Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.
Includes: 2 Days Live Instructor Led Training, Detailed Course Materials, Live Projects & Certification
Duration: 2 Days – (9.30am – 4.30pm each day)
Global Access: Yes
Special Rate £ 425
Standard Rate £ 795
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.
By the end of the program, you will be able to accomplish the following:
• 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
Live Hands on Sessions with Exercises.
This course is designed for Python developers, data analysts, and data scientists. This course is not for beginners.
Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help in understanding various concepts explained in this course.
The Big Data Analysis with Python course includes:
- 2 Days Live Instructor Led Training
- Detailed Course Materials
- Multiple Live Hands on Exercises
Once you enrol for the course you will receive a bookiing confirmation.
You will be sent a digital verision of the course materials, along with the exercises files required for the hands on exercises.
You will then be able to review each module in the course and start to plan your study time.
You will then be sent a link 7 days prior to the course with instructions of how to access the class.
Full support is provided prior to the course to make sure you are ready.
Lesson 1: The Python Data Science Stack
• Python Libraries and Packages
• Using Pandas
• Data Type Conversion
• Aggregation and Grouping
• Exporting Data from Pandas
• Visualization with Pandas
Lesson 2: 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
Lesson 3: Working with Big Data Frameworks
• Writing Parquet Files
• Handling Unstructured Data
Lesson 4: 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
Lesson 5: Handling Missing Values and Correlation Analysis
• Setting up the Jupyter Notebook
• Missing Values
• Handling Missing Values in Spark DataFrames
Lesson 6: 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
Lesson 7: Reproducibility in Big Data Analysis
• Reproducibility with Jupyter Notebooks
• Gathering Data in a Reproducible Way
• Code Practices and Standards
• Avoiding Repetition
Lesson 8: Creating a Full Analysis Report
• Reading Data in Spark from Different Data Sources
• SQL Operations on a Spark DataFrame
• Generating Statistical Measurements