Applied Python Data Engineering Specialization

By theetay.com Categories: Coursera
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About Course

Applied Python Data Engineering Specialization, Learn how to use data engineering to leverage big data for business strategy, data analysis, or machine learning and AI. By completing this course series, you’ll empower yourself with the knowledge and proficiency required to build efficient data pipelines, manage cutting-edge platforms like Hadoop, Spark, Snowflake, Databricks, and Kubernetes, and tell stories with data through visualization. You will delve into foundational big data concepts, distributed computing with Spark, Snowflake’s architecture, Databricks’ machine learning capabilities, Python techniques for data visualization, and critical methodologies like DataOps.

This course series is designed for software engineers, developers, researchers, and data scientists who want to strengthen their specialization in data science or machine learning, as well as for professionals who are interested in pursuing a career as a data-focused software engineer, data scientist, or a data engineer working in cloud, machine learning, business intelligence, or other field. The Specialization features a capstone project focused on using Databricks’ API to replicate an existing project. This provides hands-on experience working with Databricks to build a portfolio-ready data solution. You will apply Python to a variety of data engineering tasks.

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What Will You Learn?

  • Create scalable big data pipelines (Hadoop, Spark, Snowflake, Databricks) for efficient data handling.
  • Build machine learning workflows (PySpark, MLFlow) on Databricks for seamless model development and deployment.
  • Implement DataOps/DevOps to streamline data engineering processes.
  • Formulate and communicate data-driven insights and narratives through impactful visualizations with Python and data storytelling

Course Content

01. Spark, Hadoop, and Snowflake for Data Engineering

  • 001 01_welcome-to-data-engineering-platforms-with-python_instructions.html
    00:00
  • 004 02_meet-your-co-instructor-kennedy-behrman.mp4
    00:00
  • 007 03_meet-your-co-instructor-noah-gift.mp4
    00:00
  • 010 01_overview-of-big-data-platforms.mp4
    00:00
  • 011 02_big-data-platforms_quiz.html
    00:00
  • 014 03_getting-started-with-hadoop.mp4
    00:00
  • 015 04_what-is-apache-hadoop_instructions.html
    00:00
  • 016 05_apache-hadoop-concepts_quiz.html
    00:00
  • 019 06_getting-started-with-spark.mp4
    00:00
  • 020 07_what-is-apache-spark_cluster-overview.html
    00:00
  • 022 08_use-apache-spark-in-azure-databricks-optional_instructions.html
    00:00
  • 023 09_apache-spark-concepts_quiz.html
    00:00
  • 024 10_choosing-between-hadoop-and-spark_instructions.html
    00:00
  • 027 01_introduction-to-resilient-distributed-datasets-rdd.mp4
    00:00
  • 029 02_what-are-rdds_instructions.html
    00:00
  • 030 03_rdd-concepts_quiz.html
    00:00
  • 033 04_resilient-distributed-datasets-rdd-demo.mp4
    00:00
  • 034 05_getting-started-creating-rdd-s-with-pyspark_instructions.html
    00:00
  • 037 01_introduction-to-spark-sql.mp4
    00:00
  • 038 02_spark-sql-dataframes-and-datasets_instructions.html
    00:00
  • 041 03_spark-sql-concepts_quiz.html
    00:00
  • 042 04_pyspark-and-spark-sql_instructions.html
    00:00
  • 045 05_pyspark-dataframe-demo-part-1.mp4
    00:00
  • 048 06_pyspark-dataframe-demo-part-2.mp4
    00:00
  • 049 07_pyspark-dataframe-concepts_quiz.html
    00:00
  • 053 01_what-is-snowflake.mp4
    00:00
  • 054 02_accessing-snowflake_instructions.html
    00:00
  • 055 03_detailed-view-inside-snowflake_instructions.html
    00:00
  • 056 04_snowflake-architecture_quiz.html
    00:00
  • 059 05_snowflake-layers.mp4
    00:00
  • 060 06_snowflake-layers_quiz.html
    00:00
  • 063 07_snowflake-web-ui.mp4
    00:00
  • 066 01_navigating-snowflake.mp4
    00:00
  • 067 02_navigating-snowflake_quiz.html
    00:00
  • 068 03_snowsight-the-snowflake-web-interface_instructions.html
    00:00
  • 071 04_creating-a-table-in-snowflake.mp4
    00:00
  • 072 05_creating-a-table_quiz.html
    00:00
  • 073 06_working-with-warehouses_instructions.html
    00:00
  • 076 07_snowflake-warehouses.mp4
    00:00
  • 079 01_writing-to-snowflake.mp4
    00:00
  • 080 02_writing-to-snowflake_quiz.html
    00:00
  • 083 03_reading-from-snowflake.mp4
    00:00
  • 084 04_python-connector-documentation_instructions.html
    00:00
  • 085 01_snowflake_exam.html
    00:00
  • 089 02_accessing-databricks.mp4
    00:00
  • 092 03_spark-notebooks-with-databricks.mp4
    00:00
  • 095 04_using-data-with-databricks.mp4
    00:00
  • 098 05_working-with-workspaces-in-databricks.mp4
    00:00
  • 101 06_advanced-capabilities-of-databricks.mp4
    00:00
  • 102 07_pyspark-sql_quiz.html
    00:00
  • 103 01_introduction-to-databricks-machine-learning_instructions.html
    00:00
  • 106 02_pyspark-introduction-on-databricks.mp4
    00:00
  • 109 03_exploring-databricks-azure-features.mp4
    00:00
  • 110 04_what-is-the-databricks-file-system-dbfs_instructions.html
    00:00
  • 113 05_using-the-dbfs-to-automl-workflow.mp4
    00:00
  • 116 06_load-register-and-deploy-ml-models.mp4
    00:00
  • 117 07_serverless-compute-with-databricks_instructions.html
    00:00
  • 120 08_databricks-model-registry.mp4
    00:00
  • 123 09_model-serving-on-databricks.mp4
    00:00
  • 124 10_pyspark-dataframes_quiz.html
    00:00
  • 127 01_what-is-mlops.mp4
    00:00
  • 128 02_mlops-workflow-on-azure-databricks_instructions.html
    00:00
  • 131 03_exploring-open-source-mlflow-frameworks.mp4
    00:00
  • 134 04_running-mlflow-with-databricks.mp4
    00:00
  • 135 05_run-mlflow-projects-on-azure-databricks_instructions.html
    00:00
  • 138 06_end-to-end-databricks-mlflow.mp4
    00:00
  • 141 07_databricks-autologging-with-mlflow.mp4
    00:00
  • 142 08_databricks-autologging_instructions.html
    00:00
  • 143 09_mlflow-with-databricks_quiz.html
    00:00
  • 147 01_kaizen-methodology-for-data.mp4
    00:00
  • 148 02_kaizen-methodology_quiz.html
    00:00
  • 151 03_introducing-github-codespaces.mp4
    00:00
  • 152 04_github-codespaces-overview_instructions.html
    00:00
  • 155 05_compiling-python-in-github-codespaces.mp4
    00:00
  • 156 06_getting-started-with-amazon-sagemaker-studio-lab_instructions.html
    00:00
  • 159 07_walking-through-sagemaker-studio-lab.mp4
    00:00
  • 160 08_teaching-mlops-at-scale-with-github-optional_instructions.html
    00:00
  • 163 09_pytest-master-class-optional.mp4
    00:00
  • 166 01_what-is-devops.mp4
    00:00
  • 169 02_devops-key-concepts.mp4
    00:00
  • 170 03_getting-started-with-devops-and-cloud-computing_instructions.html
    00:00
  • 171 04_devops_quiz.html
    00:00
  • 174 05_continuous-integration-overview.mp4
    00:00
  • 177 06_build-an-nlp-in-cloud9-with-python.mp4
    00:00
  • 180 07_build-a-continuously-deployed-containerized-fastapi-microservice.mp4
    00:00
  • 183 08_hugo-continuous-deploy-on-aws.mp4
    00:00
  • 186 09_container-based-continuous-delivery.mp4
    00:00
  • 189 01_what-is-dataops.mp4
    00:00
  • 192 02_dataops-and-mlops-with-snowflake.mp4
    00:00
  • 193 03_dataops_quiz.html
    00:00
  • 196 04_building-cloud-pipelines-with-step-functions-and-lambda.mp4
    00:00
  • 199 05_what-is-a-data-lake.mp4
    00:00
  • 202 06_data-warehouse-vs-feature-store.mp4
    00:00
  • 205 07_big-data-challenges.mp4
    00:00
  • 208 08_types-of-big-data-processing.mp4
    00:00
  • 211 09_real-world-data-engineering-pipeline.mp4
    00:00
  • 214 10_data-feedback-loop.mp4
    00:00
  • 215 11_benefits-of-serverless-etl-technologies_instructions.html
    00:00
  • 216 01_dataops-and-operations-methodologies_exam.html
    00:00
  • A-Detailed-View-Inside-Snowflake.pdf
    00:00

02. Virtualization, Docker, and Kubernetes for Data Engineering

03. Data Visualization with Python

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