Learning Path: Python: Effective Data Analysis Using Python
Learning Path: Python: Effective Data Analysis Using Python, available at $34.99, has an average rating of 3.4, with 86 lectures, based on 20 reviews, and has 402 subscribers.
You will learn about Scrape the Twitter stream to collect real-time data Predictive methods that can forecast and predict future trends based on current data Use the Selenium module and scrape with Selenium Discover how to perform parsing with BeautifulSoup Make 3D visualizations mainly using mplot3d This course is ideal for individuals who are This course is ideal for those who are new to data analysis and for those who are already into data analytics and want to enhance their data extraction and visualization skills. It is particularly useful for This course is ideal for those who are new to data analysis and for those who are already into data analytics and want to enhance their data extraction and visualization skills.
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Summary
Title: Learning Path: Python: Effective Data Analysis Using Python
Price: $34.99
Average Rating: 3.4
Number of Lectures: 86
Number of Published Lectures: 86
Number of Curriculum Items: 86
Number of Published Curriculum Objects: 86
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Scrape the Twitter stream to collect real-time data
- Predictive methods that can forecast and predict future trends based on current data
- Use the Selenium module and scrape with Selenium
- Discover how to perform parsing with BeautifulSoup
- Make 3D visualizations mainly using mplot3d
Who Should Attend
- This course is ideal for those who are new to data analysis and for those who are already into data analytics and want to enhance their data extraction and visualization skills.
Target Audiences
- This course is ideal for those who are new to data analysis and for those who are already into data analytics and want to enhance their data extraction and visualization skills.
Over the years, almost every organization has understood the importance of analyzing data.
In fact, it would not be an overstatement to say that “No organization will be able to survive today’s cut-throat competition if it does not analyze data.”
Data analysis as we know it is the process of taking the source data, refining it to get useful information, and then making useful predictions from it.
In this Learning Path, we will learn how to analyze data using the powerful toolset provided by Python.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
Python features numerous numerical and mathematical toolkits such as Numpy, Scipy, Scikit learn,and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice of data scientists for data analysis, visualization, and machine learning.
We will have a general look at data analysis and then discuss the web scraping tools and techniques in detail. We will show a rich collection of recipes that will come in handy when you are scraping a website using Python, addressing your usual and unusual problems while scraping websites by diving deep into the capabilities of Python’s web scraping tools such as Selenium, BeautifulSoup, and urllib2.
We will then discuss the visualization best practices. Effective visualization helps you get better insights from your data, and help you make better and more informed business decisions.
After completing this Learning Path, you will be well-equipped to extract data even from dynamic and complex websites by using Python web scraping tools, and get a better understanding of the data visualization concepts. You will also learn how to apply these concepts and overcome any challenge while implementing them.
To ensure that you get the best of the learning experience, in this Learning Path we combine the works of some of the leading authors in the business.
About the authors
Benjamin Hoff spent 3 years working as a software engineer and team leader doing graphics processing, desktop application development, and scientific facility simulation using a mixture of C++ and Python. This sparked a passion for software development and developmental programming and led him to explore state-of-the art projects in natural language processing, facial detection/recognition, and machine learning.
Charles Clayton is a sole proprietor of crclayton technologies co, and an independent web developer. He is an experienced developer and Python specialist in Python web scraping solutions and tools such as Selenium, BeautifulSoup, and urllib2. He also has worked as a Reliability Engineer with West frazweer.
Dimitry Foures is a data scientist with a background in applied mathematics and theoretical physics. After completing his physics undergraduate studies in ENS Lyon (France), he studied fluid mechanics at École Polytechnique in Paris where he obtained first class in Master’s degree. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data scientist for a smart energy startup in Cambridge, in close collaboration with the university.
Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualization in Python in his blogs.
Igor Milovanović
is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems.
Course Curriculum
Chapter 1: Learning Python Data Analysis
Lecture 1: The Course Overview
Lecture 2: Getting started with Python
Lecture 3: Getting Data using the Twitter API
Lecture 4: Collecting and Storing Tweets
Lecture 5: Database Design
Lecture 6: Pandas and Databases
Lecture 7: Panda Series, Dataframes, and Columnar Operations
Lecture 8: Grouping Operations and Working with Date Columns
Lecture 9: Merging Operations and Exporting data to JSON/CSV
Lecture 10: Array Features, Bucketting Arrays and Histogram Functions
Lecture 11: Simple Aggregations
Lecture 12: Linear Algebra
Lecture 13: Introducting PyQT and MatplotLib
Lecture 14: Creating Charts
Lecture 15: Simple XY Plots with Axis Scales
Lecture 16: Introduction to the NTLK Package
Lecture 17: Bag of Words
Lecture 18: Classification of Words
Lecture 19: Stemming
Lecture 20: Simple Sentiment Analysis
Lecture 21: Grouping By Dimensions and Classification of Data Types
Lecture 22: Trend Analysis and Deriving New Metrics
Lecture 23: Correlation Analysis
Lecture 24: Course Summary
Chapter 2: Getting Started with Python Web Scraping
Lecture 1: The Course Overview
Lecture 2: When to Web Scrape
Lecture 3: What Makes up a Website
Lecture 4: How to Interact with a Website
Lecture 5: Using the Selenium Module
Lecture 6: Ethical Web Scraping
Lecture 7: Requesting HTML
Lecture 8: Using the BeautifulSoup Module
Lecture 9: Example: Parsing Wikipedia
Lecture 10: Bypassing the Browser
Lecture 11: Introduction to APIs
Lecture 12: Working with APIs
Chapter 3: Python Data Visualization Solutions
Lecture 1: The Course Overview
Lecture 2: Importing Data from CSV
Lecture 3: Importing Data from Microsoft Excel Files
Lecture 4: Importing Data from Fix-Width Files
Lecture 5: Importing Data from Tab Delimited Files
Lecture 6: Importing Data from a JSON Resource
Lecture 7: Importing Data from a Database
Lecture 8: Cleaning Up Data from Outliers
Lecture 9: Importing Image Data into NumPy Arrays
Lecture 10: Generating Controlled Random Datasets
Lecture 11: Smoothing Noise in Real-World Data
Lecture 12: Defining Plot Types and Drawing Sine and Cosine Plots
Lecture 13: Defining Axis Lengths and Limits
Lecture 14: Defining Plot Line Styles, Properties, and Format Strings
Lecture 15: Setting Ticks, Labels, and Grids
Lecture 16: Adding Legends and Annotations
Lecture 17: Moving Spines to Center
Lecture 18: Making Histograms
Lecture 19: Making Bar Charts with Error Bars
Lecture 20: Making Pie Charts Count
Lecture 21: Plotting with Filled Areas
Lecture 22: Drawing Scatter Plots with Colored Markers
Lecture 23: Adding a Shadow to the Chart Line
Lecture 24: Adding a Data Table to the Figure
Lecture 25: Using Subplots
Lecture 26: Customizing Grids
Lecture 27: Creating Contour Plots
Lecture 28: Filling an Under-Plot Area
Lecture 29: Drawing Polar Plots
Lecture 30: Visualizing the filesystem Tree Using a Polar Bar
Lecture 31: Creating 3D Bars
Lecture 32: Creating 3D Histograms
Lecture 33: Animating with OpenGL
Lecture 34: Plotting with Images
Lecture 35: Displaying Images with Other Plots in the Figure
Lecture 36: Plotting Data on a Map Using Basemap
Lecture 37: Generating CAPTCHA
Lecture 38: Understanding Logarithmic Plots
Lecture 39: Creating a Stem Plot
Lecture 40: Drawing Streamlines of Vector Flow
Lecture 41: Using Colormaps
Lecture 42: Using Scatter Plots and Histograms
Lecture 43: Plotting the Cross Correlation Between Two Variables
Lecture 44: The Importance of Autocorrelation
Lecture 45: Drawing Barbs
Lecture 46: Making a Box-and-Whisker Plot
Lecture 47: Making Gantt Charts
Lecture 48: Making Error Bars
Lecture 49: Making Use of Text and Font Properties
Lecture 50: Understanding the Difference between pyplot and OO API
Instructors
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Packt Publishing
Tech Knowledge in Motion
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 0 votes
- 3 stars: 4 votes
- 4 stars: 6 votes
- 5 stars: 7 votes
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