Category: Data Science

How to Learn Data Science: A Step-by-Step Guide

Data Science is an ever-growing field of technology that has come to the forefront in recent years. It is an exciting and rapidly growing field, offering a range of career opportunities and possibilities. But how can you learn data science? Progressing in data science can seem overwhelming, but with the right steps and dedication, it is possible to become a proficient learner. In this article, we will discuss how to learn data science step-by-step, as well as how much time it takes to learn data science.

Data Science – Step by Step Guide:

Step 1: Develop Foundational Knowledge

What is the fastest way to learn Data Science? Perhaps this question is hovering in your mind. Well, when it comes to quickly learning data science, step one is to develop foundational knowledge. The best way to do this is by taking courses in mathematics and computer science. These topics provide the groundwork for understanding the principles behind data science and how to use them effectively.

To get started, look for online courses that focus on topics like calculus, linear algebra, probability theory, statistics, and machine learning algorithms. It may also be beneficial to learn a programming language such as Python or R which are widely used in data science projects. Additionally, taking courses about databases and big data systems will help you understand how large datasets can be managed effectively.

After building your foundation of knowledge through these courses, it is important to practice what you have learned by implementing projects using real-world datasets.

Step 2: Learn Programming

Data science is a rapidly growing field that requires a number of complex and technical skills. Programming languages such as Python are essential for manipulating, analyzing, and interpreting large datasets. There are many free online resources available that provide beginners with the opportunity to learn data science for free.

The best way to get started with programming is by working on projects specific to data science. These projects will help you develop the necessary coding skills needed in order to analyze datasets and build custom algorithms. Additionally, there are plenty of tutorials available online which allow learners to go at their own pace and pick up core concepts quickly and easily. With a bit of effort, anyone can become proficient in Python or any other language used for data science without spending any money!

Step 3: Acquire Data & Explore It

In this step, you will be gathering all the relevant datasets you need for your project and then familiarizing yourself with them. You may find your datasets from public databases such as Kaggle or other open-source websites, or you may have to collect them from surveys or interviews. Once you have gathered the required datasets, use visualization techniques such as histograms and scatter plots to better understand them. By doing so, you will get an insight into how different variables interact with each other and spot any patterns or anomalies that might exist within the dataset.

Step 4: Apply Machine Learning Algorithms

To successfully apply machine learning algorithms, learners should first understand how the different types work and how to implement them into their projects. Machine Learning algorithms can be broken down into supervised, unsupervised, semi-supervised, and reinforcement learning categories – each with its own set of techniques and approaches to solving problems. Additionally, learners should understand basic programming concepts like classes/objects and functions that are common practice when utilizing ML libraries such as Scikit-learn or TensorFlow.

Step 5: Visualize Data & Report Results

This step requires a deep understanding of the analysis techniques used to collect and interpret the data. Using these techniques, you can create visualizations that clearly show patterns or relationships between different pieces of data. These visuals can be used as evidence when presenting findings from your analysis to stakeholders, colleagues, or clients. It is also important to understand how to write up a report on your findings so that all readers are able to easily understand them, regardless of their level of experience with data science concepts.

Can I learn Data Science on my own?

Data Science is one of the most popular and in-demand skills today. With its vast potential to help businesses make better decisions and improve customer experience, many people are now wondering if they can learn Data Science on their own.

The answer is yes! With the right resources and dedication, anyone can learn Data Science. You can find a variety of online courses, tutorials, and books that will help you understand the fundamentals of Data Science. It’s also important to practice regularly by working on projects related to data analysis or machine learning. Joining online forums or communities is another great way to stay up to date with the latest trends in Data Science and get advice from experienced professionals.

Overall, learning Data Science takes time and effort but it can be done independently with commitment and perseverance.

About the Author

Sonali Trehon

Blog Author

Sonali is an accomplished Author, Content Writer, Copywriter, and Ghostwriter, known for her ability to create engaging and captivating communications. With over 11 years of experience, she has developed a diverse industry background in Education, Travel, Retail, Events, and Fashion . Quality over quantity is what she firmly believes in. At UniKakhsa, she leads a team of talented technical writers, dedicated to delivering valuable content to readers through our blogs.

Want To Be A Data Scientist? Here Are The Top Skills You Need

It is no secret that data scientists have become one of the most sought-after jobs in the world over the past few years. When companies realized how crucial data is to their businesses, the demand for professionals in the Data Science field started growing in every sector. Today, data science is considered a core component of analytics, mining, and extraction, natural language processing, machine learning, artificial intelligence, etc.

Data Scientist – what is it and who does it?

In recent years, the term “data science” has become increasingly popular. But what is data science? In simple terms, data science is the process of extracting insights from data. This can be done using a variety of methods, including machine learning, statistical analysis, and visualization.

A data scientist is a professional responsible for collecting, analyzing, and interpreting large data sets to identify patterns and trends. They utilize their findings to help organizations make better business decisions. Data scientists typically have a strong background in computer science and mathematics. However, with the right training, anyone can learn the skills necessary to become a data scientist.

Data scientists are in high demand these days, as more and more companies are looking to make data-driven decisions. But what does it take to be a data scientist? In this article, we will explore the top skills required to become a data scientist.

The skills you need:

1. Technical skills: programming, statistics, machine learning

Data scientists must have strong technical skills in programming, statistics, and machine learning as they are responsible for collecting, analyzing, and interpreting large data sets
Programming skills are critical for data scientists. They need to be able to write code that can clean, manipulate, and analyze data.
Statistical skills are important for understanding how to model data and make predictions.
Machine learning skills are necessary for building algorithms that can learn from data and make predictions on new data sets.

2. Business skills: domain knowledge, communication, teamwork
Data scientists must have strong business skills to be successful in this role as the major key responsibility involves analyzing data to help companies make better business decisions.

Domain knowledge is indispensable for data scientists. They need to understand the business they’re working in and the data they’re analyzing. Without this knowledge, they won’t be able to effectively solve problems or communicate their findings.
Communication is another key skill for data scientists. They need to be able to clearly explain their analysis and findings to non-technical colleagues. This requires both strong writing and oral communication skills.
Finally, data scientists must be good team players. They often work on cross-functional teams and need to be able to collaborate effectively with others.

3. Soft skills: critical thinking, problem solving, creativity

There are many soft skills required to become a data scientist, but critical thinking, problem-solving, and creativity are some of the most important.

The ability to think critically is a key skill for data scientists because they need to be able to analyze data and understand what it means.
Data scientists need to have strong problem-solving skills to be able to identify problems. In addition, they must be able to think creatively about how to solve problems.
They should be able to come up with new ideas and ways of looking at things, as well as find creative solutions.

How to acquire these skills?

As the world becomes increasingly driven by data, those with the skills to analyze and make sense of it are in high demand. If you’re looking to start or further your career in data science, the four-month part-time course from Unikaksha is the perfect way to get started.

The course is 100% live-conducted online, so you can study at your own pace and fit it around your other commitments. You’ll learn key data science concepts and how to apply them in real-world scenarios. By the end of the course, you’ll be confident using industry-standard tools and techniques to manipulate data and draw insights from it.

Data scientists typically have a strong background in computer science and mathematics. However, with our Data Science Program, anyone can learn the skills necessary to become a data scientist.

Final Thoughts

Data science is a rapidly growing field with immense potential. It is not all about deep learning and artificial general intelligence when it comes to data science. It can be used in several fields. Additionally, as discussed above, soft skills like communication, storytelling, and problem-solving are also necessary. So, if you want to become a data scientist you need to master these skills. With these skills, you will be well on your way to a successful career as a data scientist.

About the Author

Sonali Trehon

Blog Author

Unlock Your Data-Driven Future with a Course in Data Science

Everyone in the tech industry seems to be raving about Data Science, which has been deemed the “hottest job of the 21st century“. Using data science to extract actionable insights from a company’s data is revolutionizing the way businesses use their data, improving efficiency and increasing profits.

Why is this field particularly relevant today? This can be attributed to the staggering growth of data. As we consume and produce large amounts of data every day, businesses and industries are heavily dependent on data. Essentially, data science focuses on collecting, organizing, and analyzing data to figure out hidden patterns and trends that can solve problems previously unknown, making it one of today’s “in-demand jobs.” A professional with the capability of handling, managing, analyzing, and understanding data trends is crucial for businesses and industries. Because many people aspire to become data scientists, it is important to know the pros of becoming one.

Let’s see why you should consider upskilling yourself with a data science course from Unikaksha

Its The Hottest Job Of The 21st Century
In this new age, data drives industries and businesses forward. Insights derived from hidden patterns and trends in data can transform the operations of a business leading to being more efficient and profitable. In order to gain a competitive edge, companies are looking for data scientists who are able to process data and generate useful information.

The Vast Availability Of Vacancies
A high level of demand exists for this profile. Thus, prospective job seekers have a variety of options based on their industry of choice. The healthcare, banking, financial services, and FMCG industries, governments, education, and entertainment industries are all seeking data scientists to improve their business processes. Companies are only now realizing the importance of data and thus creating the need for more data scientists across industries.

A Highly Successful Career
The demand for data scientists is high, but there are only a handful of people with the right skills. As a result, it has become one of the most rewarding professions in the market today. Businesses are paying high packages to hire data scientists because of a lack of supply in the field.

It’s All About Data Science
There is a place for data science in almost every industry. E-commerce, retail, entertainment, education, banking, and healthcare are just some of the industries where it is widely used! The field of data science is therefore very versatile.

If all of this appeals to you, then Unikaksha’s data science course fits the bill. This up-and-coming industry will dominate the future, which is why Unikaksha brings to you a rigorous 4 month part-time course in data science. Make sure you are ready for the future by enrolling now!

About the Author

Sonali Trehon

Blog Author

Exploring the Wonders of Data Analytics

The digital world of today relies heavily on data analytics. Despite hearing it often, you might not fully comprehend what it means. Data analytics is rapidly becoming the go-to tool for businesses and individuals alike. By unlocking the wonders of data analytics, businesses can gain a competitive edge, make informed decisions, and pave the way for growth and success. It is used to make decisions based on analyzing data and uncovering underlying patterns, connections, and correlations between data points. A data analyst interprets both large amounts of data, called “big data,” as well as smaller amounts such as company records.

The question is, what exactly is data analytics, and what is it like to work in it? Let’s take a closer look at this industry of Data Analytics.

Data Analytics: What does it mean?

It is a discipline that combines data science, statistics, and machine learning to uncover insights from large sets of structured and unstructured data. By understanding how to extract knowledge from complex datasets, businesses can gain valuable insights into customer behavior, market trends, operational efficiency, and more.

How is it done?

In practical terms, Data Analytics involves collecting huge volumes of raw data from multiple sources such as surveys, websites or existing databases. This information then needs to be processed using sophisticated algorithms so that it can be stored and queried with ease. Once this process is complete the results are then used to generate meaningful reports which help organizations make better decisions about their strategies and operations.

Wonders of Data Analytics

Data analytics is quickly becoming an integral part of the business world and its importance can no longer be ignored. It is used in almost every industry, from banking and healthcare to retail and entertainment.

The role of data analytics in today’s digital economy is considered as it helps organizations make informed decisions about their products or services, improve operational efficiency and enhance customer experience.

Organizations are now relying on data analytics to identify potential customers, spot trends in consumer behavior, measure marketing ROI (return on investment), predict customer churn, and optimize pricing strategies; this list goes on.

Common Types of Data Analytics

The first type of data analytics is descriptive or diagnostic analysis. This type focuses on understanding what has happened in the past by extracting relevant information from existing datasets, such as customer purchase history or web traffic statistics. Descriptive analysis helps businesses identify patterns and draw conclusions about their customers’ behaviors.

The second type is predictive analysis, which uses past data to make predictions about future outcomes. Predictive models can be used for marketing campaigns or risk management purposes, allowing businesses to anticipate customer needs and optimize operations accordingly.

Navigating a Career in Data Analytics

A tremendous amount of data is produced daily, and this quantity will surely increase in the upcoming years. The internet of things and data collection are integral parts of today’s digital world, which makes data analytics a valuable career path. As an industry, data science and analytics jobs are projected to grow by 26% by 2026. It is essential for companies to hire business professionals who are able to make use of their collected data. Data analysts’ salaries have also become more competitive as a result of high demand. Furthermore, data analysts collaborate with top management.

So If you have an eye for detail, strong problem-solving skills, and excellent communication abilities and are thinking about switching careers or are interested in learning more about data analytics, join our free Guru class on 10th December 2022 to understand its fundamentals and gain insights into utilizing data analysis to your advantage.

About the Author

Sonali Trehon

Blog Author

Tech-ready in just 180 days… Let’s see how

Since the demand for tech skills is so high, many people wonder if they’re smart enough or tech ready to pursue a career in web development, design, or data analysis. The answer to this question really depends on your technical skills and abilities. Therefore, the question is, are you Tech ready with the current technical skills? Are you ready for the dynamic tech industry? Are you looking for a well-paying job in the tech industry?

This is where Unikaksha comes in. The demand for tech skills is growing rapidly and is expected to do so for a very long time. We’ve designed Techfit courses in data science, java, and full-stack web development to take you from new to job-ready in as little as 9 months, and send you off on a path to more freedom and higher earning potential. But how? Let’s see.

How do we do this?

Flexible learning options: You can enrol in a class at your convenience during the week or on weekends.
Learn from the best in the industry: Our trainers are industry experts, IIT alumni, and FAANG developers with 20+ years of industry experience. As you progress through your course, they will guide you, help structure your learning, and evaluate your performance.
100% placement guarantee: Our Java Full Stack Developer Program, Full Stack Web Developer Program, and Data Science with Python Program come with job placement guarantees. You can trust us to assist you in finding the job in technology you want or you can receive your money back if we fail to do so.
Curriculum relevant to the tech industry: We offer a curriculum designed by leading industry experts to help you succeed in your professional career. It’s our goal to keep you and us at the forefront of technology through continuous innovation.

What should be your next step t become tech ready?

Find out if java, data science, or full stack web development is right for you by talking to our program counsellors.
Choose the learning format and outcome that best meets your needs. Depending on your needs, you can enroll in placement classes, learning classes, or self-paced courses.
Attend one of our FREE Guru Classes with industry experts. Let’s find out what Unikaksha Guru classes are about. Guru Classes are live, 1.5 hours immersion workshops delivered by industry experts, company CTOs, Product Directors, Project Leads, and Senior Developers from reputed companies. These classes intend to provide real-time exposure to on-demand technologies and techniques used by experts to solve a particular problem.
Finally, become a qualified java developer, full stack web developer, or data scientist in just 9 months—complete with a job guarantee.

Learn the skills that employers are looking for and get started on your new career today. So if you’re looking for the best way to get #skillfit and create your tech career, look no further!

About the Author

Sonali Trehon

Blog Author

Top 4 In-Demand TECH Skills That Are Dominating The Tech-World

Adapting to cutting-edge technology is becoming increasingly important in the highly competitive world of today. In today’s rapidly advancing tech landscape, staying competitive requires honing in-demand tech skills that are driving innovation and propelling professionals to new heights of success. The COVID-19 outbreak in 2020 resulted in a lot of changes across a wide range of industries worldwide. While the world came to a halt, the progress of technology was the only thing that continued. It comes as no surprise that it’s one of the most sought-after careers today and in the future. With technologies rapidly evolving, new career opportunities are being created. Being proficient in new-age technology can give you the edge you need to succeed.

With Industry 4.0 comes a new set of tech skills that you will need to succeed.

Let’s take a look at the top 4 tech skills:

1. Data Science and Analytics

With more and more businesses collecting data, the demand for people who can analyze it and extract valuable insights is only going to grow. If you’re good with numbers and have strong problem-solving skills, consider a career in data science or analytics.

2. Programming

The foundation of any data science application is programming. Compared to other industries in the IT sector, the developer job market is growing faster. The number of developer jobs is expected to grow by 21% by 2028 while the number of application developer jobs is expected to grow by 28%. Over the next few years, the number of programming jobs will grow to a whopping 28 million, globally. As a result of the growing demand for computer software and mobile phone apps, this industry is expanding rapidly.

Languages such as Python, JAVA, and C+ are among the most popular programming languages. It is expected that the demand for developers will rise as mobile apps become more popular.

3. Full Stack Development

The demand for full-stack developers has increased exponentially, as companies seek to create more efficient and agile development teams. As full stack developers are able to work across the entire spectrum of web development, from front-end design and user experience to back-end server administration, this versatility makes them an indispensable asset to any organization looking to stay ahead of the curve in today’s competitive market. The ability to work across the entire stack also gives full-stack developers a unique perspective on how web applications are built and deployed. This deep understanding of the inner workings of web applications is extremely valuable in troubleshooting and debugging code.

So if you’re looking to get ahead in your career, then learning full stack development skills is a must

4. AI, Machine Learning & IoT

The fourth industrial revolution is the current trend of automation and data exchange in manufacturing technologies. It includes a number of new technologies such as the internet of things (IoT), artificial intelligence, machine learning, and 3D printing.

Artificial intelligence & Machine Learning is becoming increasingly important in various industries, from retail to healthcare. Essentially, AI-based innovations aim to eliminate repetitive programming by enabling autonomous machine behaviors. As more and more organizations have remote workers, AI and machine learning are becoming increasingly important. If you’re interested in working with cutting-edge technology, consider a career in AI, ML, and/or IoT

Get SkillFit with Unikaksha’s Tech-Edge

Since we are increasingly moving towards a digital future, it is imperative that IT professionals, such as data scientists, software engineers, UI developers, system architects, system engineers, and full stack developers, will be in great demand in the future. This will mean higher wages, higher compensation, and investments by organizations to hire qualified and certified IT professionals.

Unikaksha, one of the leading Ed-Tech training platforms, offers a wide variety of courses that can help you stay ahead of the curve. In addition to skills, having a certification from Unikaksha will prove to be an added advantage. Our courses are designed by industry experts and cover a wide range of topics, from basic coding to more advanced concepts. Whether you’re just starting out or are looking to brush up on your skills, Unikaksha has a course for you. Click here to find out more:

https://www.unikaksha.com/

About the Author

Sonali Trehon

Blog Author

5 Proven Ways to Grow your Business with Data Science

Data science is used in various sectors and has extensive applications. Every industry, starting from tiny enterprises to bigger ones, relies on data science for fulfilling its objectives, including deciphering hidden patterns, identifying possibilities, and expanding its business operations. By delivering superior services to end-users, data science plays a critical part in making businesses more profitable, expandable, and reachable.

As per a report published by the US Bureau of Labor Statistics, the number of jobs in the data science field will grow by approximately 28% by 2026. Grow your business with data science technologies are emerging in every business domain and area. Data has become extremely important in today’s world of business. It implies organizations operating internationally dealing with volumes of data to make choices and expand their businesses in the direction that data suggests. In today’s highly competitive environment, making the most of data, uncovering insights, and comprehending hidden patterns are critical for successful business growth.

Grow your Business with Data Science in the following ways :

1. Making Better Decisions with Business Intelligence:

Business intelligence used to be more detailed and static. It has, however, evolved into a more dynamic profession with the advent of data science. Thanks to data science, company intelligence may now include a wide variety of business processes

Businesses require a data scientist to evaluate and draw valuable insights from the data as the volume of data grows exponentially. Data science organizations will be able to assess data on a wide scale and develop essential decision-making methods with significant insights. The review and appraisal of many aspects involved in decision-making is a necessary part of the process. There are four steps to make a decision:

Recognizing the context and nature of the problem must be solved.
Investigate and measure the data’s quality.
Proper implementation of problem-solving algorithms and tools.
Results turned into a broader understanding for teams via storytelling.
Businesses will require data science to help them make decisions using this strategy.

2. Improving Products :

Companies must be able to get clients to buy their items. They must create products that meet their clients’ needs and give them a guarantee of satisfaction. As a result, industries require data to develop their goods to their full potential.

The procedure entails analyzing client feedback to determine which goods are the best match. This analysis is carried out using Data Science’s powerful analytical techniques. In addition, companies use current market trends to create items for the general public. These market trends give businesses information about the product’s current demand. As data grows, industries can deploy newer items and varied and inventive tactics.

3. Automating Recruitment Processes :

Data science has been essential in introducing automation to a variety of businesses. Resume screening is a tedious and repetitious process for companies. Some corporations might get hundreds of applicants for a single post. Data science is used by businesses to go through all of these resumes and choose the best applicant.

Image recognition and other data science tools can turn visual information from a resume into a digital version. The data is then processed using various analytical methods like clustering and classification to find the best applicant for the position.

4. Using Predictive Analytics to Forecast Outcomes:

The most crucial aspect of the company is predictive data analytics. Companies’ capacity to deal with various data has grown due to enhanced prediction tools and technology. Predictive analytics is the statistical study of data that employs multiple machine learning algorithms to predict future outcomes based on previous data. There are a variety of predictive analytics technologies available, including IBM SPSS, SAS, SAP HANA, and others.

Customer segmentation, sales forecasting, risk assessment, and market analysis are predictive analytics applications in the business world. Businesses may get an advantage over their competitors by using predictive data analytics to forecast future occurrences and take appropriate action in response

5. Efficient Business Management :

Businesses now have access to any data to get insights through data analysis. Data science platforms uncover hidden patterns in data and aid in creating evaluations and forecasts of occurrences. Businesses may govern themselves more effectively with data science. Large corporations and small enterprises may benefit from data science to expand their volume.

A data scientist plays a pivotal role in different organizations assisting in analyzing a company’s health. Companies can forecast the success rate of their strategy using data science. Data scientists are responsible for converting unprocessed data into processed data—this aid in evaluating the company’s success and the product’s health.

Data Science identifies critical parameters for assessing company success. Based on this information, the company may take crucial measures to quantify and analyze its performance and take appropriate management action. Businesses may also support leadership growth by using data science to track their performance, success rate, and other vital indicators. Industries may assess what is best for their employees using workforce analytics.

Final Thoughts

We live in a digital world, and having data gives a company a strategy for making more money. Organizations that wish to achieve significant commercial development will need to invest in data science in a few years. Regardless of the sector, big data is becoming an integral component of doing business. Furthermore, big data resonate with government and public-sector entities, which is a positive indication for those businesses based worldwide since it will help increase public-private collaboration in various areas.

In today’s commercial world, data science plays a critical role. Business intelligence, product improvement, company management skills, and predictive analytics are some examples of how data science is employed. In data-driven mindsets, it allows for more flexibility and innovative approaches. Data science may help businesses achieve maximum earnings and reach by implementing these five proven professional ways for business growth.

Did you like the article? Feel free to share! Learn in-Demand tech skills with 100+ Courses and Certifications with multilingual support, 24×7 doubt clearing sessions, and regular assessments to track progress via a distinct Social media-like experience (FB) at UniKaksha.

About the Author

Sonali Trehon

Blog Author

Is Data Science a Best Career Choice in 2022 and beyond?

In 2012, Harvard Business Review predicted Data Science would be the most promising career of the 21st century. Many, taking a cue, started upskilling themselves and successfully transitioned into a data scientist or a data science analyst role in some of the most prestigious global organizations. Beginning 2016, ‘Data Scientist’ consistently ranked number one on the US job market until it recently lost its sheen in 2020. But even today, it is ranked third on the list of the most coveted jobs in the US. It shows how despite the Covid-19 slowdown, the demand for data scientists is still pretty high.

But many would argue that losing the top position to front-end engineers and Java developers reflects that data science is no longer the career it once was. It is exactly what we would explore through this post; is data science the best career choice in 2022 and beyond?

What is Data Science?

In simple words, Data Science involves studying and using data to make informed business decisions and create new products and services. And the people responsible for executing the process are data scientists and data science analysts.

What Do Data Scientists and Data Science Analysts Do?

Data Scientists and Data Science Analysts are responsible for analyzing past user data, predicting future user behavior, and then presenting these insights to the management to help them make informed business decisions. The job typically involves creating statistical and economic models and working with advanced machine learning tools to summarize and visualize data.

Data Scientist vs. Data Science Analyst

So what makes a data scientist different from a data science analyst ?

A data scientist does all that a data science analyst can do, plus much more. Data scientists have more experience working in the data science field for diverse domains, regardless of whether they have data science degrees. They can use their expertise to identify issues and formulate end-to-end solutions. Not only are they experts at empirical and computational skills, but they also possess the insight necessary to identify problems that require the application of data science to make informed business decisions.

But despite the difference, both a data scientist and a data science analyst play a significant role in modern-day organizations. The following section will discuss factors indicating why data science professionals will not be going out of work soon and why data science is still a good career choice in 2022 and beyond.

Why is Data Science the best Career Choice in 2022 and beyond?

Let’s explore some factors indicating why data science is still a good career choice in 2022 and beyond.

1. High Demand

Today, data is, undoubtedly, the most valuable of all commodities, perishable or non-perishable. Be it Google, Amazon, Facebook (now Meta), all leading organizations use data to enhance customer experience, maximize profits, and achieve strategic goals. The problem is, data exists in various formats and structured or unstructured forms across multiple locations. As such, companies have and will always need data science professionals who can extract data, structure and analyze it, draw meaningful insights, and present it for informed business decision-making.

Data science jobs have grown by a phenomenal 650% since 2012. Glassdoor reported 1,700 jobs in 2016 were posted with data science as the primary role. The same stood at 4,500 in 2018 and 6,500 in 2020. In 2019, data science was ranked by LinkedIn as the most promising job, with an approximately 60% rise in job openings across the US alone. Even the Covid-19 crisis could not slow down the demand for data science professionals, with 42% of data science organizations suffering no impact and approximately 8% growing in size.

2. Short Supply

When people started upskilling themselves for data science jobs from 2012 onwards, it was mainly through books as not much information was available on the internet, and advanced programming languages did not exist. Neither did the traditional educational system offer specialized courses or data science degrees to equip those who wanted to make a career in data science. Till today, the data science field remains relatively new and unexplored. According to some estimates, there is currently a shortage of 1,50,000 – 2,00,000 hardcore data science professionals worldwide. So, if ever there was a right time to take up data science as a career, it is now.

3. Future Growth Potential

The data science field is flush with opportunities, with the demand consistently topping the supply. According to a report published by the US Bureau of Labor Statistics, the number of jobs in the data science field will grow by approximately 28% by 2026, translating into nearly 11.5 million new data science jobs. Even the Future of Work Report 2020, prepared by The World Economic Forum, has predicted that the job with the highest growth and demand by 2025 would be that of a data scientist.

4. Fat Pay Packages

Growth potential and opportunities aside, the amount of money a career choice translates into forms an important consideration for pursuing it. And data science jobs do translate into thriving pay packages. According to Glassdoor, the median salary for the US workforce stands at $49,800, while the median salary for data science professionals is $1,08,000, more than double the national average. According to the 2021 India Talent Trends report submitted by recruitment firm Michael Page, data science professionals with experience ranging between 3-10 years stand to earn anything between 25-65 lakhs per annum. Those who carry more experience command more than a crore.

5. Availability of Exclusive Courses and Data Science Degrees

The role of data science professionals is evolving every day and requires expert domain knowledge. Many professional associations acknowledge this and want to introduce certification and licensure requirements for data science professionals. Fortunately, unlike a few years ago, several technical institutions have today included data science in their undergraduate curriculum to equip future data science professionals with the requisite knowledge, skills, and universally-recognized data science degrees

Final Thoughts

Emerging technologies like Quantum and Cloud computing, Blockchain, AI, and Machine Learning have changed the dynamics of the global workforce, including data science professionals. However, the exponential growth in demand, future potential in the data science field, and the surge in the number of institutions offering specialized data science degrees are reasons enough to suggest data science professionals are not going anywhere for a long time.

So, if you are still wondering if data science is a good career choice in 2022 and beyond, the answer is a resounding YES.

About the Author

Sonali Trehon

Blog Author

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