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Data Scientist I Solve Problems You Don T Know Notebook

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Data Scientist I Solve Problems You Don T Know Notebook

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Author : Genius Job Utility Journal Notebooks
language : en
Publisher:
Release Date : 2019-12-28

Data Scientist I Solve Problems You Don T Know Notebook written by Genius Job Utility Journal Notebooks and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-28 with categories.

Are you passionate about Science ? Do you Love Data ? Are you good at your Job but can not remember everything? No Problem. This Notebook will help you remember it all! It comes with: - 110 Pages - 6 x 9 inch size - beautiful matte cover - simple yet elegant design An awesome Gift Idea for Birthdays, Christmas, Anniversaries, Graduation or any other present giving occasion.

Data Scientist I Solve Problems You Don T Know Notebook

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Author : Genius Job Utility Journal Notebooks
language : en
Publisher:
Release Date : 2019-12-12

Data Scientist I Solve Problems You Don T Know

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Author : Happy Books journal boukharta
language : en
Publisher:
Release Date : 2019-12-31

Data Scientist I Solve Problems You Don T Know written by Happy Books journal boukharta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-31 with categories.

This funny 6 x 9 inch blank lined journal .Data Scientist I Solve Problems You Don't Know This fantastic 120-page notebook is just perfect for all your noting needs. Whether you're scribbling down your most secret inner thoughts or carefully copying out your favourite recipe for (avocado) dip; recording reflections and reminiscences or setting out your aims and objectives for the coming year. You can keep it hidden by your bed, carry it conveniently in your school-bag or pull it out, with more than a little operatic flourish, at your next big meeting. You can fill it up with lines from Gilbert & Sullivan or your own private ballads, songs and snatches. Rhyme and reason not required! Key Features: 6" x 9" - conveniently sized, and just perfect for your school bag, backpack, or desk 120 fully usable white lined pages Printed on high-quality paper throughout Full-colour glossy cover bearing an image of the Avogato winking playfully and wishing you 'Mew Bueno!' Perfect for use as a journal, notebook, diary or...well, you choose

Data Scientist I Solve Problems You Don T Know Notebook

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Author : Genius Job Utility Journal Notebooks
language : en
Publisher:
Release Date : 2019-12-19

Practical Data Science With Jupyter

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Author : Prateek Gupta
language : en
Publisher: BPB Publications
Release Date : 2021-03-01

Practical Data Science With Jupyter written by Prateek Gupta and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-01 with Computers categories.

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

Data Science For Dummies

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Author : Lillian Pierson
language : en
Publisher: John Wiley & Sons
Release Date : 2017-02-21

Data Science For Dummies written by Lillian Pierson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-21 with Computers categories.

Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

Data Science From Scratch

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Author : Joel Grus
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2015-04-14

Data Science From Scratch written by Joel Grus and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-14 with Computers categories.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Data Science

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Author : Herbert Jones
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-11

Data Science written by Herbert Jones and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11 with categories.

Did you know that the value of data usage has increased job opportunities, but that there are few specialists? These days, everyone is aware of the role that data can play, whether it is an election, business or education. But how can you start working in a wide interdisciplinary field that is occupied with so much hype? This book, Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't, presents you with a step-by-step approach to Data Science as well as secrets only known by the best Data Scientists. It combines analytical engineering, Machine Learning, Big Data, Data Mining, and Statistics in an easy to read and digest method. Data gathered from scientific measurements, customers, IoT sensors, and so on is very important only when one can draw meaning from it. Data Scientists are professionals that help disclose interesting and rewarding challenges of exploring, observing, analyzing, and interpreting data. To do that, they apply special techniques that help them discover the meaning of data. Becoming the best Data Scientist is more than just mastering analytic tools and techniques. The real deal lies in the way you apply your creative ability like expert Data Scientists. This book will help you discover that and get you there. The goal with Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't is to help you expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data centric issues. At the end of this book, you will learn how to combine Machine Learning, Data Mining, analytics, and programming, and extract real knowledge from data. As you read, you will discover important statistical techniques and algorithms that are helpful in learning Data Science. When you have finished, you will have a strong foundation to help you explore many other fields related to Data Science. This book will discuss the following topics: What Data Science is What it takes to become an expert in Data Science Best Data Mining techniques to apply in data Data visualization Logistic regression Data engineering Machine Learning Big Data Analytics And much more! Don't waste any time. Grab your copy today and learn quick tips from the best Data scientists!

Software Engineering For Data Scientists

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Author : Catherine Nelson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-04-16

Software Engineering For Data Scientists written by Catherine Nelson and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-16 with Computers categories.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more

Data Scientist Diploma Master S Level City Of London College Of Economics 6 Months 100 Online Self Paced

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Author : City of London College of Economics
language : en
Publisher: City of London College of Economics
Release Date :

Data Scientist Diploma Master S Level City Of London College Of Economics 6 Months 100 Online Self Paced written by City of London College of Economics and has been published by City of London College of Economics this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.

Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link.

Data Scientist I Solve Problems You Don T Know Notebook

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