Python For Data Science: Python Programming & Data Analysis

Transform data into insights using Python and its powerful Libraries such as Numpy, Pandas, MatplotLib, Seaborn etc.

What you'll 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

Requirements

No prior experience in Python or data analysis is required; just basic computer skills and access to a computer with an internet connection are necessary to start this course.

Description

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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course resources

Section 2: Getting Started with Python

Lecture 3 Introduction to Python

Lecture 4 Variables in Python: Declaration and Use

Lecture 5 Data types in Python

Lecture 6 Python String

Lecture 7 String methods

Section 3: Data Structures in Python

Lecture 8 List in Python

Lecture 9 Tuples in Python

Lecture 10 Dictionaries in Python

Lecture 11 Sets in Python

Section 4: Conditional Statements in Python

Lecture 12 Python Conditional Expressions

Lecture 13 Exploring Operators and Conditional Expressions in Python

Section 5: Loops in Python

Lecture 14 For loops in Python

Lecture 15 While loops in Python

Section 6: Python Functions

Lecture 16 Function in Python

Lecture 17 Recursion in Python

Lecture 18 Lambda function

Section 7: File handling in Python

Lecture 19 File I/O in Python

Section 8: NumPy Library

Lecture 20 Introduction to NumPy arrays

Lecture 21 Accessing the elements of NumPy arrays

Lecture 22 Leveraging Data Types, Shapes, and Array Stacking in NumPy

Lecture 23 Exploring Diverse Approaches to Creating NumPy Arrays

Lecture 24 Mathematical operations on arrays

Section 9: Pandas Library

Lecture 25 Introduction to Pandas Library

Lecture 26 Exploring Series and DataFrame in Python

Lecture 27 Essential Data Analysis Methods in Python

Lecture 28 Missing Data Handling in Python

Lecture 29 Manipulating DataFrame in Python

Section 10: Matplotlib Library

Lecture 30 Introduction to Matplotlib Library

Lecture 31 Data Visualization with Matplotlib: Plotting Essentials and Customization

Lecture 32 Exploring Subplots, Scatter Plots, and Customization

Lecture 33 Crafting Bar Plots, Histograms, Pie Charts with Customization Using Matplotlib

Section 11: Seaborn Library

Lecture 34 Introduction to Seaborn Library

Lecture 35 Exploring Seaborn: Univariate and Bivariate Analysis for Data Visualization

Lecture 36 Advanced Data Visualization with Seaborn: Pairplot and Barplot Customization

Lecture 37 Advanced Visualizations with Countplot and Heatmap Using Seaborn

Section 12: Conclusion

Lecture 38 About your certificate

Lecture 39 Bonus lecture

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.