16 Jul 2022

An Introduction To Data Science Field | Ekeeda

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An Introduction To Data Science Field

Data science is one of the most in-demand fields and techniques that has gained pace in the last couple of years. It provides valuable insights on raw data that will be very important to enhance business operations and boost profit margins. Today, Data is everywhere and it’s estimated that every day, a staggering 2.5 quintillion bytes of data is generated. With this mind-boggling number, it’s easy to see how much of an impact data can have. With data taking over the world, you might be anxious to learn a bit about data science. So, here we offer a basic introduction to data science. We’ll discuss what is Data Science, its importance, and what the future hold for data science.

What Is Data Science?

Everyone is so curious to know about the invention of Data Science and how did it originate? In 2001, William S Cleveland combined computer science with data mining to build a more technical approach to statistical analysis. Thus, as a result of this combination, data science was born. To put it simply, data science is the combination of programming, mathematics, and statistics to extract valuable insights. It is an umbrella term that encompasses many techniques, tools, and concepts related to useful data. Data science uses various tools & techniques to identify patterns, anomalies, and correlations in data.

How Did Big Data Rise?

When you read about data science, you’ll find that it is termed as Big Data. But did you know what is Big Data? It’s a term used to describe large sets of data that need to be broken down using tools and techniques to obtain representative data. Big data sets are very large and complex wherein traditional software cannot process them. Although big data has been around for a longer period of time, 2010 did bring a spike in the use of data science. More and more businesses were turning towards data science to draw insights. Since then, the total data amount floating around has increased exponentially.

By 2020, a whopping 90% of the world’s data is created in the prior two years. The fast development of data use is immense. With so much data analysis and getting through, data science has become an integral part of businesses and systems. 

Importance Of Data Science

Now that we’ve gone through the introduction of data science, let’s take a look at what makes data science so important. Why do we need to pull and do data analysis? The data collected from data science will be used in various forms within a business. It’s not just the data teams that utilize it, even marketers, software engineers, IT executives, and whoever makes the business decisions will make use of data science. Through data analysis, we can determine the trends and patterns to let us make better and informed business decisions. These decisions will vary greatly based on the type of business, but data acts as a powerful and useful tool to make informed business decisions. Rather than going in blind and assuming the outcome of a campaign or a business strategy, we can interpret and analyze data sets. By doing so, we’ll have evidence that our work has the potential to make a difference.

The Impact Of Data Science

Other than helping make business decisions regarding strategies and plans, data can also inform us of what not to do. If we see negative trends and correlations within data, this tells us that whatever caused it should be avoided. As a result, we can then test other elements to find a more favourable outcome. The use of data can be efficient for growth, reduce uncertainty, and can save money on what would otherwise be wasted efforts. Big companies such as Netflix, Facebook, and Twitter use big data in various ways to retain customers, create the right content and save on spending.

Data Science Process & Concepts

How does such a powerful process work? First, let's take a look at the data science process and discuss some data science concepts. The process of data collection will be as follows:

  • Collect data from a variety of sources
  • Structure collected data for analysis
  • Perform data analysis
  • Determine valuable insights
  • Visualize insights in the business-oriented form
  • Share insights with team members and stakeholders
  • Make informed decisions based on findings

The steps you perform and the tools you use will totally depend on the data and business. To find out more about data science career opportunities, job roles, pros, and cons read: Why Is Data Science A Good Career Option

Data Science Concepts

There are a lot of concepts used in the field of data science. Let’s explore a few of them: 

Data Mining

With so much data being collected, not everything is useful. Thus, it's important to work with useful data – else, we won’t have sufficient time to analyse it all. Thus, the industry explored data mining, where we can extract useful information from larger raw data sets. The process uses mathematical and statistical algorithms to detect correlations, patterns, or anomalies in data.

Machine Learning

Wonder what the connection between data science to machine learning is? Well, it’s a crucial concept in data science. Machine learning is a type of AI – Artificial intelligence that will allow machines to learn from data and improvise. ML algorithms are used to teach computers to think in the same way just like humans. ML uses past experiences to better understand statistical patterns in the future. As the machine is provided more data to consume, it will gradually become better to identify data differences.

Data Visualisation

When you have huge sets of data to work around and derive a lot of insights, it can be tedious, time-consuming, and frustrating at times to review the data. Also, you will have to share data with other members that may be unable to understand and process data. Therefore, data visualization tools come into the picture. These tools let you create charts, graphs, maps, dashboards, and more than visualize meaningful data.  Data visualization tools are an excellent way to ‘translate’ data to others. We’ll take a look at some examples of these tools in a moment.

Some Important Data Science Tools

  • Programming languages – Programming skills can help you extract data, clean data, and even visualize it. Some of the most common use and compatible languages with data science are Python, R, and SQL
  • Visualization Tools – Such tools are used to make data digestible. Some data visualization tools include Tableau, Google charts, and data wrapper.
  • Database management systems (DBMS) – It is used to store and manage vast amounts of data; some examples of DBMS include Filemaker, Clipper, FoxPro, and Microsoft Access.

To learn everything about data science tools, concepts, and more sign up with Ekeeda Data Science Program and advance your career today!

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