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Course Description

Navy rectangle with the gold and blue interlocking python logo with the word big in blue and data in gold. Grey clouds in the top right with rain represented by falling electrical components

 

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. 

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.

Course Outline

  • 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
    • Hadoop
    • Spark
    • 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
    • Correlation
  • 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

Learner Outcomes

At the end of this program, you will be able to:

  • 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

Notes

Learn about more data analytics topics here

Recommendations

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.

 

 

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in

Type
Virtual: Instructor Led
Days
T, Th
Time
3:00PM to 6:00PM
Dates
May 04, 2021 to May 18, 2021
Schedule and Location
Contact Hours
14.0
Course Fee(s)
Tuition non-credit $1,395.00
Section Notes

Enrollment Deadline is Tuesday,  April 27, 2021  at 5 PM CST. Beyond this date, please call 314-935-4444 to register.

THIS IS A VIRTUAL COURSE--Attendee can participate from a location of their choosing. The live instructor teaches the course and provides the opportunity for remote attendees to participate in discusses and exercises with both in-person and remote attendees. Some courses involve hands-on activities and labs. These activities are performed via a secure cloud-accessible environment. Live online courses are through Zoom (or Webex); Video camera, microphone and speakers are necessary to participate in this class.

CANCELLATION POLICY

A full refund will be given when a registrant cancels more than five business days prior to the start of the class. Cancellations received within 5 business days of the start of the class and no-shows will be billed in full. Another person may be substituted at any time at no additional charge. 

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