Data science for dummies:
Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help. What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you c...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley
[2021]
|
| Edition: | 3rd edition |
| Series: | Learning made easy
For dummies |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help. What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects.Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book |
| Item Description: | Includes index "Deploy math and stats to improve decision-making ; supercharge your business growth with data science wins ; monetize your data resources and data science skills" -- Cover |
| Physical Description: | xii, 416 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781119811558 1119811554 |
Staff View
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047654763 | ||
| 003 | DE-604 | ||
| 005 | 20230207 | ||
| 007 | t | ||
| 008 | 211227s2021 a||| |||| 00||| eng d | ||
| 020 | |a 9781119811558 |9 978-1-119-81155-8 | ||
| 020 | |a 1119811554 |9 1119811554 | ||
| 035 | |a (OCoLC)1294713699 | ||
| 035 | |a (DE-599)BVBBV047654763 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T |a DE-739 | ||
| 084 | |a ST 530 |0 (DE-625)143679: |2 rvk | ||
| 100 | 1 | |a Pierson, Lillian |e Verfasser |0 (DE-588)1069696161 |4 aut | |
| 245 | 1 | 0 | |a Data science for dummies |c by Lillian Pierson |
| 250 | |a 3rd edition | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c [2021] | |
| 300 | |a xii, 416 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Learning made easy | |
| 490 | 0 | |a For dummies | |
| 500 | |a Includes index | ||
| 500 | |a "Deploy math and stats to improve decision-making ; supercharge your business growth with data science wins ; monetize your data resources and data science skills" -- Cover | ||
| 505 | 8 | |a Introduction -- Getting started with Data Science. Wrapping your head around data science ; Tapping into critical aspects of data engineering -- Using data science to extract meaning from your data. Machine learning means... using a machine to learn from data ; Math, probability, and statistical modeling ; Grouping your way into accurate predictions ; Coding up data insights and decision engines ; Generating insights with software applications ; Telling powerful stories with data -- Taking stock of your data science capabilites. Developing your business acumen ; Improving operations ; Making marketing improvements ; Enabling improved decision-making ; Decreasing lending risk and fighting financial crimes ; Monetizing data and data science expertise -- Assessing your data sciene options. Gathering important information about your company ; Narrowing in on the optimal data science use case ; Planning for future data science project success ; Blazing a path to data science career success -- The part of tens. Ten phenomenal resources for open data ; Ten free or low-cost data science tools and applications | |
| 520 | 3 | |a Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help. What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects.Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book | |
| 650 | 0 | 7 | |a Algorithmus |0 (DE-588)4001183-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Visualisierung |0 (DE-588)4188417-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenstruktur |0 (DE-588)4011146-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Programmiersprache |0 (DE-588)4047409-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Information technology | |
| 653 | 0 | |a Information retrieval | |
| 653 | 0 | |a Databases | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Business / Data processing | |
| 653 | 0 | |a Business / Data processing | |
| 653 | 0 | |a Data mining | |
| 653 | 0 | |a Databases | |
| 653 | 0 | |a Information retrieval | |
| 653 | 0 | |a Information technology | |
| 653 | 6 | |a Handbooks and manuals | |
| 689 | 0 | 0 | |a Datenstruktur |0 (DE-588)4011146-5 |D s |
| 689 | 0 | 1 | |a Algorithmus |0 (DE-588)4001183-5 |D s |
| 689 | 0 | 2 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | 1 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 1 | 2 | |a Datenstruktur |0 (DE-588)4011146-5 |D s |
| 689 | 1 | 3 | |a Visualisierung |0 (DE-588)4188417-6 |D s |
| 689 | 1 | 4 | |a Programmiersprache |0 (DE-588)4047409-4 |D s |
| 689 | 1 | 5 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 1 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-119-81166-4 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-119-81161-9 |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033038738&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033038738 | ||
Record in the Search Index
| _version_ | 1804183121473568768 |
|---|---|
| adam_text | Table of Contents INTRODUCTION........................................................................... 1 About This Book.................................................................................... 3 Foolish Assumptions............................................................................. 3 Icons Used in This Book........................................................................4 Beyond the Book.................................................................................. 4 Where to Go from Here........................................................................4 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 Inspecting the Pieces of the Data Science Puzzle................................ 10 Collecting, querying, and consuming data..................................... 11 Applying mathematical modeling to data science tasks...............12 Deriving insights from statistical methods................................... 12 Coding, coding, coding — it s just part of the game..................... 13 Applying data science to a subject area......................................... 13 Communicating data insights....................................................... 14 Exploring Career Alternatives That Involve Data Science................... 15 The data implementer.................................................................. 16 The data
leader............................................................................. 16 The data entrepreneur.................................................................. 17 CHAPTER 2: Tapping into Critical Aspects of Data Engineering..............................................19 Defining Big Data and the Three Vs.................................................... 19 Grappling with data volume........................................................... 21 Handling data velocity.................................................................. 21 Dealing with data variety...............................................................22 Identifying Important Data Sources....................................................23 Grasping the Differences among Data Approaches............................ 24 Defining data science.................................................................... 25 Defining machine learning engineering......................................... 26 Defining data engineering............................................................. 26 Comparing machine learning engineers, data scientists, and data engineers........................................................................27 Storing and Processing Data for Data Science................................... 28 Storing data and doing data science directly in the cloud............. 28 Storing big data on-premise......................................................... 32 Processing big data in real-time....................................................35
PART 2: USING DATA SCIENCE TO EXTRACT MEANING FROM YOUR DATA.............................................. 37 CHAPTER 3: Machine Learning Means... Using a Machine to Learn from Data.......................39 Defining Machine Learning and Its Processes................................... 40 Walking through the steps of the machine learning process....... 40 Becoming familiar with machine learning terms.........................41 Considering Learning Styles............................................................... 42 Learning with supervised algorithms............................................ 42 Learning with unsupervised algorithms.........................................43 Learning with reinforcement......................................................... 43 Seeing What You Can Do.................................................................... 43 Selecting algorithms based on function.........................................44 Using Spark to generate real-time big data analytics.....................48 CHAPTER 4: Math, Probability, and Statistical Modeling...........51 Exploring Probability and Inferential Statistics................................... 52 Probability distributions................................................................ 53 Conditional probability with Naïve Bayes..................................... 55 Quantifying Correlation...................................................................... 56 Calculating correlation with Pearson s r......................................... 56 Ranking variable-pairs using Spearman s rank
correlation............58 Reducing Data Dimensionality with Linear Algebra............................ 59 Decomposing data to reduce dimensionality................................59 Reducing dimensionality with factor analysis................................63 Decreasing dimensionality and removing outliers with PCA....... 64 Modeling Decisions with Multiple Criteria Decision-Making............... 65 Turning to traditional MCDM......................................................... 65 Focusing on fuzzy MCDM...............................................................67 Introducing Regression Methods....................................................... 67 Linear regression........................................................................... 67 Logistic regression..........................................................................69 Ordinary least squares (OLS) regression methods........................ 70 Detecting Outliers............................................................................... 70 Analyzing extreme values............................................................... 70 Detecting outliers with univariate analysis................................... 71 Detecting outliers with multivariate analysis................................73 Introducing Time Series Analysis....................................................... 73 Identifying patterns in time series................................................ 74 Modeling univariate time series data............................................ 75
CHAPTERS: Grouping Your Way into Accurate Predictions..... 77 Starting with Clustering Basics............................................................. 78 Getting to know clustering algorithms............................................ 79 Examining clustering similarity metrics.......................................... 81 Identifying Clusters in Your Data........................................................... 82 Clustering with the к-means algorithm.......................................... 82 Estimating clusters with kernel density estimation (KDE)............. 84 Clustering with hierarchical algorithms......................................... 84 Dabbling in the DBScan neighborhood......................................... 87 Categorizing Data with Decision Tree and Random Forest Algorithms............................................................................................. 88 Drawing a Line between Clustering and Classification....................... 89 Introducing instance-based learning classifiers............................. 90 Getting to know classification algorithms.......................................90 Making Sense of Data with Nearest Neighbor Analysis....................... 93 Classifying Data with Average Nearest Neighbor Algorithms.............. 94 Classifying with К-Nearest Neighbor Algorithms................................. 97 Understanding how the к-nearest neighbor algorithm works... .98 Knowing when to use the к-nearest neighbor algorithm............. 99 Exploring common applications of к-nearest neighbor
algorithms....................................................................................... 100 Solving Real-World Problems with Nearest Neighbor Algorithms.. .100 Seeing к-nearest neighbor algorithms in action.......................... 101 Seeing average nearest neighbor algorithms in action............... 101 CHAPTER 6: Coding Up Data Insights and Decision Engines......................................................... юз Seeing Where Python and R Fit into Your Data Science Strategy................................................................................................. 104 Using Python for Data Science........................................................... 104 Sorting out the various Python datatypes................................... 106 Putting loops to good use in Python.............................................. 109 Having fun with functions............................................................. 110 Keeping cool with classes............................................................... 112 Checking out some useful Python libraries................................... 114 Using Open Source R for Data Science................................................ 120 Comprehending R s basic vocabulary............................................ 121 Delving into functions and operators............................................ 124 Iterating in R.................................................................................... 127 Observing how objects work......................................................... 129 Sorting out R s popular
statistical analysis packages................... 131 Examining packages for visualizing, mapping, and graphing in R.......................................................................... 133
CHAPTER?: Generating Insights with Software Applications..................................................... 137 Choosing the Best Tools for Your Data Science Strategy.................. 138 Getting a Handle on SQL and Relational Databases......................... 139 Investing Some Effort into Database Design....................................... 144 Defining data types......................................................................... 144 Designing constraints properly...................................................... 145 Normalizing your database........................................................... 145 Narrowing the Focus with SQL Functions.......................................... 147 Making Life Easier with Excel............................................................... 151 Using Excel to quickly get to know your data............................... 152 Reformatting and summarizing with PivotTables......................... 157 Automating Excel tasks with macros............................................ 158 CHAPTER 8: Telling Powerful Stories with Data......................... 161 Data Visualizations: The Big Three...................................................... 162 Data storytelling for decision makers............................................ 162 Data showcasing for analysts........................................................ 163 Designing data art for activists...................................................... 164 Designing to Meet the Needs of Your Target Audience..................... 164 Step 1: Brainstorm (All about
Eve)................................................ 165 Step 2: Define the purpose............................................................166 Step 3: Choose the most functional visualization type for your purpose..................................................................... 166 Picking the Most Appropriate Design Style.........................................167 Inducing a calculating, exacting response..................................... 167 Eliciting a strong emotional response.......................................... 168 Selecting the Appropriate Data Graphic Type..................................... 170 Standard chart graphics................................................................. 171 Comparative graphics..................................................................... 173 Statistical plots................................................................................ 176 Topology structures....................................................................... 179 Spatial plots and maps................................................................... 180 Testing Data Graphics........................................................................... 183 Adding Context.................................................................................... 184 Creating context with data............................................................. 184 Creating context with annotations................................................ 185 Creating context with graphical elements..................................... 186 PART 3: TAKING STOCK OF YOUR
DATA SCIENCE CAPABILITIES................................................. 187 CHAPTER 9: Developing Your Business Acumen......................... i89 Bridging the Business Gap................................................................... 189 Contrasting business acumen with subject matter expertise... .190 Defining business acumen............................................................. 191
Traversing the Business Landscape.................................................... 192 Seeing how data roles support the business in making money................................................................................ 192 Leveling up your business acumen................................................ 195 Fortifying your leadership skills...................................................... 196 Surveying Use Cases and Case Studies.............................................. 197 Documentation for data leaders.................................................... 199 Documentation for data implementers........................................ 202 chapter 10: Improving Operations............................................. 205 Establishing Essential Context for Operational Improvements Use Cases............................................................................................. 206 Exploring Ways That Data Science Is Used to Improve Operations........................................................................................... 207 Making major improvements to traditional manufacturing operations............................................................. 208 Optimizing business operations with data science..................... 210 An Al case study: Automated, personalized, and effective debt collection processes................................................ 211 Gaining logistical efficiencies with better use of real-time data.................................................................................. 216 Another Al case study: Real-time
optimized logistics routing... .217 Modernizing media and the press with data science and Al ... .222 Generating content with the click of a button...............................222 Yet another case study: Increasing content generation rates .. .224 CHAPTER 11: Making Marketing Improvements ........................ 229 Exploring Popular Use Cases for Data Science in Marketing........... 229 Turning Web Analytics into Dollars and Sense...................................232 Getting acquainted with omnichannel analytics........................... 233 Mapping your channels................................................................. 233 Building analytics around channel performance......................... 235 Scoring your company s channels.................................................. 235 Building Data Products That Increase Sales-and-Marketing ROI... .238 Increasing Profit Margins with Marketing Mix Modeling................... 239 Collecting data on the four Ps....................................................... 240 Implementing marketing mix modeling........................................ 241 Increasing profitability with МММ................................................ 243 CHAPTER 12: Enabling Improved Decision-Making..................... 245 Improving Decision-Making................................................................. 245 Barking Up the Business Intelligence Tree........................................ 247 Using Data Analytics to Support Decision-Making............................. 249 Types of
analytics.......................................................................... 252 Common challenges in analytics.................................................... 252 Data wrangling................................................................................ 253
Increasing Profit Margins with Data Science...................................... 254 Seeing which kinds of data are useful when using data science for decision support.................................................. 255 Directing improved decision-making for call center agents........257 Discovering the tipping point where the old way stops working.................................................................................. 262 chapter із: Decreasing Lending Risk and Fighting Financial Crimes.......................................... 265 Decreasing Lending Risk with Clustering and Classification.............. 266 Preventing Fraud Via Natural Language Processing (NLP)................267 chapter 14: Monetizing Data and Data Science Expertise....... 275 Setting the Tone for Data Monetization............................................. 275 Monetizing Data Science Skills as a Service........................................ 278 Data preparation services............................................................ 279 Model building services................................................................. 280 Selling Data Products.......................................................................... 282 Direct Monetization of Data Resources.............................................. 283 Coupling data resources with a service and selling it.................. 283 Making money with data partnerships........................................ 284 Pricing Out Data Privacy....................................................................... 285 PART 4: ASSESSING YOUR DATA
SCIENCE OPTIONS........289 CHAPTER 15: Gathering Important Information about Your Company.................................. 291 Unifying Your Data Science Team Under a Single Business Vision.................................................................................... 292 Framing Data Science around the Company s Vision, Mission, and Values.............................................................................. 294 Taking Stock of Data Technologies...................................................... 296 Inventorying Your Company s Data Resources................................... 298 Requesting your data dictionary and inventory........................... 298 Confirming what s officially on file................................................ 300 Unearthing data silos and data quality issues............................. 300 People-Mapping.................................................................................... 303 Requesting organizational charts.................................................. 303 Surveying the skillsets of relevant personnel............................... 304 Avoiding Classic Data Science Project Pitfalls.....................................305 Staying focused on the business, not on the tech....................... 305 Drafting best practices to protect your data science project... .306 Tuning In to Your Company s Data Ethos.......................................... 306 Collecting the official data privacy policy...................................... 307 Taking Al ethics into
account........................................................ 307 Making Information-Gathering Efficient........................................... 308
CHAPTER 16: Narrowing In on the Optimal Data Science Use Case................................... 311 Reviewing the Documentation........................................................... 312 Selecting Your Quick-Win Data Science Use Cases............................. 313 Zeroing in on the quick win........................................................... 313 Producing a POTI model................................................................. 314 Picking between Plug-and-Play Assessments.................................... 316 Carrying out a data skill gap analysis for your company............. 317 Assessing the ethics of your company s Al projects and products................................................................................. 318 Assessing data governance and data privacy policies.................323 CHAPTER 17: Planning for Future Data Science Project SUCCeSS................................................ 327 Preparing an Implementation Plan..................................................... 328 Supporting Your Data Science Project Plan........................................ 335 Analyzing your alternatives........................................................... 335 Interviewing intended users and designing accordingly.............. 337 POTI modeling the future state..................................................... 338 Executing On Your Data Science Project Plan.................................... 339 CHAPTER 18: Blazing a Path to Data Science Career Success.... 341 Navigating the Data Science Career
Matrix........................................ 341 Landing Your Data Scientist Dream Job.............................................. 343 Leaning into data science implementation.................................. 345 Acing your accreditations............................................................... 346 Making the grade with coding bootcamps and data science career accelerators....................................................348 Networking and building authentic relationships....................... 349 Developing your own thought leadership in data science......... 350 Building a public data science project portfolio........................... 351 Leading with Data Science.................................................................. 354 Starting Up in Data Science................................................................ 357 Choosing a business model for your data science business....... 357 Selecting a data science start-up revenue model......................... 359 Taking inspiration from Kam Lee s success story......................... 361 Following in the footsteps of the data science entrepreneurs.. .364 PART 5: THE PART OF TENS..................................................... 367 CHAPTER 19: Ten Phenomenal Resources for Open Data.......... 369 Digging Through data.gov................................................................... 370 Checking Out Canada Open Data....................................................... 371 Diving into data.gov.uk........................................................................ 372 Checking Out US
Census Bureau Data................................................ 373
Accessing NASA Data.......................................................................... 374 Wrangling World Bank Data................................................................. 375 Getting to Know Knoema Data........................................................... 376 Queuing Up with Quandi Data........................................................... 378 Exploring Exversion Data..................................................................... 379 Mapping OpenStreetMap Spatial Data .............................................. 380 CHAPTER 20: Ten Free or Low-Cost Data Science Tools and Applications................................... зві Scraping, Collecting, and Handling Data Tools................................... 382 Sourcing and aggregating image data with ImageQuilts.............. 382 Wrangling data with DataWrangler................................................ 383 Data-Exploration Tools........................................................................ 384 Getting up to speed in Gephi......................................................... 384 Machine learning with the WEKA suite.......................................... 386 Designing Data Visualizations............................................................. 387 Getting Shiny by RStudio............................................................... 387 Mapmaking and spatial data analytics with CARTO..................... 388 Talking about Tableau Public......................................................... 390 Using RAWGraphs for web-based data visualization...................
392 Communicating with Infographics......................................................393 Making cool infographics with Infogram...................................... 394 Making cool infographics with Piktochart.....................................395 INDEX............................................................................................ 397
|
| adam_txt |
Table of Contents INTRODUCTION. 1 About This Book. 3 Foolish Assumptions. 3 Icons Used in This Book.4 Beyond the Book. 4 Where to Go from Here.4 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 Inspecting the Pieces of the Data Science Puzzle. 10 Collecting, querying, and consuming data. 11 Applying mathematical modeling to data science tasks.12 Deriving insights from statistical methods. 12 Coding, coding, coding — it's just part of the game. 13 Applying data science to a subject area. 13 Communicating data insights. 14 Exploring Career Alternatives That Involve Data Science. 15 The data implementer. 16 The data
leader. 16 The data entrepreneur. 17 CHAPTER 2: Tapping into Critical Aspects of Data Engineering.19 Defining Big Data and the Three Vs. 19 Grappling with data volume. 21 Handling data velocity. 21 Dealing with data variety.22 Identifying Important Data Sources.23 Grasping the Differences among Data Approaches. 24 Defining data science. 25 Defining machine learning engineering. 26 Defining data engineering. 26 Comparing machine learning engineers, data scientists, and data engineers.27 Storing and Processing Data for Data Science. 28 Storing data and doing data science directly in the cloud. 28 Storing big data on-premise. 32 Processing big data in real-time.35
PART 2: USING DATA SCIENCE TO EXTRACT MEANING FROM YOUR DATA. 37 CHAPTER 3: Machine Learning Means. Using a Machine to Learn from Data.39 Defining Machine Learning and Its Processes. 40 Walking through the steps of the machine learning process. 40 Becoming familiar with machine learning terms.41 Considering Learning Styles. 42 Learning with supervised algorithms. 42 Learning with unsupervised algorithms.43 Learning with reinforcement. 43 Seeing What You Can Do. 43 Selecting algorithms based on function.44 Using Spark to generate real-time big data analytics.48 CHAPTER 4: Math, Probability, and Statistical Modeling.51 Exploring Probability and Inferential Statistics. 52 Probability distributions. 53 Conditional probability with Naïve Bayes. 55 Quantifying Correlation. 56 Calculating correlation with Pearson's r. 56 Ranking variable-pairs using Spearman's rank
correlation.58 Reducing Data Dimensionality with Linear Algebra. 59 Decomposing data to reduce dimensionality.59 Reducing dimensionality with factor analysis.63 Decreasing dimensionality and removing outliers with PCA. 64 Modeling Decisions with Multiple Criteria Decision-Making. 65 Turning to traditional MCDM. 65 Focusing on fuzzy MCDM.67 Introducing Regression Methods. 67 Linear regression. 67 Logistic regression.69 Ordinary least squares (OLS) regression methods. 70 Detecting Outliers. 70 Analyzing extreme values. 70 Detecting outliers with univariate analysis. 71 Detecting outliers with multivariate analysis.73 Introducing Time Series Analysis. 73 Identifying patterns in time series. 74 Modeling univariate time series data. 75
CHAPTERS: Grouping Your Way into Accurate Predictions. 77 Starting with Clustering Basics. 78 Getting to know clustering algorithms. 79 Examining clustering similarity metrics. 81 Identifying Clusters in Your Data. 82 Clustering with the к-means algorithm. 82 Estimating clusters with kernel density estimation (KDE). 84 Clustering with hierarchical algorithms. 84 Dabbling in the DBScan neighborhood. 87 Categorizing Data with Decision Tree and Random Forest Algorithms. 88 Drawing a Line between Clustering and Classification. 89 Introducing instance-based learning classifiers. 90 Getting to know classification algorithms.90 Making Sense of Data with Nearest Neighbor Analysis. 93 Classifying Data with Average Nearest Neighbor Algorithms. 94 Classifying with К-Nearest Neighbor Algorithms. 97 Understanding how the к-nearest neighbor algorithm works. .98 Knowing when to use the к-nearest neighbor algorithm. 99 Exploring common applications of к-nearest neighbor
algorithms. 100 Solving Real-World Problems with Nearest Neighbor Algorithms. .100 Seeing к-nearest neighbor algorithms in action. 101 Seeing average nearest neighbor algorithms in action. 101 CHAPTER 6: Coding Up Data Insights and Decision Engines. юз Seeing Where Python and R Fit into Your Data Science Strategy. 104 Using Python for Data Science. 104 Sorting out the various Python datatypes. 106 Putting loops to good use in Python. 109 Having fun with functions. 110 Keeping cool with classes. 112 Checking out some useful Python libraries. 114 Using Open Source R for Data Science. 120 Comprehending R's basic vocabulary. 121 Delving into functions and operators. 124 Iterating in R. 127 Observing how objects work. 129 Sorting out R's popular
statistical analysis packages. 131 Examining packages for visualizing, mapping, and graphing in R. 133
CHAPTER?: Generating Insights with Software Applications. 137 Choosing the Best Tools for Your Data Science Strategy. 138 Getting a Handle on SQL and Relational Databases. 139 Investing Some Effort into Database Design. 144 Defining data types. 144 Designing constraints properly. 145 Normalizing your database. 145 Narrowing the Focus with SQL Functions. 147 Making Life Easier with Excel. 151 Using Excel to quickly get to know your data. 152 Reformatting and summarizing with PivotTables. 157 Automating Excel tasks with macros. 158 CHAPTER 8: Telling Powerful Stories with Data. 161 Data Visualizations: The Big Three. 162 Data storytelling for decision makers. 162 Data showcasing for analysts. 163 Designing data art for activists. 164 Designing to Meet the Needs of Your Target Audience. 164 Step 1: Brainstorm (All about
Eve). 165 Step 2: Define the purpose.166 Step 3: Choose the most functional visualization type for your purpose. 166 Picking the Most Appropriate Design Style.167 Inducing a calculating, exacting response. 167 Eliciting a strong emotional response. 168 Selecting the Appropriate Data Graphic Type. 170 Standard chart graphics. 171 Comparative graphics. 173 Statistical plots. 176 Topology structures. 179 Spatial plots and maps. 180 Testing Data Graphics. 183 Adding Context. 184 Creating context with data. 184 Creating context with annotations. 185 Creating context with graphical elements. 186 PART 3: TAKING STOCK OF YOUR
DATA SCIENCE CAPABILITIES. 187 CHAPTER 9: Developing Your Business Acumen. i89 Bridging the Business Gap. 189 Contrasting business acumen with subject matter expertise. .190 Defining business acumen. 191
Traversing the Business Landscape. 192 Seeing how data roles support the business in making money. 192 Leveling up your business acumen. 195 Fortifying your leadership skills. 196 Surveying Use Cases and Case Studies. 197 Documentation for data leaders. 199 Documentation for data implementers. 202 chapter 10: Improving Operations. 205 Establishing Essential Context for Operational Improvements Use Cases. 206 Exploring Ways That Data Science Is Used to Improve Operations. 207 Making major improvements to traditional manufacturing operations. 208 Optimizing business operations with data science. 210 An Al case study: Automated, personalized, and effective debt collection processes. 211 Gaining logistical efficiencies with better use of real-time data. 216 Another Al case study: Real-time
optimized logistics routing. .217 Modernizing media and the press with data science and Al . .222 Generating content with the click of a button.222 Yet another case study: Increasing content generation rates . .224 CHAPTER 11: Making Marketing Improvements . 229 Exploring Popular Use Cases for Data Science in Marketing. 229 Turning Web Analytics into Dollars and Sense.232 Getting acquainted with omnichannel analytics. 233 Mapping your channels. 233 Building analytics around channel performance. 235 Scoring your company's channels. 235 Building Data Products That Increase Sales-and-Marketing ROI. .238 Increasing Profit Margins with Marketing Mix Modeling. 239 Collecting data on the four Ps. 240 Implementing marketing mix modeling. 241 Increasing profitability with МММ. 243 CHAPTER 12: Enabling Improved Decision-Making. 245 Improving Decision-Making. 245 Barking Up the Business Intelligence Tree. 247 Using Data Analytics to Support Decision-Making. 249 Types of
analytics. 252 Common challenges in analytics. 252 Data wrangling. 253
Increasing Profit Margins with Data Science. 254 Seeing which kinds of data are useful when using data science for decision support. 255 Directing improved decision-making for call center agents.257 Discovering the tipping point where the old way stops working. 262 chapter із: Decreasing Lending Risk and Fighting Financial Crimes. 265 Decreasing Lending Risk with Clustering and Classification. 266 Preventing Fraud Via Natural Language Processing (NLP).267 chapter 14: Monetizing Data and Data Science Expertise. 275 Setting the Tone for Data Monetization. 275 Monetizing Data Science Skills as a Service. 278 Data preparation services. 279 Model building services. 280 Selling Data Products. 282 Direct Monetization of Data Resources. 283 Coupling data resources with a service and selling it. 283 Making money with data partnerships. 284 Pricing Out Data Privacy. 285 PART 4: ASSESSING YOUR DATA
SCIENCE OPTIONS.289 CHAPTER 15: Gathering Important Information about Your Company. 291 Unifying Your Data Science Team Under a Single Business Vision. 292 Framing Data Science around the Company's Vision, Mission, and Values. 294 Taking Stock of Data Technologies. 296 Inventorying Your Company's Data Resources. 298 Requesting your data dictionary and inventory. 298 Confirming what's officially on file. 300 Unearthing data silos and data quality issues. 300 People-Mapping. 303 Requesting organizational charts. 303 Surveying the skillsets of relevant personnel. 304 Avoiding Classic Data Science Project Pitfalls.305 Staying focused on the business, not on the tech. 305 Drafting best practices to protect your data science project. .306 Tuning In to Your Company's Data Ethos. 306 Collecting the official data privacy policy. 307 Taking Al ethics into
account. 307 Making Information-Gathering Efficient. 308
CHAPTER 16: Narrowing In on the Optimal Data Science Use Case. 311 Reviewing the Documentation. 312 Selecting Your Quick-Win Data Science Use Cases. 313 Zeroing in on the quick win. 313 Producing a POTI model. 314 Picking between Plug-and-Play Assessments. 316 Carrying out a data skill gap analysis for your company. 317 Assessing the ethics of your company's Al projects and products. 318 Assessing data governance and data privacy policies.323 CHAPTER 17: Planning for Future Data Science Project SUCCeSS. 327 Preparing an Implementation Plan. 328 Supporting Your Data Science Project Plan. 335 Analyzing your alternatives. 335 Interviewing intended users and designing accordingly. 337 POTI modeling the future state. 338 Executing On Your Data Science Project Plan. 339 CHAPTER 18: Blazing a Path to Data Science Career Success. 341 Navigating the Data Science Career
Matrix. 341 Landing Your Data Scientist Dream Job. 343 Leaning into data science implementation. 345 Acing your accreditations. 346 Making the grade with coding bootcamps and data science career accelerators.348 Networking and building authentic relationships. 349 Developing your own thought leadership in data science. 350 Building a public data science project portfolio. 351 Leading with Data Science. 354 Starting Up in Data Science. 357 Choosing a business model for your data science business. 357 Selecting a data science start-up revenue model. 359 Taking inspiration from Kam Lee's success story. 361 Following in the footsteps of the data science entrepreneurs. .364 PART 5: THE PART OF TENS. 367 CHAPTER 19: Ten Phenomenal Resources for Open Data. 369 Digging Through data.gov. 370 Checking Out Canada Open Data. 371 Diving into data.gov.uk. 372 Checking Out US
Census Bureau Data. 373
Accessing NASA Data. 374 Wrangling World Bank Data. 375 Getting to Know Knoema Data. 376 Queuing Up with Quandi Data. 378 Exploring Exversion Data. 379 Mapping OpenStreetMap Spatial Data . 380 CHAPTER 20: Ten Free or Low-Cost Data Science Tools and Applications. зві Scraping, Collecting, and Handling Data Tools. 382 Sourcing and aggregating image data with ImageQuilts. 382 Wrangling data with DataWrangler. 383 Data-Exploration Tools. 384 Getting up to speed in Gephi. 384 Machine learning with the WEKA suite. 386 Designing Data Visualizations. 387 Getting Shiny by RStudio. 387 Mapmaking and spatial data analytics with CARTO. 388 Talking about Tableau Public. 390 Using RAWGraphs for web-based data visualization.
392 Communicating with Infographics.393 Making cool infographics with Infogram. 394 Making cool infographics with Piktochart.395 INDEX. 397 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Pierson, Lillian |
| author_GND | (DE-588)1069696161 |
| author_facet | Pierson, Lillian |
| author_role | aut |
| author_sort | Pierson, Lillian |
| author_variant | l p lp |
| building | Verbundindex |
| bvnumber | BV047654763 |
| classification_rvk | ST 530 |
| contents | Introduction -- Getting started with Data Science. Wrapping your head around data science ; Tapping into critical aspects of data engineering -- Using data science to extract meaning from your data. Machine learning means... using a machine to learn from data ; Math, probability, and statistical modeling ; Grouping your way into accurate predictions ; Coding up data insights and decision engines ; Generating insights with software applications ; Telling powerful stories with data -- Taking stock of your data science capabilites. Developing your business acumen ; Improving operations ; Making marketing improvements ; Enabling improved decision-making ; Decreasing lending risk and fighting financial crimes ; Monetizing data and data science expertise -- Assessing your data sciene options. Gathering important information about your company ; Narrowing in on the optimal data science use case ; Planning for future data science project success ; Blazing a path to data science career success -- The part of tens. Ten phenomenal resources for open data ; Ten free or low-cost data science tools and applications |
| ctrlnum | (OCoLC)1294713699 (DE-599)BVBBV047654763 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 3rd edition |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05042nam a2200745 c 4500</leader><controlfield tag="001">BV047654763</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230207 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">211227s2021 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119811558</subfield><subfield code="9">978-1-119-81155-8</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1119811554</subfield><subfield code="9">1119811554</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1294713699</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047654763</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 530</subfield><subfield code="0">(DE-625)143679:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Pierson, Lillian</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1069696161</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data science for dummies</subfield><subfield code="c">by Lillian Pierson</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">3rd edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley</subfield><subfield code="c">[2021]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 416 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">24 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Learning made easy</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">For dummies</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">"Deploy math and stats to improve decision-making ; supercharge your business growth with data science wins ; monetize your data resources and data science skills" -- Cover</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Introduction -- Getting started with Data Science. Wrapping your head around data science ; Tapping into critical aspects of data engineering -- Using data science to extract meaning from your data. Machine learning means... using a machine to learn from data ; Math, probability, and statistical modeling ; Grouping your way into accurate predictions ; Coding up data insights and decision engines ; Generating insights with software applications ; Telling powerful stories with data -- Taking stock of your data science capabilites. Developing your business acumen ; Improving operations ; Making marketing improvements ; Enabling improved decision-making ; Decreasing lending risk and fighting financial crimes ; Monetizing data and data science expertise -- Assessing your data sciene options. Gathering important information about your company ; Narrowing in on the optimal data science use case ; Planning for future data science project success ; Blazing a path to data science career success -- The part of tens. Ten phenomenal resources for open data ; Ten free or low-cost data science tools and applications</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help. What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects.Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Algorithmus</subfield><subfield code="0">(DE-588)4001183-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Visualisierung</subfield><subfield code="0">(DE-588)4188417-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenstruktur</subfield><subfield code="0">(DE-588)4011146-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Programmiersprache</subfield><subfield code="0">(DE-588)4047409-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Information technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Information retrieval</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Databases</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Data mining</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business / Data processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Business / Data processing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Data mining</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Databases</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Information retrieval</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Information technology</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Handbooks and manuals</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Datenstruktur</subfield><subfield code="0">(DE-588)4011146-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Algorithmus</subfield><subfield code="0">(DE-588)4001183-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="2"><subfield code="a">Datenstruktur</subfield><subfield code="0">(DE-588)4011146-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="3"><subfield code="a">Visualisierung</subfield><subfield code="0">(DE-588)4188417-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="4"><subfield code="a">Programmiersprache</subfield><subfield code="0">(DE-588)4047409-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="5"><subfield code="a">Statistik</subfield><subfield code="0">(DE-588)4056995-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-119-81166-4</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-119-81161-9</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033038738&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033038738</subfield></datafield></record></collection> |
| id | DE-604.BV047654763 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:50:50Z |
| indexdate | 2024-07-10T09:18:25Z |
| institution | BVB |
| isbn | 9781119811558 1119811554 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033038738 |
| oclc_num | 1294713699 |
| open_access_boolean | |
| owner | DE-29T DE-739 |
| owner_facet | DE-29T DE-739 |
| physical | xii, 416 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Wiley |
| record_format | marc |
| series2 | Learning made easy For dummies |
| spelling | Pierson, Lillian Verfasser (DE-588)1069696161 aut Data science for dummies by Lillian Pierson 3rd edition Hoboken, NJ Wiley [2021] xii, 416 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Learning made easy For dummies Includes index "Deploy math and stats to improve decision-making ; supercharge your business growth with data science wins ; monetize your data resources and data science skills" -- Cover Introduction -- Getting started with Data Science. Wrapping your head around data science ; Tapping into critical aspects of data engineering -- Using data science to extract meaning from your data. Machine learning means... using a machine to learn from data ; Math, probability, and statistical modeling ; Grouping your way into accurate predictions ; Coding up data insights and decision engines ; Generating insights with software applications ; Telling powerful stories with data -- Taking stock of your data science capabilites. Developing your business acumen ; Improving operations ; Making marketing improvements ; Enabling improved decision-making ; Decreasing lending risk and fighting financial crimes ; Monetizing data and data science expertise -- Assessing your data sciene options. Gathering important information about your company ; Narrowing in on the optimal data science use case ; Planning for future data science project success ; Blazing a path to data science career success -- The part of tens. Ten phenomenal resources for open data ; Ten free or low-cost data science tools and applications Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help. What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects.Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book Algorithmus (DE-588)4001183-5 gnd rswk-swf Visualisierung (DE-588)4188417-6 gnd rswk-swf Datenstruktur (DE-588)4011146-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Programmiersprache (DE-588)4047409-4 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Information technology Information retrieval Databases Data mining Business / Data processing Handbooks and manuals Datenstruktur (DE-588)4011146-5 s Algorithmus (DE-588)4001183-5 s Data Mining (DE-588)4428654-5 s DE-604 Datenanalyse (DE-588)4123037-1 s Visualisierung (DE-588)4188417-6 s Programmiersprache (DE-588)4047409-4 s Statistik (DE-588)4056995-0 s Erscheint auch als Online-Ausgabe 978-1-119-81166-4 Erscheint auch als Online-Ausgabe 978-1-119-81161-9 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033038738&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pierson, Lillian Data science for dummies Introduction -- Getting started with Data Science. Wrapping your head around data science ; Tapping into critical aspects of data engineering -- Using data science to extract meaning from your data. Machine learning means... using a machine to learn from data ; Math, probability, and statistical modeling ; Grouping your way into accurate predictions ; Coding up data insights and decision engines ; Generating insights with software applications ; Telling powerful stories with data -- Taking stock of your data science capabilites. Developing your business acumen ; Improving operations ; Making marketing improvements ; Enabling improved decision-making ; Decreasing lending risk and fighting financial crimes ; Monetizing data and data science expertise -- Assessing your data sciene options. Gathering important information about your company ; Narrowing in on the optimal data science use case ; Planning for future data science project success ; Blazing a path to data science career success -- The part of tens. Ten phenomenal resources for open data ; Ten free or low-cost data science tools and applications Algorithmus (DE-588)4001183-5 gnd Visualisierung (DE-588)4188417-6 gnd Datenstruktur (DE-588)4011146-5 gnd Statistik (DE-588)4056995-0 gnd Programmiersprache (DE-588)4047409-4 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4001183-5 (DE-588)4188417-6 (DE-588)4011146-5 (DE-588)4056995-0 (DE-588)4047409-4 (DE-588)4428654-5 (DE-588)4123037-1 |
| title | Data science for dummies |
| title_auth | Data science for dummies |
| title_exact_search | Data science for dummies |
| title_exact_search_txtP | Data science for dummies |
| title_full | Data science for dummies by Lillian Pierson |
| title_fullStr | Data science for dummies by Lillian Pierson |
| title_full_unstemmed | Data science for dummies by Lillian Pierson |
| title_short | Data science for dummies |
| title_sort | data science for dummies |
| topic | Algorithmus (DE-588)4001183-5 gnd Visualisierung (DE-588)4188417-6 gnd Datenstruktur (DE-588)4011146-5 gnd Statistik (DE-588)4056995-0 gnd Programmiersprache (DE-588)4047409-4 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Algorithmus Visualisierung Datenstruktur Statistik Programmiersprache Data Mining Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033038738&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT piersonlillian datasciencefordummies |