Data Science in a growing digital economy, is creating a buzz in every industry vastly. With a steady flow of information in the form of complex data, the need to transform it into actionable insights is more important than ever.
It is difficult to understand the implications of insights derived from the amounts of data that are to be made available over time. In this blog, we will learn about data science in its entirety so that we can grasp how to develop a plan to excel in our data science careers.
What Is Data Science?
Data science is the broad field that includes statistical analysis, data analysis, machine learning techniques, data modelling, data preprocessing, and other methods for gleaning insights from unstructured data.
Let’s look at an example in layman’s words. How the search engines collect user data and make suggestions based on their choices (data points). On streaming websites, organizations employ recommendation engines built with various machine learning algorithms to forecast recommendations that will best serve the user’s history.
Overall, data science is the field of study in which data is processed using advanced statistical and mathematical ideas, as well as machine learning techniques, to create valuable insights to address issue statements or business challenges.
How Does Data Science Work?
The following is a description of how data science works:
- Raw data from multiple sources is gathered to describe the business context
- Data modelling is performed using various statistical analysis and machine learning methodologies to provide the best solutions that best describe the business problem.
- Data science provides insights that can be used to solve business problems.
The Life Cycle of Data Science
Setting up a business problem
Any data science project certainly begin with the formulation of a business problem. A simple example of a business challenge is to have sales data from the previous year for a retail store. You must predict or forecast sales for the next three months using machine learning methodologies. This will assist the business in creating an inventory assist in reducing wastage of goods and to shorter its lifespan than other products.
Data extraction, conversion, and uploading
The following phase in the data science life cycle is to build a data flow in which relevant data is retrieved from the source and processed into machine-readable format before being loaded into the programme or machine learning pipeline to get things going.
To forecast sales in the above example, we will require data from the store that will be beneficial in developing an efficient machine learning model. Keeping this in mind, we would generate distinct data points that may or may not be influencing sales at that specific store.
The real process begins in the third phase. We will generate relevant data using statistical analysis, exploratory data analysis, data wrangling, and data manipulation. Preprocessing is done to evaluate the numerous data points and develop hypotheses that best explain the relationships between the various data points.
For example, in order to predict sales, the data for the shop’s sales problems must be in a time series format. The hypothesis testing will determine the series’ stationarity, and subsequent computations basically reveal numerous trends, seasonality, and other related patterns in the data.
Complex machine learning principles added is basically used for feature transformation, feature selection, data standardisation, data normalisation, and other tasks. Choosing the finest algorithms based on data from the preceding steps will assist you in creating a model that will efficiently provide a forecast for the months mentioned in the preceding example.
For example, we can use the Time Series forecasting approach to solve a commercial problem with high-dimensional data. We will use various dimensionality reduction strategies to develop a forecasting model that will forecast revenues for the upcoming quarter using an AR, MA, or ARIMA model.
Acquiring Valuable Insights
The last step is to collect insights from the problem statement to draw conclusions to explain the business challenge.
For example, using the Time series model, one can obtain monthly or weekly sales for the next three months. These insights will then assist experts in developing a strategy plan to address the issue at hand.
Solutions to Business Issues
Business problem solutions are nothing more than ideas that will fix the problem using evident knowledge. For instance, the Time series model’s projection will give a precise estimate of store sales over the following three months. Using this knowledge, the store may arrange their inventory to reduce the waste of delicate items.
Why is data science important?
Currently, skilled and credential data scientists are in high demand across sectors. They are among the highest-paid professionals in the information technology business. A data scientist is the top job in America, according to Glassdoor. Only a few people have the expertise to extract significant insights from raw data.
In recent years, there has been rapid progress in the field of the Internet of Things, resulting in 90% growth. As the IoT grows, the amount of data generated now has increased to 2.5 quintillion bytes each day.
The data comes from a number of sources, such as
- Shopping centres employ sensors also gather data from patrons.
- Posts on social networking platforms
- Phone-captured digital photos and movies
- E-commerce transactions
- Big data is what this entails.
Enormous amounts of data is provided to organizations and businesses. As a result, it is critical to understand what to do with this data and how to use it. Basically, it enhance many abilities, like statistic, maths, and business domain experts, and assists organisation in finding ways to:
- Cut extra charges
- Enter new markets.
- Make use of various demographics.
- Determine the impact of marketing campaigns.
- Introduce new items or services.
And the list goes on and on! Above all, despite of industry sector, data science is likely to play a critical role in any organization’s success.