DS-TOOLS-PYTHON.AD1 ISBN-Nummer: 978-1-64459-252-6
Using Data Science Tools in Python
Learn key Python Data Science tools required to work with data and create clear, easy-to-understand visuals.
What you will be able to do
- Set up a Python environment for data science using Anaconda and Jupyter Notebook
- Create and manage data arrays with NumPy for fast and efficient data analysis
- Analyze and transform data using Pandas, enabling easy manipulation of large datasets
- Visualize data with clear charts and graphs using Matplotlib and Seaborn
- Scrape data from websites with Beautiful Soup for real-world data collection
- Handle different types of data, from simple arrays to complex data frames
- Apply data science techniques to solve problems and make data-driven decisions
Beginner Selbstgesteuert · 1 Jahr Zugang 4.7/5 (269 Rezension)
33 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
01 / About
Über diesen Kurs
Try Free →
Keine Kreditkarte benötigt
This course teaches you how to use the most important tools for data science in Python like NumPy, Pandas, and Matplotlib. You’ll gain hands-on experience with real-world data, learning how to manage, analyze, and visualize it in simple ways. By the end of this data science with Python course, you’ll be able to use Python programming to work with large data sets, create clear charts and graphs, and solve data problems.
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
8 Interaktive Lektionen · 30 topics01 Introduction 3 topics +
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
02 Setting Up a Python Data Science Environment 4 topics · 1 LiveLab +
- Topic A: Select Python Data Science Tools
- Topic B: Install Python Using Anaconda
- Topic C: Set Up an Environment Using Jupyter Notebook
- Summary
1 LiveLab in this lesson — see the labs panel →
03 Managing and Analyzing Data with NumPy 4 topics · 6 LiveLab +
- Topic A: Create NumPy Arrays
- Topic B: Load and Save NumPy Data
- Topic C: Analyze Data in NumPy Arrays
- Summary
6 LiveLab in this lesson — see the labs panel →
04 Transforming Data with NumPy 3 topics · 9 LiveLab +
- Topic A: Manipulate Data in NumPy Arrays
- Topic B: Modify Data in NumPy Arrays
- Summary
9 LiveLab in this lesson — see the labs panel →
05 Managing and Analyzing Data with pandas 5 topics · 5 LiveLab +
- Topic A: Create Series and DataFrames
- Topic B: Load and Save pandas Data
- Topic C: Analyze Data in DataFrames
- Topic D: Slice and Filter Data in DataFrames
- Summary
5 LiveLab in this lesson — see the labs panel →
06 Transforming and Visualizing Data with pandas 4 topics · 4 LiveLab +
- Topic A: Manipulate Data in DataFrames
- Topic B: Modify Data in DataFrames
- Topic C: Plot DataFrame Data
- Summary
4 LiveLab in this lesson — see the labs panel →
07 Visualizing Data with Matplotlib and Seaborn 6 topics · 8 LiveLab +
- Topic A: Create and Save Simple Line Plots
- Topic B: Create Subplots
- Topic C: Create Common Types of Plots
- Topic D: Format Plots
- Topic E: Streamline Plotting with Seaborn
- Summary
8 LiveLab in this lesson — see the labs panel →
08 Appendix A: Scraping Web Data Using Beautiful Soup 1 topics +
- Topic A: Scrape Web Pages
Praktische Übungen Our edge
33 LiveLabs- Setting Up a Jupyter Notebook Environment
- Creating a NumPy Array
- Using the NumPy Array Attributes
- Loading and Saving NumPy Data
- Analyzing Data in a NumPy Array
- Using Fancy Indexing
- Using the NumPy Statistical Summary Functions
- Manipulating Data in a NumPy Array
- Using the reshape Function
- Using the ravel and flip Functions
- Using the transpose and concatenate Functions
- Using the sort and argrsort Functions
- Using the insert and delete Functions
- Using the Arithmetic Functions and Operators
- Using the Comparison Functions and Operators
- Modifying Data in NumPy Arrays
- Creating Series and DataFrames
- Using the Series and DataFrame Attributes
- Loading and Saving DataFrame Data
- Analyzing Data in a DataFrame
- Slicing and Filtering Data in a DataFrame
- Manipulating Data in a DataFrame
- Modifying Data in a DataFrame
- Using the DataFrame Arithmetic Functions and Operators
- Creating a Scatter Plot
- Creating a Line Plot
- Creating Subplots
- Creating Box Plots
- Creating a 3-D Scatter Plot
- Creating a Histogram
- Formatting Plots
- Creating a JointGrid
- Creating a Linear Regression Plot
Labore laufen direkt im Browser — nichts zu installieren.
03 / FAQS
Fragen, bevor Sie beginnen
How Python can be used for data science? +
Python is widely used in data science for tasks like managing large datasets, analyzing data patterns, and creating visualizations. With its simple syntax and powerful libraries like NumPy, Pandas, and Matplotlib, Python makes data analysis and visualization faster and more efficient.
What is the Python tool for data science? +
Popular Python tools for data science include libraries like NumPy for numerical data, pandas for managing and analyzing datasets, Matplotlib and Seaborn for visualizing data, and Beautiful Soup for web scraping.
Who is this course ideal for? +
This course is perfect for beginners who want to establish a career in data science and Python programming or anyone who is looking for a smart way to upskill.
How do I practice Python for data science? +
You can practice working on Python data analysis tools inside our hands-on labs and simulations, where you’ll work with real-world datasets in a risk-free environment and apply your skills to solve data challenges.
Data Science with Python Made Easy
Learn, practice, and use Python’s most powerful tools for data science, with step-by-step guidance.
- 1 Jahr voller Zugang
- 33 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt