In recent years, data science has gained a lot of traction across many different industries. From retail to government to healthcare—businesses and organizations are generating so much raw information these days, but they need the right resources to make something meaningful out of it.

This is where data science comes in. This fast-growing discipline helps companies to analyze raw data, and provides valuable information about consumers, the market, and employees.

What Is Data Science?

Data science is a multidisciplinary blend of data inferences, algorithm development, and technology. It combines technical expertise and programming skills with the knowledge of mathematics and statistics to extract meaningful insights from chunks of raw data. Much of what goes on in the field of data science remains a mystery to the workforce at large, including the skills required, education options, and job opportunities. This article provides an insight into this ever-expanding field and how aspiring students can get a foothold in the sector.

How Is Data Science Used?  

Data science brings together numerous disciplines and areas of expertise to produce a holistic, thorough, and refined approach to raw data. Unlike the structured data in traditional systems, most of the data generated today is either unstructured or semi-structured. Going by current trends, by 2021 most data will be unstructured - which means there will be an even greater need for talented data scientists.  

Since an exorbitant amount of data is generated by various sources, we need advanced analytical software and algorithms for processing, analyzing, and drawing trends and metrics out of the data. Data scientists are able to turn these bits of information into useful predictions that guide company actions.

Common Areas of Data Science Application

Below are the areas where data science is most commonly used:

Social media analytics – Data science has numerous applications across social media. By observing particular keywords, marketers can identify commonly discussed topics through social media interactions. The topics can then be scrutinized across social platforms to classify them.

Predictive analysis – Weather forecasting as a good example of where predictive analysis can be used. Data from radars and satellites is analyzed to build mathematical models, which can forecast weather events. This helps in predicting the occurrence of natural calamities, and even controlling potential damage.

Targeted ads – Using data science, marketers and advertisers analyze endless information (like online search histories) to deliver relevant advertisements in real-time.

Augmented Reality (AR) – The AR system will understand reality and reconstruct it to create its digital twin. It also enables a user to interact with both digital twins and digital data.

Recommendation engines – Certain models track products, articles, or pages that are browsed together, and present relevant recommendations based on this data.

Healthcare imaging – Data science finds use in medical imaging where computers interpret MRIs, X-rays, and other types of images, and then identify patterns in the data to detect things like tumors and organ anomalies. Researchers at Stanford have developed data-driven models to diagnose irregular heart rhythms from ECGs even quicker than a cardiologist. They can even distinguish between images showing benign and malignant lesions.

Role of a Data Scientist

Data scientists are highly valued employees, and for good reason. Their ability to design, implement, and evaluate advanced statistical models and approaches for application are vitally important to addressing key business issues. They build econometric and statistical models for various problems including projections, classification, clustering, pattern analysis, sampling, and simulations.

Data scientists also perform ad hoc data mining and exploratory statistics tasks on large data sets in accordance with business strategies. They prepare reports and presentations that provide insights to inform high-level decision making.

Data scientists research new ways of predicting and modeling end-user behavior. To do this, they investigate data summarization and visualization techniques to convey key applied analytics findings.

Pursuing a Career in Data Science

A distinct lack of skilled data scientists in a world that is increasingly reliant on data for decision making has led to a sharp rise in demand for the role. As a result, data science is now one of the most popular emerging careers in America. Reports indicate that the average remuneration for a data scientist is as high as $91,570 in the US. The average salary package for a data scientist in the US is actually 80% higher than that for all other job postings across the nation, as of May 2019. And thousands of new job openings are posted for data scientists each year.

Typical job titles in the field of data science include:

Learning Data Science

Core skills that will pave the way for career opportunities in data science include:

Programming Languages: Java/Python/R

Python and R are open-source languages and are easy to learn due to their similarity to English. Java is also one of the oldest and most versatile programming languages. Numerous videos and technical blogs on the internet explain the basics of these programming languages for free. A candidate can also enroll in paid classroom sessions to gain a deeper understanding and hands-on experience in coding in these languages.

Applied Mathematics and Statistical Analysis

Data science hopefuls should understand the fundamentals of statistics and probability, as well as basic and advanced machine learning algorithms. For people with intermediate skill sets, knowledge of big data, deep learning, and reinforcement learning is expected.

Working Knowledge of Hadoop and Spark

Apache Hadoop is a software framework that enables the distributed processing of large data sets across several clusters of computers, with the help of simple programming models. Apache Spark works great with unstructured data and has a myriad of functions from simple data loading to analytics and machine learning.

Databases: SQL and NoSQL

Knowledge of either of these databases is beneficial for a career in data science and analytics. SQL is the standard language for dealing with relational databases, which define relationships in the form of tables. SQL programming can be effectively used to insert, search, update, delete database records. NoSQL is a non-relational DMS, that does not require a fixed schema, avoids joins, and is easy to scale. NoSQL databases are used for distributed data stores with humongous data storage needs. They’re used for big data and real-time web apps.

Neural Networks and Machine Learning  

Neural networks strive to recognize underlying relationships within data sets, mimicking a human brain. Machine learning, on the other hand, involves the building of complex mathematical models based on sample data. It’s the study of computer algorithms designed to improve automatically through experience, giving it a predictive nature.

Proficiency in Deep Learning Frameworks

Deep learning has become highly valued in terms of accuracy with large amounts of data. It plays a big role to fill gaps in scenarios that would otherwise be challenging for the human brain. The commonly used tools are open source (available for free) and are based on Machine Learning and Neural Networks.  These include TensorFlow, Keras, and Pytorch.

Creative Mindset and Industry Knowledge

Industry experience will always be looked upon favorably in data science candidates, but perhaps even more important is a creative mindset and exploratory outlook. Data science is a highly dynamic field that’s suited to those who are self-motivated and have a lifelong passion for learning.

Study Options for Data Science

If you’re interested in learning data science via conventional means, several universities across the US offer graduate and undergrad level programs in data science. Some prominent schools like MIT and Harvard also offer short-term internship and certification programs in addition to their regular four-year and two-year courses. Some of these programs come with financial aid, as well as distance learning and part-time learning options.

Online Data Science Courses

Of course, a traditional degree isn’t the only way to get hired as a data scientist. Many individuals working in data science have self-trained themselves through free online resources and distance learning programs as a full-time college program isn’t feasible for them. So if money is a constraint or if you are transitioning from a different field of study, there are many free or subsidized online courses also available. These include:

Coursera – Coursera provides online data science courses through Johns Hopkins University. Their Data Science Specialization and Data-Driven Decision Making programs are quite popular.  

edXData Science Essentials is a course offered by Microsoft and is also a part of their Professional Program Certificate in Data Science, though it can also be taken as a stand-alone course through edX. Students are expected to have beginner knowledge of either R or Python. Subjects covered include statistics and probability, data exploration, visualization, and an introduction to machine learning using the Microsoft Azure framework. Although all of the course material is free, students can pay for a formal certificate on completion.

IBM – IBM provides numerous free online courses through its portal, Cognitive Class (formerly known as Big Data University). The Data Science Fundamentals program covers data science methodology, hands-on applications, and programming in R.  

Dataquest – This is an independent online training portal and is not affiliated with any university. It offers free access to most of its course content, although you can pay for premium services like tutored projects.

Thinkful – If you’re interested in a comprehensive program that’s tailored to your availability, Thinkful offers an online data science bootcamp. The full-time course is an accelerated program, while the part-time format offers more flexibility. In both courses, you’ll get one-on-one mentorship and professional guidance to get you into the field of data science. You’ll learn how to use Python and SQL for analytics and experimentation. Besides machine learning (supervised and unsupervised), you’ll also learn advanced skills in specialized areas of data science. If you want to start a career in data science as soon as possible, this course will get you on a proven path for long-term career growth.

Next Steps

Data science is a promising career option for anyone with a passion for tech and a mind for problem-solving. Your career options and chances to succeed are growing in this field by the day. You’ll never stop learning—and the healthy paycheck will be a nice bonus too.

If you’re looking for guidance on your journey to becoming a data scientist, Thinkful’s team is here for you. We’re dedicated to supporting you while you pursue a career that you’ll love in the field of data science. Schedule a call with our admissions reps to go through your options at a time that works for you to get started.

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