Python for Data Science- Python Programming Data Analysis

Python for Data Science: Python Programming & Data Analysis, available at $54.99, has an average rating of 4.27, with 41 lectures, 19 quizzes, based on 27 reviews, and has 3102 subscribers.

You will learn about Gain a thorough understanding of Python syntax, script writing, and fundamental programming concepts such as variables, data types, and string operations Become adept at using lists, dictionaries, tuples, and sets for organizing and managing data effectively within Python Master the use of conditional statements and loops in Python to automate and optimize data processing tasks Learn to design reusable Python functions to perform repetitive tasks efficiently, including knowledge of recursion and lambda functions Acquire skills in reading from and writing to files in Python, crucial for data processing tasks in real-world applications Understand how to use NumPy arrays for complex mathematical computations and effectively handle large datasets with high performance Master the use of Pandas for data manipulation and analysis; learn how to explore, clean, and transform data into a suitable format for analysis Develop the ability to create insightful visual representations of data using Matplotlib and Seaborn libraries of Python This course is ideal for individuals who are Aspiring Data Scientists: Beginners who are interested in entering the field of data science and need to build foundational skills in programming and data handling. or Professionals Seeking a Career Transition: Individuals in various fields such as business, finance, or healthcare, who wish to transition into data-centric roles and require practical skills in data manipulation and analysis. or Hobbyists and Personal Learners: Anyone with a curiosity about data science and how Python programming can be applied to sort, analyze, and visualize data in personal projects or informal learning. or Students in STEM Fields: College students or high school seniors who are studying subjects like statistics, mathematics, or computer science and want to enhance their data analysis capabilities. It is particularly useful for Aspiring Data Scientists: Beginners who are interested in entering the field of data science and need to build foundational skills in programming and data handling. or Professionals Seeking a Career Transition: Individuals in various fields such as business, finance, or healthcare, who wish to transition into data-centric roles and require practical skills in data manipulation and analysis. or Hobbyists and Personal Learners: Anyone with a curiosity about data science and how Python programming can be applied to sort, analyze, and visualize data in personal projects or informal learning. or Students in STEM Fields: College students or high school seniors who are studying subjects like statistics, mathematics, or computer science and want to enhance their data analysis capabilities.

Enroll now: Python for Data Science: Python Programming & Data Analysis

Summary

Title: Python for Data Science: Python Programming & Data Analysis

Price: $54.99

Average Rating: 4.27

Number of Lectures: 41

Number of Quizzes: 19

Number of Published Lectures: 41

Number of Published Quizzes: 19

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a thorough understanding of Python syntax, script writing, and fundamental programming concepts such as variables, data types, and string operations

Become adept at using lists, dictionaries, tuples, and sets for organizing and managing data effectively within Python

Master the use of conditional statements and loops in Python to automate and optimize data processing tasks

Learn to design reusable Python functions to perform repetitive tasks efficiently, including knowledge of recursion and lambda functions

Acquire skills in reading from and writing to files in Python, crucial for data processing tasks in real-world applications

Understand how to use NumPy arrays for complex mathematical computations and effectively handle large datasets with high performance

Master the use of Pandas for data manipulation and analysis; learn how to explore, clean, and transform data into a suitable format for analysis

Develop the ability to create insightful visual representations of data using Matplotlib and Seaborn libraries of Python

Who Should Attend

Aspiring Data Scientists: Beginners who are interested in entering the field of data science and need to build foundational skills in programming and data handling.

Professionals Seeking a Career Transition: Individuals in various fields such as business, finance, or healthcare, who wish to transition into data-centric roles and require practical skills in data manipulation and analysis.

Hobbyists and Personal Learners: Anyone with a curiosity about data science and how Python programming can be applied to sort, analyze, and visualize data in personal projects or informal learning.

Students in STEM Fields: College students or high school seniors who are studying subjects like statistics, mathematics, or computer science and want to enhance their data analysis capabilities.

Target Audiences

Aspiring Data Scientists: Beginners who are interested in entering the field of data science and need to build foundational skills in programming and data handling.

Professionals Seeking a Career Transition: Individuals in various fields such as business, finance, or healthcare, who wish to transition into data-centric roles and require practical skills in data manipulation and analysis.

Hobbyists and Personal Learners: Anyone with a curiosity about data science and how Python programming can be applied to sort, analyze, and visualize data in personal projects or informal learning.

Students in STEM Fields: College students or high school seniors who are studying subjects like statistics, mathematics, or computer science and want to enhance their data analysis capabilities.

Are you aspiring to become a data scientist or aiming to enhance your data analysis skills? Have you ever found yourself overwhelmed by data, wondering how to turn it into actionable insights? If your goal is to not only understand the vast world of data science but also to apply this knowledge practically, then this course is designed with you in mind. Dive into the transformative world of Python and its powerful libraries, and start your journey towards becoming a proficient data scientist.

This course offers a comprehensive guide to mastering Python programming and data analysis, tailored specifically for data science applications. By engaging with this course, you will:

Develop a solid foundation in Python programming, from basic syntax to advanced functions.

Master the art of handling and analyzing data using Python’s most powerful libraries, including NumPy for numerical data, Pandas for data manipulation, Matplotlib and Seaborn for data visualization.

Create compelling data visualizations that communicate your findings effectively.

Implement data manipulation techniques to clean, transform, and prepare your data for analysis.

Solve real-world data analysis problems by applying practical programming solutions.

Why is learning about this topic crucial?

In today’s data-driven world, the ability to analyze and interpret data is indispensable. Python, being at the forefront of data science, offers an extensive ecosystem of libraries and tools that make data analysis accessible and powerful. Whether you’re analyzing customer data to inform business decisions, researching for academic purposes, or exploring datasets for personal projects, Python provides the capabilities to turn data into insights.

Throughout this course, you’ll engage in hands-on activities such as coding exercises, real-world data analysis projects, and creating data visualizations. These practical experiences are designed to cement your learning and give you the confidence to apply your skills in a professional setting.

What sets this course apart is not just the breadth of topics covered but the focus on practical application. You’ll learn not just the theory but how to apply these concepts in real-world scenarios, preparing you for immediate application in your work or studies.

Don’t let data overwhelm you any longer. Take the first step towards unlocking its potential by enrolling in Python for Data Science: Python Programming & Data Analysis today. Transform data into insights and become an invaluable asset in the field of data science.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course resources

Lecture 3: This is a milestone

Chapter 2: Getting Started with Python

Lecture 1: Introduction to Python

Lecture 2: Variables in Python: Declaration and Use

Lecture 3: Data types in Python

Lecture 4: Python String

Lecture 5: String methods

Chapter 3: Data Structures in Python

Lecture 1: List in Python

Lecture 2: Tuples in Python

Lecture 3: Dictionaries in Python

Lecture 4: Sets in Python

Chapter 4: Conditional Statements in Python

Lecture 1: Python Conditional Expressions

Lecture 2: Exploring Operators and Conditional Expressions in Python

Chapter 5: Loops in Python

Lecture 1: For loops in Python

Lecture 2: While loops in Python

Chapter 6: Python Functions

Lecture 1: Function in Python

Lecture 2: Recursion in Python

Lecture 3: Lambda function

Chapter 7: File handling in Python

Lecture 1: File I/O in Python

Chapter 8: NumPy Library

Lecture 1: Introduction to NumPy arrays

Lecture 2: Accessing the elements of NumPy arrays

Lecture 3: Leveraging Data Types, Shapes, and Array Stacking in NumPy

Lecture 4: Exploring Diverse Approaches to Creating NumPy Arrays

Lecture 5: Mathematical operations on arrays

Chapter 9: Pandas Library

Lecture 1: Introduction to Pandas Library

Lecture 2: Exploring Series and DataFrame in Python

Lecture 3: Essential Data Analysis Methods in Python

Lecture 4: Missing Data Handling in Python

Lecture 5: Manipulating DataFrame in Python

Chapter 10: Matplotlib Library

Lecture 1: Introduction to Matplotlib Library

Lecture 2: Data Visualization with Matplotlib: Plotting Essentials and Customization

Lecture 3: Exploring Subplots, Scatter Plots, and Customization

Lecture 4: Crafting Bar Plots, Histograms, Pie Charts with Customization Using Matplotlib

Chapter 11: Seaborn Library

Lecture 1: Introduction to Seaborn Library

Lecture 2: Exploring Seaborn: Univariate and Bivariate Analysis for Data Visualization

Lecture 3: Advanced Data Visualization with Seaborn: Pairplot and Barplot Customization

Lecture 4: Advanced Visualizations with Countplot and Heatmap Using Seaborn

Lecture 5: The final milestone!

Chapter 12: Conclusion

Lecture 1: About your certificate

Lecture 2: Bonus lecture

Instructors

Start-Tech Trainings
Analytics and ML academy

Start-Tech Academy
5,000,000+ Enrollments | 4.5 Rated | 160+ Countries

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 7 votes

4 stars: 13 votes

5 stars: 14 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!