Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for Bi Systems

ISBN-13: 9781484241080 / Angielski / Miękka / 2018 / 374 str.

Ossama Embarak

Spis treści

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 programming

1.6 Fundamental python programming techniques 

1.6.1 The Tabular data, and data formats

1.6.2 Python pandas data science library 

1.6.3 Python lambdas, and the numpy library. 

1.6.4 Introduce the data cleaning and manipulation techniques

1.6.5 Introduce the abstraction of the Series and DataFrame

1.6.6 Run basic inferential statistical analysis. 

1.7 Exercises and answers

Chapter 2: The importance of data visualization in business intelligence

2.1 Shift from input to output data preference

2.2 Why Data visualization is important?

2.3 How is the modern business needs Data visualization? 

2.4 The future of Data Visualization

2.5 How data visualization is used for Business decision making 

2.6 Introduce data visualization tchniques 

2.6.1 Loading libraries

2.6.2 Popular Libraries for Data Visualization in Python

Matplotlib

Seaborn

Geoplotlib 

Pandas

Plotly

2.6.3 Introduce Plots in Python

2.7 Exercises and answers

Chapter 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 Dictionaries

3.4.1 Create dictionaries

3.4.2 Updating and accessing values in dictionary  

3.4.3 Delete dictionary elements  

3.4.4 Built-in dictionary functions & methods  

3.5 Tuples

3.5.1 Create tuples

3.5.2 Updating tuples

3.5.3 Accessing values in tuples

3.5.4 Basic tuples operations

3.6 Series data structure  

3.7 DataFrame data structure  

3.8 Panel data structure  

3.9 Exercises and answers

Chapter 4: File I/O processing & Regular expressions  

4.1 File I/O processing  

4.1.1 Screen in/out processing  

4.1.2 Opening and closing files  

4.1.3 The file object attributes  

4.1.4 Reading and writing files  

4.1.5 Directories in python  

4.2 Regular expressions  

4.2.1 Regular expression patterns  

4.2.2 Special character classes  

4.2.3 Repetition cases  

Alternatives  

Anchors  

4.3 Exercises and answers  

Chapter 5: Data gathering and cleaning  

5.1 Data cleaning  

Check missing values  

Handle the missing values  

5.2 Read and clean csv file  

5.3 Data integration  

5.4 Read the json file  

5.5 Reading the html file  

5.6 Exercises and answers  

Chapter 6:  Data exploring and analysis

 

6.1 Series data structure  

6.1.1 Create a series  

6.1.2 Accessing data from series with position  

6.2 DataFrame data structure  

6.2.1 Create a DataFrame  

6.2.2 Updating and accessing DataFrame  

Column selection  

Column addition  

Column deletion  

Row selection  

Row addition  

Row deletion  

6.3 Panel data structure  

6.3.1 Create panel  

6.3.2 Accessing data from panel with position  

6.4 Data analysis  

6.4.1 Statistical analysis  

6.4.2 Data grouping  

Iterating through groups  

Aggregations  

Transformations  

Filtration  

6.5 Exercises and answers  

Chapter 7: Data visualization

 

7.1 Direct plotting  

Line plotting  

Bar plotting  

Pie chart  

Box plotting  

Histogram plotting  

A scatterplot  

7.2 Seaborn plotting system  

Strip plotting  

Boxplot  

Swarmplot  

Jointplot  

7.3 Matplotlib plotting  

Line plotting  

Bar chart  

Histogram plotting  

Scatter plot  

Stack plots  

Pie chart  

7.4 Exercises.

 

Chapter 8: Case Study 

8.1 Business case 

8.2 Case data gathering

8.3 Case data analysis 

8.4 Case data Visualization

O autorze

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.

Blurb

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. 

Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. 

In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far.

You will:

• Use Python programming techniques for data science
  
• Master data collections in Python 
  
• Create engaging visualizations for BI systems
  
• Deploy effective strategies for gathering and cleaning data
  
• Integrate the Seaborn and Matplotlib plotting systems