In this blog, you will get to know everything about “What Is Data Science?” in detail.
Data is making a splash in every imaginable industry in a growing digital economy. Unstructured data is constantly being produced. Which make it more important than ever to transform it into insights that can be used.
One can only fathoms the impact of actionable insights that can be taken from the vast amount of data. Which is predicted to be released in the market over the next ten years.
To understand how one might develop a path to succeed in their data science profession. We will study about data science in its entirety in this post.
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What Is Data Science?
Statistical analysis, data analysis, machine learning algorithms, data modelling, and other concepts. And these all are the part of the process of extracting useful insights from unstructures data.
In layman’s terms: Let’s think about an illustration. A case study that was later adaptes into the Hollywood motion picture “Moneyball.”
By analysing the statistical data points of each player and quantifying their performances to win the game, the video depicts how an underdog team went on to compete at the top level of the baseball tournament. It can be in line with how data science functions in practise.
How does Data Science Work?
The following can be use to describe how data science functions:-
- Raw data that illustrates the business issue is acquires from many sources.
- To find the best solutions that adequately explain the business problem, data modelling is carries out using a variety of statistical analysis and machine learning techniques.
- Actionable insights that will help solve the business issues identifies by data science.
Let’s use an example to better grasp this. Assume that a company is looking for possible leads for their sales force. To use data science to obtain an ideal answer, they can take the following course of action:
- Assemble historical information on completes sales.
- Utilize statistical analysis to identify the trends that the leads that were closes follows.
- Utilize machine learning to provide practical insights for identifying possible leads.
- To separate potential leads that have a high likelihood of being closes, use the new data on sales leads.
Let’s talk about the history of data science and how it has developes into an emerging field for the future now that we have explores how it functions.
Data Science Life Cycle
The Data Science lifecycle comprises of the following:
Formulating a Business Problem
Any data science issue will begin with the formulation of a business issue. The challenges that might be resolve with knowledge obtain from a successful Data Science solution are explain by a business problem.
You have sales data for a retail store going back a year. This is a straightforward example of a business challenge.
You must predict or forecast the store’s sales over the next three months using machine learning techniques in order to enable the retailer build an inventory that will reduce the wastage of goods with shorter shelf lives than other goods.
Data Extraction, Transformation, Loading
The creation of a data pipeline is the following phase in the data science life cycle.
In this step, the pertinent data is taken from the source, translate into machine-readable format, and then loaded into the programme or machine learning pipeline to get things going.
However, for the aforementiones scenario, we will need data from the shop that will be helpful in creating an effective machine learning model in order to estimate the sales.
In light of this, we would generate distinct data points that might or might not be influencing the sales for that specific store.
The magic happens in the third phase. We will produce relevant data by using statistical analysis, exploratory data analysis, data wrangling, and data manipulation.
Moreover, the preprocessing is carries out to evaluate the different data points and create hypotheses that best explain the relationship between the different elements in the data.
For instance, in order to forecast store sales, it is necessary for the data to be in a time series format.
The series’ stationarity will be examines by hypothesis testing, and further calculations will reveal numerous trends, seasonality, and other relationship patterns in the data.
Advance machine learning techniques are utilises in this step for feature selection, feature transformation, data standardisation, data normalisation, etc.
You can build a model that will effectively produce a forecast for the specifies months and the example above by selecting the best algorithms bases on evidence from the aforementiones phases.
For a business challenge where high dimensional data may be present, for instance, we can use the time series forecasting approach.
We’ll create a forecasting model utilising an AR, MA, or ARIMA model and other dimensionality reduction approaches to predict the sales for the upcoming quarter.
Gathering Actionable Insights
Getting insights from the aforementiones problem description is the last stage of the data science life cycle. From the entire process, we derive conclusions and results that would most effectively explain the business issue.
Solutions For the Business Problem
The only things that will address the business challenge are practical insights supportes by data are actionable insights.
As an illustration, our projection bases on the time series model will provide a reliable estimation of the shop sales for the following three months.
The store can plan its inventory using such data to minimise the loss of perishable goods.
Everything relates to data, including “What Is Data Science”, how it functions, has already been coveres. We therefore anticipate that you will find our blog to be very helpful. And that it will allay any questions you may have about data science.