What is data science?

Aspire Thought Leadership! Ever wondered about what is data science?. Find out more on what has changed with data science in the current age. Come rig

As the world started to enter into an era of big data, the need to store this data has seen an increase in need as well. It became one of the biggest challenges and concerns for the enterprise industries until 2010 is also to critically understand what is data science. The main focus for this was to build a framework and then find some solutions in order to store the data that you have. Now that there are a few different frameworks out there that have been successful at solving this kind of problem with the storage, the focus has changed a bit, and we see that the shift is now on processing all of the data that we have.

This is where components of data science are going to come into play. Data science is going to be the future of technology and the future of Artificial Intelligence. This is why it is so important to understand what data science is all about and how you can use it to add some value to your own business.

What is data science?

The first thing that we need to take a look at is why do we need data science. Traditionally, the data that companies had was going to be structured and pretty small in size. This allowed companies to analyze by using simple BI tools. Unlike some of the data that we saw with traditional systems, which was pretty structured and easy to work with, today most of the data that a business is going to receive will be either unstructured or semi-structured.

There are a lot of different places where this data can be generated from in our modern world. Some of the places include text files, financial logs, multimedia forms, sensors, and even instruments. While the simple BI may have worked well in the past, they are just not capable of processing all of the data, either the variety and the volume of the data that is there. This is why we need to find tools that are more advanced and more complex in order to handle the data and help us to process, analyze, and get good insights and predictions out of that information.

Of course, this is just one of the reasons why the field of data science has become so big and so popular over the years. There are a lot of different domains that we can use with data science, and it has helped to change the business world and even some of the products that consumers have been able to enjoy over the past few years.

Let us look at a few examples. How would it help a business if they were able to take a look at some information on their customers, including their income, age, purchase history, and past browsing history to understand the exact requirements that your customer needs to be answered. You may have had access to this kind of information before using data science, but now you can use data science, and some of the machine learning algorithms that are out there, in order to train the right models. In the end, this helps you to be more effective at recommending products to your customers with a precision that wouldn’t have been possible before. This can help you with bringing in more business to your organization.

We can look at another scenario as well to help us understand how this whole process works a bit. What if you had a car that had enough intelligence to drive you home. This car is going to work because it can collect live data from the sensors in it, including lasers, cameras, and radars. This helps the car to create a pretty accurate map of the things around it, and it takes that data to make decisions like when to make a turn, when to go faster or slower, where to overtake, and more with the help of advanced algorithms of machine learning.

Many companies like to go with data science because it can help them make a lot of predictions. Weather forecasting is a good option to see how this works. Data that comes from satellites, radars, aircraft, and ships are going to be collected. When it is collected, it can then be analyzed before building up models. These models are not only going to be able to help us forecast the weather, but can even help us make predictions about a natural calamity happening. It is going to help you to make sure you take the right measures ahead of time and can save many lives.

What is data science

With some of the information above in mind, it is time to take a look at what exactly data science is about and why it can be so important in helping your business out. You may have found that the use of the term of data science is growing all of the time, but what does this really mean? What skills do you need to have in order to become a data scientist in the first place? And what really are the differences that you can find between data science and BI? How are predictions and decisions made in data science? These are just a few of the questions that you may have when it comes to what data science is all about and how you will be able to use these for your needs.

The first thing that we need to take a look at is what data science is. To keep it simple, data science is going to be a blend of different tools, algorithms, and other supervised machine learning principles with the goal to discover some of the hidden patterns that are found in the raw data you have. This brings up the question of how this is different from what statisticians have already been doing? The difference is going to come from the difference between predicting and explaining.

When you meet with a data analyst, they are going to really explain what is going on by processing the history of any data that you have. The data scientist is going to do things a bit different. Not only are they going to discover some insights from the exploratory analysis, but they will use some of the algorithms in machine learning in order to identify the occurrence of a particular event in the future and how likely it is to occur. The data scientist is also going to look at the data you have in many different angles, hopefully including some perspectives that were not known earlier.

So, to help us understand this a bit better, data science is primarily going to be used in order to make predictions and decisions using prescriptive analytics, predictive causal analytics, and machine learning introduction. Let us explore each of these parts so we can gain a better understanding of how this is going to work.

First, we have to look at predictive causal analytics. If you want to work with a model that has the capabilities of predicting the possibilities of a specific event and if it will happen in the future, you need to use the idea of predictive causal analytics. This could be something like if you choose to provide money on credit, then this is going to be the probability of customers making future credit payments on time because this is a big concern for you.

With this information, and perhaps some of the background information that comes with the customer, you would be able to build up a model that is able to do the predictive analytics. This would take a look at the past payment history of your customer and use that, often with other factors, to predict if the customer will be able to make payments in the future on time, or they are not going to make these payments.

Another thing that you need to work on is known as prescriptive analytics. If you want to work with a model that can have intelligence enough to make its own decisions, and you want it to be able to modify with the dynamic parameters of your choice, then you have to work with what is known as prescriptive analytics to make it happen. This is actually a newer field that is going to be used in order to provide advice to a company based on the information that is present.

In other terms, it is not only going to make predictions based on the raw data that you have, but it can take those predictions and then suggest a few prescribed actions that you can take, along with the outcomes that would be associated with this as well.

An excellent example of this would be the self-driving car from Google. The data that the vehicle is able to gather can then be used to train these cars. You can add on some algorithms to this data to ensure that there is some intelligence added to it. This helps the vehicle to make some decisions like when it should turn, which path it is going to take to get at the right destination, and when to slow down or speed up.

Of course, we need to have unsupervised machine learning thrown into the mix to help us make some predictions. If you have any transactional data, such as what you can typically find in a financial company, and you want to work with building up a model to determine the future trend, then machine learning algorithms are often going to be the best option to go with. This is going to fall into the category of reinforcement learning because you have to work with data that you can use as the basis of training the machines. For example, you can work with a model for fraud detection that can be used to have a historical record of fraudulent purchases.

And finally, we can work with machine learning to discover some of the patterns that show up in your data. If you do not already have the right parameters based on which you can make some predictions, then you have to be able to go through the set of data and find some of the patterns that are hidden so you can come up with some good predictions. This is an excellent example of an unsupervised model because you do not have the parameters and examples in place, and this means there are not any predefined labels to help you out with this kind of grouping. One of the most common algorithms that you can use to make this happen and to help you to discover some patterns in the set of data will include clustering.

Let us say that your job is with a phone company, and you need to be able to establish a new network doing so by putting towers in the region. Then, you can work with the algorithm of clustering to find those tower locations, the ones that are in the best place so that the users are going to receive the best strength of the signal.

Now that we know a bit about data science, we need to look at how it compares to business intelligence or the BI that we talked about before. BI is not going to go as in-depth with things as we will see with the data science. BI is basically going to analyze some of the previous data in order to find some hindsight and some insight to help describe the different trends that show up for that business.

BI is going to enable the user to take data from a combination of internal and external sources. Then they can prepare it, run queries on it, and create dashboards to help them answer questions like an analysis on the quarterly revenue or business problems. It is also able to evaluate the impact of different kinds of events that may be needed in the near future.

On the other hand, data science workflow is going to be an approach that is more forward-looking. It is more of an exploratory way with the focus on analyzing the past or current data and predicting the outcomes in the future to make more informed decisions. You will find that it helps to answer more open-ended questions, including how and what.

What is Data Science: The lifecycle of data science

Now that we have a better idea of data science and what it is all about, it is time to take a look at some of the main phases that happen in the lifecycle of data science. Knowing these six stages will be so crucial for helping us understand more about what data science is, and ensures that we are able to make this work for our needs. The stages and phases that we need to focus on when looking at the Data Science Lifecycle include:

Phase 1 of Discovery. 

Before you get started on any project, it is vital for you to understand some of the different requirements, specifications, priorities, and the required budget of something before getting started. You can also possess the ability to start asking questions that will get you the right results. Here, you are going to take a look around and decide if you have the right resources in place in terms of time, data, technology, and people in order to support this project as a whole in this phase. You also need to frame the business problem that you are hoping to solve and then come up with the initial hypotheses that you would like to test.

Phase 2 of Data preparation. 

In this phase, you are going to need to work with a kind of analytical sandbox so that you can perform different types of analytics for the rest of your project. You need to spend time exploring, preprocessing, and conditioning the data before you try to work on the modeling. Besides, you will do a process known as ETLT, which stands for extract, transform, load, and transform, in order to get that data over into the created sandbox.

Many programmers like to work with R to help out with the data cleaning, the transformation, and the visualization. This is going to be one of the best things to do ahead of time because it helps you to spot some of the outliers, and can establish a good relationship that is needed between the different variables. Once you have had some time to clean and prepare any data that you want to use, it is time to do a bit of exploratory analytics to see how it works.

Phase 3 of the model planning. 

This is the step where you are going to determine the different techniques and methods that you want to use in order to draw up the various relationships that you should see between the variables. Knowing these relationships is important because it is going to set the base for which algorithms you can use in the next phase. You will need to use the Exploratory Data Analytics here using a variety of visualization tools and statistical formulas.

There are going to be three main tools that are brought out when it is time to work with model planning. You can use the one that makes the most sense for the project you are exploring, or you can try out something else if that makes the most sense. The three main tools that are often used in model planning with include:

1. R: This programming language is going to have a complete set of modeling capabilities, and it is a great environment to help build up an interpretive model as you want.
2. SQL Analysis Services. This is going to help you perform in-database analytics using some of the common functions of data mining as well as some basic models of predicting.
3. SAS/ACCESS. This can be used when we would like to access data from Hadoop, and it is really useful when we want to create some reusable and repeatable model flow diagrams for our needs.

Although there are a lot of tools on the market to help you out with this part, most people are going to use R (or phyton). This one is the easiest to use for this kind of work and opens up the most doors for success for your needs.

Phase 4 the model building. 

When we get to this phase, we are going to spend some time developing the sets of data that are needed for both the training and the testing of everything. You will need to take a look at your current tools and decide if they are going to be enough for running the models that you want. Sometimes these will work just fine, and other times, you will need an environment that is more robust and able to do things like fast and parallel processing.

During this phase, you are going to do a lot of analyzing. In addition to looking at whether the tools you have are going to be enough for the model you want to create, you also need to analyze some of the different learning techniques, including clustering, association, and classification to see how they can fall into your building of a new model.

Phase 5 of operationalize. 

In this fifth phase, your goal is to deliver final code, technical documents, briefings, and reports. Also, depending on the type of project that you are working on and how successful it is at this point, you can release a pilot project and implement it inside of a real-time production environment. This is going to provide you with a clear picture of the performance and some of the other constraints that can show up, but this is on a smaller scale before you deploy the thing on a full scale.

Phase 6 of communicating results. 

Now it is important to evaluate if you have been able to achieve the goal that you had gone through and planned out in the very first phase. So, in this last phase, your goal is to look at all of the key findings, communicate to the stakeholders, and then determine if the results of the projects are a success or a failure based on the criteria developed in Phase 1 in the beginning.

Data science is going to make it so much easier for businesses to keep up in this competitive world, for them to make a lot more decisions that are based on fact rather than worrying about second-guessing and getting things wrong, and helps them to provide better products to those customers who use them the most. And with so many different algorithms and methods that the business can choose, they will be able to pick out the option that works the best for their data and for what they wish to do with that data. [what is big data?]

Now, all of these stages need to be taken into account when you are working on different projects in data science, even when you are doing machine learning. We will delve into this a bit more with some details about working on a machine learning project in particular later on, but missing out or skimping on one of the steps can be really detrimental to your project, so you have to make sure that you are adding them all in.

It is normal to take a look at some of these steps and assume that one of them is not that important or that you do not need to spend all that much time on them. But as you get more into machine learning and data science, as well as some of the projects that you will be doing with these, you will find that all of the steps are important. Skipping out on one or not giving it the attention that is needed can really harm your project, and adds in a lot more work in the end because you have to go back and redo a step and see where you went wrong in the process. Taking your time and valuing all of the parts is so critical in helping you see the results that you want.

As we will see as we dive more into machine learning and all of the different things that you are able to do with that, there is so much more you can do with these algorithms and this information than just promote a business and come up with predictions. These algorithms are able to learn, meaning that they can help with smart cars, voice and face recognition, and even with things like search engines.

It is no wonder that the field of data science is growing so much in recent years. It seems like many businesses are still happy and fond of working with some of the older and traditional forms of predictions and running their business, but this puts a lot of risks back on their shoulders and can be a dangerous thing to do in this modern world. Working with data science, and especially with machine learning, could help to solve some of this problem.