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 4. Statistical Concepts for Predictive Modelling

There are a few statistical concepts, such as hypothesis testing, p-values, normal distribution, correlation, and so on without which grasping the concepts and interpreting the results of predictive models becomes very difficult. Thus, it is very critical to understand these concepts, before we delve into the realm of predictive modelling.

In this chapter, we will be going through and learning these statistical concepts so that we can use them in the upcoming chapters. This chapter will cover the following topics:

  • Random sampling and central limit theorem: Understanding the concept of random sampling through an example and illustrating the central limit theorem's application through an example. These two concepts form the backbone of hypothesis testing.
  • Hypothesis testing: Understanding the meaning of the terms, such as null hypothesis, alternate hypothesis, confidence intervals, p-value, significance level, and so on...
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End of Section 5
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