What is data analytics?

The importance of setting up a good strategy 🙂

In recent years and in the future, the use of data has gone up significantly. Here in Data Analytics Ireland we will look to explain the concept, please share or link to this article so others can see its contents.

The reason for this is that the digital economy has taken off for several reasons:

(A) Automation of tasks has become easier.

(B) Less use of paper, becoming a greener economy.

(C) Technology improvements have meant that storage and processing make the process of delivering services easier.

As a result of all this:

(A) A large amount of data is captured.

(B) Consumers habits about how they use a service or the information they look for now has a digital footprint.

(C) Now once a consumer has used a service ( whether purchased or not), the ability to understand their habits can be captured to deliver them better:

  1. The services they want.
  2. The products they want.
  3. Quicker turnaround time.

How can this help with all the data that is captured and stored?

Let’s understand what Data Analytics is doing!

So in this article, we have already outlined what the background is as to how the industry has evolved to where it is now.

As outlined, all information traditionally would not have been stored in a format that was not easily accessible.

Now, with Data Analytics, the work can be broken down as follows:

Step 1 – Data Capture

To understand what you want to analyse, a data analyst will work with their technical colleagues to ensure that the correct data is captured.

Once the completeness and accuracy are fulfilled, your data quality will become less of an issue.

Step 2 – Analysing

Once you have all your data saved, then the next step is to understand the data. Understanding your data can happen in several ways:

(A) You create visual charts of it; this allows the viewer of the information to get an initial view of the information without looking at the underlying data. Sometimes this will show patterns in data or clusters.

(B) Use statistics to see if they can explain the data. This could show information such as how data is correlated or otherwise. Also, probabilities could be calculated to show what outcomes might happen in the future.

(C) Data analysts might also need to understand how to build a machine learning model to use complex algorithms, to explain the data better, sometimes patterns that are not immediately understood can be unearthed and investigated further. There are two approaches commonly used for this, namely supervised and unsupervised machine learning; this link will explain thoroughly what they are about.

Step3 – Presenting

Now that all the steps have happened to have the data you want, as a decision-maker will it be feasible to look over rows and columns of data, and get a sense of what it all means? An emphatic NO is an answer, but this is where some of the data analytics visualisation tools come in!

Visually presenting data points, very quickly allows a viewer of the information to come to a decision quickly, and the tools that are outlined below will help with that process. These are a handful that will allow the data to be sliced and diced, there are many more out there, but they all allow data to be drilled down into and get to a real understanding what is going on.

Some of the tools include Tableau, Power BI and Python( it has libraries that do a nice job)

Step4 – Decision Making

So after all this analysing, there need to be decisions made. The output of any data analytics work is to:

(A) Have the data in the correct format.

(B) In a place that it can be accessed and reviewed.

(C) Relevant to when the decision needs to be made

From the outset, as part of the work of performing the data analytics, an assessment needs to be made as to how often a decision will need to be made, with what data and when.

At this point, the decision-makers should have a set of data ready for them to look over and reliably make a decision based on what they have in front of them, if they can’t make a decision then possibly, steps 1-3 should be reviewed and revisited.

Often what happens in the data analytics world, is what information was required to make a change needs to be updated, improved upon, or additional data needs to be added on.

It is the job in the data analytics teams to source that data and or change how they present it, to now reflect the decision that needs to be made.

supervised machine learning vs unsupervised machine learning

If you want to unlock supervised machine learning vs unsupervised machine learning, this is a place to stop off and review what you can learn more about the topic. In this video, you will be taken through the steps to make a decision on how to approach this, analyse your data and get better meaning from it.

It will be a great building block if you want to go on and learn about classification, k-means, linear regression or logistic regression. Some of the discussion talks about clustering and how that can influence the model you build.

planning your machine learning model

Planning your machine learning model is one of the most important steps you will take in order to achieve the best results you are looking for.

In looking at how to plan a machine learning project, this video takes you through 3 steps:

a. Researching

b. Building your model

c. Testing your model

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how to build a machine learning model

In this video on how to build a machine learning model, the first of eight, we will take you through the initial questions you should as before looking to build your model.

This is the first video of eight that will explain what is machine learning, the benefits of it and some examples of companies doing it at the moment.

This series of machine learning model tutorials, will look to explain the concepts and steps involved to help get a better understanding of how you would go about building the model to better understand your data and customers.

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python constructor self and __init__explained

Here in python constructor self and __init__ explained, we will take you through an explanation of self and __init__, how you can use, and the concepts behind them.

We show how to create an object, initiate a class and pass the parameters to the __init__ constructor.

Also we take you through the concept of self and how it can be used to process an object’s parameters and return values where necessary.

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how to create charts in Tkinter

In how to create charts in Tkinter, Tkinter is one of many libraries that can be used to create charts and graphical user interfaces in Python. Its ease of use and ability to quickly roll out what you desire, make it one of the most popular in use today. In the below video, we create a:

  • Pie chart, a bar chart and a line chart
  • We also use functions and classes to help manage the creation of the charts,
  • It is recommended you follow the video to the end as it will give you a great insight into how these can be used in a video.

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how to add sine and cosine in python code

Explaining what this post is about
I was recently online and providing help to a fellow python coder, and a query came up about how you would rewrite some code that had included the sine function  and the cosine function

We were asked to see if we could translate this code:

%Position of the end effetor
x= q_0+L_1*cos(q_1)-L_2*cos(pi()-q_1-q_2)

into its python equivalent.

Some background about the output

In order to get the desired result, there is a need to import a package to provide some of the mathematical analysis required, and was achieved through using Numpy statistical analysis

This package allows the following functions to be used in the logic:

  • Pi
  • Cosine
  • Sine

And the result is

As a result, the below shows the output of the above question converted to its Python equivalent:

import numpy as np

a= np.pi
print("PI value is ", a)


print("L_1 value is",L_1)
print("L_2 value is",L_2)
print("q_0 value is",q_0)
print("q_1 value is",q_1)
print("q_2 value is",q_2)

x= (q_0+(L_1*(np.cos(q_1)))-(L_2*(np.cos(a-q_1-q_2))))
y= (L_1*(np.sin(q_1))+(L_2*(np.sin(a-q_1-q_2))))

print("x value is " , x)
print("y value is " , y)

with its output showing:

PI value is  3.141592653589793
L_1 value is 1
L_2 value is 1.5
q_0 value is 0.5
q_1 value is 0.7853981633974483
q_2 value is 0.5235987755982988
x value is  1.5953353488403288
y value is  2.15599552062015


how to create an instance of a class

Here in how to create an instance of a class, as described herein, how to create a class in Python, we will further explore the instance of class and how this can be used within a program to assign values to an object. This allows that object to inherit those values contained within the class, making it easier to have consistency regards functionality and data.

This video covers off

(a) creating an instance of a class

(B) Using the __init__ within the class

(C) define the constructor method __init__

(D) Creating an object that calls a class and uses the class to process some piece of data.

What are the benefits of this?

  • You only need to create one class that holds all the attributes required.
  • That class can be called from anywhere within a program, once an instance of it is created.
  • You can update the class, and once completed, those new values will become available to an instance of that class.
  • Makes for better management of objects and their properties, not multiple different versions contained within a program



How to create a class in Python

How to create a class in Python : In this video explaining classes will be the main topic on how they are constructed,  we explain how to create an instance of a class. Also, we look at what class attributes are and how they can be used to assign key data that can be called anywhere within a program.

The steps involve the following:

(a) Create a class

(B) Assign attributes to the class

(C) Create a method within the class ( similar to a function)

(D) Create an instance of a class to call its attributes and methods.

This video is a follow on from object oriented programming – Python Classes explained

Python Tutorial: Pandas groupby columns ( video 2)

Pandas groupby using column values

In this second video how to groupby using pandas and as part of expanding the data analytics information of this website, we are looking to explain how you can use a groupby selection but only using the column values and not the column names.

Below we import our data into a dataframe, and then group as follows:

  • Aggregate function
  • Using the cut function and assigning values to bins.
  • Assigning labels to the data frame output based on the bin values.



Why would you want to use Pandas groupby and column values?

This video looks to help understand the why going by values might be easier than column names:

  • Column names can change from project to project, using by values allows easy implementation of getting the output regardless of the names used.
  • You could apply this to any Python class, and as long as you can inherit will allow the code to run smoothly.
  • Implementing by value allows a clear understanding of the desired output as the values are clearly understood to generate what is required.
  • You need to understand how data within your data set falls within a particular cohort:
    • This use of values in different programs just needs to change, the underlying logic remains the same.
    • Using column names still means that to group them, the logic still needs to be written.