
Unlock the power of big data with our Building Big Data Pipelines with PySpark, MongoDB, and Bokeh course. This course offers comprehensive training on handling massive volumes of data using advanced tools and methods. With a focus on data processing, machine learning, and visualisation, the course is perfect for those eager to upgrade their skills in big data analytics, data science, or data engineering.
Learn about the vast world of big data with our expertly structured curriculum. Starting with setup and installations, dive into data processing with PySpark and MongoDB. Discover machine learning techniques with PySpark and MLlib. Understand the captivating realm of data visualisation and create data pipeline scripts to manage and interpret complex data efficiently. With source code included, you get to learn by doing, strengthening your conceptual understanding and practical skills.
The Building Big Data Pipelines with PySpark, MongoDB, and Bokeh course empowers you to navigate the landscape of big data confidently. Whether you’re a data analyst seeking new insights or a data engineer looking to streamline data management processes, this course equips you with the skills to drive decision-making and shape the future of your business. Start your big data journey with us and gain a competitive edge in your career!
Upon completion of the Building Big Data Pipelines with PySpark MongoDB and Bokeh course, you should be able to:
Understand and apply PySpark for big data processing.
Implement MongoDB for flexible document modelling.
Use Bokeh for effective data visualisation.
Gain hands-on experience in creating data pipeline scripts.
Explore machine learning techniques with PySpark and MLlib.
Gain a competitive edge with practical understanding of big data analytics.
Beginners aspiring to delve into data science.
Professionals looking to upgrade their big data skills.
Machine learning enthusiasts interested in pipeline construction.
Individuals keen on gaining proficiency in PySpark, MongoDB, and Bokeh.
After studying the course materials of the Building Big Data Pipelines with PySpark MongoDB and Bokeh course, there will be a written assignment test which you can take either during or at the end of the course. After passing the test, you will have a range of certification options. A CPD Accredited PDF Certificate costs £4.99, while a CPD Accredited Hardcopy Certificate is £8.00. We also offer transcript services. A PDF Transcript costs £4.99, and a Hardcopy Transcript is £9.99. Select according to your needs, and we assure timely delivery of your chosen certificate.
Our course will help you to pursue a range of career paths, such as:
Data Scientist: £35,000-£80,000
Big Data Engineer: £40,000-£90,000
Machine Learning Engineer: £45,000-£100,000
Data Analyst: £30,000-£65,000
Database Administrator: £32,000-£75,000
Business Intelligence Developer: £40,000-£85,000
Section 01: Introduction | |||
Introduction | 00:10:00 | ||
Section 02: Setup and Installations | |||
Python Installation | 00:03:00 | ||
Installing Third Party Libraries | 00:03:00 | ||
Installing Apache Spark | 00:12:00 | ||
Installing Java (Optional) | 00:05:00 | ||
Testing Apache Spark Installation | 00:06:00 | ||
Installing MongoDB | 00:04:00 | ||
Installing NoSQL Booster for MongoDB | 00:07:00 | ||
Section 03: Data Processing with PySpark and MongoDB | |||
Integrating PySpark with Jupyter Notebook | 00:05:00 | ||
Data Extraction | 00:19:00 | ||
Data Transformation | 00:15:00 | ||
Loading Data into MongoDB | 00:13:00 | ||
Section 04: Machine Learning with PySpark and MLlib | |||
Data Pre-processing | 00:19:00 | ||
Building the Predictive Model | 00:12:00 | ||
Creating the Prediction Dataset | 00:08:00 | ||
Section 05: Data Visualization | |||
Loading the Data Sources from MongoDB | 00:17:00 | ||
Creating a Map Plot | 00:33:00 | ||
Creating a Bar Chart | 00:09:00 | ||
Creating a Magnitude Plot | 00:15:00 | ||
Creating a Grid Plot | 00:09:00 | ||
Section 06: Creating the Data Pipeline Scripts | |||
Installing Visual Studio Code | 00:05:00 | ||
Creating the PySpark ETL Script | 00:24:00 | ||
Creating the Machine Learning Script | 00:30:00 | ||
Creating the Dashboard Server | 00:21:00 | ||
Source Code | |||
Source Code and Notebook | 00:00:00 |
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