Book Image

Python: Data Analytics and Visualization

By : Martin Czygan, Phuong Vo.T.H, Ashish Kumar, Kirthi Raman
Book Image

Python: Data Analytics and Visualization

By: Martin Czygan, Phuong Vo.T.H, Ashish Kumar, Kirthi Raman

Overview of this book

You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You’ll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan •Learning Predictive Analytics with Python, Ashish Kumar •Mastering Python Data Visualization, Kirthi Raman

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Python: Data Analytics and Visualization
Martin Czygan | Phuong Vo.T.H | Ashish Kumar | Kirthi Raman
Mar, 2017 | 14 hours 26 minutes
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Table of Contents (6 chapters)
Preface
Preface
What this learning path covers
What you need for this learning path
Who this learning path is for
Reader feedback
Customer support
Free Chapter
1
1. Module 1
1. Module 1
1. Introducing Data Analysis and Libraries
2. NumPy Arrays and Vectorized Computation
3. Data Analysis with Pandas
4. Data Visualization
5. Time Series
6. Interacting with Databases
7. Data Analysis Application Examples
8. Machine Learning Models with scikit-learn
2
2. Module 2
2. Module 2
1. Getting Started with Predictive Modelling
2. Data Cleaning
3. Data Wrangling
4. Statistical Concepts for Predictive Modelling
5. Linear Regression with Python
6. Logistic Regression with Python
7. Clustering with Python
8. Trees and Random Forests with Python
9. Best Practices for Predictive Modelling
A. A List of Links
3
3. Module 3
3. Module 3
1. A Conceptual Framework for Data Visualization
2. Data Analysis and Visualization
3. Getting Started with the Python IDE
4. Numerical Computing and Interactive Plotting
5. Financial and Statistical Models
6. Statistical and Machine Learning
7. Bioinformatics, Genetics, and Network Models
8. Advanced Visualization
B. Go Forth and Explore Visualization
4
Bibliography
Bibliography
5
Index
Index

Chapter 6. Statistical and Machine Learning

Machine learning enables you to create and use computer algorithms, learn from these algorithms, correct them, and improve them to draw any new patterns that were unknown in the past. You can also extract insights from these new patterns that were found from the data. For instance, one may be interested in teaching a computer how to recognize ZIP codes value in an image. Another example is if we have a specific task, such as to determine spam messages, then instead of writing a program to solve this directly, in this paradigm, you can seek methods to learn and become better at getting accurate results using a computer.

Machine learning has become a significant part of artificial intelligence in recent years. With the power of computing, it is very likely that we will be able to build intelligent systems using machine learning methods. With the power of computing that we have today, these tasks have become far simpler than they were two...

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