DATA SCIENCE: A WINNING STREAK OR A LOSING ONE?

How many of you have heard of Data Science? Most of you must have, but if not don’t worry, we got you!

Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. This enables business leaders to draw useful informed insights and data scientists can also uncover complex patterns by reviewing the analytical applications.

In layman's terms, a detailed study of a set of data is data science. Collecting, observing, analyzing, interpreting data, and then predicting something on basis of that data is data science. It is used to extract useful insights from important data and make informed decisions.

What do you think lies at the core of Data Science?

An ideal Data Scientist should have an interest in the following stated four fundamentals. Without any particular order of importance, the fundamentals are as follows:

● Business

● Mathematics (including Statistics and probability)

● Computer Science (software/ data engineering, programming)

● Communication (both written and verbal)

Now you must be wondering what are the real-life applications of Data Science. Where in the real world do we need to study data?

● Data Science is used in predictive analytics. Let us take the example of a case of weather forecasting. Data science lets us forecast the weather accurately to some extent by collecting data from satellites, RADARs, ships, and aircraft. This data can also inform us about some natural calamities that may happen in the near future. This helps in taking appropriate precautions and evading the damage as much as possible.

● The precision of the product recommendations has also been boosted with the evolution of data science. Drawing out vast volumes of data i.e., browsing history, purchase history, etc., training models better and effectively showing more precise recommendations, data science has surely grown a lot.

● Data Science also aids in effective decision-making. You must have seen self-driving intelligent cars in some YouTube video. It's so mesmerizing to see a car drive on its own but nothing would be possible without data science. The cars collect data from its surrounding through sensors like RADARs, cameras, and lasers to create a virtual map of the surroundings, and accordingly, its advanced algorithm takes the crucial decisions when to stop, turn, etc.

A whole variety of organizations are driven towards data science due to its wide range of applicability, which includes:

● customer analytics

● fraud detection

● risk management

● stock trading

● targeted advertising

● website personalization

● customer service

● predictive maintenance

● logistics and supply chain management

● image recognition

● speech recognition

● natural language processing

● cybersecurity

● medical diagnosis

What are the different steps involved in the process of Data Science?

The process can be better acquired from online courses or a degree course but here is just a little glimpse of the Data Science process:

1. Framing the problem

2. Collecting the raw data related to the problem

3. Processing the data to analyze

4. Exploring the data

5. Performing In-depth analysis

6. Communicating results of the analysis

What career opportunities does Data science pave a way for?

Some of the jobs that one can explore under Data science are:

● Data Scientist: One of the most famous job opportunities. Data scientists work with complex data and analytics and require strong expertise in Data Analytics, including programming languages like Python and R, Data visualization tools, and other vital skills.

● Data Engineer: A data engineer focuses on massive data and is tasked with optimization the organization’s infrastructure around several Data Analytics processes. Data Engineers require expertise not only in programming and visualization but also have to develop and test solutions according to the requirement.

●Data Analyst: The Data analyst serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions. Data Analysts need to have good expertise in data visualization and programming including Python, R, SQL, and Machine Learning Algorithms.

●Machine Learning Engineer: The Machine Learning Engineer (ML engineer) creates ML algorithms and AI Algorithms to learn and analyze data. It requires an excellent programming base, in-depth knowledge of ML.

● Data Journalist: A data journalist can collect data on a variety of topics to use in their reports. This includes making sure the data sources used are reliable. Data integrity becomes crucial here due to the far and wide viewership of the articles.

●Database Admin: Database administrators ensure databases run efficiently. Even financial information and customer shipping records are stored and secured to create and organize a system. Database administrators also ensure that this data is available only to authorized users.

● Financial Analyst: Financial analysts guide businesses and individuals in decisions about expending money to attain a profit. The performance of bonds, stocks, and other investments are assessed through the data to come to decisive conclusions.

● Business Analyst: Business analysts evaluate past and current business data with the primary goal of improving decision-making processes within organizations. The data is collected, stored, analyzed, and then data science methods are applied to measure the effectiveness of the decision.

● Product Analyst: Product analysts utilize market data to help companies develop marketing strategies for a product by comparing a company's product and industry trends and ensuring that a product is most suitable and profitable.

● Business Intelligence Analyst: A business intelligence analyst reviews data to produce finance and market intelligence reports. These reports help in highlighting patterns and trends that influence a company’s operations in a specific market.

● Market Analyst: A market analyst is responsible for studying market conditions to assess the potential sales of products and services. The basic responsibility is a Market Analyst is to assist companies to determine what products are in high demand, which consumer group will be willing to buy them, and at particular what price.

● Quantitative Analyst: A quantitative analyst, often called a “Quant”, is a specialist who specializes in the application of mathematical and statistical methods to financial and risk management projects. A quantitative analyst develops and implements complex models used by firms so that financial and business decisions about investments and pricing can be made.

●Data Visualization Specialist: Data visualization specialists translate complex statistics and data so that subject matter experts and business users can better understand them. They support companies in making data-informed decisions by presenting the data in innovative and informative ways.

● Functional Analyst: Functional analysts provide advice and fact-based assessments related to technological tools or programs and their ability to solve particular business needs. They are generally specialized in a certain product line or type of technology. These roles require excellent communication skills and analytical abilities.

● Data System Developer: Develop database code to perform specific tasks, such as extracting data for reports, making updates, or deleting data. Modify and upgrade existing databases. Use databases to design business intelligence reports. Ensure new IT and business projects meet database standards and requirements.

Lifecycle of Data Science

Let’s have a brief overview of the lifecycle of Data Science:

Phase 1- Discovery: this is done before the project execution. Extraction of all the data from different sources, understanding various requirements, specifications and ascertaining the budget required, all come into this phase.

Phase 2- Data Exploration: one needs to explore, process, and condition data before modeling the day. Before that, ETLT (Extract, Transform, Load, and Transform) is performed to get the data to the analytical sandbox.

Phase 3- Model Planning: the next step involves determining the methods and techniques that will be used to determine the relationship between the variables.

Phase 4- Model Building: this phase is responsible for developing datasets for training and testing purposes. One needs to analyze various learning techniques like classification, association, and clustering to build the model.

Phase 5- Operationalize: delivering the final reports, briefings, code, and technical documents fall under this phase. This provides a clearer image of the performance and other related constraints.

Phase 6- Communicate Results: in the last phase, one needs to picturize all the findings, communicate to the stakeholders and determine the results of the project.

Investing in Data Science worth the time?

Now, let us see, does Data science deserves the hype or is it just rumored hype.

“The jobs for data science has only grown sexier, More employers than ever are looking to hire data science professionals,” said Andrew Flowers, an economist at Indeed, based in Austin, Texas, and author of the Indeed report.

Since 2012, the Data Science sector has witnessed a 650% hike far outpacing other sectors. According to the US Bureau of Labor Statistics, the job outlook for data scientists and other computer and information research is projected to increase by 15 percent between 2019 and 2029. The employment growth rate is much faster than average for all occupations. A Deloitte research says that by 2022, the income of data scientists will rise to as high as USD 130,176.

What about India? Is it an in-demand job in India? Is there any scope for data scientists in India?

The answer is a big yes. The demand for data scientists in India is at an all-time high, which means generous pay from companies. India Inc is witnessing the rapid digitization of businesses and services, making it the second-largest hub for data science in the world. As per the latest predictions, our country will have more than 11 million job openings in this field by 2026.

Over the years, job opportunities and salaries in data science have boomed in India — a trend that pandemic-driven digital transformations have further accelerated.

Do Data Scientists earn crores of Rupees?

Well, this is not exactly the truth, according to recruitment firm Michael Page’s 2021 India Talent Trends report, data science professionals with 3-10 years of experience get annual salaries in the 25-65 lakh range, on the other hand, those with more experience can command pay packages upwards of 1 crore.

Professionals with over 15 years of experience can get paid up to 1.8 crores. Similarly, the average annual pay hike for data science professionals falls between 20-30% compared with 15-20% for professionals from other backgrounds.

According to Forbes, when you earn a PG certificate, you can expect anywhere from a 13 to 25% boost in your annual salary.

Why is Data Science a buzzword?

Data will control the twenty-first century. Over the next few years, businesses are expected to generate large amounts of data. Estimations say that the total amount of data created, copied, collected, and consumed worldwide will likely exceed 149 zettabytes by 2024, rising from 10 zettabytes in 2010.

Data Science is considered to be a fundamental concept for businesses these days. It is required by all kinds of businesses be it small or large, manufacturing or service, to analyze the trends ongoing in the market and make valuable decisions increasing profitability of the company, thus, reducing losses. About 80% of the firms across the globe are investing a large part of their earnings into creating a skillful data analytics division, thus hiring the smartest of people in the industry domain.

How can you pursue a Data Science course?

There are numerous online training platforms that are currently providing Data Science certification courses in minimalistic fees such as:

● Coursera

● Udemy

● LinkedIn

● Upgrad campus

Some prestigious institutes/ universities in India that provide bachelor and masters courses in Data Science are as follow:

● Loyola College, Chennai

● Christ University, Bangalore

● IIT Madras – Indian Institute of Technology, Chennai

● IIT Guwahati – Indian Institute of Technology, Guwahati

● IIT Indore– Indian Institute of Technology, Indore

● Narsee Monjee Institute of Management Studies, Mumbai

Conclusion-

Data Science undoubtedly is a winning streak with tremendous growing career opportunities with handsome pay. Numerous opportunities are available in India as well as outside India.

In this data-centered world, it is crucial to understand data and that is why we must learn how to read data and understand it by making meaningful judgments, which Data science teaches us how to.

Building a career in Data science would be fruitful if you are ready to put in your hard work and want to play with the data. As technologies grow, so should we.