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.

• Fundamentals of Data Science
  • Overview and importance.
  • Key concepts and workflows.
• Data Preprocessing
  • Getting the Dataset
    • Acquiring and loading data.
  • Data Preparation
    • Missing & categorical data.
    • Splitting datasets and scaling.

• Various Regression Models
  • Simple Linear Regression
    • Building & evaluating simple LM.
  • Multiple Linear Regression
    • Handling multiple predictors.
  • Polynomial Regression
    • Modelling nonlinear relationships.
  • Support Vector Regression
    • Advanced regression techniques.
  • Decision Tree & Random Forest
    •  Tree-based regression models.

• Various Classification Models
  • Logistic Regression
    • Binary classification.
  • K-Nearest Neighbors (K-NN)
    • Distance-based classification.
  • Support Vector Machine (SVM)
    • Hyperplane Based.
  • Naive Bayes
    • Probabilistic classification.
• Decision Tree & Random Forest
• Tree-based models.

• Clustering Models
  • K-Means Clustering
    • Partitioning data into clusters.
  • Hierarchical Clustering
    • Creating a hierarchy.

• Association Rule Learning
  • Market basket analysis rules.
• Reinforcement Learning
  • Basics of reinforcement.
  • Applications and algorithms.
• Natural Language Processing
  • Text processing & analysis.
• Dimensionality Reduction
  • Techniques like PCA for Reduction.
• Model Selection
  • Best Model Selection Criteria.
  • Cross-validation techniques.

Real World Project

Data Science using Python involves leveraging the Python programming language to analyze and interpret complex data. It includes techniques in data manipulation, statistical analysis, machine learning, and data visualization, all tailored to extract actionable insights from data.

This course is ideal for aspiring data scientists, data analysts, AI professionals, statisticians, researchers, and anyone interested in building a career in data science using Python. It is also suitable for career changers looking to enter the field of data science.

Basic knowledge of programming concepts and familiarity with Python is beneficial. However, the course is designed to accommodate beginners, so prior experience in data science is not required.

You will learn core concepts of data science including data cleaning, statistical analysis, machine learning algorithms, data visualization, and how to apply Python libraries such as Pandas, NumPy, Scikit-Learn, and Matplotlib.

The course spans over 35+ hours of comprehensive, trainer-led instruction. The training is structured to cover both foundational and advanced topics, with flexible scheduling options available.

Training is available in multiple formats including classroom sessions, online classes, and hybrid models. This flexibility allows you to choose the mode that best fits your schedule and learning preferences.

Yes, the course includes practical sessions with real-world projects and case studies to reinforce learning and ensure you gain hands-on experience in applying Python to data science problems.

You will receive continuous support from experienced trainers, including guidance on assignments, project work, and one-on-one consultations. Our trainers are available to help you through any challenges you may encounter.

The course equips you with essential skills in data science and Python programming, making you a competitive candidate for roles in data science, machine learning, and analytics. We also provide career support including resume building, interview preparation, and job placement assistance.

Yes, you will earn a certification upon successful completion of the course, which validates your expertise in Data Science using Python and enhances your credentials in the field.

Yes, you will work on practical projects throughout the course to build a strong portfolio. This portfolio will showcase your ability to apply Python in real-world data science scenarios.

Absolutely! Contact us to schedule a free demo session, where you can learn more about the course content, meet the trainers, and get a feel for the training format.