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About This Book
Learn basic steps of data analysis and how to usePythonand its packages
A step-by-step guide to predictive modeling including tips, tricks, and best practices
Effectively visualize a broad set of analyzed data and generate effective results
Who This Book Is For
This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.
What You Will Learn
Get acquainted with NumPy and use arrays and array-oriented computing in data analysis
Procss and analyze data using the time-series capabilities of Pandas
Understand the statistical andmathematicalconcepts behindpredictive analyticsalgorithms
Data visualization with Matplotlib
Interactive plotting with NumPy, Scipy, and MKL functions
Build financial models using Monte-Carlo simulations
Create directed graphs and multi-graphs
Advanced visualization with D3
In Detail
You will start the course with an introduction to theprinciplesof 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 ordatabaseusing 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 mediaand 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
Style and approach
The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization
Table of Contents
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. 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
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
Packt.Python.Data.Analytics.and.Visualization.2nd.azw3.zip
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