Theory

• Introduction to Python
  • Overview of Python and its history.
  • Key features and benefits.
• Anaconda IDE
  • Installing and setting up Anaconda.
  • Overview of Anaconda features.

Installing Python and Anaconda IDE

• Step-by-step guide to installing Python.
• Installing and configuring Anaconda IDE on various platforms.

• Arithmetic Operators
  • + – * / %% //
• Relational Operators:
  • <, >, <>, =, >=, <=
• Bitwise Operators
  • AND, OR, XOR, NOT, shifts.
• Increment and Decrement
  • Using +=, -=, etc.
• Logical Operators
  • AND, OR, NOT.

• Do While Loop
  • Syntax and examples.
• While Loop
  • Syntax and practical use cases
• For Loop
  • Iterating over sequences.
• Break and Continue
  • Controlling loop flow.

• Lists
  • Creating, accessing, modifying.
  • List comprehensions methods.
• Sets
  • Creating and using sets.
  • Set operations and methods
• Tuples
  • Creating and accessing tuples.
  • Tuple methods immutability.
• Dictionary
  • Creating & using dictionaries.
  • Methods operations.
• Strings
  • Manipulation and methods.
  • Formatting & Operations.
• Numbers
  • Numeric types in Python.
  • Operations & Conversions.

• Create Class
  • Defining & instantiating classes.
• Creating Objects
  • Object instantiation and usage.
• Member Variables
  • Instance and class variables.
• Member Methods
  • Defining and calling methods.

• Default Constructor
  • Creating default constructors.
• Parameterized Constructor
  • Using parameters in constructor.

• Is-A Relationship
  • Understanding inheritance types.
• Has-A Relationship
  • Composition and aggregation.

• Compile-Time (Overloading)
  • Method overloading concepts.
• Run-Time (Overriding)
  • Overriding dynamic p-morphism.

Methods returning objects and their applications.

• Modules
  • Creating and importing modules.
• Import
  • Importing from same and different packages.
• Packages
  • Understanding and creating packages.
  • Classifying same package same class, subclass, and non-subclass.
  • Different package subclass and non-subclass.
• Abstract Class
  • Concept of abstract classes and interfaces.
• Exception Handling
  • Handling Mechanism
    • Try, except, finally blocks.
  • Raising Exceptions
    • Custom exception raising.
  • User-Defined Exceptions
    • Creating and using custom exceptions.
• Multithreading
  • Introduction to multithreading.
  • Creating and managing threads.

• Reading and Writing Files
  • Reading from files.
  • Writing to files.

• Date Object
  • Working with dates.
• Date Time Object
  • Handling date & time.
• Time Object
  • Working with time.
• Time Zone Object
  • Managing time zones.

• Connecting Database
  • Setting up DB connections.
• Transactions
  • Commit and rollback.
• Queries
  • SELECT, INSERT, UPDATE
  • CREATE, ALTER, DROP.
• Batch Processing
  • Executing batch operations.

• Event Scheduler
  • Setting up and using event schedulers.
• Logging
  • Implementing logging in Python applications.
• Regular Expressions
  • Patterns and Matchers
    • Creating and using regular expressions.
  • Wildcards and Split
    • Advanced regex techniques.

• Data Frame Operations
  • Create Data Frames
    • Creating & initializing.
  • Structure and Summary
    • Understanding summarizing.
  • Subsetting Data Frames
    • Filtering & slicing data frames.
  • Modifying Data Frames
    • Adding new columns & rows.
    • Formatting applying functions.
• Data Reading and Writing
  • CSV, Excel, JSON, and Text Files
    • Reading & Writing file formats.
  • Database Integration
    • Reading from databases.
  • Other Formats
    • Reading SAS, SPSS, Stata files.

• Creating Charts and Graphs
  • Matplotlib
    • Bar charts, pie charts, boxplots, histograms, line graphs, scatterplots.
  • Plotly
    • Interactive visualizations with Plotly.

Python Analytics refers to using Python programming for data analysis, visualization, and machine learning. Python is a versatile language with extensive libraries like Pandas, NumPy, and Scikit-learn, making it ideal for analyzing large datasets, building predictive models, and visualizing insights. Learning Python Analytics enhances your ability to work in data-driven roles across various industries, from finance to healthcare, and is a crucial skill for aspiring data scientists.

Our Python Analytics Training course covers a comprehensive range of topics, including Python programming fundamentals, data manipulation with Pandas, data visualization using Matplotlib and Seaborn, statistical analysis, machine learning algorithms, and real-world data projects. The course is designed to equip you with the skills necessary to analyze and interpret complex data, build predictive models, and make data-driven decisions.

This course is ideal for professionals and students who want to enhance their data analysis skills, including data analysts, business analysts, aspiring data scientists, software developers, and anyone interested in leveraging Python for data analytics. Whether you’re new to Python or looking to expand your data analytics capabilities, this course provides the knowledge and hands-on experience needed to succeed.

No prior programming experience is required to enroll in this course. Our Python Analytics Training course is designed to start with the basics and gradually progress to advanced topics. However, familiarity with basic programming concepts and statistics can be helpful, but it is not mandatory.

The course duration is approximately 35+ hours of trainer-led instruction. The exact time to complete the course may vary depending on your learning pace, participation in hands-on projects, and additional study. Our flexible scheduling options allow you to learn at your own pace, whether you choose classroom sessions, online learning, or a hybrid format.

During the Python Analytics Training course, you will work on real-world projects that involve analyzing large datasets, building predictive models, and visualizing insights. These projects are designed to reinforce your learning and provide practical experience in applying Python to solve data analytics challenges in various industries.

Yes, upon successfully completing the Python Analytics Training course, you will receive a course completion certificate from Ampersand Academy. This certificate validates your expertise in Python Analytics and demonstrates your commitment to advancing your career in data analytics.

This course will equip you with the skills and knowledge required to excel in data analytics roles. You’ll learn how to analyze and interpret data, build predictive models, and communicate insights effectively. With the growing demand for data professionals, this course will enhance your employability and open doors to opportunities in various sectors, including finance, healthcare, marketing, and technology.

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The Python Analytics Training course is available in both classroom and online formats. We offer flexible learning options to accommodate your schedule and learning preferences. Whether you prefer in-person instruction or the convenience of online learning, our trainer-led sessions are designed to provide a comprehensive and interactive learning experience.