This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn Master the mathematical foundations required for business analytics Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if youre new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine data sets and handle missing data Reshape, tidy, and clean data sets so theyre easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large data sets with groupby Leverage Pandas advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the best one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning ...

Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Learn how to integrate data-driven methodologies and machine learning into your business decision-making processes with 'The Art of Data-Driven Business.' This comprehensive guide shows you how to apply Python-based machine learning techniques to real-world challenges, transforming your organization into an innovative and well-informed enterprise. What this Book will help me do Create professional-quality data visualizations using Python's seaborn library to derive business insights. Analyze customer behavior, including predicting churn, with machine learning techniques. Apply clustering algorithms to segment customers for targeted marketing campaigns. Utilize pandas effectively for pricing and sales analytics to optimize your pricing strategies. Forecast outcomes of promotional strategies to determine costs and benefits and maximize performance. Author(s) None Palacio is an experienced data scientist and educator who specializes in the application of machine learning to solve business problems. With extensive real-world industry experience, Palacio brings practical insights and methodologies to learners. Their teaching connects technical knowledge to actionable business strategies. Who is it for? This book is ideal for business professionals aiming to incorporate data science into their strategies and technical experts seeking to leverage machine learning for business scenarios. Beginners to Python can find foundational help, while data scientists will appreciate the focused practical applications. It's perfect for individuals seeking a strong data-driven perspective in marketing, sales, and customer management.

A swift and practical introduction to building interactive data visualization apps in Python, known as dashboards. Youâ??ve seen dashboards before; think election result visualizations you can update in real time, or population maps you can filter by demographic. With the Python Dash library youâ??ll create analytic dashboards that present data in effective, usable, elegant ways in just a few lines of code. The book is fast-paced and caters to those entirely new to dashboards. It will talk you through the necessary software, then get straight into building the dashboards themselves. Youâ??ll learn the basic format of a Dash app by building a twitter analysis dashboard that maps the number of likes certain accounts gained over time. Youâ??ll build up skills through three more sophisticated projects. The first is a global analysis app that compares country data in three areas: the percentage of a population using the internet, percentage of parliament seats held by women, and CO2 emissions. Youâ??ll then build an investment portfolio dashboard, and an app that allows you to visualize and explore machine learning algorithms. In this book you will: â?¢Create and run your first Dash apps â?¢Use the pandas library to manipulate and analyze social media data â?¢Use Git to download and build on existing apps written by the pros â?¢Visualize machine learning models in your apps â?¢Create and manipulate statistical and scientific charts and maps using Plotly Dash combines several technologies to get you building dashboards quickly and efficiently. This book will do the same.

Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. Youâ??ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. You will discover Pythonâ??s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.

The Pandas Workshop offers a detailed journey into the world of data analysis using Python and the pandas library. Throughout the book, you'll build skills in accessing, transforming, visualizing, and modeling data, all while focusing on real-world data science challenges. You will gain the knowledge and confidence needed to dissect and derive insights from complex datasets. What this Book will help me do Understand how to access and load data from various formats including databases and web-based sources. Manipulate and transform data for analysis using efficient pandas techniques. Create insightful visualizations using Matplotlib integrated with pandas for clearer data presentation. Build predictive and descriptive data models and glean data-driven insights. Handle and analyze time-series data to uncover trends and seasonal effects in data patterns. Author(s) Blaine Bateman, Saikat Basak, Thomas Joseph, and William So collectively bring diverse expertise in data analysis, programming, and teaching. Their goal is to make cutting-edge data science techniques accessible through clear explanations and practical exercises, helping learners from varied backgrounds master the pandas library. Who is it for? This book is best suited for novice to intermediate programmers and data enthusiasts who are already familiar with Python but are new to the pandas library. Ideal readers are those interested in honing their skills in data analysis and visualization, as well as leveraging data for informed decision-making. Whether you're an analyst, aspiring data scientist, or business professional seeking to strengthen your analytical toolkit, this book provides beneficial insights and techniques.

Explore the comprehensive world of data science with "Building Data Science Solutions with Anaconda." This book covers essential topics like managing environments with Anaconda, detecting and overcoming bias, and ensuring model interpretability. Delve into practical tools and solutions, all explained in an approachable way to help you become proficient in data science workflows. What this Book will help me do Master environment management for data science projects using Anaconda and conda. Detect and mitigate dataset biases to ensure fair and ethical machine learning models. Learn advanced data science techniques with tools like NumPy, pandas, and Jupyter Notebooks. Understand and explain your machine learning models using LIME and SHAP. Grow your expertise in selecting and fine-tuning AI/ML algorithms for diverse applications. Author(s) None Meador combines extensive expertise in data science with a thorough understanding of Anaconda tools and open source software. With a background in engineering and AI model management, None provides an insightful perspective on the field. Their practical and analogy-driven approach makes technical concepts accessible to learners of any level. Who is it for? This book is ideal for data analysts, aspiring machine learning engineers, and data science professionals who wish to deepen their knowledge and make the most of Anaconda's capabilities. A prior understanding of Python and basic data science principles is assumed. If you're looking to optimize your data science workflows and gain hands-on practice, this book is for you.

Learn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications. You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts. Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed Matplotlib with different frameworks. The concluding chapters cover how to visualize data with Pandas and Matplotlib, Seaborn, and how to work with the real-life data and visualize it. After reading Hands-on Matplotlib you'll be proficient with Matplotlib and able to comfortably work with ndarrays in NumPy and data frames in Pandas. What You'll Learn Understand Data Visualization and Python using Matplotlib Review the fundamental data structures in NumPy and Pandas Work with 3D plotting, visualizations, and animations Visualize images and audio data Who This Book Is For Data scientists, machine learning engineers and software professionals with basic programming skills.

Practical Data Science with Python guides you through the entire process of leveraging Python tools to analyze and gain insights from data. You'll start with foundational concepts and coding essentials, progressing through statistical analysis, machine learning techniques, and ethical considerations. What this Book will help me do Clean, prepare, and explore data using pandas and NumPy. Understand and implement machine learning models such as random forests and support vector machines. Perform statistical tests and analyze distributions to enhance data insights. Utilize SQL with Python for efficient data interaction. Generate automated reports and dashboards for data storytelling. Author(s) Nathan George has extensive professional experience as a data scientist and Python developer. He specializes in the application of machine learning and statistical methods to solve real-world problems. His writing combines technical depth with an approachable style, aiming to provide readers with actionable knowledge and skills. Who is it for? This book is perfect for data science beginners who have a basic understanding of Python and want to build practical data analysis skills. Students in analytics programs or professionals looking to transition into a data science role will find value in its approachable yet comprehensive coverage. Aspiring data analysts and career changers will gain firsthand exposure to Python-based data science best practices. If you're eager to develop practical, hands-on experience in the data science field, this is the guide for you.

Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software. In Pandas in Action you will learn how to: Import datasets, identify issues with their data structures, and optimize them for efficiency Sort, filter, pivot, and draw conclusions from a dataset and its subsets Identify trends from text-based and time-based data Organize, group, merge, and join separate datasets Use a GroupBy object to store multiple DataFrames Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You’ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data. About the Technology Data analysis with Python doesn’t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It’s a perfect way to up your data game. About the Book Pandas in Action introduces Python-based data analysis using the amazing pandas library. You’ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you’ll find in the real world. What's Inside Organize, group, merge, split, and join datasets Find trends in text-based and time-based data Sort, filter, pivot, optimize, and draw conclusions Apply aggregate operations About the Reader For readers experienced with spreadsheets and basic Python programming. About the Author Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries. Quotes Of all the introductory pandas books I’ve read—and I did read a few—this is the best, by a mile. - Erico Lendzian, idibu.com This approachable guide will get you up and running quickly with all the basics you need to analyze your data. - Jonathan Sharley, SiriusXM Media Understanding and putting in practice the concepts of this book will help you increase productivity and make you look like a pro. - Jose Apablaza, Steadfast Networks Teaches both novice and expert Python users the essential concepts required for data analysis and data science. - Ben McNamara, DataGeek

In 'Data Science for Marketing Analytics', you'll embark on a journey that integrates the power of data analytics with strategic marketing. With a focus on practical application, this guide walks you through using Python to analyze datasets, implement machine learning models, and derive data-driven insights. What this Book will help me do Gain expertise in cleaning, exploring, and visualizing marketing data using Python. Build machine learning models to predict customer behavior and sales outcomes. Leverage unsupervised learning techniques for effective customer segmentation. Compare and optimize predictive models using advanced evaluation methods. Master Python libraries like pandas and Matplotlib for data manipulation and visualization. Author(s) Mirza Rahim Baig, Gururajan Govindan, and Vishwesh Ravi Shrimali combine their extensive expertise in data analytics and marketing to bring you this comprehensive guide. Drawing from years of applying analytics in real-world marketing scenarios, they provide a hands-on approach to learning data science tools and techniques. Who is it for? This book is perfect for marketing professionals and analysts eager to harness the capabilities of Python to enhance their data-driven strategies. It is also ideal for data scientists looking to apply their skills in marketing across various roles. While a basic understanding of data analysis and Python will help, all key concepts are introduced comprehensively for beginners.

This book contains 25 short programs that will challenge your understanding of Pandas. Like any big project, the Pandas developers had to make some design decisions that at times seem surprising. This book uses those quirks as a teaching opportunity. By understanding the gaps in your knowledge, you'll become better at what you do. Some of the teasers are from the author's experience shipping bugs to production, and some from others doing the same. Teasers and puzzles are fun, and learning how to solve them can teach you to avoid programming mistakes and maybe even impress your colleagues and future employers. Working with data is central to nearly everything we do, from disease contact tracing and analyzing health records to smart meters that track utility consumption behavior. With the power of Python's pandas library, you can process and analyze this data in a highly efficient and simple-to-understand way. And with 25 brain teasers designed to turn this technology's quirks into a teaching opportunity, you'll be honing your data science skills while having fun at the same time. Following a simple format, you'll challenge yourself and your understanding of pandas. Read a short Python program that uses pandas, try to guess the output, run the code yourself, and then go to the next page for an explanation of the solution. From common pitfalls and hidden gotchas to unexpected twists and turns, you'll deepen your understanding of pandas, learn to write more efficient code, and reduce the number of bugs in the software you develop. You may even impress your colleagues and your employers, both present and future. Learn the tricks of the trade with Python's pandas, in one of the most fun and creative ways around. What You Need: To run the code you'll need Python version 3.8 or upper and Pandas version 1.0 or upper installed. We use Python version 3.8.3 and Pandas version 1.0.5; the output might change in future versions.

Data Science Projects with Python offers a hands-on, project-based approach to learning data science using real-world data sets and tools. You will explore data using Python libraries like pandas and Matplotlib, build machine learning models with scikit-learn, and apply advanced techniques like XGBoost and SHAP values. This book equips you to confidently extract insights, evaluate models, and deliver results with clarity. What this Book will help me do Learn to load, clean, and preprocess data using Python and pandas. Build and evaluate predictive models, including logistic regression and random forests. Visualize data effectively using Python libraries like Matplotlib. Master advanced techniques like XGBoost and algorithmic fairness. Communicate data-driven insights to aid decision making in practical scenarios. Author(s) Stephen Klosterman is an experienced data scientist with a strong focus on practical applications of machine learning in business. Combining a rich academic background with hands-on industry experience, he excels at explaining complex concepts in an approachable way. As the author of 'Data Science Projects with Python,' his goal is to provide learners with the skills needed for real-world data science challenges. Who is it for? This book is ideal for beginners in data science and machine learning who have some basic programming knowledge in Python. Aspiring data scientists will benefit from its practical, end-to-end examples. Professionals seeking to expand their skillset in predictive modeling and delivering business insights will find this book invaluable. Some foundation in statistics and programming is recommended.

'Hands-On Data Analysis with Pandas' guides you to gain expertise in the Python pandas library for data analysis and manipulation. With practical, real-world examples, you'll learn to analyze datasets, visualize data trends, and implement machine learning models for actionable insights. What this Book will help me do Understand and implement data analysis techniques with Python. Develop expertise in data manipulation using pandas and NumPy. Visualize data effectively with pandas visualization tools and seaborn. Apply machine learning techniques with Python libraries. Combine datasets and handle complex data workflows efficiently. Author(s) Stefanie Molin is a software engineer and data scientist with extensive experience in analytics and Python. She has worked with large data-driven systems and has a strong focus on teaching data analysis effectively. Stefanie's books are known for their practical, hands-on approach to solving real data problems. Who is it for? This book is perfect for aspiring data scientists, data analysts, and Python developers. Readers with beginner to intermediate skill levels in Python will find it accessible and informative. It is designed for those seeking to build practical data analysis skills. If you're looking to add data science and pandas to your toolkit, this book is ideal.

Dive into the intricacies of data cleaning, a crucial aspect of any data science and machine learning pipeline, with 'Cleaning Data for Effective Data Science.' This comprehensive guide walks you through tools and methodologies like Python, R, and command-line utilities to prepare raw data for analysis. Learn practical strategies to manage, clean, and refine data encountered in the real world. What this Book will help me do Understand and utilize various data formats such as JSON, SQL, and PDF for data ingestion and processing. Master key tools like pandas, SciPy, and Tidyverse to manipulate and analyze datasets efficiently. Develop heuristics and methodologies for assessing data quality, detecting bias, and identifying irregularities. Apply advanced techniques like feature engineering and statistical adjustments to enhance data usability. Gain confidence in handling time series data by employing methods for de-trending and interpolating missing values. Author(s) David Mertz has years of experience as a Python programmer and data scientist. Known for his engaging and accessible teaching style, David has authored numerous technical articles and books. He emphasizes not only the technicalities of data science tools but also the critical thinking that approaches solutions creatively and effectively. Who is it for? 'Cleaning Data for Effective Data Science' is designed for data scientists, software developers, and educators dealing with data preparation. Whether you're an aspiring data enthusiast or an experienced professional looking to refine your skills, this book provides essential tools and frameworks. Prior programming knowledge, particularly in Python or R, coupled with an understanding of statistical fundamentals, will help you make the most of this resource.

Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

The Data Science Workshop provides a comprehensive introduction to building real-world data science projects. Through a hands-on approach, you will learn how to analyze data, build machine learning models, and deploy them effectively in various scenarios. This book is designed to equip you with the skills to confidently tackle data science challenges. What this Book will help me do Understand the differences between supervised and unsupervised learning to select the appropriate technique. Master data manipulation and analysis using popular Python libraries like pandas and scikit-learn. Develop skills in regression, classification, and clustering to solve diverse data science problems. Learn advanced methods to improve model accuracy, including hyperparameter tuning and feature engineering. Implement and deploy machine learning models efficiently in production workflows. Author(s) The authors of The Data Science Workshop are experienced professionals and educators in the field of data science and machine learning. They have extensive expertise in using practical methods to solve data challenges and have a passion for teaching others through engaging and clear instructional material. Who is it for? This book is ideal for aspiring data analysts, data scientists, and business analysts who wish to build foundational skills in data science. It caters to those new to the field and professionals transitioning to a data-centric role, providing practical knowledge without requiring an advanced mathematical background. Familiarity with Python is recommended.

The Data Wrangling Workshop is your beginner's guide to the essential techniques and practices of data manipulation using Python. Throughout the book, you will progressively build your skills, learning key concepts such as extracting, cleaning, and transforming data into actionable insights. By the end, you'll be confident in handling various data wrangling tasks efficiently. What this Book will help me do Understand and apply the fundamentals of data wrangling using Python. Combine and aggregate data from diverse sources like web data, SQL databases, and spreadsheets. Use descriptive statistics and plotting to examine dataset properties. Handle missing or incorrect data effectively to maintain data quality. Gain hands-on experience with Python's powerful data science libraries like Pandas, NumPy, and Matplotlib. Author(s) Brian Lipp, None Roychowdhury, and Dr. Tirthajyoti Sarkar are experienced educators and professionals in the fields of data science and engineering. Their collective expertise spans years of teaching and working with data technologies. They aim to make data wrangling accessible and comprehensible, focusing on practical examples to equip learners with real-world skills. Who is it for? The Data Wrangling Workshop is ideal for developers, data analysts, and business analysts aiming to become data scientists or analytics experts. If you're just getting started with Python, you will find this book guiding you step-by-step. A basic understanding of Python programming, as well as relational databases and SQL, is recommended for smooth learning.

Embark on an interactive journey into the world of data science with 'The Applied Data Science Workshop'. By following real-world scenarios and hands-on exercises, you will explore the fundamentals of data analysis and machine learning modeling within Jupyter Notebooks, leveraging Python libraries like pandas and sci-kit learn to draw meaningful insights from data. What this Book will help me do Master the process of setting up and using Jupyter Notebooks effectively for data science tasks. Learn to preprocess, analyze, and visualize data using Python libraries such as pandas, Matplotlib, and Seaborn. Discover methods to train and evaluate machine learning models using real-world data scenarios. Apply techniques to assess model performance and optimize them with advanced validation. Gain the skills to communicate insights through well-documented analyses and stakeholder-ready reports. Author(s) None Galea, an accomplished author in the data science domain, focuses on making technical concepts understandable and relatable. With this book, Galea leverages years of experience to introduce readers to practical applications of data science using Python. The author's approach ensures that readers not only learn the concepts but also apply them hands-on. Who is it for? This book caters to aspiring data scientists and developers interested in data analysis and practical applications of data science techniques. Beginners will find the step-by-step methodology approachable, while those with a basic understanding of Python programming or machine learning can quickly extend their skills. It suits anyone eager to apply data science in their professional toolbox.