We live in a technology-driven world, and data is now referred to as the ‘New Gold.’ There are multiple channels, and each produces massive data volumes each day. Having said that, there is a need to ensure that this massive volume is analysed and sorted in a proper manner. And that is where data science has become a key part of various industries. With data science on the upswing, there are various data science techniques that different industries adopt to enhance the business growth and improve their CX.
What is Data Science?
The simplest answer to the question ‘what data science is’ – it is a field of study pertaining to analysing massive data volumes by using cutting-edge technology and techniques to discover patterns, important information, and insights that drive effective decision making.”
Today’s data scientists use tools like AI and complex machine learning algorithms to build predictive models for the purpose. The data used for the purpose of analysis comes from various sources such as websites, apps, social media, third-party websites, marketing campaigns, as well as customer support platforms. The diversity of sources also leads to different formats of data that require analysis.
The Significance of Data Science
Data science is a crucial thing that organizations collecting data consistently focus on. These organizations need data science experts to find actionable insights out of the data silos, and leverage those for business growth.
Moreover, with multiple sources entering the scene, the data storage volumes are growing by the day. And, the past practice of building infrastructure to store the data on premises and subsequently processing it over a period of time to find insights is becoming increasingly outdated. Therefore, frameworks like Hadoop, which can handle storage have gained popularity.
Further, businesses are increasingly focusing on using advanced analytics tools to process the data with greater efficiency. To understand it better, let’s take a look at the data science lifecycle, and its integration with big data processes.
The Data Science Lifecycle
There are five clearly distinguishable stages in the data science lifecycle, with a peculiar set of tasks to be performed at each stage:
- Data Capture – It involves Data Acquisition, Data Entry, Signal Reception, and Data Extraction. The first stage of the data science lifecycle pertains to compiling raw, structured, and unstructured data from different sources.
- Data Maintenance – It involves Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture. At this stage, the raw data stored is converted into a format suitable for business usage.
- Data Processing – It involves Data Mining, Clustering/Classification, Data Modelling, and Data Summarization. Data processing is the stage where data scientists would use the refined data to analyse it and determine patterns, ranges, and biases. This stage is also where data’s usability in analytics is ascertained.
- Data Analytics – It involves Exploratory/Confirmatory and Predictive Analysis, Regression, Text Mining, Qualitative Analysis. It won’t be wrong to say that this is the most crucial aspect of a data science professional’s job. At this stage different analytics are performed on the data available.
- Communication – In the final stage of the data science lifecycle, tasks such as Data Reporting, Data Visualization, Business Intelligence, Decision Making are undertaken to communicate the insights generated in an impactful manner to the business leaders and decision makers.
Prerequisites for a Career in Data Science
Data science is a highly complex and advanced technology driven field. There are some specific technologies and concepts that a data scientist should be well versed with, such as:
1. Machine Learning
For a data scientist, machine learning expertise is as crucial as understanding the basics of statistics and data structuring.
2. Data Modelling
By adopting mathematical models, it is possible to speed up calculations and data-driven predictions. Modelling is integral to machine learning and would require the person to identify the best algorithm to resolve the problems and how to train those models.
Statistics are the basic language for anyone using data to extract insights and achieve desired outcomes.
For anyone looking to enter the data science field, some programming proficiency is essential, therefore, the common programming languages such as Python, and R must be learned. Python is one of the most popular options considering its ease of learning, and ability to support multiple data science and ML libraries. Good data science courses will also include learning of programming among other things.
The working of databases, creating them, managing, and extracting meaningful data, etc., are all part of a data science professional’s job profile.
Aspiring professionals are focusing on data science training to acquire the essential skills and grasp that a data scientist is expected to have. Data science has continuously evolved as a highly promising and rewarding career option for talented individuals. To become a successful data professional, one must now go beyond the practice of analysing the huge data volumes, data mining and programming. Modern data scientists are expected to be masters of the entire data science life cycle. They have to be dynamic and quick-thinking experts who can understand scenarios and develop strategies that maximize the returns at each stage of the process.
Roles and Responsibilities of a Data Scientist
The role of a data scientist is to analyse business data and generate actionable insights. They primarily use data to find solutions to the problems faced by the organization. There are certain steps to the process that need to be performed by the data scientist. We have outlined them below:
- Probing related to the problem before embarking on the data analytics lifecycle to understand what exactly needs to be done.
- Identifying the data that is needed for analysis.
- Gathering data from diverse sources such as the organization’s own data sources or from public or third-party sources.
- Structuring the compiled data for uniformity, completeness, relevance, and accuracy.
- Once the data is structured, it is analysed through a Machine Learning algorithm or a statistical model.
- Insights are generated and solutions are discovered.
The final task for a data scientist is to turn the insights into reports and presentations to share with the stakeholders for further communication and implementation.
Why Become a Data Scientist?
The data science field is rapidly growing. It is estimated that the market will grow by nearly 28% by 2026. Hence, there are ample high paying opportunities for qualified data science professionals, and the demand will only rise with time. The important thing right now would be to focus on acquiring the right qualifications by undergoing data science training or a data analytics course from a reputed institute.
Where Would You Fit in the Data Science Industry?
A career in data science means that the candidates get to focus on and achieve mastery in one particular dimension of the field. There are different job profiles available for aspirants to join the exciting, growing, and demanding field of data science. Let’s take a look at some of them:
As discussed above, a data scientist’s job profile would involve identifying the problem, probing about it, finding the right data, structuring, analysing and presenting the data driven insights.
A Data Scientist’s job role is to determine what the problem is, what questions need answers, and where to find the data. It also includes mining, cleaning, and presenting the relevant data. The candidates would need to undergo a data science course that imparts essential skills such as programming (SAS, R, Python), storytelling and visualization, statistical and mathematical abilities, knowledge of Hadoop, SQL, and Machine Learning.
A data analyst is the connecting link between the data scientists and business analysts. His/her job is to organize and analyse data to answer the organization’s challenges/questions. A data analyst would convert the technical analysis into clearly understandable business action inputs. Apart from a data analytics course or a certificate in data science that would impart necessary Statistical and mathematical skills, and programming skills (SAS, R, Python), the right candidate should have some experience in data analysis and visualization.
A data engineer would focus on developing, deploying, managing, and optimizing the data setup and sources for an organization. Their role is crucial for data scientists as they act as a support system in transferring and transforming data for questions. A data engineer would be one who has undergone a data science program and acquired skills such as NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages including Java and Scala, and frameworks (Apache Hadoop).
Best Data Science Courses for Industry Aspirants
With the surge in demand for data science professionals, there are some high-quality courses offered by Emeritus, one of the reputed international learning institutes. Founded in 2015, Emeritus is an internationally acclaimed career learning and development institute with over 2000 employees working in offices across Mumbai, New Delhi, Shanghai, Singapore, Palo Alto, Mexico City, New York, Boston, London, and Dubai.
Emeritus India is committed to providing most advanced and futuristic data science skills to individuals, companies, and governments all over the world. The company offers best learning opportunities facilitated by its collaboration with over 50 top-tier universities across the United States, Europe, Latin America, Southeast Asia, India and China. Learners can enrol for a data science course or a data analytics course, short term courses, long-duration data science training, degree programs, boot camps and senior executive programs to acquire new skills and transform their lives, companies, and organizations. We offer a unique model which involves innovative curriculum, state-of-the-art technology, and hands-on instruction from senior faculty, mentors, and coaches. These unique and highly impactful courses benefit over 250,000 students across 55+ countries, each year.
Companies ranging from startups to large enterprises, are all dependent on data science today, and the right course from Emeritus India would provide the skills that are necessary for making a distinguished career in the arena.