A Career in Data Science
Data science is the field of study that combines expertise in the line of business or the industry the organisation is in, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights. It involves preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.
Data science thus is an interdisciplinary field that uses scientific methods, processes, instruction sets and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining, machine learning and big data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and business and industry knowhow.
Before starting a data science project, it is necessary to understand the data requirements and issues. The objectives of the project need to be identified to make valuable predictions. This knowledge will make it clear what to acquire and what to leave.
A data science project’s lifecycle consists of five distinct stages, each with its own tasks. These stages are:
Capture
After understanding the project requirements, it’s time to gather data. This stage involves gathering raw, structured and unstructured data from different sources. Here, we need to think about questions such as what kind of data we actually require? From where it has to be pulled out? How to obtain and store this data?
Maintain
This stage covers taking the raw data and putting it in a form that can be used. Here we have to extract useful data by the cleaning process. It is the most time-consuming step. It includes removing unnecessary data, preparing useful data from raw data, avoiding inconsistencies that affect here, etc. There may be some missing values or mixed data that creates problems in this stage. So, the step needs to be done carefully.
Process
In this stage, data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis. Here, we need to determine the methods and techniques to build a model that shows relation between different variables. This phase of the life cycle includes some tools for planning a model and helping in predictive modelling of the life cycle.
Analyse
This stage involves performing the various analyses on the data. The two primary methods for data analysis are qualitative data analysis techniques and quantitative data analysis techniques. These data analysis techniques can be used independently or in combination with the other to help business leaders and decision-makers acquire business insights from different data types.
Communicate
Finally, we came to the end of the project life cycle. Here it is important to evaluate the things that we have achieved from the start of this cycle. We need to communicate the results of our experiment to the stakeholders to find the success or failure of this process. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
Data Scientist
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.
Data Analyst
Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Data Engineer
Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
Quantitative Analyst
Quantitative analysts, sometimes called “quants”, use advanced statistical analyses to answer questions and make predictions related to finance and risk. Needless to say, most data science programming skills are immensely useful for quantitative analysis, and a solid knowledge of statistics is fundamental to the field. Understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.
Business Intelligence Analyst
A business analyst is essentially a data analyst who is focused on analyzing market and business trends. This position sometimes requires familiarity with software-based data analysis tools, but many data science skills are also crucial for business intelligence analyst positions, and many of these positions will also require programming skills in languages used in the data science field such as Python.
Business Analyst
Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest term, a business analyst helps companies answer questions and solve problems. This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them. But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make a person a more compelling candidate for almost any business analyst role.
As organisations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources the demand for data scientists is on the rise. Whether it is to refine the process of product development, improve customer retention, or mine through data to find new business opportunities, organisations are increasingly relying on data scientist skills to sustain, grow, and stay one step ahead of the competition.
So, the data scientists need to acquire and demonstrate the following competencies in addition to the technical skills:
- Strong business acumen to discern the real problems and potential challenges that need to be solved.
- Ability to use statistical methods and techniques to perform statistical analysis.
- Ability to acquire domain knowledge and utilize it effectively in developing solutions.
- Strong communication skills to successfully communicate findings with business teams.
- Great data intuition to look beyond the surface for insightful information.
- Critical thinking to uncover and synthesize connections that are not always so clear.
- To be able to tell a compelling story with data to get the point across using data visualisation.
- Presentation skills with the use of statistical charts and diagrams.
- Curiosity to continuously observe events and ask questions with an insatiable hunger for knowledge and understanding.
- Ability to learn on his or her own since the field is new and evolving
| Education Pathway | ||
|---|---|---|
| Undergraduate | Post-Graduate | Some Colleges |
|
B.Sc. (Data Science) B.Tech (Artificial Intelligence and Data Science) B.Tech (Computer Science & Engineering with Data Science) |
M.Sc. (Data Science) M.Tech (Computer Science & Engineering – Data Science) |
|
