Advanced Data engineering training and placement

•  Module 1: Introduction to Python
•  Overview of Python
•  Installing Python and setting up the environment
• Writing your first Python program
• Understanding the Python interpreter

• Python syntax and semantics
•  Variables and data types
•  Basic operators (arithmetic, comparison, logical)
• Input and output functions Descriptive statistics, inferential statistics,
• regression analysis.

Tools:

•   Python 

•   Conditional statements (if, elif, else)
•  Loops (for, while)
• Control flow tools (break, continue, pass)

• Lesson 4: Lists and Tuples.
• Creating and using lists
•  Understanding tuples and their uses 

• Creating and using dictionaries
•  Dictionary methods and operations
•  Understanding sets and their uses

• String operations and methods
  
•  String formatting
  
• Working with regular expressions

• Lesson 7: Functions
• Defining and calling functions
  
• Function arguments and return values
  
• Scope and lifetime of variables
  
•  Lambda functions

• Importing modules
  
• Standard library overview
  
•  Creating and using packages
  
• Managing dependencies with pip

• Lesson 9: Classes and Objects
  
•  Introduction to OOP
  
•  Creating classes and objects

•  Instance variables and methods

•  Inheritance and polymorphism
  
• Encapsulation and abstraction
  
•  Magic methods and operator overloading 

• Lesson 11: File Handling
  
•  Reading from and writing to files
  
• Working with file paths
  
•  Using context managers
  

• Understanding exceptions
  
•  Try, except, else, and finally blocks
  
•  Creating custom exceptions
  
  

• Lesson 13: JSON and CSV
  
•  Reading and writing JSON data
  
•  Working with CSV files
  
• Parsing and processing data
  
  

• Lesson 14: Understanding Comprehensions
• List comprehensions
• Dictionary comprehensions
• Set comprehensions
  
  

• Nested comprehensions
• Conditional comprehensions
  
  
  

• Comparing comprehensions with loops
• Best practices and common pitfalls
  
  
  

• Lesson 17: Iterator Protocol
• Understanding iter  () and   next  ()
• Built-in iterators in Python

   

  
  
  

• Implementing your own iterator classes
• Use cases for custom iterators
  
  
  

• count(), cycle(), chain(),
• Combining iterators with comprehensions
  
  
  

• Lesson 20 : Introduction to Generators 
• Understanding yield keyword
• Generator functions regular functions
  
   
  

• Syntax and use cases
• Comparison with list comprehensions
   
  

• Chaining generators
• Generators for data streaming and processing
   
  

• Lesson 23: Basics of Regular Expressions
• Introduction to regex syntax
• Using the re module in Python
   
  

• Matching and searching
• Grouping and capturing
• Replacing and splitting text
   
  

• Lookahead and lookbehind assertions
• Non-capturing groups
• Practical examples in data validation and parsing
   
  

• Lesson 26: Introduction to Datetime Module
• Understanding datetime, date, time, and timedelta
• Creating and formatting dates and times 
  

• Adding and subtracting dates and times
• Comparing dates and times
   
  

• Working with pytz module
• Converting between time zones
   
  

• Lesson 29: Web Scraping 

• Introduction to web scrapings

•  

  Using libraries like Beautiful Soup and Scrapy
• Parsing HTML and XML

• Module 1: Introduction to SQL and Databases
• Lesson 1: Overview of Databases
• Understanding Databases: Types and Uses
• Relational Databases vs. NoSQL Databases
• Introduction to SQL (Structured Query Language)

• Installing and Setting Up a SQL Database (SQL SERVER)
• Using SQL Interfaces (SSMS STUDIO)
• Connecting to a Database

• Lesson 1: Introduction to SQL Syntax
• Basic SQL Commands: SELECT, FROM,
• Filtering Data with WHERE Clauses
• SQL Syntax Rules and Best Practices

• Selecting Specific Columns
• Using Aliases for Columns and Tables
• Sorting Data with ORDER BY

• Using Comparison Operators
• Using Logical Operators (AND, OR, NOT)
• Handling NULL Values

• Lesson 1: Aggregate Functions
•  Introduction to Aggregate Functions: COUNT, SUM, AVG, MAX, MIN
• Combining Aggregate Functions with GROUP BY
• Filtering Grouped Data with HAVING

• Understanding GROUP BY Clause
• Grouping by Multiple Columns
• Using ROLLUP and CUBE for Advanced Grouping

• Lesson 1: Understanding Join
• Introduction to Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
• Joining Multiple Tables
• Aliasing Tables in Joins

• Self Joins
• Cross Joins
• Using Subqueries with Joins

• Lesson 1: Subqueries and Nested Queries
• Writing Subqueries in SELECT, FROM, and WHERE Clauses
• Correlated Subqueries
• Using Subqueries for Data Analysis

• Introduction to Window Functions
• Using ROW_NUMBER, RANK, and DENSE_RANK
• Applying PARTITION BY and ORDER BY in Window Functions

• Introduction to CTEs
• Writing Recursive CTEs
• Using CTEs for Complex Queries

• Lesson 1: Inserting Data
• Basic INSERT Statements
• Inserting Multiple Rows
• Using SELECT for Inserting Data

• Basic UPDATE Statements
• Using Subqueries in UPDATE
• DELETE Statements and Safe Deletion Practices

• Lesson 1: SQL and Python
• Using SQL with Python (pandas, SQL Alchemy)
• Integrating SQL Queries in Python Workflows
• Analysing SQL Data in Jupyter Notebooks

• Module 1: Introduction to NoSQL and MongoDB 
• NoSQL database overview
• Installing and setting up MongoDB
• Basic CRUD operations (Create, Read, Update, Delete)
• MongoDB data modeling and schema design

• Aggregation framework and pipeline
• Indexing for performance optimization
• Working with geospatial data
• Backup and restore strategies

• Using PyMongo to interact with MongoDB in Python
• Data analysis with MongoDB and Pandas
• Visualizing MongoDB data with popular libraries
• Case studies and real-world applications

• Module 1: Introduction to Big Data and PySpark 
• Understanding Big Data concepts
• Setting up PySpark environment
• Basics of RDDs (Resilient Distributed Datasets)
• Transformations and Actions in PySpark

• Introduction to DataFrames
• Performing SQL operations on DataFrames
• Data manipulation and cleaning with PySpark
• Working with different file formats (CSV, JSON, Parquet)

• Machine learning with PySpark MLlib
• Performance tuning and optimization
• Handling large-scale data processing
• Real-world project: End-to-end data pipeline with PySpark

• Module 1: Introduction to NumPy
• Lesson 1: Getting Started with NumPy
• What is NumPy and why use it?
• Installing NumPy
• Importing NumPy
• Basic operations with NumPy
• Understanding NumPy arrays

• Creating arrays from lists and tuples
• Using built-in NumPy functions to create arrays (arange, zeros, ones, full, linspace, eye)
• Array attributes (shape, size, dtype, ndim)
• Reshaping arrays
• Indexing and slicing arrays
• Array broadcasting

• Lesson 3: Operations on Arrays
• Arithmetic operations
• Universal functions (ufuncs)
• Aggregate functions (sum, mean, std, var, min, max)
• Boolean operations and masking
• Sorting arrays
• Unique elements

• Basic linear algebra with NumPy
• Matrix operations (dot product, cross product)
• Solving linear equations
• Eigenvalues and eigenvectors
• Matrix decomposition (LU, QR, SVD)

• Lesson 5: Structured Arrays and Record Arrays
• Understanding structured arrays
• Creating and manipulating structured arrays
• Record arrays and their use cases
• Field access and modification

• Reading data from files (text, CSV)
• Writing arrays to files
• Handling large datasets with memory mapping
• Saving and loading NumPy objects with save, np.load, np.savez

• Lesson 7: Broadcasting and Vectorization
• Deep dive into broadcasting rules
• Vectorized operations for performance
• Using vectorize for vectorization

• Module 1: Introduction to Pandas
• Understanding Pandas and its role in data science
• Installation and setup

• Series: Creation, manipulation, and operations
• DataFrame: Creation, manipulation, and operations

• Indexing and selecting data
• Handling missing data
• Data alignment
• Merging, joining, and concatenating data
• GroupBy operations
• Pivot tables and cross-tabulations

• Handling duplicates
• Data transformation
• String operations

• Reading and writing data from/to different file formats (CSV, Excel, SQL, )

• Date and time data types and tools
• Time series basics
• Resampling and frequency conversion

• Window functions
• Performance improvement using categorical data and memory optimization

• Module 1: Introduction to GitHub
• Overview of Version Control Systems
• Setting up Git and GitHub accounts
• Basic Git commands (clone, commit, push, pull)
• Creating and managing repositories

• Branching and merging strategies
• Pull requests and code reviews
• Managing issues and milestones
• Best practices for collaborative projects

• Module 1: Getting Started with VSCode
• Installing and configuring VSCode
• Key features and extensions for data science
• Customizing the editor for efficiency
• Integrated terminal and version control

• Setting up Python environment and interpreter
• Debugging and testing Python code
• Using Jupyter Notebooks within VSCode
• Popular extensions for data science (Python, Jupyter, Pylance)

• Module 1: Introduction to Jupyter Notebooks
• Installing Jupyter Notebook
• Notebook interface and basic features
• Markdown and code cells
• Creating and organizing notebooks

• Importing and exploring data with Pandas
• Data visualization with Matplotlib and Seaborn
• Interactive widgets with ipywidgets
• Sharing notebooks with JupyterHub and nbviewer