2.18 GB | 00:24:21 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
001 Chapter 1 Introduction (43.44 MB)
002 Chapter 1 Key features of Julia from a data scientist s perspective (48.05 MB)
003 Chapter 1 Usage scenarios of tools presented in the book (8.64 MB)
004 Chapter 1 Julia s drawbacks (15.16 MB)
005 Chapter 1 What data analysis skills will you learn (3.99 MB)
006 Chapter 1 How can Julia be used for data analysis (15.62 MB)
007 Chapter 1 Summary (4.41 MB)
008 Part 1 Essential Julia skills (5.19 MB)
009 Chapter 2 Getting started with Julia (31.7 MB)
010 Chapter 2 Defining variables (15.76 MB)
011 Chapter 2 Using the most important control-flow constructs (43.65 MB)
012 Chapter 2 Defining functions (29.47 MB)
013 Chapter 2 Understanding variable scoping rules (18.58 MB)
014 Chapter 2 Summary (4.54 MB)
015 Chapter 3 Julia s support for scaling projects (29.67 MB)
016 Chapter 3 Using multiple dispatch in Julia (19.68 MB)
017 Chapter 3 Working with packages and modules (27.49 MB)
018 Chapter 3 Using macros (20.5 MB)
019 Chapter 3 Summary (3.98 MB)
020 Chapter 4 Working with collections in Julia (77.44 MB)
021 Chapter 4 Mapping key-value pairs with dictionaries (20.41 MB)
022 Chapter 4 Structuring your data by using named tuples (19.72 MB)
023 Chapter 4 Summary (4.35 MB)
024 Chapter 5 Advanced topics on handling collections (52.93 MB)
025 Chapter 5 Defining methods with parametric types (34.21 MB)
026 Chapter 5 Integrating with Python (21.44 MB)
027 Chapter 5 Summary (9.8 MB)
028 Chapter 6 Working with strings (19.32 MB)
029 Chapter 6 Splitting strings (12.26 MB)
030 Chapter 6 Using regular expressions to work with strings (11.41 MB)
031 Chapter 6 Extracting a subset from a string with indexing (21.13 MB)
032 Chapter 6 Analyzing genre frequency in movies dat (18.63 MB)
033 Chapter 6 Introducing symbols (13.53 MB)
034 Chapter 6 Using fixed-width string types to improve performance (17.62 MB)
035 Chapter 6 Compressing vectors of strings with PooledArrays jl (19.37 MB)
036 Chapter 6 Choosing appropriate storage for collections of strings (9.01 MB)
037 Chapter 6 Summary (17.8 MB)
038 Chapter 7 Handling time-series data and missing values (45.05 MB)
039 Chapter 7 Working with missing data in Julia (30.43 MB)
040 Chapter 7 Getting time-series data from the NBP Web API (16.14 MB)
041 Chapter 7 Analyzing data fetched from the NBP Web API (29.65 MB)
042 Chapter 7 Summary (12.06 MB)
043 Part 2 Toolbox for data analysis (10.62 MB)
044 Chapter 8 First steps with data frames (46.9 MB)
045 Chapter 8 Loading the data to a data frame (28.34 MB)
046 Chapter 8 Getting a column out of a data frame (27.47 MB)
047 Chapter 8 Reading and writing data frames using different formats (22.4 MB)
048 Chapter 8 Summary (9.6 MB)
049 Chapter 9 Getting data from a data frame (72.47 MB)
050 Chapter 9 Analyzing the relationship between puzzle difficulty and popularity (26.13 MB)
051 Chapter 9 Summary (9.64 MB)
052 Chapter 10 Creating data frame objects (76.11 MB)
053 Chapter 10 Creating data frames incrementally (70.33 MB)
054 Chapter 10 Summary (12.32 MB)
055 Chapter 11 Converting and grouping data frames (89.75 MB)
056 Chapter 11 Grouping data frame objects (46.27 MB)
057 Chapter 11 Summary (8.76 MB)
058 Chapter 12 Mutating and transforming data frames (54.95 MB)
059 Chapter 12 Computing additional node features (33.98 MB)
060 Chapter 12 Using the split-apply-combine approach to predict the developer s type (52.28 MB)
061 Chapter 12 Reviewing data frame mutation operations (21.53 MB)
062 Chapter 12 Summary (14.81 MB)
063 Chapter 13 Advanced transformations of data frames (56.34 MB)
064 Chapter 13 Investigating the violation column (24.2 MB)
065 Chapter 13 Preparing data for making predictions (49.53 MB)
066 Chapter 13 Building a predictive model of arrest probability (47.99 MB)
067 Chapter 13 Reviewing functionalities provided by DataFrames jl (17.89 MB)
068 Chapter 13 Summary (12.8 MB)
069 Chapter 14 Creating web services for sharing data analysis results (47.81 MB)
070 Chapter 14 Implementing the option pricing simulator (57.44 MB)
071 Chapter 14 Creating a web service serving the Asian option valuation (40.14 MB)
072 Chapter 14 Using the Asian option pricing web service (41.65 MB)
073 Chapter 14 Summary (13.54 MB)
074 Appendix A First steps with Julia (7.71 MB)
075 Appendix A Getting help in and about Julia (8.16 MB)
076 Appendix A Managing packages in Julia (45.32 MB)
077 Appendix A Reviewing standard ways to work with Julia (6.73 MB)
078 Appendix B Solutions to exercises (62.87 MB)
079 Appendix C Julia packages for data science (11.69 MB)
080 Appendix C Scaling computing with Julia (4.99 MB)
081 Appendix C Working with databases and data storage formats (8.68 MB)
082 Appendix C Using data science methods (8.53 MB)
083 Appendix C Summary (3.6 MB)
002 Chapter 1 Key features of Julia from a data scientist s perspective (48.05 MB)
003 Chapter 1 Usage scenarios of tools presented in the book (8.64 MB)
004 Chapter 1 Julia s drawbacks (15.16 MB)
005 Chapter 1 What data analysis skills will you learn (3.99 MB)
006 Chapter 1 How can Julia be used for data analysis (15.62 MB)
007 Chapter 1 Summary (4.41 MB)
008 Part 1 Essential Julia skills (5.19 MB)
009 Chapter 2 Getting started with Julia (31.7 MB)
010 Chapter 2 Defining variables (15.76 MB)
011 Chapter 2 Using the most important control-flow constructs (43.65 MB)
012 Chapter 2 Defining functions (29.47 MB)
013 Chapter 2 Understanding variable scoping rules (18.58 MB)
014 Chapter 2 Summary (4.54 MB)
015 Chapter 3 Julia s support for scaling projects (29.67 MB)
016 Chapter 3 Using multiple dispatch in Julia (19.68 MB)
017 Chapter 3 Working with packages and modules (27.49 MB)
018 Chapter 3 Using macros (20.5 MB)
019 Chapter 3 Summary (3.98 MB)
020 Chapter 4 Working with collections in Julia (77.44 MB)
021 Chapter 4 Mapping key-value pairs with dictionaries (20.41 MB)
022 Chapter 4 Structuring your data by using named tuples (19.72 MB)
023 Chapter 4 Summary (4.35 MB)
024 Chapter 5 Advanced topics on handling collections (52.93 MB)
025 Chapter 5 Defining methods with parametric types (34.21 MB)
026 Chapter 5 Integrating with Python (21.44 MB)
027 Chapter 5 Summary (9.8 MB)
028 Chapter 6 Working with strings (19.32 MB)
029 Chapter 6 Splitting strings (12.26 MB)
030 Chapter 6 Using regular expressions to work with strings (11.41 MB)
031 Chapter 6 Extracting a subset from a string with indexing (21.13 MB)
032 Chapter 6 Analyzing genre frequency in movies dat (18.63 MB)
033 Chapter 6 Introducing symbols (13.53 MB)
034 Chapter 6 Using fixed-width string types to improve performance (17.62 MB)
035 Chapter 6 Compressing vectors of strings with PooledArrays jl (19.37 MB)
036 Chapter 6 Choosing appropriate storage for collections of strings (9.01 MB)
037 Chapter 6 Summary (17.8 MB)
038 Chapter 7 Handling time-series data and missing values (45.05 MB)
039 Chapter 7 Working with missing data in Julia (30.43 MB)
040 Chapter 7 Getting time-series data from the NBP Web API (16.14 MB)
041 Chapter 7 Analyzing data fetched from the NBP Web API (29.65 MB)
042 Chapter 7 Summary (12.06 MB)
043 Part 2 Toolbox for data analysis (10.62 MB)
044 Chapter 8 First steps with data frames (46.9 MB)
045 Chapter 8 Loading the data to a data frame (28.34 MB)
046 Chapter 8 Getting a column out of a data frame (27.47 MB)
047 Chapter 8 Reading and writing data frames using different formats (22.4 MB)
048 Chapter 8 Summary (9.6 MB)
049 Chapter 9 Getting data from a data frame (72.47 MB)
050 Chapter 9 Analyzing the relationship between puzzle difficulty and popularity (26.13 MB)
051 Chapter 9 Summary (9.64 MB)
052 Chapter 10 Creating data frame objects (76.11 MB)
053 Chapter 10 Creating data frames incrementally (70.33 MB)
054 Chapter 10 Summary (12.32 MB)
055 Chapter 11 Converting and grouping data frames (89.75 MB)
056 Chapter 11 Grouping data frame objects (46.27 MB)
057 Chapter 11 Summary (8.76 MB)
058 Chapter 12 Mutating and transforming data frames (54.95 MB)
059 Chapter 12 Computing additional node features (33.98 MB)
060 Chapter 12 Using the split-apply-combine approach to predict the developer s type (52.28 MB)
061 Chapter 12 Reviewing data frame mutation operations (21.53 MB)
062 Chapter 12 Summary (14.81 MB)
063 Chapter 13 Advanced transformations of data frames (56.34 MB)
064 Chapter 13 Investigating the violation column (24.2 MB)
065 Chapter 13 Preparing data for making predictions (49.53 MB)
066 Chapter 13 Building a predictive model of arrest probability (47.99 MB)
067 Chapter 13 Reviewing functionalities provided by DataFrames jl (17.89 MB)
068 Chapter 13 Summary (12.8 MB)
069 Chapter 14 Creating web services for sharing data analysis results (47.81 MB)
070 Chapter 14 Implementing the option pricing simulator (57.44 MB)
071 Chapter 14 Creating a web service serving the Asian option valuation (40.14 MB)
072 Chapter 14 Using the Asian option pricing web service (41.65 MB)
073 Chapter 14 Summary (13.54 MB)
074 Appendix A First steps with Julia (7.71 MB)
075 Appendix A Getting help in and about Julia (8.16 MB)
076 Appendix A Managing packages in Julia (45.32 MB)
077 Appendix A Reviewing standard ways to work with Julia (6.73 MB)
078 Appendix B Solutions to exercises (62.87 MB)
079 Appendix C Julia packages for data science (11.69 MB)
080 Appendix C Scaling computing with Julia (4.99 MB)
081 Appendix C Working with databases and data storage formats (8.68 MB)
082 Appendix C Using data science methods (8.53 MB)
083 Appendix C Summary (3.6 MB)
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