data science with python

An original practical guide to data science with Python: NumPy, pandas, visualisation and scikit-learn. Covers the complete beginner-to-job workflow with code examples and practice questions. Ideal for data science students.

You are now able to: Explain why Python should be integrated with Hadoop Outline the ecosystem and architecture of Hadoop Explain the functioning of MapReduce Discuss Apache Spark functions and their benefits Write Python programs for Hadoop operations

Define web scraping and explain its importance List the steps involved in the web scraping process Describe basic terminologies, such as parser, object, and tree associated with the BeautifulSoup Explain various operations, such as searching, modifying, and navigating the tree to yield the required result

Explain what data visualization is and its importance Illustrate why Python is considered one of the best data visualization tools Describe matplotlib and its data visualization features in Python List the types of plots and the steps involved in creating these plots

Define natural language processing Explain the importance of natural language processing List the applications using natural language processing Outline the modules to load content and category Apply feature extraction techniques Implement the approaches of natural language processing

Define machine learning Explain the machine learning approach List relevant terminologies that help you understand a dataset Discuss features of supervised and unsupervised learning models Explain algorithms, such as regression, classification, clustering, and dimensionality reduction

Explain Pandas and its features List different data structures of Pandas Outline the process to create series and DataFrame with data inputs Explain how to view, select, and access elements in a data structure Describe the procedure to handle vectorized operations Illustrate how to handle missing values Analyze data with different data operation methods

Everything there is to know about Scipy

Explain NumPy and its importance Discuss the basics of NumPy, including its fundamental objects Demonstrate how to create and print a NumPy array Analyze and perform basic operations in NumPy Utilize shape manipulation and copying methods Demonstrate how to execute linear algebraic functions Build basic programs using NumPy

Explain Anaconda and Jupyter notebook installation List the important data types supported by Python Discuss data structures, such as lists, tuples, sets, and dicts Explain slicing and accessing the four data structures Discuss basic operators and functions Outline the important control flow statements

Differentiate between statistical and non-statistical analysis Illustrate the two major categories of statistical analysis and their differences Describe statistical analysis process Calculate mean, median, mode, and percentile Describe data distribution and the various methods of representing it Explain types of frequencies Outline correlation matrix and its uses

Describe Data Analytics process and its steps List the skills and tools required for data analysis Understand the challenges of the Data Analytics process Explain Exploratory data analysis technique Illustrate data visualization techniques Describe Hypothesis testing

These documents provide specific information about Data Science and the use of Python to manipulate it.

The document consists of zero to end steps and proper explanation on how we can scrape a website to get the unstructured data and convert it in to structured data in to files such csv format for better understanding. It is very useful technique in the role of a data analyst.

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