Master Data Science with Python: From Basics to Advanced

Master Data Science with Python: From Basics to Advanced, available at $54.99, has an average rating of 2.83, with 216 lectures, based on 3 reviews, and has 70 subscribers.

You will learn about Understand the basics of data analysis and the various types involved Learn how to effectively use NumPy for data manipulation and statistical analysis Gain proficiency in using Pandas for data manipulation and analysis in Python Master data visualization techniques using Matplotlib and Seaborn This course is ideal for individuals who are Aspiring data scientists looking to get started with data science in Python or Python developers wanting to learn about data analysis and visualization or Students and professionals seeking to enhance their data manipulation skills using Python libraries or Anyone interested in learning data science with practical Python applications It is particularly useful for Aspiring data scientists looking to get started with data science in Python or Python developers wanting to learn about data analysis and visualization or Students and professionals seeking to enhance their data manipulation skills using Python libraries or Anyone interested in learning data science with practical Python applications.

Enroll now: Master Data Science with Python: From Basics to Advanced

Summary

Title: Master Data Science with Python: From Basics to Advanced

Price: $54.99

Average Rating: 2.83

Number of Lectures: 216

Number of Published Lectures: 216

Number of Curriculum Items: 216

Number of Published Curriculum Objects: 216

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics of data analysis and the various types involved
Learn how to effectively use NumPy for data manipulation and statistical analysis
Gain proficiency in using Pandas for data manipulation and analysis in Python
Master data visualization techniques using Matplotlib and Seaborn

Who Should Attend

Aspiring data scientists looking to get started with data science in Python
Python developers wanting to learn about data analysis and visualization
Students and professionals seeking to enhance their data manipulation skills using Python libraries
Anyone interested in learning data science with practical Python applications

Target Audiences

Aspiring data scientists looking to get started with data science in Python
Python developers wanting to learn about data analysis and visualization
Students and professionals seeking to enhance their data manipulation skills using Python libraries
Anyone interested in learning data science with practical Python applications

This comprehensive course on Basic Data Science with Python is designed to equip you with the fundamental skills and knowledge required to excel in the field of data science. Through a combination of theoretical lessons and practical hands-on activities, this course will guide you through the essential concepts and tools used in data analysis, manipulation, and visualization.

You will begin with an introduction to data analysis, exploring what it entails and the different types of data analysis. This foundational knowledge will set the stage for understanding the goals and methodologies of data analysis. From there, you will delve into NumPy, the cornerstone of numerical computing in Python. You will learn how to create and manipulate arrays, perform statistical operations, and leverage NumPy’s powerful functionalities to manage data efficiently.

As you progress, the course will introduce you to Pandas, a versatile library for data manipulation and analysis. You will master the creation and manipulation of dataframes, perform data cleaning, and execute complex data transformations. With Pandas, you will be able to handle real-world data scenarios with ease and precision.

The journey continues with a deep dive into data visualization. You will explore Matplotlib, a comprehensive plotting library, learning how to create a wide range of static, animated, and interactive visualizations. You will understand the principles of effective data visualization and how to communicate insights clearly and effectively.

In addition to Matplotlib, you will be introduced to Seaborn, a library built on top of Matplotlib, which provides beautiful and informative statistical graphics. You will learn how to create various types of plots, customize them, and interpret the visual representations of data.

Throughout the course, you will engage in numerous hands-on activities and projects that reinforce the concepts and skills learned. These practical exercises are designed to provide you with real-world experience and build your confidence in applying data science techniques using Python.

By the end of this course, you will have a solid understanding of data science fundamentals, be proficient in using essential Python libraries, and possess the skills to analyze, manipulate, and visualize data effectively. Whether you are an aspiring data scientist, a Python developer, or a professional looking to enhance your data skills, this course will provide you with the knowledge and tools to succeed in the dynamic field of data science.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Get to Know Your Instructor

Lecture 2: Course Contents – What You Will Learn

Chapter 2: Introduction to Data Analysis

Lecture 1: What is Data Analysis – Explained

Lecture 2: Types of Data Analysis

Lecture 3: Goal for Data Analysis

Chapter 3: NumPy: Your First Steps Into Data Science in Python

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Variable Properties

Lecture 3: Importing NumPy Library Into A Jupyter Notebook

Lecture 4: Creating a List in Python

Lecture 5: Creating Arrays from the List

Lecture 6: Practical Uses of NumPy Variable Properties

Lecture 7: Basic Operations for Statistical Analysis Using NumPy

Lecture 8: Issues Regarding NumPy Array Reshaping

Chapter 4: Working with Numpy Arrays: Indexing & Slicing

Lecture 1: Indexing and Slicing NumPy Arrays

Lecture 2: Assigning Values to Arrays

Lecture 3: Copy and Slicing Difference

Lecture 4: Queries Regarding Assigning Values to Arrays

Lecture 5: 2-D Slicing and Referencing

Lecture 6: Queries Regarding Indexing and Slicing

Lecture 7: What You Have Learned So Far

Lecture 8: Queries Regarding Accessing to The NumPy Work File

Lecture 9: Array programming with NumPy – Recap

Chapter 5: Operations on NumPy Arrays

Lecture 1: Numpy Argmin and Argmax Explained

Lecture 2: Boolean Array in NumPy

Lecture 3: Suggestions Regarding Operations on NumPy Arrays

Lecture 4: Mathematical Operations on NumPy Arrays – Addition and Subtraction

Lecture 5: Use of numpy.zeros() and numpy.ones() Function in Python

Chapter 6: Hands-on NumPy Activities

Lecture 1: Creating an Array for a Given List

Lecture 2: Solving an Undefined Variable NameError in Python

Lecture 3: Common Operations with NumPy

Lecture 4: Using np.zeros() and np.ones() Functions

Chapter 7: NumPy Array Functions With Examples

Lecture 1: Using numpy.eye() Function to Create an Identity Matrix

Lecture 2: Creating NumPy Array Using np.arrange() Function

Lecture 3: np.linspace() and np.arange() Function in NumPy

Lecture 4: Where to Save Work File

Chapter 8: Pandas Library in Python

Lecture 1: Introducing Pandas Objects

Lecture 2: Series and Dataframe

Lecture 3: Creating Series Using Pandas

Lecture 4: Queries Regarding Creating Series Using Pandas

Lecture 5: Conditional Selection of Selected Elements

Lecture 6: Referencing Using Index Values

Lecture 7: What You Learnt So Far

Lecture 8: Pandas Library – Recap

Chapter 9: Working with Pandas Library

Lecture 1: Accessing to Work File

Lecture 2: Using Series in Python

Lecture 3: How to Download and Access Work File

Lecture 4: Queries Regarding Downloading and Accessing Work File in Jupyter

Lecture 5: Creating Series Of Cities

Chapter 10: Pandas Dataframe Operations

Lecture 1: Getting Any Column From Dataframe

Lecture 2: Dataframe from Single List

Lecture 3: Creating Dataframe from Dictionary

Lecture 4: Creating Dataframe from Lists Using Zip

Chapter 11: Working With Dataframe Operations

Lecture 1: Getting Subset of Columns from Dataframe

Lecture 2: DataFrame set_index() and DataFrame.loc() Method

Lecture 3: Getting The Integer Index of a Pandas DataFrame Row Fulfilling A Condition

Lecture 4: Queries Regarding Conditional Selection of Selected Elements

Chapter 12: Matplotlib — Visualization with Python

Lecture 1: Introduction to Matplotlib

Lecture 2: Plots with Matplotlib

Lecture 3: Two Methods for Plots

Chapter 13: Introduction to Plotting with Matplotlib

Lecture 1: Queries Regarding Accessing to The Work File

Lecture 2: What We Have Learnt So Far

Lecture 3: Basics of Matplotlib

Lecture 4: Plots with Matplotlib and Methods of Matplotlib

Lecture 5: Matplotlib Scatter Plot

Lecture 6: Sample Structure of Matplotlib

Lecture 7: Histograms in Matplotlib

Lecture 8: Object Oriented Method – Explained

Lecture 9: How to Install Matplotlib Using Anaconda Prompt

Lecture 10: Some Useful Links

Chapter 14: Working With Matplotlib in Python

Lecture 1: Installing Matplotlib Using Anaconda Prompt

Lecture 2: Creating Some Numeric Data For Visualization

Lecture 3: What is numpy.linspace() in Python

Lecture 4: Queries Regarding x.shape

Lecture 5: Queries Regarding Using the NumPy linspace() Function

Chapter 15: Methods of Plotting in Matplotlib

Lecture 1: Functional Method of Creating a Single Plot

Lecture 2: Object Oriented Method of Creating a Figure and Only One Subplot

Lecture 3: Understanding Figure object in Matplotlib

Lecture 4: Plot Labeling in Matplotlib

Lecture 5: Queries Regarding Accessing the Matplotlib Work File

Lecture 6: Object Oriented Method of Creating Two Subplots

Lecture 7: Creating Four Polar axes and Accessing them Through the Returned Array

Lecture 8: Queries Regarding Creating Four Polar axes

Lecture 9: Queries Regarding Scattering Plot

Chapter 16: What We Have Learnt So Far

Lecture 1: What Is Matplotlib and How It Works

Instructors

Peter Alkema
Business | Technology | Self Development
Regenesys Business School
Regenesys Business School

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 1 votes
5 stars: 0 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!

Menu