Solve business problems with data-driven techniques and easy-to-follow Python examples Ê KEY FEATURESÊÊ _ Essential coverage on statistics and data science techniques. _ Exposure to Jupyter, PyCharm, and use of GitHub. _ Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTIONÊÊ This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you willÊ clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN _ Rapid understanding of Python concepts for data science applications. _ Understand and practice how to run data analysis with data science techniques and algorithms. _ Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. _ Become self-sufficient to perform data science tasks with the best tools and techniques. Ê WHO THIS BOOK IS FORÊÊ This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Ê TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All ChaptersÕ Learning

Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.

Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

Step-by-step guide to practising data science techniques with Jupyter notebooksKey features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you'll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques.Who this book is forThe book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience.Table of contents1. Data Science Fundamentals2. Installing Software and Setting up3. Lists and Dictionaries4. Function and Packages5. NumPy Foundation6. Pandas and Dataframe7. Interacting with Databases8. Thinking Statistically in Data Science9. How to import data in Python?10. Cleaning of imported data11. Data Visualization12. Data Pre-processing13. Supervised Machine Learning14. Unsupervised Machine Learning15. Handling Time-Series Data16. Time-Series Methods 17. Case Study - 118. Case Study - 219. Case Study - 320. Case Study - 4About the authorPrateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more