9Natree

Excel for Busy Professionals (Mark J. Roger)

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- Interesting Topics:
- A Seven-Minute Learning Format for Time-Constrained Professionals
- Pivot Tables as a Faster Route from Raw Data to Summaries
- VLOOKUP and XLOOKUP for Reliable Cross-Table Data Retrieval
- Dashboards and KPIs for Clearer Operational Reporting
- Filtering, Sorting, and Introductory VBA to Reduce Repetitive Work

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Excel for Busy Professionals by Mark J. Roger is a practical workplace-skills guide aimed at people who need to improve their spreadsheet work without committing to a lengthy technical course. Presented as a seven-minute-a-day crash course, it focuses on commonly used Excel capabilities that support reporting, data analysis, routine administration, and managerial communication. The available description positions the book around a clear problem: professionals often lose time to manual cleanup, fragile formulas, repetitive updates, and difficulty answering data questions quickly. Roger addresses this problem through tools such as Pivot Tables, VLOOKUP, XLOOKUP, dashboards, filtering and sorting, key performance indicators, and introductory VBA automation. Rather than treating Excel as a purely technical subject, the book frames it as an operational tool whose quality affects reliability and perceived competence at work. Its stated purpose is to help nontechnical and time-constrained readers develop usable systems, reduce avoidable errors, and apply spreadsheet skills directly to recurring workplace tasks.

Learning Spark: Lightning-Fast Data Analytics (Jules S. Damji)

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- Interesting Topics:
- Structured APIs replace much of the complexity of low-level distributed processing
- Execution planning, the Spark UI, and configuration connect code to real performance
- Data source integration makes Spark a practical layer for heterogeneous analytics
- Structured Streaming applies a consistent model to batch and continuous data
- Delta Lake, MLlib, and MLflow extend Spark from analysis into managed pipelines

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Learning Spark: Lightning-Fast Data Analytics is a technical, practice-oriented guide to Apache Spark, written for data engineers and data scientists working with data too large, varied, or fast-moving for single-machine workflows. The second edition, updated for Spark 3.0, presents Spark not merely as a distributed computation engine but as a unified platform for SQL analytics, batch processing, streaming, and machine learning. Its organizing idea is that structured interfaces, especially DataFrames, Datasets, and Spark SQL, make distributed processing more accessible and more optimizable than lower-level approaches alone. The book combines conceptual explanations with code-driven walkthroughs across Python, SQL, Scala, and Java. It addresses the full path from establishing a Spark application and reading external data to inspecting execution plans, tuning jobs, building streaming workloads, and managing machine learning pipelines. Rather than focusing solely on isolated APIs, it emphasizes how Spark components interact in production-oriented analytical systems.

Practical Linear Algebra for Data Science (Mike X Cohen)

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- Interesting Topics:
- Vectors and matrices as computational representations of data and transformations
- Matrix arithmetic and dimensional structure behind numerical algorithms
- Independence, rank, and inverses as tests of information and solvability
- LU and QR decompositions as practical alternatives to direct matrix inversion
- Eigendecomposition and singular value decomposition for modeling and dimensionality reduction

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Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python is a technical, application-oriented introduction to the mathematical structures underlying computational analysis. Written by Mike X Cohen, it presents linear algebra not chiefly as a proof-centered academic subject, but as a working language for algorithms implemented on computers. The book connects fundamental objects such as vectors and matrices to Python-based computation and to domains including data science, machine learning, deep learning, simulation, and biomedical data processing. Its stated coverage spans matrix arithmetic, linear independence, rank, inverses, LU and QR decompositions, eigendecomposition, singular value decomposition, least-squares fitting, and principal components analysis. This scope gives readers both the vocabulary for interpreting common numerical methods and the operational perspective needed to implement them. Rather than treating formulas as isolated rules, the book positions them as descriptions of transformations, representations of data, and tools for solving structured problems. It is therefore best understood as a bridge between foundational linear algebra and the matrix-based workflows used in modern scientific and technical computing.

Think Bayes: Bayesian Statistics in Python (Allen B. Downey)

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- Interesting Topics:
- Bayesian Updating Is Presented as an Explicit Model of Changing Belief
- Discrete Probability Distributions Replace Much of the Usual Calculus
- Estimation and Prediction Are Treated as Distributional Questions
- Evidence, Hypothesis Testing, and Decisions Depend on Competing Models
- Simulation and Multidimensional Models Connect Fundamentals to Real Data

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Think Bayes: Bayesian Statistics in Python is a practical introduction to Bayesian inference by computer scientist Allen B. Downey. Written for readers who can program in Python and have some familiarity with probability, the book treats statistics primarily as a computational activity rather than a calculus-heavy branch of mathematics. Its central purpose is to show how prior beliefs can be represented as probability distributions, revised with observed evidence, and used to answer questions about uncertainty. The second edition uses Python, Jupyter notebooks, and discrete approximations to make the mechanics of Bayesian updating inspectable and executable. It begins with small probability puzzles involving coins, dice, cookies, and similar examples before moving toward estimation, prediction, decision analysis, hypothesis testing, simulation, and models with multiple parameters. Rather than presenting Bayesian methods as a collection of formulas to memorize, Downey emphasizes a reusable workflow: state assumptions, encode uncertainty, update the model with data, and interpret the resulting distribution in the context of a real decision or prediction.

Hands-On Programming with R: Write Your Own Functions and Simulations (Garrett Grolemund)

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- Interesting Topics:
- Casino simulations turn abstract R syntax into connected programming problems
- Function writing is introduced as the basis for reusable analytical work
- R data objects and environments explain how programs retain and retrieve information
- Control flow and vectorization are treated as alternative ways to express computation
- S3 classes, packages, and debugging extend lessons beyond one-off scripts

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Hands-On Programming with R: Write Your Own Functions and Simulations is a practical introduction to the R language by Garrett Grolemund, published by O Reilly in 2014. It is aimed primarily at people who use R for statistics or data analysis but have not yet learned to program systematically, as well as complete newcomers to R. Rather than organizing instruction around isolated syntax lessons, the book develops programming ability through increasingly substantial casino game projects. Readers build tools for weighted dice, card games, and a slot machine while learning how R represents data, evaluates code, and supports simulation. This structure links programming decisions to questions familiar in data science, including random sampling, probabilities, and the behavior of repeated experiments. The book moves beyond basic commands into functions, control flow, environments, object-oriented tools, vectorization, packages, and debugging. Its purpose is not merely to help readers run existing analyses, but to enable them to create reliable R programs that express a problem, automate repeated work, and investigate it through simulation.

Mindmasters (Sandra Matz)

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- Interesting Topics:
- Digital footprints reveal more than deliberate self-presentation
- Psychological targeting links prediction to tailored persuasion
- The same predictive capacity can support welfare or enable manipulation
- Generative AI increases the scale and granularity of personalized influence
- Data governance must move beyond notice and individual consent

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Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior is a nonfiction examination of psychological targeting by Sandra Matz, a computational social scientist and professor at Columbia Business School. The book explains how data generated through ordinary digital activity can be analyzed to infer traits, preferences, values, and likely decisions. Its subject is not merely personalized advertising, but the expanding ability of organizations to model the psychological conditions under which people can be persuaded. Matz combines behavioral-science research, data analytics, and practical examples to show how this capability operates in commercial and social settings. She treats the technology as inherently dual-use: it can help people make healthier, better-informed choices, while also enabling intrusive influence and reducing individual agency. The book therefore has two linked purposes. It clarifies the mechanisms behind algorithmic profiling for non-specialists, and it asks what ethical governance should look like when intimate inferences can be drawn from seemingly routine data.

Grokking Algorithms (Aditya Bhargava)

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- Interesting Topics:
- Algorithmic efficiency is introduced through concrete growth-rate comparisons
- Data structures are treated as choices that shape the cost of operations
- Recursion and divide-and-conquer reveal reusable ways to reduce problems
- Graph algorithms model relationships, reachability, and least-cost routes
- Greedy methods, dynamic programming, and NP-completeness clarify the limits of simple choices

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Grokking Algorithms by Aditya Bhargava is an illustrated introductory guide to algorithms for programmers and other technically curious readers. Rather than treating algorithms chiefly as formal mathematical objects, it presents them as practical procedures for solving recurring programming problems efficiently. The book uses approachable language, diagrams, short examples, exercises, and annotated Python code to explain why a method works, where it fits, and what trade-offs it brings. Its coverage begins with foundational ideas such as binary search, selection sort, recursion, arrays, and linked lists, then moves through hash tables, graph traversal, shortest paths, greedy strategies, dynamic programming, and the limits represented by NP-complete problems. The central purpose is not exhaustive theoretical coverage or proof-based instruction. It is to build algorithmic intuition: the ability to recognize a problem pattern, compare possible approaches, estimate their cost, and select a suitable technique. This makes the book a bridge between basic programming fluency and more systematic computer science problem solving.

Machine Learning for Algorithmic Trading (Stefan Jansen)

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- Interesting Topics:
- The End-to-End Research Workflow Connects Models to Tradeable Decisions
- Market, Fundamental, and Alternative Data Expand the Search for Alpha
- Feature Engineering and Alpha Research Matter More Than Algorithm Novelty
- Multiple Learning Paradigms Serve Distinct Financial Research Tasks
- Backtesting Must Address Overfitting, Costs, and Changing Market Conditions

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Machine Learning for Algorithmic Trading by Stefan Jansen is a technical, Python-oriented guide to building systematic investment strategies from financial data. Positioned between a machine learning text and a quantitative-finance workflow manual, it explains how predictive methods can be applied from data collection through feature research, model evaluation, portfolio construction, and backtesting. The book addresses conventional market and fundamental datasets as well as alternative sources such as regulatory filings, earnings-call transcripts, and financial news. Its central purpose is not simply to introduce algorithms, but to show how an apparent prediction can become a tradeable signal under the constraints of noisy, changing markets. Jansen covers supervised, unsupervised, and deep-learning approaches while emphasizing the practical research process around them. Readers are expected to have some familiarity with Python and core machine-learning concepts. The result is a broad applied reference for researchers seeking to connect data science methods to realistic long-short and other systematic trading strategies.

Computational Geometry: Algorithms and Applications (Mark de Berg)

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- Interesting Topics:
- Geometric predicates turn spatial questions into algorithmic decisions
- Plane sweep organizes intersection problems around a moving frontier
- Voronoi diagrams and Delaunay triangulations capture proximity structure
- Search structures make geometric data useful after preprocessing
- Planning and visibility chapters connect theory to constrained motion

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Computational Geometry: Algorithms and Applications is a textbook by Mark de Berg, Otfried Cheong, Marc van Kreveld, and Mark Overmars that introduces the design and analysis of algorithms for geometric data. It addresses problems in which the input consists of points, segments, polygons, or spatial subdivisions, and where a useful solution depends on exploiting geometric structure rather than treating the data as arbitrary numbers. The book is primarily concerned with two and three dimensional settings that arise in computer graphics, geographic information systems, robotics, and engineering. Its purpose is not merely to catalogue geometric problems, but to show how a relatively small set of algorithmic ideas can yield efficient, understandable solutions across many settings. Chapters typically begin from an application-shaped problem and then identify the abstract geometric task beneath it. The result is a broad, algorithm-centered introduction for advanced undergraduates, early graduate students, and independent readers who already understand data structures, asymptotic analysis, and basic algorithm design.

Basics of Power BI Modeling (Reza Rad)

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- Interesting Topics:
- Star Schema as the Organizing Structure for Reporting Models
- Fact Tables, Dimension Tables, and the Importance of Declaring Grain
- Relationships Define How Filters Travel Through a Power BI Model
- Power Query Supports Reshaping Data Before It Becomes a Reporting Model
- Model Quality Affects Performance, Usability, and Trust in Reports

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Basics of Power BI Modeling by Reza Rad is a concise technical guide to the data-modeling decisions that sit behind dependable Power BI reports. Rather than treating a dashboard as a collection of imported tables and visuals, the book frames reporting as the outcome of a deliberately shaped model. Its subject is foundational dimensional modeling for Power BI, with emphasis on organizing data into fact and dimension tables, defining relationships, and using Power Query and related Power BI components to prepare tables for analysis. The book is written for Power BI users, including beginners, and does not require extensive prior knowledge of database design. At the same time, its central principles extend beyond one Microsoft product: clear table roles, consistent keys, appropriate grain, and understandable relationships are general business-intelligence concerns. Rad positions modeling as a practical prerequisite for performance and trustworthy analysis. The purpose is not advanced DAX instruction or visual-design advice, but a learning path for building a semantic foundation that makes those later activities more reliable.