Python Programming for Data Professionals

Enrolled : 129 students
Duration : 74 Mins
No. of Videos: 38
Categories: eLearning
Certification: Yes
Access:Lifetime
Type:Animated Video Series

Python is one of the most popular and widely used programming languages across various industries, and it's also one of the easiest to learn. Learning Python can open up a wide range of career opportunities.

Whether you are a beginner or a data professional, our Python Programming for Data Professionals course will take you from basic to advanced Python concepts. This affordable course (just ₹99) covers:

1.Python Basics: Learn the fundamentals of Python programming.

2.Operators and Control Structures: Understand the building blocks of Python programs.

3.Functions and Error Handling: Write reusable code and handle errors effectively.

4.Data Structures: Work with lists, tuples, dictionaries, and sets.

5.Modules, Packages, and File Handling: Organize and manage your Python code.

6.Object-Oriented Programming: Learn to create classes and objects.

7.Data Analysis and Visualization: Explore powerful libraries like NumPy, Pandas, and Matplotlib.

8.Machine Learning and Big Data: Get a basic understanding of Python's applications in these fields.

Enroll in our course and unlock the power of Python to advance your data science career.

Python Programming for Data Professionals

Python Programming for Data Professionals

Course Features

1. Introduction to Python and Development Environment

1.1 Overview of Python and its applications 3.2 Min

1.1 Overview of Python and its applications

1.2 Installing Python: Download and installation steps 2.3 Min

1.2 Installing Python: Download and installation steps

1.3 Overview of popular IDEs: Jupyter Notebook, PyCharm, Visual Studio Code 3.2 Min

1.3 Overview of popular IDEs: Jupyter Notebook, PyCharm, Visual Studio Code

2. Python Basics

2.1 Understanding Data Types 3.2 Min

2.1 Understanding Data Types

2.2 Variables and Constants 1.3 Min

2.2 Variables and Constants

2.3 Basic Input/Output 1.2 Min

2.3 Basic Input/Output

2.4 Python Syntax and Comments 1.2 Min

2.4 Python Syntax and Comments

3. Operators and Control Structures

3.1 Operators: Arithmetic, Comparison, Logical, Assignment-1 3.5 Min

3.1 Operators: Arithmetic, Comparison, Logical, Assignment-1

3.2 Operators: Arithmetic, Comparison, Logical, Assignment-2 2.3 Min

3.2 Operators: Arithmetic, Comparison, Logical, Assignment-2

3.3 Typecasting and Data Type Conversion 1.4 Min

3.3 Typecasting and Data Type Conversion

3.4 Conditional Statements 2.1 Min

3.4 Conditional Statements

3.5 Looping Constructs 3.1 Min

3.5 Looping Constructs

4. Functions and Error Handling

4.1 Defining Functions and Lambda Functions 2.3 Min

4.1 Defining Functions and Lambda Functions

4.2 Function Arguments and Return Values 1.3 Min

4.2 Function Arguments and Return Values

4.3 Scope of Variables 1.5 Min

4.3 Scope of Variables

4.4 Error and Exception Handling-I 2.1 Min

4.4 Error and Exception Handling-I

4.5 Error and Exception Handling-II 3.0 Min

4.5 Error and Exception Handling-II

5. Data Structures

5.1 Data Structures (List, Tuples, Dictionaries, Sets)-1 2.2 Min

5.1 Data Structures (List, Tuples, Dictionaries, Sets)-1

5.2 Data Structures (List, Tuples, Dictionaries, Sets)-2 2.4 Min

5.2 Data Structures (List, Tuples, Dictionaries, Sets)-2

5.3 Advanced Data Structures - 1 2.3 Min

5.3 Advanced Data Structures - 1

5.4 Advanced Data Structures-2 3.0 Min

5.4 Advanced Data Structures-2

6. Modules, Packages, and File Handling

6.1 Working with Modules in Python 2.0 Min

6.1 Working with Modules in Python

6.2 Packages and Dependency Management 2.4 Min

6.2 Packages and Dependency Management

6.3 File Handling 2.0 Min

6.3 File Handling

7. Object-Oriented Programming (OOP)

7.1 Introduction to OOP Concepts: Classes and Objects 3.0 Min

7.1 Introduction to OOP Concepts: Classes and Objects

7.2 Inheritance: All types of Inheritance 1.4 Min

7.2 Inheritance: All types of Inheritance

7.3 Polymorphism: Method Overriding 1.2 Min

7.3 Polymorphism: Method Overriding

7.4 Data Abstraction and Encapsulation 2.1 Min

7.4 Data Abstraction and Encapsulation

8. Data Analysis and Visualization

8.1 Introduction to NumPy, Pandas 3.2 Min

8.1 Introduction to NumPy, Pandas

8.2 Data Cleaning and Preprocessing 2.0 Min

8.2 Data Cleaning and Preprocessing

8.3 Exploratory Data Analysis (EDA) 2.5 Min

8.3 Exploratory Data Analysis (EDA)

8.4 Data Visualization: Introduction to Matplotlib and Seaborn 3.5 Min

8.4 Data Visualization: Introduction to Matplotlib and Seaborn

9. Introduction to Machine Learning

9.1 Overview of machine learning concepts 1.5 Min

9.1 Overview of machine learning concepts

9.2 Supervised vs. unsupervised learning 4.0 Min

9.2 Supervised vs. unsupervised learning