Before we delve deeper into the subject of this post, let’s understand what data science is.

With data at its core, data science works involve multiple disciplines that include technology, algorithm development, and data inference. A data scientist is someone who works with big data extensively utilizing his/her expertise in multiple disciplines and analyzes it to generate business value. And data scientists are often assumed to perform multiple roles – from data miner, data analyst, and software engineer to manager, business communicator, and a key person in any data-driven business that helps the management in decision-making.

However, the responsibilities of a data scientist always understood easily and are often used to describe a broad range of data-related work. If you’re planning to pursue a data scientist career, it’s important that you develop a clear understanding of the working process of a data scientist.

The working process of a data scientist

Though the working methods of data scientists may vary a bit based on their approaches and project goals, they generally follow these steps.

• Acquisition, collection, as well as, storage of data
• Goal identification
• Access, absorb, and integrate data
• Data munging/wrangling (processing and cleaning)
• EDA (exploratory data analysis)
• Choosing algorithms and one or multiple potential models
• Applying data science techniques and methods (artificial intelligence, machine learning, statistical modeling etc)
• Validation and tuning of results
• Presenting and/or communicating final results

Based on the final results, business stakeholders make business decisions and/or implement changes.

Now, let’s have a look at the typical deliverables and goals accomplished by a data scientist using the above process.

• Recommendations (think Netflix and Amazon recommendations)
• Prediction of a value based on provided inputs
• Grouping and pattern detection
• Actionable insights
• Recognition of text, image, audio, video etc
• Segmentation (think demographic-based marketing)
• Forecasts (think revenue and sales)
• Automated decision-making and processes

Each of the above is dedicated toward solving a certain problem and/or addressing a certain goal. While these may not seem like serious issues initially, in reality, these are the pillars of the success of any data-driven business.

By reading till now, if you feel interested to kick-start your data science career and want to take a relatively affordable and quicker pathway, the popular data science bootcamp in Silicon Valley offered by Magnimind Academy is what you should opt for.

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To learn more about data science, click here and read our another article.

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1- Major reasons to become a machine learning professional

Here’re some major forecasts about machine learning’s future.

1.1- Enhanced personalization

Personalized recommendations can entice users to complete certain actions. With the help of machine learning personalization algorithms, the information in a data can be synthesized to make appropriate conclusions like a user’s interests. For instance, on an e-commerce website, it can be deduced from a user’s browsing activity that he/she is looking for a garden chair.

1.2- Better cognitive services

Cognitive services offered by machine learning professionals all developers to incorporate intelligent capabilities into their applications. Developer can empower those applications to perform various duties including speech detection, vision recognition, speech understanding. As machine learning is evolving continuously, we can expect to see the emergence of highly intelligent applications which can increasingly see, speak, hear, and even reason with surroundings.

1.3- Fraud prevention

Machine learning professionals can analyze transactions of an e-commerce website and the machine will pick out the fraud unlike rules-based, traditional system. In the future, we’re likely to witness machine learning technology becoming more sophisticated and the machines will modify, through self-learning, to prevent fraud.

1.4- Better use of quantum computing

The machine learning spectrum can be transformed through implementation of quantum machine learning algorithms. If machine learning can be integrated into quantum computers, it could result in faster processing of data that could dramatically accelerate the ability of synthesizing information and derive insights – what the future probably holds for us.

Though many people consider machine learning to be in its nascent stage, its future is clearly bright and so is the future of machine learning professionals.

2- Key career opportunities as a machine learning professional

Let’s have a look the major job opportunities as a machine learning professional.

2.1- Data scientist

Professionals, who extract meaning from a massive amount of data and analyze and interpret it.

2.2- Machine learning engineer

Programmers, who develop the machines and systems that can learn and apply the acquired knowledge without requiring any specific direction or lead.

2.3- Deep learning engineer

These professionals specialize in developing tasks related to AI with the help of deep learning platforms.

3- Roadmap to become a machine learning professional

As you’re aware of the reach of machine learning, let’s discuss how you can become a machine learning professional. Though this field involves a broad range of skill sets, there’re plenty of resources like Magnimind Academy that would help you master all these skills. They offer some valuable machine learning mini bootcamps for students with different skill sets. And if you’re interested in becoming a data scientist, you can always join their full-fledged data science bootcamp in Silicon Valley to move toward that direction.

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To learn more about machine learning, click here and read our another article.

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First of all, data science is often described as a multidisciplinary field which uses scientific processes, methods, systems, and algorithms to derive insights and knowledge from data. The emergence of big data has promoted the development of new algorithms, systems, and computing paradigms. Data science as a field essentially uses the most powerful hardware, most powerful programming system, and algorithms to obtain the solution of problems.

When it comes to identifying the most powerful idea in data science, we can say it depends on patterns and the way you want to use them. And which one of the patterns is useful to you, depends on the goals you’re trying to accomplish.

Though the most fundamental definition of data science is it’s a field that involves capturing, storing, organizing, and analyzing huge amounts of data, it all boils down to identifying patterns and drawing conclusions that can help either to identify the solution to a present business problem or to predict future scopes. And when it comes to identifying patterns, probably the best idea is to split a dataset. Then having the analysts focus on one part, come up with their insights derived from that part, and finally using the other part of the dataset to check their conclusions.

It’s important to understand that in recent years, the field of data science has eventually become less about the data and more about different types of tools and technologies that are being used to interact with it. High-end solutions like artificial intelligence, machine learning together with robust and advanced analytics tools now make it possible not only to process and comprehend huge amounts of data but at unprecedented speeds.

If reading till now and learning about the most important idea of data science make you interested in the field, let’s have a quick discussion on the things that are critical to start your journey. Some of the obvious subjects include programming, mathematics, descriptive statistics, linear algebra, and machine learning. There’re lots of online courses offered by reputable institutes that can help you gain a robust understanding of all these subjects. Then there’re data science master’s programs, along with certificate courses, which can help you gain more advanced skills in the field.

Keeping the key goal of data science and the present situation of the tech landscape, it won’t be difficult to say that the future of data science should become more expansive than ever – as the field touches almost every enterprise-level process. And we can expect to see this progress becoming more expedited with the help of automation, machine learning, and more advanced and efficient solutions.

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To learn more about data science, click here and read our another article.

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1- What is a data scientist?

A data scientist is a trained individual who can accumulate, organize, and analyze data, thus helping businesses from every walk of industry make informed decisions. These high-tech breeds work extensively with massive amounts of structured as well as unstructured data to derive valuable insights in order to meet specific business goals and needs. Essentially, data scientists wear multiple hats. They’re part computer scientist, part mathematician, part analyst, and part trend-spotter, apart from having some critical non-technical skills as well. We’ll delve deeper into this later, but first let’s have a look at the common industries that are being benefitted by data scientists.

2- Industries able to leverage the power of data science

Each industry comes with its own big data profile that can be analyzed by a data scientist. Here’re some of the common industries that can leverage big data.

• Government: Here, security and compliance are two prime concerns for data scientist They help governments to monitor overall performance, support constituents, and develop decisions. Data science training is appropriate for these.
• Business: In today’s competitive business landscape, it’s simply impossible for a business to thrive without leveraging the power of big data. From customer loyalty to production errors to inventory management to efficiency improvement – analysis of business data is needed to excel.
• Healthcare: Today, healthcare facilities are heavily dependent on electronic medical records with assurance of adherence to security and compliance. Here, data scientists can aid in improving health services by analyzing big data.
• Telecommunication: Every electronic device gathers data and it needs to be stored, managed, interpreted and analyzed to improve product and/or service quality, eliminate bugs, and enhance customer experience.
• Science: Though data has always been a key area of interest to scientists, emergence of data science empowers these professionals to gather, maintain, and analyze data through experiments.
• Finance: For a functioning business in the finance domain, it’s imperative to analyze massive amount of data on different kinds of transactions and accounts. Like the government sector, maintaining security and compliance of sensitive data is one of the key responsibilities of a data scientist working in finance and related companies.

Apart from these, other notable industries, which are on the constant look out for data scientists, include social networking, ecommerce, smart appliances, and utility providers, among others.

3- Key responsibilities of a data scientist

Though the key responsibilities of a data scientist depend on the project he/she is working on, it can be said that all of them are based on big data or complicated inputs. And all these responsibilities need a deep curiosity in order to be performed accurately. Let’s have a look at the common responsibilities of a data scientist, regardless of the nature and volume of the business.

• Analytics: Analytical roles are imperative to data scientists where he/she designs, implements, and assesses advanced statistical models as well as approaches that are related to the business’s most critical issues. Here, statistical and econometric models are developed encompassing different problems related to pattern analysis, clustering, simulations, projections, sampling etc.
• Strategy: A data scientist helps in developing business strategies to form new approaches by analyzing customer trends and behaviors etc – which are essential for product performance optimization and increasing revenue. Here, advanced statistical techniques are implemented to gain actionable insights. Sometimes, unstructured datasets are also analyzed by the data science professional to create manageable analytical processes.

Apart from these two, a data scientist has to stay updated about relevant industry’s trends continually to provide useful recommendations to the business. Value-based programs and strategic initiatives are two of the key areas that are such a professional focuses upon. It’s important to understand that a data scientist’s role has to be collaborative. It means in order to solve complex business issues, he/she has to closely work with other teams like the IT department, product managers, data engineers, data analytics team etc.

4- Future scope for data scientists

Now that you know why these professionals are in massive demand, it’s important to see whether there exist an adequate number of data scientists. The truth is, the number of these professionals is just a handful, while the demand for them seems to be increasing by the day. As businesses lean more and more toward machine learning and artificial intelligence, there’ll be more jobs than available experts to fill them, and perhaps this is the reason why data science has become one of the fastest growing tech employment fields today.

5- How to become a data scientist?

To begin with, you need to have a robust acumen of sophisticated visualization and adequate knowledge of statistical techniques that are used to derive forward-looking insights. So, what are the critical skills and attributes of a data scientist? Let’s have a look.

• Coding: You should know how to write codes and should be comfortable dealing with an array of programming tasks. These programming skills should encircle different aspects – from handling large volumes of data, dealing with real-time data to managing unstructured data, managing cloud computing and working with statistical models – everything should be taken care of. Python is the coding language typically used in data science roles, together with Perl, Java, C/C++ etc.
• Critical thinking: It’s one of those skills that would set you apart from the pack. Critical thinking enables data scientists to implement objective analysis of facts on a given problem or fact before they can render judgments or formulate opinions. A business decision or problem has to be understood clearly before a model, which is critical to resolving the issue or supporting the decision, can be developed. It’s all about evaluating a situation or problem from multiple points of view, and not about looking at things with the eyes wide open.
• Mathematics: If you don’t like or aren’t proficient in mathematics, probably becoming a data scientist shouldn’t be your career goal. You’ve already learned that massive amounts of data are what you will have to work with in this field. Thus, it’s imperative for you to leverage deep knowledge in mathematics to develop statistically relevant, complex operational or financial models that are capable of shifting or developing key business strategies.
• AI and machine learning: Adequate knowledge of high-end techniques like reinforcement learning, neural networks, supervised machine learning, natural language processing, adversarial learning etc are a must for a data scientist. As huge volumes of data are being constantly collected, these cutting-edge technologies help a data science professional to make the data speak what’s needed by itself.
• Communication: Regardless of how advanced a technology is, there’s always some integration between data, applications, systems and humans. A good data scientist should be able to communicate properly with different people using data as a major attribute. Type of communication can vary to a great extent – from the challenges with data quality, to technology and computational resources to distill complex technical information into accurate, easy-to-understand format – everything should be properly communicated to help business leaders direct business processes.

6- Education requirements:

Though there’re different paths to become a data scientist, it’s absolutely impossible to land into the field without a bachelor’s degree. In addition, if your aim is to get an advanced leadership position, having a doctorate or a master’s degree should be your best bet. There’re data science degrees offered by some schools that can empower you with the skills necessary to process and analyze a complex set of massive data. Most of these programs boast of an analytical and creative element, apart from technical aspects related to analysis techniques, statistics, computers, and more. Some of the common fields of degrees that can help you become a data scientist include statistics, computer science, mathematics, economics etc.

Final takeaway

Before you delve deeper into your endeavor of becoming a data scientist, it’s important to understand that you’ll have to work in different settings, with different teams and in collaboration. The actual work environment can vary largely based on the organization and the nature of business you’ll work for. There’re lots of satisfying factors to becoming a data scientist like having a unique yet challenging career, options for working for a diverse range of companies, getting engaged in interesting and unique subjects and topics that offer you a wide perspective, and working with the latest technologies – among others. On the flip side, there’re some clear drawbacks too. For instance, the technologies you’ll be using will be evolving constantly, which means you may find extreme variety of software and systems, which you’ll have to learn on a constant basis. However, as data science is required by almost every organization and business across the globe, and all of them are increasingly relying on data for developing strategies, the need for data scientists will become all the more important, making the demand in data increasing steadily in the near future.

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To learn more about data science requirements, click here and read our another article.

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1. You learn better with instructors than on your own

Most online courses have the curriculum divided in chapters, which you can study on your own – at your convenience. Since the information available in such courses is often huge, you may feel overwhelmed, confused, and even demotivated. And if you get stuck or can’t understand a concept, you have no one to get advice from or ask questions. The handful of courses that give you limited access to instructors may not help as figuring out everything on your own is tough. In contrast, bootcamps (like Magnimind Academy) give you one-on-one access to instructors, which is imperative to quick and proper learning. Such as Magnimind Academy empowers trainees through dynamic instructors.

2. You get a strong peer environment

A huge advantage of bootcamps is the community of people who are attending the course with you. You get to discuss with your peers the concepts that are taught, brainstorm to complete your allotted tasks, and explain/help others who may need to understand a specific concept/chapter using group chats, video hangouts, message boards, emails in the event spaces. This not only helps you to have a better grasp on what you’ve learned, but even build a good support network of peers who share their passion and interests for what they are learning and doing. At the same time, this strong peer environment can even motivate you to push yourself harder and be better in what you are aiming at.

3. High-impact, quick learning

The duration of bootcamps is much shorter than online courses. Depending on the subject, it can be anywhere from 6 weeks (for data science) to coding (10-12 weeks). Since the curriculum of a bootcamp focuses only on the relevant and important topics that students need to learn, it’s planned meticulously to have extremely trimmed, to-the-point course structure ideal for fast-paced learning. During your office hours, you can practice what you have learned and work on projects that have been assigned to you. Since the instructors are often highly experienced professionals in their fields, everything you learn from a bootcamp is extremely relevant and up-to-date with the present state of the industry. Thus, you not only get to learn topics that are relevant and popular to the current industry status, but even do it pretty fast and in the most effective way possible.

4. Learn to collaborate and network with others

In modern world, you often have to work as a part of team where you need to collaborate with a diverse group of people almost regularly. By meeting and learning with a diverse group of people from different fields in a bootcamp, you will learn how you should collaborate with others to achieve a common goal. Also, as you work with people from different backgrounds and having varying perspectives, you will get to expand your thinking and see the same problem/issue from different points of view. This, in turn, is likely to encourage creativity and innovation. Also, this is a great advancement for your career. By making new friends and keeping in touch with them after the bootcamp, you can even take advantage of a solid network that opens door to significant career opportunities.

The next time you are in a dilemma about whether to choose a bootcamp or an online course, go for the former without a second thought.

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To learn more about data science bootcamps, click here and read our another article.

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