This is a book regarding data science with Python. A python is an excellent tool for several analyzers because of its libraries for manipulating, storing and gaining insight from data. Python code is ideal for tackling day-to-day problems like- visualizing different types of data; manipulating, transforming, and cleaning data; using data to build machine learning or statistical models. It is terribly merely that it should have a reference for scientific computing in Python. It is meant to assist Python users by learning the way to use Python’s data science stack libraries like- IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.
What you will learn:
• Jupyter and IPython – In several, Python using data scientists work with these packages and provide the computational environment.
• NumPy -This library provides the array object for economical storage and manipulation of dense data arrays in Python.
• Pandas- For efficient storage and manipulation of labeled/columnar data in Python the Data Frame object is provided by the library.
• Matplotlib- Capabilities of a versatile range of data visualizations in Python is provided by the library.
• Scikit-Learn- This library provides economical and clean Python with implementations of the foremost necessary and established machine learning algorithms.
This book will help you in getting familiar with data science using Python 3.5; Save time (and effort) with all the essential tools explained and additionally produce effective data science projects which can avoid common problems with the help of hints prescribed by experiences and examples. Get trendy vision into the core of Python data which incorporates the latest versions of NumPy, Jupyter notebooks, sci-kit-learn, and pandas. This book gives the complete overview of all the visualization and deployment instruments which makes it an easier option to present your results to an audience of both business users and data science experts; principal of machine learning algorithms and graph analysis techniques.
What you will learn:
• By using a Python scientific environment on Windows, Mac, and Linux how you can set up your data science toolbox
• Get data prepared for your data science project
• Explore, Manipulate and fix data so as to resolve data science problems
• For testing your data science hypotheses how you can set up an experimental pipeline
• For your data science tasks how to select the foremost effective and scalable learning algorithm
• Forgetting the best performance of how to optimize your machine learning models
• Taking advantage of interconnections and links in your data by exploring and the cluster of graphs.
In this book, Master data science uses Python and its libraries in many ways. This comprehensive guide helps you to move beyond the enhancement and transform the theory which provides a hands-on and advanced study of data science using python, and also easy-to-follow. Data science is comparatively a new cognitive content which is employed by various organizations to produce data-driven decisions. Informing high-end visualizations in Python matplot library is used and also uncovers the basics of machine learning. All the topics covered in this book can be used in real-world circumstances.
What You Will Learn:
•Perform linear algebra and manage data in Python; evaluate and apply linear and logistic regression techniques in various application techniques for estimating the relationships among variables.
•Derive assumptions from the analysis by mining data to reveal hidden patterns and trends and performing inferential statistics
• Resolve data science issues in Python
•With the help of various collaborative filtering algorithms, how you can build recommendation engines
• Apply the ensemble ways to boost your predictions
• For handling data at large scale how we work with large data technologies
•Produce mine for patterns and data visualizations.
•The four basics of Data Science with Python having advanced techniques like- data mining, machine learning, data visualization, and data analysis
•Perform clustering together with an analysis of unstructured data with completely different text mining techniques and to invest the power of Python in big data analytics.
The book begins with setting up the environment for Anaconda platform so as to make it accessible for tools and frameworks like- Jupyter, pandas, matplotlib, Python, R, Julia, and more. Anaconda is an open source platform which brings along the simplest tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages.
Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how to perform data science operations in the real world. It is ideal for data analysts and data science professionals who want to boost the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.
What you will learn:
· Perform cleaning, sorting, classification, clustering, regression, prediction, and building machine learning models and optimizing them and dataset modeling using Anaconda
· Use the package manager conda and discover, install, and use functionally efficient and scalable packages
· Get comfortable with heterogeneous data exploration using multiple languages within a project
· Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
· Tackle advanced data prediction issues
· Explore all the necessities information of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, and many more.
· Find out how to visualize data using the packages available for Julia, Python, and R. Analyze your data efficiently with the foremost powerful data science stack.