Here's what to expect: 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. There is a small portion of data science world that focuses on using data to write better programs. Python for Data Science For Dummies: Mueller, John Paul, Massaron, Luca: Amazon.com.au: Books 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. Listen to "Data Science For Dummies 2nd Edition" by Lillian Pierson available from Rakuten Kobo. Forensic scientists are wanted all Read more, Your email address will not be published. Also, R’s data visualizations capabilities are somewhat more sophisticated than Python’s, and generally easier to generate. The descriptions below should help you do that. Audience: In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value. Part 1: Getting Started with Data Science 5, Chapter 1: Wrapping Your Head around Data Science 7, Seeing Who Can Make Use of Data Science 8, Analyzing the Pieces of the Data Science Puzzle 10, Collecting, querying, and consuming data 10, Applying mathematical modeling to data science tasks 11, Deriving insights from statistical methods 12, Coding, coding, coding — it’s just part of the game 12, Applying data science to a subject area 12, Exploring the Data Science Solution Alternatives 14, Outsourcing requirements to private data science consultants 15, Leveraging cloud-based platform solutions 15, Letting Data Science Make You More Marketable 16, Chapter 2: Exploring Data Engineering Pipelines and Infrastructure 17, Grasping the Difference between Data Science and Data Engineering 21, Comparing data scientists and data engineers 23, Storing data on the Hadoop distributed file system (HDFS) 27, Putting it all together on the Hadoop platform 28, Identifying Alternative Big Data Solutions 28, Introducing massively parallel processing (MPP) platforms 29, Data Engineering in Action: A Case Study 30, Solving business problems with data engineering 32, Chapter 3: Applying Data-Driven Insights to Business and Industry 33, Benefiting from Business-Centric Data Science 34, Converting Raw Data into Actionable Insights with Data Analytics 35, Distinguishing between Business Intelligence and Data Science 39, The kinds of data used in business intelligence 40, Technologies and skillsets that are useful in business intelligence 40, Defining Business-Centric Data Science 41, Kinds of data that are useful in business-centric data science 42, Technologies and skillsets that are useful in business-centric data science 43, Making business value from machine learning methods 43, Differentiating between Business Intelligence and Business-Centric Data Science 44, Knowing Whom to Call to Get the Job Done Right 45, Exploring Data Science in Business: A Data-Driven Business Success Story 46, Part 2: Using Data Science to Extract Meaning from Your Data 49, Chapter 4: Machine Learning: Learning from Data with Your Machine 51, Defining Machine Learning and Its Processes 51, Walking through the steps of the machine learning process 52, Getting familiar with machine learning terms 52, Selecting algorithms based on function 54, Using Spark to generate real-time big data analytics 58, Chapter 5: Math, Probability, and Statistical Modeling 61, Exploring Probability and Inferential Statistics 62, Conditional probability with Naïve Bayes 65, Calculating correlation with Pearson’s r 66, Ranking variable-pairs using Spearman’s rank correlation 66, Reducing Data Dimensionality with Linear Algebra 67, Decomposing data to reduce dimensionality 67, Reducing dimensionality with factor analysis 69, Decreasing dimensionality and removing outliers with PCA 70, Modeling Decisions with Multi-Criteria Decision Making 70, Ordinary least squares (OLS) regression methods 74, Detecting outliers with univariate analysis 76, Detecting outliers with multivariate analysis 77, Chapter 6: Using Clustering to Subdivide Data 81, Looking at clustering similarity metrics 85, Estimating clusters with kernel density estimation (KDE) 87, Clustering with hierarchical algorithms 88, Categorizing Data with Decision Tree and Random Forest Algorithms 91, Recognizing the Difference between Clustering and Classification 94, Getting to know classification algorithms 95, Making Sense of Data with Nearest Neighbor Analysis 97, Classifying Data with Average Nearest Neighbor Algorithms 98, Classifying with K-Nearest Neighbor Algorithms 101, Understanding how the k-nearest neighbor algorithm works 102, Knowing when to use the k-nearest neighbor algorithm 103, Exploring common applications of k-nearest neighbor algorithms 104, Solving Real-World Problems with Nearest Neighbor Algorithms 104, Seeing k-nearest neighbor algorithms in action 104, Seeing average nearest neighbor algorithms in action 105, Chapter 8: Building Models That Operate Internet-of-Things Devices 107, Overviewing the Vocabulary and Technologies 108, Getting context-aware with sensor fusion 111, Digging into the Data Science Approaches 111, Advancing Artificial Intelligence Innovation 113, Part 3: Creating Data Visualizations That Clearly Communicate Meaning 115, Chapter 9: Following the Principles of Data Visualization Design 117, Data storytelling for organizational decision makers 118, Designing to Meet the Needs of Your Target Audience 119, Step 3: Choose the most functional visualization type for your purpose 121, Picking the Most Appropriate Design Style 122, Inducing a calculating, exacting response 122, Eliciting a strong emotional response 123, Creating context with graphical elements 125, Selecting the Appropriate Data Graphic Type 127, Chapter 10: Using D3.js for Data Visualization 141, Knowing When to Use D3.js (and When Not To) 142, Bringing in the Cascading Style Sheets (CSS) 146, Implementing More Advanced Concepts and Practices in D3.js 147, Getting to know transitions and interactions 153, Chapter 11: Web-Based Applications for Visualization Design 157, Designing Data Visualizations for Collaboration 158, Visualizing and collaborating with Plotly 159, Visualizing Spatial Data with Online Geographic Tools 162, Mapmaking and spatial data analytics with CartoDB 164, Visualizing with Open Source: Web-Based Data Visualization Platforms 166, Making pretty data graphics with Google Fusion Tables 166, Using iCharts for web-based data visualization 167, Using RAW for web-based data visualization 168, Knowing When to Stick with Infographics 170, Making cool infographics with Infogr.am 170, Making cool infographics with Piktochart 172, Chapter 12: Exploring Best Practices in Dashboard Design 173, Chapter 13: Making Maps from Spatial Data 179, Map projections and coordinate systems 185, Getting Started with Open-Source QGIS 191, Chapter 14: Using Python for Data Science 201, Checking Out Some Useful Python Libraries 210, Getting up close and personal with the SciPy library 213, Bonding with MatPlotLib for data visualization 214, Analyzing Data with Python — an Exercise 216, Installing Python on the Mac and Windows OS 216, Chapter 15: Using Open Source R for Data Science 225, Sorting Out Popular Statistical Analysis Packages 236, Examining Packages for Visualizing, Mapping, and Graphing in R 238, Visualizing R statistics with ggplot2 238, Analyzing networks with statnet and igraph 239, Mapping and analyzing spatial point patterns with spatstat 240, Chapter 16: Using SQL in Data Science 241, Getting a Handle on Relational Databases and SQL 242, Investing Some Effort into Database Design 245, Integrating SQL, R, Python, and Excel into Your Data Science Strategy 249, Narrowing the Focus with SQL Functions 249, Chapter 17: Doing Data Science with Excel and Knime 255, Using Excel to quickly get to know your data 256, Reformatting and summarizing with pivot tables 261, Using KNIME for Advanced Data Analytics 264, Using KNIME to make the most of your social data 265, Using KNIME for environmental good stewardship 266, Part 5: Applying Domain Expertise to Solve Real-World Problems Using Data Science 267, Chapter 18: Data Science in Journalism: Nailing Down the Five Ws (and an H) 269, Bringing Data Journalism to Life: The Black Budget 273, When does the audience care the most? ).push ( { } ) ; your email address will not be published by Lillian Pierson available from Kobo... 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