What you'll get
- 13+ Hours
- 6 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Apply Python alongside Spark to perform advanced Big Data analysis
- Master the latest Spark DataFrame syntax for efficient data handling
- Gain hands-on experience through consulting-style projects that reflect real-world challenges
- Predict customer churn using Logistic Regression techniques
- Implement Random Forests in Spark for effective classification tasks
- Explore Spark's Gradient Boosted Trees for robust predictive modeling
- Build high-performance machine learning models leveraging Spark's capabilities
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Pyspark Beginner | 2h 16m | ✔ | View Curriculum |
| Pyspark Intermediate | 2h 02m | ✔ | View Curriculum |
| Pyspark Advance | 1h 18m | ✔ | View Curriculum |
| Apache Spark Advanced | 5h 47m | ✔ | View Curriculum |
| Project on Apache Spark: Building an ETL Framework | 2h 1m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Apache Spark Fundamentals | 1h 38m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
The Spark and Python for Big Data with PySpark course introduces learners to the powerful combination of Python and Apache Spark, a leading Big Data analysis platform. Focused on developing valuable skills, the course teaches professionals how to handle extensive datasets with precision and efficiency. Industry leaders like Google, Facebook, Netflix, Airbnb, Amazon, and NASA rely on Spark to tackle complex data challenges, and its speed, up to 100x faster than Hadoop MapReduce, has created high demand for Spark expertise.
Participants start with a concise Python refresher before mastering Spark DataFrames using the latest Spark syntax. Through hands-on exercises and mock consulting projects, learners apply their skills to realistic scenarios that simulate real-world data challenges.
The Spark and Python for Big Data with PySpark course also covers advanced Spark technologies, including Spark SQL, Spark Streaming, and machine learning models like Gradient Boosted Trees. By the end, learners will feel confident listing Spark and PySpark on their resumes. The course includes a 30-day satisfaction guarantee and awards a LinkedIn Certificate upon completion.
For anyone ready to dive into Python, Spark, and Big Data, the Spark and Python for Big Data with PySpark course provides a practical, comprehensive path to mastering these essential technologies.
Requirements
- Intended for learners who can read, write, and comprehend code in Python
- Requires a 64-bit system running Windows, macOS, or Linux
- Minimum hardware specification: 8 GB RAM to support practical exercises and project work
Target Audience
- Designed for engineers and system designers building scalable data systems with Apache Spark
- Ideal for developers transitioning into Spark-focused Data Engineering roles
- Suitable for Python users expanding into large-scale data processing
- Helpful for professionals proficient in other languages who want to learn Spark efficiently
