Internet of Things (IoT) Course Roadmap

Here is the series of courses that participants may find useful if they want to embark on the journey of Internet of Things (IoT) development.

Python Code Camp (3 Days) For Kids Aged 12-18

Learning to code is all the rage nowadays. Even if you are not preparing for a career in the IT world, learning to program has the following benefits:

• It fosters problem-solving skills
• It promotes critical thinking
• It prepares you for the future!

While it is never too late to start learning programming, the ideal age is to start as early as possible. And it is the exact motivation for this course.

This course is targeted at kids from the age of 12 to 18, who want to get started in programming. With so many programming languages available, what is the best language to get started? The answer is: Python. Python is a high-level programming language that is widely in use. Python code is readable, and its syntax ensures programmers write code that is well-indented and clear. Python is supported on most platforms and is well suited for a wide variety of tasks – ranging from automating your daily platform tasks, to running as a standalone Web Server.

What’s more, Python will be the language taught in the new O-level Computing subject (Secondary 3 level), starting in 2017.

In this course, kids will learn about the Python language, and how it can be used to solve problems. More importantly, kids will learn how to use Python in the real-world, such as how to fetch weather information continuously from a Web service and display it in a chart.

Topics

• Introduction to programming
• Getting started with Python
• Strings
• Arithmetic
• Looping
• Functions
• Lists, Dictionaries, Tuples, Sets
• Data Types
• Modules
• Connecting and fetching data from the outside world!
• Displaying charts and graphs

Fee and Dates

• 28-30 November 2016 (Mon-Wed)
• 9am to 5pm
• $950 per student (nett, no GST)
• Early bird discount: Register before 30 Sep 2016 and receive a discount of $200!
• Receive an additional discount of $50 when you register 2 students or more
• Each student will get a free Python Code Camp T-shirt 
• Course fee includes a lunch and 2 tea-breaks per day
• Participant need to bring along his/her own laptop

Venue

        IBIS HOTEL

        170 Bencoolen Street 

        Singapore 189657

Data Science Workshop @ NDC Sydney 2016

Learning Data Science using Python

Come and join us in our first Data Science series of courses:

IOT201 - Learning the Python Programming Language
IOT202 - Introduction to Data Science using Python

In IOT201, you will learn about the Python Programming language, and the various features that makes it one of the most popular programming languages for beginners as well as advanced programmers.

Once you have mastered the basics of Python, head over to IOT202, where you will learn the various modules and libraries that allow Python to crunch large amount of data. In particular, you will learn:

• NumPy - an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
• Pandas - a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
• Matplotlib - a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. 

Once you have learned the basics of NumPy, Pandas, Matplotlib, it is time to make use of them to analyse your data. In particular, you will analyse:

• Birth Rates in Singapore 
• Different types of infectious diseases affecting Singapore 
• Prevalence of diseases in adults in Singapore 
• Historical trends of stocks
• And more!

Data Visualization Using Python, Pandas and Matplotlib

Internet of Things (IoT) has been the buzzwords of late. While most people associate IoT with the collection of data using sensors and transmitted to central servers, an integral part of IoT involves processing the data collected. The ability to visualize data and make intelligent decisions is the cornerstone of IoT systems.

Python is one of the preferred languages for data analytics, due to its ease of learning and its huge community support of modules and packages designed for number crunching. In this article, I am going to show you the power of Python and how you can use it to visualize data.

Collection of Blood Glucose Data

With the advancement in technologies, heathcare is one area that is receiving a lot of attention. One particular disease – diabetes, garners a lot of attention. According to the World Health Organization (WHO), the number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014. The care and prevention of diabetes is hence of paramount importance. Diabetics need to regular prick their fingers to measure the amount of blood sugar in their body.

For this article, I am going to show you how to visualize the data collected by a diabetic so that he can see at a glance on how well he is keeping diabetes in control.

Storing the Data

For this article, I am assuming that you have a CSV file named readings.csv, which contains the following lines:

,DateTime,mmol/L

0,2016-06-01 08:00:00,6.1

1,2016-06-01 12:00:00,6.5

2,2016-06-01 18:00:00,6.7

3,2016-06-02 08:00:00,5.0

4,2016-06-02 12:00:00,4.9

5,2016-06-02 18:00:00,5.5

6,2016-06-03 08:00:00,5.6

7,2016-06-03 12:00:00,7.1

8,2016-06-03 18:00:00,5.9

9,2016-06-04 09:00:00,6.6

10,2016-06-04 11:00:00,4.1

11,2016-06-04 17:00:00,5.9

12,2016-06-05 08:00:00,7.6

13,2016-06-05 12:00:00,5.1

14,2016-06-05 18:00:00,6.9

15,2016-06-06 08:00:00,5.0

16,2016-06-06 12:00:00,6.1

17,2016-06-06 18:00:00,4.9

18,2016-06-07 08:00:00,6.6

19,2016-06-07 12:00:00,4.1

20,2016-06-07 18:00:00,6.9

21,2016-06-08 08:00:00,5.6

22,2016-06-08 12:00:00,8.1

23,2016-06-08 18:00:00,10.9

24,2016-06-09 08:00:00,5.2

25,2016-06-09 12:00:00,7.1

26,2016-06-09 18:00:00,4.9

The CSV file contains rows of data that are divided into three columns – index, date and time, and blood glucose readings in mmol/L.

Reading the Data in Python

While Python supports lists and dictionaries for manipulating structured data, it is not well suited for manipulating numerical tables, such as the one stored in the CSV file. As such, you should use pandas. Pandas is a software library written for Python for data manipulation and analysis.

Let’s see how pandas work. Note that for this article, I am using IPython Notebook for running my Python script. The best way to use IPython Notebook is to download Anaconda (https://www.continuum.io/downloads). Anaconda comes with the IPython Notebook, as well as pandas and matplotlib (more on this later).

Once Anaconda is installed, launch the IPython Notebook by typing the following command in Terminal:

$ ipython notebook

When IPython Notebook has started, click on New | Python 2:

Type the following statements into the cell:

import pandas as pd

data_frame = pd.read_csv('readings.csv', index_col=0, parse_dates=[1])

print data_frame

You first import the pandas module as pd, then you use the read_csv() function read the data from the CSV file to create a dataframe. A dataframe in pandas behaves like a two-dimensional array, with an index for each row. The index_col parameter specifies which column in the CSV file will be used as the index (column 0 in this case) and the parse_dates parameter specifies the column that should be parsed as a datetime object (column 1 in this case). To run the Python script in the cell, press Ctrl-Enter.

When you print out the dataframe, you should see the following:

              DateTime  mmol/L

0  2016-06-01 08:00:00     6.1

1  2016-06-01 12:00:00     6.5

2  2016-06-01 18:00:00     6.7

3  2016-06-02 08:00:00     5.0

4  2016-06-02 12:00:00     4.9

5  2016-06-02 18:00:00     5.5

6  2016-06-03 08:00:00     5.6

7  2016-06-03 12:00:00     7.1

8  2016-06-03 18:00:00     5.9

9  2016-06-04 09:00:00     6.6

10 2016-06-04 11:00:00     4.1

11 2016-06-04 17:00:00     5.9

12 2016-06-05 08:00:00     7.6

13 2016-06-05 12:00:00     5.1

14 2016-06-05 18:00:00     6.9

15 2016-06-06 08:00:00     5.0

16 2016-06-06 12:00:00     6.1

17 2016-06-06 18:00:00     4.9

18 2016-06-07 08:00:00     6.6

19 2016-06-07 12:00:00     4.1

20 2016-06-07 18:00:00     6.9

21 2016-06-08 08:00:00     5.6

22 2016-06-08 12:00:00     8.1

23 2016-06-08 18:00:00    10.9

24 2016-06-09 08:00:00     5.2

25 2016-06-09 12:00:00     7.1

26 2016-06-09 18:00:00     4.9

You can print out the index of the dataframe by using the index property:

print data_frame.index

You should see the index as follows:

Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,

            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],

           dtype='int64')

You can also print out the individual columns of the dataframe:

print data_frame['DateTime']

This should print out the DateTime column of the dataframe:

0    2016-06-01 08:00:00

1    2016-06-01 12:00:00

2    2016-06-01 18:00:00

3    2016-06-02 08:00:00

4    2016-06-02 12:00:00

5    2016-06-02 18:00:00

6    2016-06-03 08:00:00

7    2016-06-03 12:00:00

8    2016-06-03 18:00:00

9    2016-06-04 09:00:00

10   2016-06-04 11:00:00

11   2016-06-04 17:00:00

12   2016-06-05 08:00:00

13   2016-06-05 12:00:00

14   2016-06-05 18:00:00

15   2016-06-06 08:00:00

16   2016-06-06 12:00:00

17   2016-06-06 18:00:00

18   2016-06-07 08:00:00

19   2016-06-07 12:00:00

20   2016-06-07 18:00:00

21   2016-06-08 08:00:00

22   2016-06-08 12:00:00

23   2016-06-08 18:00:00

24   2016-06-09 08:00:00

25   2016-06-09 12:00:00

26   2016-06-09 18:00:00

Name: DateTime, dtype: datetime64[ns]

Likewise, you can also print the mmol/L column:

print data_frame['mmol/L']

You should see the following:

0      6.1

1      6.5

2      6.7

3      5.0

4      4.9

5      5.5

6      5.6

7      7.1

8      5.9

9      6.6

10     4.1

11     5.9

12     7.6

13     5.1

14     6.9

15     5.0

16     6.1

17     4.9

18     6.6

19     4.1

20     6.9

21     5.6

22     8.1

23    10.9

24     5.2

25     7.1

26     4.9

Name: mmol/L, dtype: float64

Visualizing the Data

Let’s now try to visualize the data by displaying a chart. For this purpose, let’s use matplotlib. Matplotlib is a plotting library for the Python language and is integrated right into pandas.

Add the following statements in bold to the existing Python script:

%matplotlib inline

import pandas as pd

import numpy as np

data_frame = pd.read_csv('readings.csv', index_col=0, parse_dates=[1])

print data_frame

print data_frame.index

print data_frame['DateTime']

print data_frame['mmol/L']

data_frame.plot(x='DateTime', y='mmol/L')

The “%matplotlib inline” statement instructs IPython notebook to plot the matplotlib chart inline. You can directly plot a chart using the dataframe’s plot() function. The x parameter specifies the column to use for the x-axis and the y parameter specifies the column to use for the y-axis.

This will display the chart as follows:

  

You can add a title to the chart by importing the matplotlib module and using the title() function:

%matplotlib inline

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

data_frame = pd.read_csv('readings.csv', index_col=0, parse_dates=[1])

print data_frame

print data_frame.index

print data_frame['DateTime']

print data_frame['mmol/L']

data_frame.plot(x='DateTime', y='mmol/L')

plt.title('Blood Glucose Readings for John', color='Red')

A title is now displayed for the chart:

By default, matplotlib will display a line chart. You can change the chart type by using the kind parameter:

%matplotlib inline

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

data_frame = pd.read_csv('readings.csv', index_col=0, parse_dates=[1])

print data_frame

print data_frame.index

print data_frame['DateTime']

print data_frame['mmol/L']

data_frame.plot(kind='bar', x='DateTime', y='mmol/L')

plt.title('Blood Glucose Readings for John', color='Red')

The chart is now changed to a barchart:

Besides displaying as a barchart, you can also display an area chart:

data_frame.plot(kind='area', x='DateTime', y='mmol/L')

The chart is now displayed as an area chart:

You can also set the color for the area chart by using the color parameter:

data_frame.plot(kind='area', x='DateTime', y='mmol/L', color='r')

The area is now in red:

Learning More

This article is just touching on the surface of what Python can do in the world of data analytics. To learn more about using Python for data analysis, come join my workshop (Introduction to Data Science using Python) at NDC Sydney 2016 on the 1-2 August 2016. See you there!