Data Engineering Course in Mysuru Overview


Data Engineering is a field focused on designing, building, and managing the infrastructure and systems required to collect, store, process, and analyze large volumes of data. Data engineers work to ensure that data is accessible, reliable, and efficiently processed for use by data scientists, analysts, and other stakeholders.

Basics of Python:

  • Variables, data types, operators
  • Control structures (if-else, loops)
  • Functions and modules

  • Advanced Python:

  • List comprehensions, lambda functions
  • Error handling (exceptions)
  • File I/O, working with CSV, JSON, and other file formats
  • NumPy:

  • Arrays, array operations
  • Mathematical functions, broadcasting

  • Pandas:

  • Series, DataFrame basics
  • Data manipulation (filtering, sorting, merging)
  • Handling missing data, reshaping data

  • SQL Basics:

  • Introduction to SQL, querying databases
  • SQLite integration with Python

  • Working with Relational Databases:

  • MySQL, PostgreSQL integration with Python
  • Data manipulation and querying using SQLAlchemy

  • NoSQL Databases:

  • Introduction to MongoDB
  • PyMongo for interacting with heatmaps
  • Web Scraping:

  • BeautifulSoup for parsing HTML
  • Scrapy framework for structured web scraping

  • API Integration:

  • Fetching data from RESTful APIs using requests library
  • Authentication and pagination in API calls

  • Data Streaming:

  • Introduction to Kafka and Apache Kafka-Python integration
  • Processing real-time data streams with Kafka
  • Data Cleaning Techniques:

  • Handling missing values, outliers
  • Data transformation: scaling, normalization

  • Data Validation and Quality:

  • Validating and cleaning data pipelines
  • Implementing data quality checks
  • Airflow Basics:

  • Introduction to Apache Airflow
  • Creating and scheduling data pipelines

  • Workflow Management:

  • DAGs (Directed Acyclic Graphs) in Airflow
  • Managing dependencies and tasks
  • Apache Spark:

  • Introduction to distributed computing
  • PySpark API for data processing

  • Hadoop Ecosystem:

  • Overview of Hadoop, HDFS
  • Using Hadoop Streaming with Python
  • JSON and XML:

  • Parsing and generating JSON/XML data
  • Using Python libraries for serialization

  • Protocol Buffers (Protobuf):

  • Text classification using Naive Bayes, SVM
  • Introduction to Protobuf
  • Implementing data serialization with Protobuf in Python
  • Introduction to Data Warehousing:

  • Basics of dimensional modeling
  • Implementing ETL processes

  • ETL Tools:

  • Talend Open Studio for Data Integration
  • Custom ETL pipelines using Python
  • AWS Services:

  • S3 for object storage, EC2 for compute
  • Using AWS SDK (Boto3) with Python

  • Google Cloud Platform (GCP):

  • Cloud Storage, BigQuery, Dataflow
  • Python libraries for GCP integration
  • Visualization Libraries:

  • Matplotlib, Seaborn for data visualization
  • Plotly for interactive visualizations

  • Reporting Tools:

  • Generating reports with Python
  • Data Security Best Practices:

  • Encryption, access controls
  • Compliance with GDPR and other regulations
  • Version Control:

  • Git for version control, GitHub/GitLab for collaboration
  • Managing data engineering projects