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.

.  .  .

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

The post How a Data Scientist works? appeared first on Magnimind Academy.

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.

.  .  .

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

The post The most powerful idea in Data Science appeared first on Magnimind Academy.

#1. Before entering a data science master’s degree, you should ensure that you’re truly interested in what the program would entail. For example, a professional may try to find an opportunity to get some experience in working with data to gain exposure while a student may try to take a statistics class.

#2. These days, a lot of data science master’s programs are taught online which means it has become easier than ever to learn the skills required to become a data science professional. You’d be able to enjoy a lot of flexibility in terms of studying when you want, working at your own pace, picking a course schedule which suits you the best etc.

#3. Having a data science master’s degree is surely an effective way to develop data science skillsets but not a prerequisite to start your career in data science. It’s possible to step into the field without having a data science master’s degree.

#4. A data science master’s degree heavily matters when you’re applying for a position but not having a master’s degree will stop you from getting that job. For instance, some tech giants may need the applicants to have a data science master’s degree while other companies may not have those stringent criteria.

#5. While data science professionals are already in high demand, having a data science master’s degree could make your chances even better. Apart from that, you’ll be in a better position to negotiate your benefits.

#6. If you want to truly apply for a data science master’s degree, first you should decide on the pathway you want to take. If you’re willing to return to the school, obtaining such a degree can help you in defining the pathway to a good extent.

#7. Some of the data science master’s degree programs are still in the process of developing the proper curriculum which blends computer science, math, and statistics, and there’s a broad range in terms of breadth of knowledge offered, program quality etc. In addition, apart from requiring an investment of a minimum of one to two years, they can cost thousands of dollars.

#8. Some data science master’s degree programs, especially the newer ones, may risk overpromising the students and under-delivering on future employment.

#9. If you can complete a full data science undergraduate curriculum that involves statistical, computational, and professional practice aspects, it might be more comprehensive than a data science master’s degree program.

#10. If your aim is to getting a PhD, you shouldn’t look beyond a data science master’s degree program.

Final Takeaway

Probably you’ve already understood that it’ll be your call whether to go for a data science master’s degree program. Consider the above information thoroughly and make an informed decision in accordance with your future goals.

.  .  .

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

The post 10 Information Related to Data Science Master’s Degree appeared first on Magnimind Academy.

If you want to get into this attractive position, you’ve to be equipped with a wide-ranging and diverse skillset, and have the ability of balancing knowledge in various programming languages together with possessing advanced expertise in data visualization and mining. In this post, we’re going to discuss some simple yet extremely useful steps that would help you get a competitive edge as a data scientist.

Before diving deeper, it’s important to understand that the data science revolution has just begun. It’s unclear whether the job of data scientist will continue to be the century’s sexiest job, or will indicate a set of skills that most working professionals are required to have, or will become more specialized with time. But one thing is for sure – these data science masters have the ability to transform all sectors – from health and agriculture to tech and retail, and almost every industry in-between. So, these professionals will continue to remain in high demand. And this is the best time to step into this rewarding field.

1- Key reasons to become a data science master

If you still aren’t convinced enough why you should study data science, have a look at these.

1.1- Data is transforming the world

Data has gained immense popularity because of its unbelievable potential. Data science enables entirely new generations of technological solutions. And it’s not only in AI and machine learning, where data analyses enable great changes, but in other areas too where new developments are being driven by advanced statistics.

For example, predictive analytics together with data on user behavior are helping businesses improve the UI (user interfaces) of their products, or detailed performance analyses that are helping businesses track the ROI of their marketing campaigns, just to name a few. In short, data has become the backbone of digital economy, which makes the demand of data science masters extremely high.

1.2- You’ll have incredible job prospects

In reality, employers don’t find enough of these data science masters to fill their openings. With potential employers fighting over these professionals, this is the ideal time to pursue data science so that you’ll be in a position to demand more attractive salary within a couple of years from now. If you can earn a postgraduate degree i.e. a Master’s in data science, you’ll be able to double your job options in this field.

1.3- You’ll have different career options

We’ve already discussed that data is extremely crucial to almost every organization today and almost at all levels. It’s not only the big software or IT companies that need data science masters. These professionals are needed in finance, automotive, retail, healthcare, transport, energy, and virtually almost every industry you can think of.

As data helps to drive decisions, these data science masters are getting directly involved in crucial decision making processes. Once you’ve gained a solid expertise of how to “crunch the numbers”, you can become a valuable team member with any of the common job titles that include data scientist, data engineer, data architect, big data engineer, BI (Business Intelligence) architect, data visualization specialist, and many more.

2- How to become a data science master?

Perhaps the above reasons have convinced you about becoming one of those data science masters. However, before you start your journey to become a data scientist, it’s important to understand that there’re a significant number of universities and schools that offer a diverse range of courses related to data science to address the increasing market demand. As a result, you may find a lot of people, who possess adequate knowledge relevant to the field. So, if you want to break through now, you’ve to have a competitive edge. Let’s have a look at the major steps you’ll need to achieve this goal.

2.1- Become proficient in working with big data

Data scientists need to handle a huge volume of both non-segregated and segregated data on which computations often become difficult. Most data science masters use big data software such as Spark, Hadoop etc to achieve distributed processing. You should focus on mastering these software by taking courses on them.

2.2- Become a master in data visualization, munging etc

Data visualization is a crucial set of skills on which data scientists rely heavily when it comes to facilitating administrative and managerial decisions using data analysis. Data munging, which is an equally important skill, refers to the process of converting raw data into a form that’s easy to consume.

2.3- Become a master in coding

Put simply, it isn’t possible to become a good data scientist until you’ve got a solid understanding of the language through which data communicates. A chunk of usable data may be waiting for its analysis but you cannot do anything if you don’t know the script. Remember that a good coder may not be a good data scientist, but a good data scientist is always a good coder.

2.4- Obtain a solid knowledge of databases

Virtually, a huge amount of data is being generated every minute and a majority of businesses today employ database management software like Cassandra or MySQL to store and analyze data. Good knowledge of the working methods of a database management system will surely help you in your effort to become one of those data science masters.

2.5- Maintain the right balance

As an aspiring data scientist, you’d need to develop skills in statistics, mathematics, and machine learning, among others. The key to success is maintaining the right balance among these.

2.6- Become a great communicator

Great communication skills can help you rise above the competition. More often than not, data scientists find themselves explaining the findings of their data analysis to people who’re the decision makers. And data scientists, who’ve the ability to communicate effectively, often find themselves in a better position when it comes to dealing with unforeseen situations.

In addition, as a data science master, you’ll have to work with teams. So, it would help to develop the intuition needed for making decisions and analyzing data by following the workings of your peers closely.

2.7- Develop robust business acumen

In today’s competitive landscape, the hands-on component is crucial when it comes to securing great jobs. To secure an enviable job, you’ve to demonstrate how you can bring value to an organization through your expertise. A solid understanding of the industry you’re planning to work in becomes extremely crucial when it comes to problem solving.

2.8- Create a great portfolio

Once you’ve become one of the data science masters, you should focus on gaining experience. Experience is a critical component that can help you stand out of the pack. As a beginner, you should try to do some projects. And once you’ve gained a moderate level of experience, picking a specific area and specializing in that is always recommended.

2.9- Never stop learning

Once you’ve become a part of the league of data science masters, you shouldn’t put an end to your learning. You can follow websites that are great training grounds for data scientists as they keep on trying to find members and compete against each other to hone their skills and demonstrate their intuitive approaches. With the emergence of such credible websites in the industry, these competitions are quickly becoming a stage to demonstrate to your potential employers how innovatively your mind works.

Another key thing you should keep in mind is that the skills, which are necessary to become a data scientist today, may not be the same tomorrow and are likely to change within a short period. As we’re experiencing fast developments in both the commercial data science tools and in the ecosystem of tools that are used to work in the field of data science, we’re also experiencing increasing automation in several aspects of data science like data preparation and data cleansing. So, it can be said that apart from technical skills, the ability to learn and communicate well to address critical business questions and explain complex findings to non-technical people would help you go that extra mile in your career. New technologies may come and go, but domain-specific, quantitative skills, and critical thinking will always remain in demand.

Wrapping up

Regardless of whether you’ve just become a data science master or you’ve a couple of years of experience under your belt, you’ve to keep yourself up-to-date with the occurrences in the field of data science and different types of job openings being offered in the field.

Try to follow the above steps to fulfill your ambitions of becoming a good data scientist and to steer your way through increasing competition, and toward excellence.

.  .  .

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

The post Master’s in Data Science appeared first on Magnimind Academy.