Mastering PySpark to Become a Data Engineer, Data Scientist, or Data Analyst
This course aims to train you in the PySpark framework on Python, which is widely used by Data Engineers, Data Scientists, and Data Analysts to handle large volumes of data.
Acquire Fundamental Skills in PySpark
No more hunting for information on Google; the essence of your learning is concentrated in this course.
Learn Quickly for Effective Skill Development
This course is designed to familiarize you with PySpark quickly and effectively. In just a few hours and through two projects, you will possess the necessary knowledge to stand out.
Recent Course, Regularly Updated
Updated in 2024, this training is in line with the skills currently sought after in PySpark by companies.
Avoid Beginner’s Traps
The course highlights the best practices of an experienced PySpark developer to help you produce professional-quality code.
Succeed in Your Exams, Technical Tests, and PySpark Certifications
The course content is structured to effectively prepare you for your university exams, certifications, and technical tests related to PySpark.
Secure a Position in a Company or Undertake Freelance Assignments
PySpark is among the most coveted frameworks in both corporate and freelance settings. Training in this library opens the door to numerous professional opportunities.
Train for In-Demand Careers
In 2024, the demand for Data Scientists, Data Engineers, Data Analysts, and other Big Data-related professions is on the rise. Now is the perfect time to train for these professions by learning to master PySpark.
Work for Top Companies
Renowned companies such as Uber, Netflix, Airbnb, Amazon, Meta (formerly Facebook), and Microsoft, are currently seeking skilled professionals in PySpark.
Obtain a Completion Certificate
A certificate confirming that you have followed and completed the course will be awarded at the end of the training.
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3
Project Overview -
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Data Source
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5
Install PySpark on Google Colab and Create a Spark Session
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6
Import Data Into a Spark DataFrame
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7
Store Data Files on Google Drive
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8
Rename and Delete Columns in a Data Table
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9
Create and Add New Columns in a Spark DataFrame
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10
Before Continuing the Course...
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11
Filter a PySpark Dataframe with Conditions
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12
Group Data and Create New Columns Based on Existing Data
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13
Join PySpark Dataframes
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14
Perform Operations With Columns and Delete Unnecessary Data
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15
Create Statistical Columns
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16
Use Window Functions
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17
Create The Final Spark Data Table
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18
End of The PySpark Project
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