Machine learning for hackers / by Conway, Drew, Safari Books Online

Table of Contents: “…; Inferring the Types of Columns in Your Data; Inferring Meaning; Numeric Summaries; Means, Medians, and Modes; Quantiles; Standard Deviations and Variances; Exploratory Data Visualization; Visualizing the Relationships Between Columns; Chapter 3. …”
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Hands-on exploratory data analysis with Python : perform EDA techniques to understand, summarize, and investigate your data / by Mukhiya, Suresh Kumar, Ahmed, Usman, PALCI EBSCO books

Table of Contents: “…Computing indicators/dummy variables -- String manipulation -- Benefits of data transformation -- Challenges -- Summary -- Further reading -- Section 2: Descriptive Statistics -- Chapter 05: Descriptive Statistics -- Technical requirements -- Understanding statistics -- Distribution function -- Uniform distribution -- Normal distribution -- Exponential distribution -- Binomial distribution -- Cumulative distribution function -- Descriptive statistics -- Measures of central tendency -- Mean/average -- Median -- Mode -- Measures of dispersion -- Standard deviation -- Variance -- Skewness…”
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Big Data Analytics with Hadoop 3 : Build highly effective analytics solutions to gain valuable insight into your big data. by Alla, Sridhar, ProQuest Ebook Subscriptions

Table of Contents: “…Record readerMap; Combiner; Partitioner; Shuffle and sort; Reduce; Output format; MapReduce job types; Single mapper job; Single mapper reducer job; Multiple mappers reducer job; SingleMapperCombinerReducer job; Scenario; MapReduce patterns; Aggregation patterns; Average temperature by city; Record count; Min/max/count; Average/median/standard deviation; Filtering patterns; Join patterns; Inner join; Left anti join; Left outer join; Right outer join; Full outer join; Left semi join; Cross join; Summary; Chapter 4: Scientific Computing and Big Data Analysis with Python and Hadoop; Installation.…”
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Learning pandas / by Heydt, Michael, ProQuest Ebook Subscriptions

Table of Contents: “…Categorical values as an index -- CategoricalIndex -- Indexing by date and time using DatetimeIndex -- Indexing periods of time using PeriodIndex -- Working with Indexes -- Creating and using an index with a Series or DataFrame -- Selecting values using an index -- Moving data to and from the index -- Reindexing a pandas object -- Hierarchical indexing -- Summary -- Chapter 7: Categorical Data -- Configuring pandas -- Creating Categoricals -- Renaming categories -- Appending new categories -- Removing categories -- Removing unused categories -- Setting categories -- Descriptive information of a Categorical -- Munging school grades -- Summary -- Chapter 8: Numerical and Statistical Methods -- Configuring pandas -- Performing numerical methods on pandas objects -- Performing arithmetic on a DataFrame or Series -- Getting the counts of values -- Determining unique values (and their counts) -- Finding minimum and maximum values -- Locating the n-smallest and n-largest values -- Calculating accumulated values -- Performing statistical processes on pandas objects -- Retrieving summary descriptive statistics -- Measuring central tendency: mean, median, and mode -- Calculating the mean -- Finding the median -- Determining the mode -- Calculating variance and standard deviation -- Measuring variance -- Finding the standard deviation -- Determining covariance and correlation -- Calculating covariance -- Determining correlation -- Performing discretization and quantiling of data -- Calculating the rank of values -- Calculating the percent change at each sample of a series -- Performing moving-window operations -- Executing random sampling of data -- Summary -- Chapter 9: Accessing Data -- Configuring pandas -- Working with CSV and text/tabular format data -- Examining the sample CSV data set -- Reading a CSV file into a DataFrame.…”
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Data Wrangling with JavaScript / by Davis, Ashley, Safari, an O'Reilly Media Company, Safari Books Online

Table of Contents: “…8.7.7 Filtering using queries -- 8.7.8 Discarding data with projection -- 8.7.9 Sorting large data sets -- 8.8 Achieving better data throughput -- 8.8.1 Optimize your code -- 8.8.2 Optimize your algorithm -- 8.8.3 Processing data in parallel -- Summary -- Chapter 9: Practical data analysis -- 9.1 Expanding your toolkit -- 9.2 Analyzing the weather data -- 9.3 Getting the code and data -- 9.4 Basic data summarization -- 9.4.1 Sum -- 9.4.2 Average -- 9.4.3 Standard deviation -- 9.5 Group and summarize -- 9.6 The frequency distribution of temperatures -- 9.7 Time series -- 9.7.1 Yearly average temperature -- 9.7.2 Rolling average -- 9.7.3 Rolling standard deviation -- 9.7.4 Linear regression -- 9.7.5 Comparing time series -- 9.7.6 Stacking time series operations -- 9.8 Understanding relationships -- 9.8.1 Detecting correlation with a scatter plot -- 9.8.2 Types of correlation -- 9.8.3 Determining the strength of the correlation -- 9.8.4 Computing the correlation coefficient -- Summary -- Chapter 10: Browser-based visualization -- 10.1 Expanding your toolkit -- 10.2 Getting the code and data -- 10.3 Choosing a chart type -- 10.4 Line chart for New York City temperature -- 10.4.1 The most basic C3 line chart -- 10.4.2 Adding real data -- 10.4.3 Parsing the static CSV file -- 10.4.4 Adding years as the X axis -- 10.4.5 Creating a custom Node.js web server -- 10.4.6 Adding another series to the chart -- 10.4.7 Adding a second Y axis to the chart -- 10.4.8 Rendering a time series chart -- 10.5 Other chart types with C3 -- 10.5.1 Bar chart -- 10.5.2 Horizontal bar chart -- 10.5.3 Pie chart -- 10.5.4 Stacked bar chart -- 10.5.5 Scatter plot chart -- 10.6 Improving the look of our charts -- 10.7 Moving forward with your own projects -- Summary -- Chapter 11: Server-side visualization -- 11.1 Expanding your toolkit -- 11.2 Getting the code and data.…”
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Hands-on data analytics for beginners with Google Colaboratory / by Safari Books Online

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