When to Use Them
- Components of a Graph
- Which Tool Should Be Used?
- Types of Graphs
- Pandas DataFrames and Grouped Data
- Changing Plot Design: Modifying Graph Components
- Exporting Graphs
- Working with Big Data Frameworks
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Diving Deeper with Spark
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Handling Missing Values and Correlation Analysis
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Exploratory Data Analysis
- 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
- Reproducibility in Big Data Analysis
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Creating a Full Analysis Report
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
