Diploma in Python -Big Data, Data Science, SQL and Data Engineering

Diploma in Python -Big Data, Data Science, SQL and Data Engineering

The “Diploma in Python -Big Data, Data Science, SQL and Data Engineering” is a comprehensive program designed to equip learners with essential skills in Python programming and its applications in the domains of data science, big data management, SQL, and data engineering. This diploma spans multiple modules, each tailored to provide a well-rounded understanding of key concepts and practical techniques. Starting with the basics of Python programming, participants will progress through intermediate and professional levels, covering areas such as variable manipulation, error handling, and advanced data manipulation using popular libraries like Pandas and PySpark.

As the course advances, students will delve into database essentials, gaining proficiency in PostgreSQL and SQL for efficient data management. The program also includes an in-depth exploration of Spark Dataframe APIs and Spark SQL, crucial tools for handling large-scale datasets and performing complex operations. Additionally, learners will master the art of building robust data engineering pipelines, ensuring seamless data flow from source to destination. Throughout this diploma, emphasis is placed on hands-on exercises, enabling students to apply their knowledge in real-world scenarios. By the end of this program, participants will have acquired a versatile skill set, empowering them to excel in roles requiring expertise in Python, data science, big data, SQL, and data engineering.

Course Key Learning 

• Master foundational Python concepts including variables, data types, loops, and error handling.
• Set up a local development environment, explore object-oriented programming, and utilize external modules effectively.
• Learn to clean, sort, and perform operations on data using Pandas, and create visualizations with Matplotlib and Plotly.
• Gain proficiency in PostgreSQL, database creation, indexing, and executing advanced SQL queries for efficient data manipulation.
• Develop core programming skills, work with data structures, and leverage Pandas for data manipulation and database interaction.
• Dive into PySpark and Spark Dataframes, focusing on data transformation techniques and advanced manipulation for handling large datasets.
• Design end-to-end data pipelines using Spark and Python, including ETL processes, and implement robust error handling and logging practices.

Module -1  B E G I N N E R   P Y T H O N

• Variables in Python
• String Manipulation
• Input and Print Functions
• Variable Naming
• Mathematical Operations in Python
• DataTypes
• Converting types
• Conditionals IF/ELIF/ELSE
• Logical Operators
• Error Handling
• Functions
• For Loops
• Code blocks and Indentation
• While Loops
• Python Dictionaries and Lists
• Nested Collections
• Returning Functions
• Return vs. Print

Module-2  I N T E R M E D I A T E   P Y T H O N

• Local Development Environment Setup
• PyCharm Tips and Tricks
• Python Object-Oriented Programming
• Creating Classes in Python
• Using External Python Modules/Import
• Getting / Setting Attributes
• Python Methods Class Initialisers Module Aliasing

Module-3  P R O F E S S I O N A L   P Y T H O N

• Packing and Unpacking Functions in Python
• Strongly Dynamic Typing
• Error Handling and Exceptions
• Try / Except/ Raise
• Working with date and time
• Hosting Python Code Online with PythonAnywhere

Module-4  INTRODUCTION TO DATA SCIENCES 

• Dataframe Inspection
• Data Cleaning
• Sorting Values in Dataframes
• Arithmetic Operations with Pandas
• Creating Line Charts with Matplotlib
• Using Jupyter Notebook
• Creating Scatterplots with Matplotlib
• Creating Bar Charts, Pie Charts, Donut Charts, Box Plots with
• Plotly
• Creating NumPy arrays
• Array Slicing and Subsetting
• Matrix Multiplication
• Bitwise and Operators in Pandas

Module 5: Database Essentials for Data Engineering

• Introduction to PostgreSQL and Database Management
• Creating and Managing Tables
• Indexing and Query Optimization
• Utilizing Pre-defined Functions in Data Engineering
• Advanced SQL Queries for Data Manipulation

Module 6: Data Engineering Programming with Python

• Basic Programming Constructs in Python
• Working with Collections (Lists, Dictionaries, etc.)
• Data Manipulation with Pandas Library
• Database Interaction with Python
• Error Handling and Exception

Module 7: Data Engineering with Spark Dataframe APIs (PySpark)

• Introduction to PySpark and Spark Dataframes
• Data Transformation with select, filter, groupBy, orderBy, etc.
• Advanced Data Manipulation Techniques
• Joins and Aggregations with Dataframes

Module 8: Advanced Data Engineering with Spark SQL (PySpark and Spark SQL)

• Writing High-Quality Spark SQL Queries
• Complex SQL Operations: SELECT, WHERE, GROUP BY, ORDER BY, etc.
• Window Functions in Spark SQL
• Optimization Techniques for Spark SQL

Module 9: Spark Metastore and Integration

• Understanding Spark Metastore and its Role
• Integrating Dataframes and Spark SQL
• Managing Metadata in Spark

Module 10: Building Data Engineering Pipelines with Spark and Python

• Designing Data Pipelines with Spark and Python
• Implementing ETL Processes
• Error Handling and Logging in Data Pipelines

Module 11: Working with Different File Formats

• Handling Parquet, JSON, CSV, and Other Formats
• Data Serialization and Deserialization
• File Formats for Efficient Data Storage and Processing

International Student Fee : 1,000 USD