Data science is the study of raw data that encompasses data analytics, data mining, and machine learning under one roof. Data science study helps us in finding meaningful patterns and insights from raw and unstructured data and is used to tackle big data that includes data cleansing, preparation, and analysis. As a data scientist, you have to gather raw data from various sources and then apply several techniques such as machine learning, predictive analytics, or sentiment analysis to collect meaningful information.
With data science, you can bring structure to big data, search for compelling patterns, and advise the decision-makers to bring in the changes effectively that suits your business needs.
The Lifecycle of a Data Science
There are multiple phases in the lifecycle of data science. Let’s understand it better with a real-life example. Imagine that you run a retail shop and your primary goal is to improve the sales of the shop. To identify the factors that drive your sales numbers, you must answer a few questions, such as which products are the most profitable? Are you gaining any benefit from the in-store promotions? These questions are better explained by following the steps involved in the lifecycle of data science.
A data science life cycle includes the following steps:
Data Discovery
The data discovery phase consists of the multiple sources from which you discover the raw and unstructured data such as videos, images, text files, etc. So, as per the above example, you need a clear understanding of the factors that affect your sales to procure the data that will be relevant for your further analysis. You can consider the following factors: store location, staff, working hours, promotions, product pricing, and so on.
Data Preparation
The next stage of the data science lifecycle is preparing the raw and unstructured data for further analysis. For this, you need to convert the data into a standard format so that you can work on it seamlessly. This phase includes steps for exploring, pre-processing, and conditioning of data. After your data is cleaned and pre-processed, it is much easier to perform exploratory analytics on it.
Model Planning
The model planning phase includes the methods and techniques that you will use to determine the relationships between variables. This relationship can act as a base for the algorithms that are used at the time of model building. You can use several different tools for model planning, such as SQL analysis services, R programming, or SAS/access. Out of all these tools, R programming is the most commonly used tool in model planning.
Model Building
In the model-building phase, you will create different datasets for training and testing purposes. For this purpose, you can divide your dataset into the 70 and 30 per cent ratio. 70% of data will be used to train the model, and the remaining 30% of data will be used to test the trained model. You can use techniques such as classification, association, or clustering to build your model.
To gain expertise in the data science field, you need skills in the three major areas: mathematics, computer science, and the respective domain knowledge. If you have the required expertise in mathematics, then you can quickly analyze and visualize the data. You should acquire good domain knowledge to understand the business problems clearly. You should also have excellent coding skills (computer science) to implement different algorithms in machine learning and data analysis.
Data analysts are well-rounded and data-driven professionals with high-level technical skills. Data analysts have the required skills to build complex quantitative algorithms for organizing and synthesizing large amounts of information that is used to answer questions and drive strategy in their organization. They bridge the gap between data scientists and business analysts.
The requirement for data analysts is growing as organizations take a thoughtful approach to develop unique analytics strategies and drive impactful outcomes. The job of a data analyst is high-paid in India as well as abroad and will be the most sought after job in the coming few years. As per the Salary Study, analytics professionals out-earn their Java counterparts by almost 50% in India. The study indicates that there is an increase of 1.8% in the salaries of entry-level professionals who have experience ranging between 0 to 3 years.
Currently, the demand for DSA skills is growing in all industries, and the highest number of openings are in three sectors: finance and insurance, information technology, and professional, scientific, and technical services. There is a demand of approximately 59% of all Data Science and Analytics (DSA) jobs in sectors such as Finance and Insurance, Professional Services, and IT. The following table shows an analysis of the DSA job category demand by industry.
Top Trends in Data Science Job
The following infographic shows the list of domains where data science is creating a significant impression.
with Some Simple Steps to get a thorough understanding.
Strategies Required for Building Your Own Data Science and Analytics Pipeline
To build a better pipeline of talent, businesses and higher education need better ways of signalling for the skills of the future.
Structuring of the People Plan for the Digital Economy
As we are advancing into the digital economy, companies need to focus on new approaches for recruitment and development that are defined by the set of data science and analyst skills. These skills are required by the companies so that they can build cohesive, multidisciplinary teams that will deliver business results. A competent people plan indicates the skills and competencies for each role that a company has. Companies need a comprehensive plan to assess how they can organize people with the right skills, right knowledge, and proper experience in the right departments.
Modernize the Training and Development
If you are recruiting candidates from companies that have made it big in the field of data science and analytics, you will end up paying colossal compensation, and there is no guarantee that the candidates will stay in the company for long-term. To ensure long-term employability, companies should focus more on bringing all the conventional training methods that focus on updating the skills of your employees. Companies can also offer external degree and certificate programs, internal coursework, and on-the-job training.
Build Multidisciplinary Strength using Data Science
Expertise in data science and analytics provides the ability to data analysts and scientists to thrive in multidisciplinary teams. To achieve this goal, companies should launch programs that bring domain, computer science, data science, and machine learning together through a diverse range of skills, expertise, and experience. A company should provide more opportunities for candidates to indulge in applying data science to real-life problems. This approach will help them to develop a host of other much-needed skills such as critical thinking, how to communicate effectively, and how to collaborate with a diverse group of people.
Final Thoughts
Undoubtedly, our future belongs to data scientists and data analysts. With the advancement in digital technology, more and more data is being generated, providing opportunities to drive critical business decisions.
For more information about data scientists, see the following video on Youtube.