000			03768nam a2200229Ia 4500
003			NULRC
005			20250520103033.0
008			250520s9999 xx 000 0 und d
020			_a9781801071970
040			_cNULRC
050			_aQA 76.73.P98 .G46 2021
100			_aGeorge, Nathan _eauthor
245		0	_aPractical data science with Python : _blearn tools and techniques from hands-on examples to extract insights from data / _cNathan George
260			_aBirmingham, UK : _bPackt Publishing, Limited, _cc2021
300			_axxiii, 595 pages ; _c24 cm.
365			_bUSD52
504			_aIncludes index.
505			_aAn Introduction and the Basics -- Chapter 1: Introduction to Data Science -- The data science origin story -- The top data science tools and skills -- Python -- Other programming languages -- GUIs and platforms -- Cloud tools -- Statistical methods and math -- Collecting, organizing, and preparing data -- Software development -- Business understanding and communication -- Specializations in and around data science -- Machine learning -- Business intelligence -- Deep learning -- Data engineering -- Big data Statistical methods -- Natural Language Processing (NLP) -- Artificial Intelligence (AI) -- Choosing how to specialize -- Data science project methodologies -- Using data science in other fields -- CRISP-DM -- TDSP -- Further reading on data science project management strategies -- Other tools -- Test your knowledge -- Summary -- Chapter 2: Getting Started with Python -- Installing Python with Anaconda and getting started -- Installing Anaconda -- Running Python code -- The Python shell -- The IPython Shell -- Jupyter -- Why the command line? -- Command line basics Installing and using a code text editor -- VS Code -- Editing Python code with VS Code -- Running a Python file -- Installing Python packages and creating virtual environments -- Python basics -- Numbers -- Strings -- Variables -- Lists, tuples, sets, and dictionaries -- Lists -- Tuples -- Sets -- Dictionaries -- Loops and comprehensions -- Booleans and conditionals -- Packages and modules -- Functions -- Classes -- Multithreading and multiprocessing -- Software engineering best practices -- Debugging errors and utilizing documentation -- Debugging -- Documentation -- Version control with Git Code style -- Productivity tips -- Test your knowledge -- Summary -- Dealing with Data -- Chapter 3: SQL and Built-in File Handling Modules in Python -- Introduction -- Loading, reading, and writing files with base Python -- Opening a file and reading its contents -- Using the built-in JSON module -- Saving credentials or data in a Python file -- Saving Python objects with pickle -- Using SQLite and SQL -- Creating a SQLite database and storing data -- Using the SQLAlchemy package in Python -- Test your knowledge -- Summary -- Chapter 4: Loading and Wrangling Data with Pandas and NumPy Data wrangling and analyzing iTunes data -- Loading and saving data with Pandas -- Understanding the DataFrame structure and combining/concatenating multiple DataFrames -- Exploratory Data Analysis (EDA) and basic data cleaning with Pandas -- Examining the top and bottom of the data -- Examining the data's dimensions, datatypes, and missing values -- Investigating statistical properties of the data -- Plotting with DataFrames -- Cleaning data -- Filtering DataFrames -- Removing irrelevant data -- Dealing with missing values -- Dealing with outliers -- Dealing with duplicate values.
520			_aThe book provides a one-stop solution for getting into data science with Python and teaches how to extract insights from data.
650			_aBIG DATA
942			_2lcc _cBK
999			_c21985 _d21985