Data Analysis and Visualization Using Python (eBook)

Dr. Ossama Embarak holds a Doctorate in Computer Science from the Heriot-Watt University in Scotland, UK. He has more than 2 decades of training and teaching experience with a number of programming languages including C++, Java, C#, R, and Python. He is presently the lead CIS Program Coordinator for Higher Colleges of Technology, UAE’s largest applied higher educational institution, with over 23,000 students attending campuses throughout the region.Recently, he got an interdisciplinary research grant of 199000 AED to implement a machine learning system for mining students’ knowledge and skills.He has participated in many scholarly activities as a reviewer for journals in the field of computer and information sciences, artificial intelligence, mobile and web technologies. He has published numerous papers in datamining and knowledge discovery, and was also involved as a co-chair for the Technical Program Committee (TPC) for various regional and international conferences.

Chapter 1:  Introduction to data science with python    1.1 What is data science? 1.2 Why Python?1.3 Python learning resources.1.4 Python environment and editors (Jupyter Notebook, Netbeans , etc)1.5 The basics of the python programming1.6 Fundamental python programming techniques 1.6.1 The Tabular data, and data formats1.6.2 Python pandas data science library 1.6.3 Python lambdas, and the numpy library. 1.6.4 Introduce the data cleaning and manipulation techniques1.6.5 Introduce the abstraction of the Series and DataFrame1.6.6 Run basic inferential statistical analysis. 1.7 Exercises and answersChapter 2: The importance of data visualization in business intelligence2.1 Shift from input to output data preference2.2 Why Data visualization is important?2.3 How is the modern business needs Data visualization? 2.4 The future of Data Visualization2.5 How data visualization is used for Business decision making 2.6 Introduce data visualization tchniques 2.6.1 Loading libraries2.6.2 Popular Libraries for Data Visualization in PythonMatplotlibSeabornGeoplotlib PandasPlotly2.6.3 Introduce Plots in Python2.7 Exercises and answersChapter 3:  Data collections structure 3.1 Lists 3.1.1 Create lists 3.1.2 Accessing values in lists 3.1.3 Add and update lists 3.1.4 Delete list elements 3.1.5 Basic list operations 3.1.6 Indexing, slicing, and matrices 3.1.7 Built-in list functions & methods 3.1.8 List methods 3.1.9 List sorting and traversing 3.1.10 Lists and strings 3.2 Parsing lines 3.3 Aliasing  3.4 Dictionaries3.4.1 Create dictionaries3.4.2 Updating and accessing values in dictionary 3.4.3 Delete dictionary elements 3.4.4 Built-in dictionary functions & methods 3.5 Tuples3.5.1 Create tuples3.5.2 Updating tuples3.5.3 Accessing values in tuples3.5.4 Basic tuples operations3.6Series data structure 3.7DataFrame data structure 3.8Panel data structure 3.9 Exercises and answersChapter 4: File I/O processing & Regular expressions 4.1File I/O processing 4.1.1Screen in/out processing 4.1.2Opening and closing files 4.1.3The file object attributes 4.1.4Reading and writing files 4.1.5Directories in python 4.2Regular expressions 4.2.1Regular expression patterns 4.2.2Special character classes 4.2.3Repetition cases Alternatives Anchors 4.3Exercises and answers Chapter 5: Data gathering and cleaning 5.1 Data cleaning Check missing values Handle the missing values 5.2Read and clean csv file 5.3Data integration 5.4Read the json file 5.5Reading the html file 5.6Exercises and answers Chapter 6:  Data exploring and analysis 6.1Series data structure 6.1.1Create a series 6.1.2Accessing data from series with position 6.2DataFrame data structure 6.2.1Create a DataFrame 6.2.2Updating and accessing DataFrame Column selection Column addition Column deletion Row selection Row addition Row deletion 6.3Panel data structure 6.3.1Create panel 6.3.2Accessing data from panel with position 6.4Data analysis 6.4.1Statistical analysis 6.4.2Data grouping Iterating through groups Aggregations Transformations Filtration 6.5Exercises and answers Chapter 7: Data visualization 7.1Direct plotting Line plotting Bar plotting Pie chart Box plotting Histogram plotting A scatterplot 7.2Seaborn plotting system Strip plotting Boxplot Swarmplot Jointplot 7.3Matplotlib plotting Line plotting Bar chart Histogram plotting Scatter plot Stack plots Pie chart 7.4Exercises. Chapter 8: Case Study 8.1 Business case 8.2 Case data gathering8.3 Case data analysis 8.4 Case data Visualization