Before we take a look into the key immersive data scientist salary factors, it’s important to note that geography has an enormous influence on the income of data scientists. According to glassdoor.com, a data scientist in the United States earns an average salary of almost $118000. However, the figure is about $93000 according to payscale.com. It goes without saying that data scientists with high experience can expect to earn high salaries. When beginning his/her career as a fresher, a data scientist can look forward to earn almost $90000 as an annual salary. Those having 5 to 10 years of experience can earn a take-home pay of about $109000. Data scientists with over 10 years of experience can expect to get an annual salary of $124000.

If you wonder how geographical location influences the pay of data scientists, some figures from the US would make the picture clear. Data scientists in San Jose earn 28% more than the national average, while it’s 18% more for those in Palo Alto. Compared to the national average, San Francisco pays 21% higher salary to data scientists, while for New York, the figures stand at 6% higher.

Some say the coveted job of data scientist may be losing some of its attractive quotients as salaries for the position has started to plateau. They say with growing competition and flattening salaries, the prospects that ere once stellar may no longer be so for data scientists. But despite a slowdown in the field, the position of data science is still a lucrative one, much more than many other posts in the IT landscape, which still makes it a coveted one.

If you have your eyes set on the domain of data science, you should know about the three key immersive data scientist salary factors. You may call these three areas that you need to work upon to become a better data scientist, and thus, earn a fat pay packet. But before we go further, it’s important to know for aspiring data scientists that they don’t need to know everything to become a successful data scientist. You should plan your career realistically. After all, there is an almost unlimited number of machine learning/data science topics, but you can actually learn only a handful to begin with. Despite what some self-proclaimed experts and unrealistic job applications may say, you don’t need to possess complete knowledge of every algorithm or have 5 to 10 years of work experience to become a practicing data scientist. So, instead of feeling overwhelmed by the huge number of topics you believe you must learn, you should ideally start with the basics and build upon it from there.

So, let’s delve deeper to take a closer look at each of these three key immersive data scientist salary factors.

1- Software Engineering

When you take an academic approach to data science, you may develop the inclination of writing code that only runs once. Additionally, you may have a tendency of developing difficult-to-read code without a consistent style, having a lack of documentation, and hard-coding particular values. Such practices indicate your solitary primary objective – to create a data science solution that works just a single time for a particular dataset.

An instance could be when you work with data that initially came in 10-minute intervals. Since you didn’t think about making your code easy to read or flexible to varying inputs, you would be at a loss when data starts coming in, say 5-minute increments. Had you written the code from a software engineering viewpoint, it must have been extensively tested with a lot of different inputs. Additionally, it would be well-documented, adhere to coding standards to make it easy for other developers to understand it, and function within an existing framework. What this means is that instead of writing code as a data scientist, you should approach the task as a software engineer.

Successful data scientists write code using software engineering best practices, thus ensuring their model is robust to be deployed and fits within an architecture. If you wonder how you can do it, you should know that nothing beats practice for learning technical skills. If your present job gives you the chance to do it, you can easily learn and hone your coding skills with practice. If not, you can take up collaborative open-source projects. To work out solid coding practices, you may even read through the source code for GitHub’s popular libraries. Finding a community of software engineers and data scientists, who are more experienced than you and from whom you can get advice, is yet another efficient way.  Using these modes, you may end up learning a number of practices including:

• Following a coding style guide
• Writing unit tests
• Thorough documentation of code
• Writing functions that accept varying parameters
• Refactoring code to make it easier and simpler to read
• Getting the code reviewed by others

Additionally, you may use linting tools, which are available in plenty and help you to check if your code follows a coding style. You could even focus more on writing efficient implementations rather than using brute force methods (such as opting for vectorization in place of looping). But it’s important for you to realize that you can’t change everything overnight or all at once. Thus, the key is to focus on a small number of practices and make them habits integrated into your workflows.

Now that you know about one of the key immersive data scientist salary factors, let’s move onto the next one.

2- Scaling Data Science

One difficulty you will face in the domain of data science is scaling a predictive model or an analysis of large datasets. Like many others, you may not have access to a computing cluster and won’t want to shell out a large sum for a personal supercomputer. To solve this problem, you may tend to apply the new methods you learn to small, well-behaved datasets. The only problem is that real-world datasets don’t have cleanliness limits or adhere to an exact size, which makes it important to use different approaches to resolve problems.

One solution is to use a remote instance, like through AWS EC2, or even multiple machines. However, this would mean learning ways to link to remote machines and mastering the command line (because you won’t have access to a GUI and your mouse on your EC2, for instance).

Another problem you may encounter is when handling larger datasets the memory of the machine. One way is iterating through a dataset one portion at a time, by breaking one large dataset into several smaller pieces, and using tools like Dask or Spark with PySpark to run the subsets through a parallel pipeline. You won’t need a cluster or a supercomputer  for this approach as you can parallelize processes on a personal machine using multiple cores. Once you have access to additional resources, you can adjust the same workflow to scale up.

3- Deep Learning

With the increasing use of AI across various industries, deep learning, which uses multi-layered neural networks, is going to be big, say some experts. Your present position may not require you to learn and apply deep learning as you can deal with problems by using conventional machine learning techniques such as Random Forest, etc. However, you should know that not every dataset that you need to work upon will be structured in neat columns and rows, and when they aren’t, neural networks would be your best bet, especially for handling projects with images or texts. Thus, familiarity with deep learning and being confident in implementing some of the techniques would let you handle a wider range of problems, which in turn would make you better placed to earn a higher salary as a data scientist. No wonder why deep learning features among key immersive data scientist salary factors.

Final words

Perhaps you now understand that key immersive data scientist salary factors are as much about your present skills and competence as well as your inclination to pick up new skills and techniques that you may not need now but can apply to future situations to solve problems. So, act accordingly to ensure these three key immersive data scientist salary factors as mentioned above work in your favor.

.  .  .

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

The post Data Scientist Salary is Immersive with 3 Steps appeared first on Magnimind Academy.

A clear insight of a data scientist salary can be obtained from the annual report of the Burtch Works Study published last year. Let’s have a quick look at it.

• Data scientists’ median base salary ranges from $95,000 to $165,000 for individual contributors. For managers, it ranges from $145,000 to $250,000.
• Data scientist salary of those having a PhD is higher compared to the salary of data scientists having a Master’s degree. For example, a median base salary of $102,000 is earned by data scientists holding a PhD while the same comes down to $92,500 for those with a Master’s degree.
• At the base level, data scientist salary is up to 36% higher compared to other predictive analysis professionals. Keeping aside the higher-level managers, there’s a double-digit percentage advantage that data scientists experience with respect to the base salary.

With this kind of data scientist salary, it’s quite obvious for people to consider a career change. Surely, the supply of these professionals has increased to a good extent, but so has their demand. As a result, data scientist salary growth has moderated, but it still remains at an extremely attractive level. Becoming master in data science increases both job opportunity and data science salary.

1- Key reasons that help the role of a data scientist move to the top

People who’re looking for a career change often wonder in terms of what should be the perfect career path to move forward. From the above discussion, it’s evident that the job of a data scientist is one of the trendiest ones in the tech domain today. Let’s see a couple of factors that have helped it move to the top.

1.1- Presence of a huge number of challenges in organizing data

The role of data scientists is evolving and companies desperately require professionals who’re capable of taking on the responsibility of organizing data and preparing it for analysis. Cleaning of data and implementing different connecting tools to get that data into a usable format has become extremely important today. There’re lots of steps involved in data preparation, from translating certain system codes for making data usable to dealing with erroneous or incomplete data.

As a result, there is and always will be a demand for skilled individuals, who’re capable of eliminating bad data, which can modify results or give inaccurate insights for a company. Even with the enhanced availability of highly sophisticated data collection tools and analytics dashboard, a steady demand will always be there for skilled professionals who hold robust understanding of and have advanced skill sets required to clean as well as organize data before it comes to a state where valuable insights can be extracted from it.

1.2- Shortage of talent

Apart from the attractive data scientist salary, a huge lack of talent is another key factor that has made the role one of the most sought-after jobs. Usually, data scientists have the perfect combination of skills in analytics and statistics as well as soft skills. Companies remain in constant search of professionals who’re not only capable of understanding the numbers but also can communicate the findings to others effectively. It’s important to note that because of this huge lack of talented professionals who’re well equipped with these skill sets, data scientist salary is projected to grow significantly over the next few years.

You may ask that what has actually triggered this shortage. The answer lies in the inadequate number of trained data scientists. Though computer science programs (which act as the fundamental breeding ground for most data scientists) have been on the rise over the last few years, it’ll take some time to fill the gap. In addition, with big data on the rise, the number of job openings for data scientists will surely continue to outweigh the supply of professionals with a sophisticated and solid understanding of analysis and data to fill those openings.

1.3- Smaller companies are also able to reap the benefit of data today

Though once the demand for data scientists was only restricted to tech giants, smaller companies today have also started to realize that they can benefit from the use of data as well when it comes to making better and more informed decisions. While smaller organizations aren’t using the huge volume of data like their larger peers, handling the data to extract valuable insights can be a strong competitive advantage for them.

In addition, entry-level data scientists are getting more preference at smaller firms and startups mainly because data scientists come with a broad range of skills that seem to be advantageous for smaller organizations. The hiring processes for smaller companies are also relatively faster compared to larger organizations. As a result, data scientists continue to enjoy a steady demand across the tech landscape.

2- Things to keep in mind if you’re looking for a career change and stepping into the data science field

By reading till now, you may have become interested in stepping into the field because of the extremely attractive aspect of data scientist salary and the field’s huge prospect. But there’re some crucial things that you need to keep in mind before you plan for a career change. Let’s have a look at them.

2.1- The field is evolving rapidly

As the field of data science is evolving at a great pace, be prepared to accept changes fast and adapt to them. What got people hired two years back may not work today. And the difference of hiring standards for data scientists between today and after one or two years will probably be bigger.

Assuming you’re a beginner and know nothing about data science, it’s immensely important to find out whether you’ll like the field or not. So, reach out to some data scientists on social media platforms, follow some data science podcasts, and try to get an overview of the field. You should understand that the process of becoming a data scientist involves a significant amount of effort and time. So, you’d need to consider every aspect of the field before delving deeper.

In addition, if you’re actually starting from the scratch, it may not be the best idea to aim for a full-on data science role. Instead, you should target relatively easier positions in the field like data analytics or data visualization experts, among others. These positions usually involve working alongside data scientists, which can greatly help you in gaining some experience before you aim for the bigger goal.

2.2- Industry

When it comes to data scientist salary, where you work can be a determining factor in different ways. Different industries hold different data challenges, and offer different ranges of pay for data scientists. Usually, data scientist salary tends to be maximum for those who work with social networking/search organizations. And this is quite justifiable when you imagine the huge amount of valuable data that those giants work with. They track interactions of millions of users on their platforms and come up with reasonable conclusions based on those interactions.It’s also important to note that these companies also provide generous stock incentive bonuses that easily add a significant additional amount when it comes to the total compensation.

Often, startups are big hirers and offer attractive data scientist salary. If you don’t want to work for giant organizations, you can try in software, hardware and finance fields as well. Many companies can be appropriate to work in.

2.3- Location

The location, where you’re trying to get a job as a data scientist, also plays a crucial role in determining your compensation. For example, there’re certain areas that hold a concentration of thriving companies and top talents. United States is the highest paying country when it comes to data scientist salary.

But you should also remember that the cost of living differs from one city to another. So, when you consider the top-paying cities in the U.S., remember to compare their cost of living indexes as well. Introduction to data science can be seen easy but some things should be considered.

2.4- Roles

A significant number of people often get confused between data scientists, data engineers, and data analysts. It’s important to note that data scientist salary is much higher than the other two positions.

Sometimes, the exact roles may not be clearly mentioned in the advertisements. So, it’s recommended to look at the skills the companies expect and the type of work they’ll want the selected candidates to do.

Final thoughts

The demand for data scientists can only be expected to increase, and various ways to enter the field of data science will open up. While university programs can be a good start, attending data science boot camps has become one of the most preferred options to enter the field.

These boot camps offer a fine mixture of skills that are required for a position related to data science, which should be a good starting point for navigating toward the position of a data scientist. There are data science boot camp prices that are affordable.

.  .  .

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

The post A Career Change for Data Scientist’s Salary appeared first on Magnimind Academy.

Perhaps that’s why data science salary could act as a driving factor for those looking to make a career switch and enter the field. Even for freshers searching for the proper career path or those looking to take up new courses in data science and its related aspects to equip themselves with the requisite skills that are needed to make a mark in the domain of data science, enviable data science salary levels often seem to be the factor that attracts them the most.

Before we dive deeper to understand and decode the mystery surrounding data science salary, let’s take a look at what makes you a good professional in the field. Remember – if you don’t feel attracted to or excited about data; miss the skills, patience or experience of pondering over piles of data to find useful insights; or can’t act as a data sleuth to get information from seemingly innocuous data where others can’t see or find any, you should reconsider stepping into this field.

After all, the mere lure of high levels of data science salary won’t carry you much farther in the field. You can go to a certain distance for sure, but if you aren’t motivated by the beauty of the field itself, your drive will die soon and you may reach nowhere after having wasted a significant amount of time, effort and money on a subject that you didn’t love or feel excited about.  

1- What makes you a good professional in the field of data science?

Though the field of data science offers various positions at different levels, let’s stick to data scientists and find the three key skills one should have to excel in it.

1.1- Analytical mindset

Data preparation takes almost 70% of a data scientist’s time. From data cleaning and munging to preparing data so that it becomes fit for machine learning algorithms to be applied to it, a data scientist needs to and should be comfortable in handling huge data sets. So, possessing an analytical mindset with a strong statistical background is a must. Additionally, you should be strong in specific programming languages like R or Python, and have good knowledge of data structures as well as machine learning algorithms.

1.2- Sound domain knowledge

Before you even start dreaming of high data science salary, you should ensure to have sound domain knowledge. From the skills to understand business problems and select the most apt data science model to address them, to having an eye for the detail, and the ability to interpret the findings and arrive at the final result, you need to excel in many aspects. Apart from sound domain knowledge, you also need to have good communication skills to convey your findings in a lucid language to the wider audience.

1.3- Programming and statistical skills

Apart from being adept in programming languages like Python and R, you should also have sound knowledge of mathematics, statistics, and algorithms. But just having these skills isn’t enough. You should ideally apply your knowledge to solve real-world problems. This would need you to be not only strong with the basic concepts, but also have the ability to use the technology tools to your advantage to create great models.

The key is to learn the basics right and then build on them by applying the acquired knowledge to practical problems while understanding and overcoming the practical difficulties that you’ve to face.

Now that you have a fair share of knowledge about what makes one a good data scientist, let’s take a look at what probably interests you the most – high levels of data science salary. We’ve considered three different regions across the world (the US, the UK, and India) to find where they stand with respect to data science salary. We’ve used information from Glassdoor, PayScale, Indeed, and some additional resources for the purpose.

1.3.1- The scenario in the US

According to Glassdoor, the average base pay for a data scientist in United States stands at $117,345, but PayScale puts the figure at $92,521. Glassdoor also reveals the additional cash compensation (that includes commission, cash bonus, tips, and profit sharing) to be $11,530 on an average, while the range of such compensation is between $3,933 and $26,784.

In case you feel interested in knowing which companies occupy the top positions with respect to lucrative data science salary they pay to their data scientists, the following (the average base salary in USD) will give you an idea of what’s like to work for the reputed companies:

• Facebook – pays $135,000 per year
• Twitter – pays $134,061 per year
• Apple – pays $130,000 per year
• Airbnb – pays $130,000 per year
• Google – pays $127,000 per year
• Uber – pays $121,500 per year
• Microsoft – pays $120,000 per year
• Amazon – pays $119,979 per year
• LinkedIn – pays $119,800 per year
• Oracle – pays $110,000 per year

If you want to know more about the salaries for some related job titles, here’s what you need to know (these are the average base pay that the following jobs offer):

• Senior Data Scientist: $137K per year
• Quantitative Analyst: $116K per year
• Data Analyst – $67K per year

1.3.2- The scenario in the UK

The average base pay for a data scientist in the United Kingdom stands at £45,000 according to Glassdoor. PayScale puts the figure at £40,547, while Indeed says it’s £62,143 per year in London, which is 12% above the national average. According to Adzuna, an average data scientist earns £66,441 per year in London. Adzuna further adds that thus figure is 16.4% over the average national salary for data scientist jobs. When compared to the average salary across London, the average data science salary in London that a data scientist gets is 59.3% more.

Glassdoor didn’t reveal anything about additional cash compensation but according to it, here are some companies that pay a lucrative data science salary to their data scientists (the figures below show the average base salary in GBP):

• Barclays – pays £52,150 per year
• Capgemini – pays £50,000 per year
• Dunnhumby – pays £44,000 per year

According to PayScale, the top five companies for hiring data scientists are

• McKinsey & Company
• PwC
• Deliveroo
• Deloitte
• Bloomberg

According to Indeed, here’s what the top companies pay in terms of data science salary to their data scientists:

• Braintree Limited – pays £99,517 per year
• OHO Group– pays £68,445 per year
• Harnham – pays £65,355 per year
• Kortx Ltd – pays £120,000 per year
• Client Server – pays £62,555 per year
• X4 Group – pays £70,356 per year
• Networking People (UK) Limited – pays £72,565 per year

If you want to know more about the salaries for some related job titles, here’s what you need to know (these are the average base pay that the following jobs offer):

• Data Analyst: £30K per year
• Quantitative Analyst – £65K per year

1.3.3- The scenario in India

The average base pay for a data scientist in India stands at ₹650,000 according to Glassdoor. PayScale puts the average data science salary (for a data scientist, IT) at ₹634,645, while Indeed says it’s ₹708,076 per year.

Glassdoor didn’t reveal anything about additional cash compensation but it lists a handful of companies that pay a lucrative data science salary to their data scientists (the figures below show the average base salary in INR):

• Mu Sigma – pays ₹497,500 per year
• Tata Consultancy Services – pays ₹757,500 per year
• IBM – pays ₹1,350,000 per year

According to Indeed, here’s what the top companies pay in terms of data science salary to their data scientists:

• Future Focus Infotech– pays ₹14,63,789 per year
• 2COMS Consulting Pvt. Ltd – pays ₹13,50,526 per year
• Crescendo Global – pays ₹12,71,853 per year
• Okda Solutions – pays ₹14,32,256 per year

In case you’re interested in knowing more about the salaries offered by some related job titles, here’s a glimpse for you to ponder upon (these are the average base pay that the following jobs offer, as per Glassdoor):

• Data Analyst: ₹405K per year
• Quantitative Analyst – ₹708K per year

Final thoughts

Perhaps you now have a better idea about the data science salary structure that’s prevalent in companies across the world. You should remember that apart from your qualifications and experience, how much value you bring to the company too would play a big role in the level of data science salary you’re offered.

So, step into this field if its elements really interest you and not just because you’re chasing a fat pay packet. As we’ve said before, a high level of data science salary alone won’t take you a long way ahead unless you’re driven from the inside by pure love and interest in the field.

.  .  .

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

The post Here is The Answer to The Most Carious Question appeared first on Magnimind Academy.

In this scenario, we’ll strongly encourage you to continue your study, develop relevant technical and non-technical skills, and become a data scientist because of the good percentage of availability of part time jobs in this field.

The appeal of a high pay together with a flexible work schedule is bringing a significant number of data scientists to the part time job market. In this post, we’re going to discuss why you should try to get a part time job as a data scientist and how to get that.

1- Key advantages of having a part-time job as a data scientist

Despite the fact that working full-time for a big organization as a data scientist can be stimulating, fun, and rewarding, a significant number of professionals these days are quitting their full-time jobs to take up part time jobs in the same field. Let’s have a look at two major benefits of working as a part-time data scientist.

1.1- Freedom

The biggest benefit of taking up a part time job as a data scientist is freedom. There’re lots of people who don’t like going to the office everyday and work for the entire day. As a part-time data scientist, these people can work according to their terms and convenience.

1.2- You’ll work only as a data scientist

There’re lots of organizations where the employers don’t have a clear idea of the responsibilities of a data scientist. They often consider data scientists as a master of all things data and expect to get answers to all their data-related queries from that one person. But when it comes to working as part-time data scientist, you’ll only apply and accept the offer once both you and the employer agree on some assignments. Thus, you’ll only do what you’re hired for and nothing more.

By now, a majority of businesses have become aware of the benefits that can be gained by employing a data scientist. The reasons can be quite varied – from trying to earn more sales to making the campaigns more effective to finding better ways to serve their customers, and more. In short, whatever the goal is, employing a data scientist proves to be the best option to attain it.

From an organizational standpoint, the key reason behind the growth of the concept of offering a part time job as a data scientist is that hiring a full-time data scientist can bring in lots of financial overheads, especially when it comes to startups and small businesses. These companies often have to work within limited budgets and resources.

Mainly, there’re three options that can be utilized by these companies – hiring a full-time data scientist, trying to perform the analytics themselves, or bringing on a part-time data scientist to handle their data requirements. Among these three, we’ve already discussed the constraints of the first option. When it comes to the second option, utilizing big data is difficult and needs specialized skills that are hardly found available in employees other than data scientists. So, these businesses are best served by the third option, which is offering a part time job to a data scientist.

Even medium-scale organizations are also taking this route frequently for getting short-term assignments done while maintaining their budget and limiting additional overhead. Thus, once the assignment is completed, they don’t need to keep a data scientist on their payroll anymore as his/her help is no longer required, which wouldn’t be the case if they had hired a full-time data scientist.

2- How to get a part time job as a data scientist?

When it comes to taking up a part time job as a data scientist, there’re some prominent avenues that can connect you to businesses, which are looking for people like you. Let’s have a look at them.

2.1- Your own website

In this internet-dominated world, for data scientists who don’t want to work full-time, a robust online presence is almost a must. So, one of the most feasible ways to establish yourself as a part-time data scientist is to have your personal website. A personal website acts as your portfolio that potential employers can see and then reach you easily, if they decide to.

There’re some crucial things that you must focus on while developing your website. First of all, employers need to know who you’re and what you actually do. As you’ll be working as a data scientist, your website has to be data science-centric. Second comes the services section where all the services offered by you have to be mentioned in a clear and detailed way. You need to clearly mention your charges as well to help the clients get a clear estimate. Detailed online research should help you out in this matter.

It’s important to keep your pricing low initially, especially if you’re a beginner and trying to get a part time job as a data scientist. Do remember to highlight your previous projects, if you’ve done anything, as it’ll help clients evaluate your expertise. Even if you’re a beginner, try to do some projects for private clients or non-government agencies, which will look good on your portfolio.

2.2- Online portals

Apart from having your own website, it greatly pays to create a part-time data scientist profile on online platforms that receive a lot of traffic from potential clients. You can consider Toptal and Upwork, for example, both which boast of a high volume of part-time data scientist jobs on offer.

When it comes to getting started with Toptal, you need to prepare your portfolio, mention the relevant skills, and then submit an application. There will be some further screening processes clearing which will get you admitted to the platform.

Signing up for Upwork is relatively easier. You’ll need to submit a cover letter, and create a profile describing your technical and non-technical skills. After that, you’ll be able to browse part time jobs for data scientists posted there and apply to the ones you’re best suited for.

2.3- Participate in different community groups

Once you’ve taken care of one or both of the above aspects, it’s time to develop your presence as an expert in the field. The internet is filled with communities for data scientists looking for part time jobs. Try to collaborate, share ideas, and learn from other participants. Regular participation in these groups will not only give you exposure as a part-time data scientist with valuable insight but will advertise your skills to potential employers and peers as well.

Sometimes, data scientists taking up a new project turn to these communities for help in solving difficult problems. If you’re able to prove your expertise, your peers will potentially contact you with offers to collaborate on part time jobs. As a significant percentage of part-time work is found through these networks, you should try to put your best foot forward to maximize the advantages of increased online visibility.

2.4- Use LinkedIn

LinkedIn can help you greatly when you’re looking for a part time job as a data scientist. Create an account there and find people in different organizations that work with data. Look for HR professionals or data scientists and send them a message.

In the message, give a short introduction about yourself and mention that you’re looking for a part time job. You’re likely to be surprised to find that many of these people will be interested in helping you out. You can also use this platform to look for alumni of your university or school to network again and get a part time job as a data scientist.

2.5- Company websites

In addition to looking for part time jobs as a data scientist on different online portals, do remember to check out different company websites that work with data.

Some organizations may not be posting their part time job positions on external job boards. Do some research online and you should be able to find companies that offer part time jobs to data scientists.

Wrapping up

Though having the prior experience of doing some part time jobs as a data scientist will make the journey relatively easier for you, it’s not an absolute necessity. However, there’s one thing that you must have in order to be successful as a part-time data scientist – the right attitude.

You should always keep in mind that employers or clients want to hire someone with a can-do, positive attitude. They want someone who’s ready to go beyond and above the call of duty. If you can demonstrate this ability together with your expertise on the assignments offered, you can rest assured of receiving a significant number of high-value part time jobs as a data scientist.

We hope that all these tips will help you get a greater sense of why and how to become a successful part-time data scientist. Keep these tips in mind as you put together your brand and identity, and work toward getting part time jobs as a data scientist.

Wherever you’re on this journey, we wish you good luck!

.  .  .

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

The post To be data scientist is appropriate for looking a part time job appeared first on Magnimind Academy.

Apart from $110,000 as a median base salary, data science jobs were found to have a job satisfaction score and a job score of 4.4 and 4.8 (out of 5) respectively. Similar findings were made public in a related report of CareerCast.com where jobs in data science were shown to have one of the best growth rates in the industry over the next decade and continue to be one of the most difficult positions to be filled.

According to rjmetrics.com statistics, these findings were supported and it was stated that over the past four years, merely 50% of the projected 19,500 data scientist positions got filled. All these statistics and predictions indicate how popular the job of a data scientist is and will become in the coming days. Let’s take a deeper look into certain aspects to understand the driving factors behind this trend that makes data scientist the hottest job of the 21^st century.

IoT is transforming how we interact with the digital world of data

A research conducted by Business Insider some time ago predicted that by 2020, over 24 billion internet-connected devices will get installed globally. In other words, every person on this planet will have more than four devices to use. Together, these devices comprise the Internet of Things (IoT), and its presence is permanently changing our world.

IoT can be called the link between the digital world of data and physical world inhabited by humans. From your smartphones and smartwatches, to tablets, computers, smart TVs, and wearables – all come under the IoT. What’s more, even your everyday appliances like lights, fans, smoke detectors, and thermostats have started boasting of smart capabilities, which make them a part of the IoT. Even how you socialize, or get from one place to other (via the transportation system) is changing and will change further because of the IoT. The tech giant has been enhanced by the time over.

Why do you need a data scientist?

If you are wondering how the IoT is connected to data, here’s the answer: all these varied smart devices and appliances draw a large amount of data. A number of sources are used to collect this data, which can be categorized into two types: unstructured data and structured data, both of which come under the domain of big data. Human input is more likely to contribute to unstructured data, which is the fastest growing type of big data.

This includes your social media posts, the emails you send and receive from various sources, the videos your stream or share, the customer reviews you post etc. since unstructured data isn’t streamlined, it’s difficult to sort and manage with technology. On the other hand, structured data is collected by products, services, and electronic devices. For example, your website traffic data, or GPS coordinates collected by your smartphone fall under this category. Since such data is organized, usually by categories, a computer or a program can be used to read, sort, and organize it automatically because of demand in data.

A data scientist works with both structured and unstructured data, and sorts, organizes and analyses them to present them in easily understandable forms to the stakeholders. This in turn would help the stakeholders examine if their departmental, business and revenue goals are being met, and also help them take important business decisions. In other words, a data scientist’s job isn’t just to process and analyze data. Rather, he/she should be able to translate departmental or company goals into data-based deliverables like pattern detection analysis, prediction engines, optimization algorithms etc, which would offer the stakeholders useful insights and facilitate informed decision making.

Not having a data scientist on your team would mean that even if you sit on a pile of data, you won’t be able to leverage it for your benefit as you can’t get any meaningful insights or use them to predict trends (like the surge in interest in a particular item), which would have helped you to make timely business decisions. In today’s competitive business landscape where data never stops flowing and the nature of challenges undergoes a continuous change, it’s the data scientists who can help decision makers make a transition from ad hoc analysis to enjoying an ongoing dialogue with data.

Now that you have an idea of the role of a data scientist, let’s see what makes it the hottest job of the 21^st century.

Top 3 reasons that make the position of a data scientist the most coveted job

1- Rising demand for skilled professionals

Lack of qualified talent is one of the key reasons why data scientist jobs are in high demand. Even for the positions that are vacant at present, employers are finding it difficult to fill them as there aren’t enough skilled and qualified people around. The problem is that though companies need more data scientists, most are still not certified yet or are still studying for their degrees. And this gap between demand and the availability of talents is set to worsen since IBM has predicted that by 2020, the demand for data scientists will skyrocket to 28%.

This sets the prefect stage for aspirants seeking jobs as data scientists. Thanks to the huge vacancy in this field (which is set to increase further in the future), these aspirants can apply for and land such in-demand jobs a lot faster than their counterparts seeking other jobs. Thus, companies want skilled person who is worker.

2- You earn well and get to work with the latest technologies

As already mentioned before, the median salary of a data scientist in the US is close to $110,000. Elsewhere in the world too, the job pays extremely well. According to Burtch Works Study: Salaries of Data Scientists, the base salary of these professionals is up to 36% higher than their counterparts working with other predictive analytics. As the demand for competent data scientists is set to grow significantly, the salary for the post is likely to become better.

Apart from the lure of a fat paycheck, the excitement of working with the latest technologies is also a big draw. From Artificial Intelligence and Machine Learning (with progressive future prospects) to R and Python (considered as the most popular technologies), and MongoDB (the most popular database), a data scientist gets to work with the constant evolution of technologies. This first-hand experience together with the future prospects of these popular technologies make the position of data scientists the most coveted one.

3- Companies in different domains and of different sizes are hiring

Once, data scientists were thought to be employable only in the IT and finance sectors, and that too in large companies. But the scenario has changed today. While the bigger names in IT, Finance and Insurance sectors continue to hire these professionals in career stage, even the medium and smaller companies are now hiring them as they have realized the importance of data-driven decision making. Though these smaller companies don’t have a data bandwidth as large as their bigger counterparts, they have started hiring qualified data scientists, who can help them get valuable insights from their metrics. With this, these smaller and medium companies can get a comparable “big data” advantage as the larger companies, which in turn would help them stay competitive.

And the good news is that it’s no longer just the IT, Professional Services, and Finance and Insurance that offer jobs to data scientists. From companies in telecom, e-commerce, and BFSI (banking, financial services and insurance), to transportation and more, a lot of industries that generate or have access to a massive amount of data have woken up to the potential of leveraging such data to their business advantage. And they are now hiring data scientists to process this huge amount of data to make the most of their business decision-making potential.

Be it proactive (where you anticipate what the problem could be and try to address it before it disrupts business operations) or preventive decision making, data science professionals can help. Even spotting trends to decide on the future course of business, or steering the business to an entirely new direction (in line with changing demands, preferences etc) becomes easy with the insights generated by data science.

Automating many small decisions is another key thing which can be done easily when the right data is collected and utilized. For example, financial institutions using automated credit scoring systems to forecast their customers ‘credit-worthiness’ would not only free their employees from the task, but also bring a higher degree of accuracy, while speeding up the process and lowering the risk of not getting a return on the loans in case the customer wasn’t worthy of being granted a loan.

As the future scope of data science is extremely bright, it’s no wonder why there’s almost a mad rush to get qualified as a data scientist and find jobs in this highly lucrative domain with data science in 6 weeks in Silicon Valley. Data science bootcamp in Bay Area provides an advantage to be accepted for a job.

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

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