What is Data Science?

Data Science is the study of data. It is the development of methods for recording, visualizing and analyzing data for the extraction of useful information.

The goal of data science is to gain insights and knowledge from any type of data, be it structured and unstructured.

Put more simply, it is using data to solve problems. The range of these problems is huge. For example:
– Gearing product development by looking at how people use your products.
– Analyzing customer sentiment on social media.
– Targeting customers with the right sales messages at the right time.
– Improving internal product development processes by looking at points where faults are most likely to happen.

Below is a representation of the Data Science Life Cycle:

Data Science Life Cycle

Discovery

Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. This involves searching for different sources of data and capturing structured and unstructured data.

structured data: this is highly-organized and formatted in a way so it’s easily searchable.

unstructured data: this has no pre-defined format or organization, making it much more difficult to collect, process, and analyze.

Data Preparation

The collected data is then converted into a workable format. You require a sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data before modeling.

conditioning of data: the filtering and cleaning of data keeping only the details relevant to the specified tasks.

Model Planning

Here you determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase.

Model Building

Datasets for training and testing purposes are developed in this phase. You will examine if your existing tools will suffice for running the models.

Operationalise

At this stage, final reports, briefings, code, and technical documents are delivered. A pilot project may also be implemented in a real-time environment.

Communicate Results

In this last step, findings are communicated to stakeholders and decision-makers. You also evaluate if you have been able to achieve the goal that you had planned initially.

As you may notice, Data Science does not focus on one specific field of knowledge, but multiple ones, from computer science and mathematics to statistics and research.

Pros and Cons of Data Science

Data Science is a massive field. Given that, it is not without its faults. Let us look at some of the pros and cons of this field.

Pros

The advantages of Data Science are:
1. It is in high demand.
2. It garners an abundance of roles.
3. It is a highly paid career.
4. It is versatile.
5. It improves data.

Cons

To understand the full spectrum of Data Science, we must also know its limitations. These are as follows:
1. The term itself is not clear-cut.
2. Mastering it is next to impossible.
3. Issues with data privacy.
4. Varying sources from which data is acquired may produce unexpected results.

Why is Data Science relevant/important?

In a nutshell, Data Science solves business problems.

We are sure you have heard the term “Big Data”, yes. This is, in part, where Data Science comes. Without the expertise of professionals to turn gathered data into useful insights, Big Data is useless.

These insights and patterns acquired help in making data-driven decisions.

To better elaborate, here is a brief rundown of the importance of Data Science:
1. It helps brands understand their customers.
2. It allows brands to communicate their stories in an engaging and powerful manner.
3. Its insight and results are applicable to almost any sector.
4. Makes use of readily accessible data to facilitate the achievement of organizational goals.

As a Data Scientist, your role is to take data sets and formulate strategies as to how best to move forward.

Where is Data Science used?

As mentioned above, Data Science is applicable in multiple sectors. It is just about everywhere and applied in places we use in our daily lives and in more complex applications such as:
– Internet Search
– Digital Advertisements
– Recommender Systems
– Image Recognition
– Speech Recognition
– Gaming
– Self-driving cars
– Robots

A Self Driving Car powered by Data Science

The most obvious question now is how does one get started in Data Science? The first and most important step I would say is the decision to begin. Given that, to do well in Data Science you require three main aptitudes; Math and statistics, programming, and business knowledge.

There are countless resources and online courses, both paid and free, that can help you get started in this field. Sites like Coursera, Udemy, Codecademy, DataCamp, Dataquest all offer free courses on the subject and you never go wrong with YouTube.

What we recommend is to learn by doing, which is what the following series of articles will be covering. So join me in the next few articles.

Lastly, make sure this is something you want to do are going to enjoy.

Find a job you enjoy doing, and you will never have to work a day in your life.
-Mark Twain