10 Tips. How Data Science Works

10 Tips. How Data Science Works

Data science is one of the most attractive career paths right now, something that comes out clearly in discussions on the same over at runrex.com. Data scientists, given their broad and impressive spectrum of skills and knowledge, sometimes appear like magicians, punching in a bunch of instructions on their computers and thereafter producing amazingly detailed predictions of the future. Data science is, however, not magic, and this article, with the help of the subject matter experts over at runrex.com, will look to highlight 10 tips on how data science works.

The foundation of data science

The first tip we are going to highlight is the one on the foundation of data science, one speaking about the core skills required for one to become a data scientist, with a more detailed write-up on the same to be found over at runrex.com. If you are to understand how data science works, it is important to note that data science requires a deep understanding of statistics, algorithms, programming as well as soft skills such as communication skills, according to the gurus over at runrex.com. If you are to become a data scientist, you will need to have a deep understanding of these disciplines.

Framing the problem

Now that we have highlighted the skills required in data science, we are going to dive headfirst into how data science works, a topic covered in detail over at runrex.com. The first step on how data science works is the framing of the problem that needs solving. Here, you will need to understand who the client is, what they are asking you to solve, and how you can frame and translate their problem into actionable intel, as covered in detail over at runrex.com.

Converting ambiguous questions to data science questions

An important part of the data science process, one that will help you understand how data science works, is converting the ambiguous questions posed by clients into data science questions that can be solved with data science. An example of an ambiguous question is, “what does the sales process look like right now?”. To solve this question, you will need to convert it into a data science question(s), as covered over at runrex.com. An example of a data science question is, “how can we predict if a prospective customer is going to buy the product?”.

Identifying what data you have available to answer your questions

Once you have formulated the data science questions that you want to solve, the next step is to identify the data that you have available to you. As per the gurus over at runrex.com, the first step is asking yourself if the data you are looking for is already available. If it is available, you will need to figure out what part of it is useful, if it is not available, you will have to collect it before you can move forward with your project.

Collecting the right data

An important part of the data science process is collecting the right data, something the experts over at runrex.com agrees with. Just because you have data available to you doesn’t mean that all of it will be useful to you for a particular project. It is important to make sure that you collect the right data, to arrive at the right conclusions and so that you don’t waste your time processing data that is irrelevant based on the problems you are looking to solve.

Ethical considerations

Another tip that explains how data science works is when it comes to the ethical consideration of data collection. As revealed in discussions over at runrex.com, ethical data scientists, which is what you should be aiming to become, ensure that security and privacy are top of their agenda when collecting data for projects. You should be careful not to extract any information from the database that is personally identifiable and should be dealing with information that is anonymized and can’t be traced back to any specific customer.

Processing the data

The next tip that highlights how data science works is the processing of the data collected, which is the next step of the data science process. Given that raw data is usually not usable, as discussed over at runrex.com, you will have to process it to extract useful insights from it. Some of the reasons why raw data is rarely usable include corrupt records, errors during data collection, missing values, and other issues. Here, you will need to clean the data and convert it into a form that can be analyzed further.

Exploring the data

Once the data has been processed and cleaned, the next stage in the data science process is exploring the data. According to the experts over at runrex.com, this means understanding the information that is contained within the data at a high level. In this stage, you will be required to identify the obvious trends and correlations that you see in the data as well as the high-level characteristics and if some are more significant than the others.

In-depth analysis

We finally arrive at the crux of the matter, the analysis stage, which is the next stage after the exploration of data and is another tip that will help you understand how data science works. Here, you will be required to conduct an in-depth analysis of the data, using machine learning, algorithms, and statistical models as explained over at runrex.com. This is the stage where you will need to apply all the cutting-edge skills and tools of data analysis to glean high-value insights and predictions of future trends.

Communication of the results of your analysis

Once you have done the analysis, you will realize that you need to communicate the results, and the process you used to arrive at the results, in a way that is both comprehensible and compelling to your client or employer, given that they are not data scientists like you. This is where your data storytelling skills come into play, and why, as per the gurus over at runrex.com, communication skills are one of the most underrated but critical skills a data scientist can have. Unlike other professionals in the tech world, data scientists work with other teams from other departments in corporations who may not have a tech background and therefore need to be skilled in translating their work into a form that is understandable to others.

We hope that the above discussion will give you an idea of how data science works, with there being more information on this broad topic to be found over at the amazing runrex.com.

10 Tips. How Data Science is Used in Healthcare

10 Tips. How Data Science is Used in Healthcare

One thing we have consistently heard about data science, as revealed in discussions over at runrex.com, is that data science is applicable in pretty much every field and industry out there; which is one of the reasons why data scientists are among the most sort-after professionals out there today. One of these fields is healthcare, where data scientists have been using analytics and machine learning to revolutionize the healthcare industry. This article, with the help of the gurus over at runrex.com, will look to highlight 10 tips on how data science is being used in healthcare.

In wearables

As is revealed in discussion over at runrex.com, the human body generates about 2 terabytes of data daily. Advances in technology now mean that we can collect most of this data, which includes data on sleep patterns, heart rate, stress levels, blood glucose levels, brain activity, and many others. Using AI, machine learning, and big data, data scientists can analyze this raw data and information which can be collected through wearables to glean critical insights. This, as per discussions over at runrex.com, helps doctors detect conditions early and predict possible health issues, allowing for possible preventive care.

Drug discovery

The process of drug development, as covered over at runrex.com, includes extensive research, testing as well as time and money, before a drug can be launched safely into the market. With this in mind, the cost of bringing a drug to market can be as much as $2.6 billion, which is substantial, to say the least. This is another area where data science has been of great help in healthcare, as it leverages various sets of biomedical data from various tests, treatment results, case studies and so forth, across various disciplines. Advanced mathematical algorithms then come into play to create a simulation of how a certain drug would interact with the proteins in the body, predicting the rate of success as discussed over at runrex.com. This simulation helps speed up the drug-testing process, leading to a huge reduction in costs as well as the time of drug development, while also mitigating the risks of failure.

In diagnostics

Another way that data science is used in healthcare is in diagnostics, as explained over at runrex.com. This is because, through data science, analysts can be able to apply deep learning techniques to process extensive clinical and laboratory reports, enabling quicker and more precise diagnoses. It also allows for the detection of early signs of medical issues enabling doctors to provide preventive care and consequently better treatment to patients.

In reducing healthcare costs

Data science also plays an important role in reducing healthcare costs, yet another way in which it is used in the sector. Data scientists, as explained over runrex.com, can look into billing data and information extracted from clinical systems as pertains to charging and variables, identifying areas of potential revenue loss, closing said gaps hence contributing to the lowering of costs. Healthcare providers can also use data science to optimize their supply chains as well as review equipment maintenance to prevent unexpected breakdowns, enabling them to keep costs down. Monitoring patient recovery as well as planning discharge protocols can also help with costs as it will diminish readmissions.

In managing and improving overall public health

As is discussed over at runrex.com, there is a large amount of data that can be found in various sources such as Google Maps, wearables, social media, websites, and many other sources. Data scientists can be able to analyze this data, helping them prepare heatmaps on useful parameters such as health aliments, medical results of people in a given geographical location, population, and so forth. This enables them to understand the signs of an imminent health crisis, such as the coronavirus pandemic, allowing them to make the necessary preparations such as increasing the capacity of medical facilities in given areas and many others.

In enabling optimal staffing

One of the most crucial aspects of healthcare is staffing since understaffed facilities are likely to offer poor medical services, not to mention the fact that the staff working there will be overworked leading to burnout, while those that are overstaffed will have increased costs, as explained over at runrex.com. Data science enables medical facilities to keep optimum staff by using analytics to predict patient visit fluctuations based on historical data collected over the years, creating a pattern in staff allocation that is grounded on admission rates from the past. This ensures that facilities always have optimal staffing as well as helping in the allocation of other resources such as beds.

In enabling precision medicine

Given that we are all born with different biological make-ups, not to mention that we are all raised in different environments, a one-size-fits-all approach to treatment doesn’t make any sense, according to the gurus over at runrex.com. This is where data science comes in, in yet another way in which data science is used in healthcare, as it helps enable precision medicine. With data science, the process of genome sequencing has been reduced to a matter of hours, at a far cheaper price. This opens the door to more tailored treatment, which is more effective making precision medicine the future of healthcare.

In reducing risks in prescription medicine

On top of contributing to diagnostic accuracy and in drug discovery, data science technology is also helping reduce the risks involved in prescription medicine, in yet another way it is used in healthcare. As explained over at runrex.com, when a certain drug is prescribed to a patient, algorithms move to verify the drug with available databases, alerting the physician if it deviates from standard treatment procedures. This helps sidestep potentially lethal complications due to faulty prescriptions.

In improving patient engagement

Nowadays, healthcare organizations place huge importance on a value-based approach to healthcare, with patient engagement playing a significant role in this, according to the folks over at runrex.com. Healthcare providers now see it as crucial to increase patient participation in the treatment process. Data science is playing a big role here, given machine learning, AI, and natural language processing can be used to extract meaningful and actionable insights as well as develop predictive risk scores to improve care coordination, hence improving patient engagement.

In improving cybersecurity in healthcare

As discussions over at runrex.com will tell you, healthcare data is extremely vulnerable to data breaches since personal data such as Social Security Numbers, Medicare information, insurance information, and many others are very lucrative in the black market. One of the challenges faced by healthcare organizations nowadays is ensuring the cybersecurity of health data. To help with this, healthcare organizations are utilizing data analytics tools to flag changes in network traffic or detect the occurrence of cyber-attacks, in yet another way data science is used in healthcare. Additionally, data science helps streamline the insurance claim process, making it faster and more efficient for patients, while identifying fraudulent and inaccurate claims.

The above tips are just some of the ways data science is used in healthcare, with more information on this and other related topics to be found over at runrex.com.

10 Tips. How Can Data Science Help a Business?

10 Tips. How Can Data Science Help a Business?

Data science, as the subject matter experts over at runrex.com will tell you, has made it possible for businesses to put into good use the large amounts of data they collect daily on their customers, helping them provide better services and attract more traffic. It is safe to say that, data science has revolutionized how businesses operate, which is one of the reasons why data scientists are one of the most sort-after professionals out there. If you don’t believe that data science can be a massive asset for your business, this article, with the help of the gurus over at runrex.com, will look to open your eyes through the following 10 tips that highlight how data science can help your business.

In improving user-experience

One of the ways through which data science can help your business is by helping in improving user-experience, something backed up by the experts over at runrex.com. You can derive and analyze data from customer feedback, usability tests, or surveys, extracting user patterns with the help of a data scientist, which will help you improve your services and products, hence improving user-experience, atopic covered in detail over at runrex.com.

In price optimization

Another way through which data science can help your business is helping in price optimization, a topic that is also covered in detail over at runrex.com. The most successful businesses are those that get the balance between customer satisfaction and price right. You can use data science, with the help of a data scientist, to collect and analyze data from previous sales records, market trends as well as individual buying patterns, according to the folks over at runrex.com, all of which will help you to optimize your prices and come up with competitive prices, while still retaining good margins.

In product planning and strategy

Data science can also help your business massively in product planning and strategy. This will help ensure that you are not making the wrong products or the wrong product updates, something that has damaged many brands out there. You can extract lots of useful insights from data from the social media interactions of your customers and target audience, your current product usage data among others as discussed over at runrex.com, which will help you in planning your products and services in terms of aspects such as preferred features, future trends, and likes and dislikes.

In improving supplier management

Data science can also help a business in improving supplier management, as covered in detail over at runrex.com. Through data science, you can be able to improve supplier management through real-time data analytics, quality delivery tracking, dashboards among others. This will help you identify what the best supplying modes are, removing those that are not so efficient and making your supplier management more efficient.

Better marketing and advertising

Another way that data science can help a business is by making marketing and advertising better and more effective. Through data science, as discussed over at runrex.com, one can be able to collect, process, and analyze data to reveal customer response to certain ads. From the insights gleaned from this, businesses can be able to choose the right marketing channels, the right messages, and so forth, which will make their marketing and advertising efforts more effective.

In inventory management

Another way that data science can help a business is in improving inventory management, which is another key aspect of a business as per the gurus over at runrex.com. Data science is used to help make sure that businesses are holding the right amount of inventory as storing too much inventory could lead to wastage, while on the other hand, a short inventory could lead to loss of sales. Through data science, one can attain the sweet spot as far as inventory is concerned, reducing wastage and cost.

In offering personalized services

Yet another wat that data science can help a business is by ensuring that customers receive customized services. Through data science, one can collect and analyze data on their customers’ past transactions, their purchase preferences, payment preferences, and so forth, as discussed over at runrex.com, to offer them personalized services. Data science can also be used to drive chat-bots to offer personalized interactions that also boost user-experience.

To reduce the risk of fraud

Data science can also be used to reduce the risk of fraud, which is another way that it can help a business. Through data science, and with the help of data scientists, a business can create a network, path, or big data technique to come up with predictive fraud ability models. This will create a response and alert the company when there is an unusual type of data, which may be a sign of fraudulent activity that could cost the business money, as per the gurus over at runrex.com. This is an important way that data science helps businesses.

In customer retention

Another way that data science can help a business is in customer retention, something the gurus over at runrex.com agree with. One can collect and analyze data, with the help of data science, on the history of purchasing behavior and lost customer reports to give one pattern and statistical reasons on why customers drop out. This will help you identify and improve the loose ends of your business, helping you retain your customers better. Data science’s role in customer retention is another way that businesses can benefit from it.

In enabling forecasting

Yet another important way that data science can help a business is in enabling forecasting. Through data science, one can be able to forecast future trends and customer behavior which can help a business prepare better for future market changes, ensuring that it is well placed to provide better services to customers, as discussed over at runrex.com.

These are some of the ways that data science can help a business, with there being more to be uncovered on this and other related topics over at runrex.com.

10 Tips. Can Data Science Predict the Stock Market?

10 Tips. Can Data Science Predict the Stock Market?

Data science has found lots of uses in our daily lives, from Netflix predicting what you should watch next to targeted ads on social media platforms, among other examples discussed over at runrex.com. Given that data science has already proven useful in predicting human behavior, the next question that has been asked by many is if it can predict the stock market. This would prove extremely useful as not only will it help people know when to sell a stock for a profit, it would also help them know when to buy one that is on the verge of blowing up. This article, with the help of the gurus over at runrex.com, will look to take a closer look at this topic and highlight 10 tips on whether data science can predict the stock market.

Data science has been used on Wall Street for decades

To understand if data science can be used to predict the stock market, it is important to highlight that it has been used for decades, with Wall Street hiring data scientists as early as the 1980s as covered over at runrex.com. These data scientists helped create models that did well in predicting the stock market. The success that this heard had everyone believing that data science was the next big thing as far as operations of the stock market are concerned, as tackled over at runrex.com.

Obstacles were encountered after the initial success

However, after some initial success stories which promised much, the use of data science to predict the stock market run into some obstacles, as explained in detail over at runrex.com. This is because, while data science is extremely useful in predicting human behavior, predicting the stock market is a whole different kettle of fish. As it stands, the simple answer to whether data science can be used to predict the stock market is no, as explained by the subject matter experts over at runrex.com.

Algorithms have been found not to fare better than the average when it comes to predicting the stock market

After decades of experimenting with algorithms to find out if data science can be used to predict the stock market, it has become apparent that algorithms don’t fare much better than the average when it comes to predicting the stock market. As per the gurus over at runrex.com, this means that a person could get the same results as an algorithm by just flipping a coin. This shows that, as of yet, data science cannot be used to predict the stock market.

Predicting human behavior is not the same as predicting the stock market

One of the reasons why data science has not been able to be used to predict the stock market is because predicting human behavior, where data science has proven extremely useful, is very different from predicting the stock market. This is because, while we humans have a great individuality, with each person, by and large, having their personal preferences as covered over at runrex.com, human behavior is much more predictable than we think and much more predictable than the stock markets.

Data is always changing when it comes to the stock market

As is explained in detail over at runrex.com, algorithms do better with stationary data. This is yet another thing that goes to explain why data science, as of yet, has been unable to predict the stock market. This is because data about the stock market is far from stationary, given that data related to good investments is always changing. This makes it difficult to come up with algorithms that can get consistent results in predicting the stock market.

Stock markets and noise and signals

Experts, including those over at runrex.com, will tell you that another reason why data science has of yet been unable to predict the stock market is that there is usually more noise than signal when it comes to the data collected. This is seen in the fact that stocks will move up and down for no apparent reason. It, therefore, becomes difficult for machines to figure out what the noise is and what the signal is which is why it is difficult to predict the stock market with data science.

The data set is also not that big

One of the reasons why data science can be used to predict human behavior is because there is enough data available for a prediction to be made based on one’s past behavior. For instance, as covered over at runrex.com, most people will have uploaded hundreds of images of themselves o their phone or on social media, making it easier for data science to predict their behavior. The same cannot be said of the stock market, given that there are only 119 years of stock market data. On top of that, not all companies have been listed on the stock market for the entire 119 years, which means that there is not enough data to make accurate predictions with data science.

Changes in an unrelated area could affect a company’s stock

Another thing that makes it difficult to predict the stock market with data science is because most of the time, an event that is seemingly unrelated to a given stock could have a big impact on it according to the gurus over at runrex.com. Some of these events may be extremely difficult to predict, like say a hurricane or a coup. All these variables have played a part in explaining why data science has yet not been able to be used to predict the stock market.

You are usually dealing with very small differences and margins when it comes to the stock market

When it comes to the stock market, a small difference in a price may be the signal required to sell a stock. These differences are usually too small for machines to pick up on, according to discussions over at runrex.com, which is why it is difficult for data science to predict the stock market. When it comes to data science, machines need clearer results and patterns for their algorithms to make a prediction.

The use of alternative data

It is important to point out that data scientists today are making use of what is referred to as alternative data, combining this with traditional data, with studies showing that with the right data, this combination has computers outperforming humans by 57%. As discussed over at runrex.com, alternative data is data that is less traditional and, usually, out of the control of the company. Examples of alternative data include credit card transactions, social media activity, cell phone usage, product reviews among others. 57% may not seem like much, but it is enough of an advantage to net investors billions of dollars.

Hopefully, the above tips will help you understand if data science can be used to predict the stock market, with more on this topic to be found over at runrex.com.

10 Tips. Can Data Science be Self Taught?

10 Tips. Can Data Science be Self Taught?

As is discussed over at runrex.com, data science is one of the most trending and most highly-rated career paths right now. Because of this, more and more people are looking to get into this career path for job satisfaction and the relatively high wages. Some are even looking to pivot from their career paths, some of which are unrelated to tech, to get into data science. The question that begs is if data science can be self-taught, particularly due to its large body of knowledge. This article, with the help of the gurus over at runrex.com, will look to help in answering this question by as well as highlighting how one can be a successful, self-taught data scientist with the following 10 tips.

Yes, data science can be self-taught

The simple answer to the question posed by this article is, yes, data science can be self-taught, something the subject matter experts over at runrex.com agree with. This is because of the sheer number of online resources which have ensured that self-learning is not beyond the scope anymore when it comes to data science, and any other field too. As revealed in discussions over at runrex.com, almost all of the skills required to be a successful data scientist, from programming to machine learning can be acquired through self-learning with the help of the resources available online.

Job prospects

It would make little sense to self-learn data science only to find out that self-taught data scientists aren’t hot in the job market, which is why the next tip is going to highlight the hiring trends in data science to know if you would get a job as a self-taught data scientist. According to the gurus over at runrex.com, different companies have got different preferences when it comes to the job candidates they hire for data science jobs. Many, like LinkedIn, hire candidates based on their skill set and not necessarily on one’s educational background. You will still encounter barriers, but there is a job market for self-taught data scientists.

Paid resources

One of the avenues you can choose when looking to become a self-taught data scientist is one that makes use of paid resources. These, as discussed over at runrex.com, include Udacity, DataCamp, Dataquest among others. These online resources will not only create an education program for you that will guide you from one topic to the next, but they also require you to do very little course-planning, if any at all. If you have the money, then you can try these resources, as per the advice of the gurus over at runrex.com.

Free alternatives

If you feel like the above resources are too expensive for you, then there are free alternatives that you can explore. According to discussions over at runrex.com, sites like edX and Coursera offer free, one-off courses on specific topics, and are a great resource if you can learn well from videos or a classroom setting. There are several free data science courses which you can search for online, just make sure you choose one that is credible and the right fit for you.

Learning python

As the gurus over at runrex.com will tell you, programming is a key skill as far as data scientists are concerned, with Python being the go-to programming language here. If you are to become a self-taught data scientist, you will need to learn Python and get comfortable with its syntax. Many resources can help with this, including “Learn Python the Hard Way” by Zed Shaw, the coding challenges on CodeSignal among other resources.

Learning SQL

Another thing you will have to do if you are to become a self-taught data scientist is to learn SQL. Here, as per discussions over at runrex.com, there are several online resources you can use, including the Mode Analytics tutorial on SQL which will help you learn all the key concepts of SQL, helping you create a robust SQL foundation. On top of that, they will also provide you with their SQL editor and data, which you can use for practice.

Learning statistics and linear algebra

You will also require to have some understanding of statistics and linear algebra if you are to become a self-taught data scientist. This is because, as explained by the gurus over at runrex.com, statistics and linear algebra are a prerequisite for machine learning and data analysis. Several online resources can help you with this, with the key being to make sure you focus on descriptive analysis as this will help you in understanding data sets.

Learning machine learning

In your journey to become a self-taught data scientist, you will also have to learn machine learning; learning both the theory and application of the machine learning algorithms and applying concepts learned to real-world data as per the subject matter experts over at runrex.com. Many resources can help with this, from Udacity to the UCI machine learning repository and many others. If you have the resources, you can also try pout the Grokking Deep Learning book which provides clear and relatable examples on machine learning and its fundamentals.

Apply your learned data science skills

While it is important to learn all the above skills, and many other such as database manipulation, it is crucial that you also apply all these skills in projects if you are to be a successful self-taught data scientist. This will allow you to practice what you have learned while also showing potential employees the scope of your knowledge as far as the practical use of your skills is concerned. As discussed over at runrex.com, resources such as Kaggle, GitHub, and others will help you create a data science portfolio. It is worth noting that tech companies nowadays actively scour these online portals when looking for talent.

Gather work experience

It is important that you also go further than just learning the relevant skills needed to be a data scientist and applying them on online projects. To give yourself an edge over competitors, you will need to gather some actual work experience, as per the folks over at runrex.com. To achieve this, you should try looking for freelance gigs and internships, which will give you experience in a corporate environment.

The above discussion focusses on some of the tips to keep in mind if you are to be a self-taught data scientist, and a successful one at that, all of which show that data science can be self-taught. You can get more information on this and other related topics by visiting the highly-rated runrex.com.

10 Tips. Are Data Science Masters Worth It?

10 Tips. Are Data Science Masters Worth It?

There can be no denying that a career in data science is one of the most attractive career paths out there, as discussed over at runrex.com. It is no surprise that more and more people are looking to get into the career path and become data scientists. To gain the necessary skills required to be a data scientist, many people find themselves wondering if a master’s in data science is worth it. While master’s programs in data science provide good education, they may not be worth it, something this article, with the help of the gurus over at runrex.com, will look to articulate with the following 10 tips.

You can enter the profession without an additional degree

Those that argue that master’s degree programs in data science are not worth it do so by pointing out that most professionals don’t have these master’s degrees, as is covered over at runrex.com. Many have degrees in mathematics, statistics, and other such degree programs including degrees in economics and even political science. This, as per the gurus over at runrex.com, shows that for one to get a job as a data scientist, they don’t necessarily need a master’s degree on the same as all they need is a firm grasp of the requisite skills that will help your employer solve business problems as well as the ability to convince them you can do so.

Finances

Master’s degree programs are not cheap, as is revealed in discussions over at runrex.com, and it is something to consider if you are wondering if they are worth it. Some will argue that a career in data science is a lucrative one, and as such the costs don’t matter as your salary will more than make up for this, but this is not always the case as, while there are data science jobs that pay highly, the national average when it comes to annual salary is lower than what is reported out there, as explained over at runrex.com. Salary is still significant, but not enough to disregard the costs of a master’s degree. If finances may be an issue for you, then the master’s degree may not be worth it.

Time and lost wages

A master’s degree in data science will also require you to commit at least 2 years of your time to graduate. This may also be a factor when deciding if it is worth it, as in most cases, you will not be able to earn in those two years you will be in school. Given that, as mentioned above and discussed over at runrex.com, you may not require a master’s degree to get a role in data science, it may not be worth it, considering the time required and lost wages that come with it.

You may not be academically prepared

If you have a bachelor’s degree in mathematics, statistics, or computer science among other such degrees, then you could transition to a master’s in data science no problem. However, if you have an unrelated degree like say a degree in art, then doing a master’s degree in analytics or any other data science master’s degree may be problematic as per the folks over at runrex.com. While interdisciplinary learning is a good thing, if you don’t have any training in math and statistics or any experience with programming or databases, then you may not be academically prepared for a master’s degree in data science, and as such it may not be worth it.

Your motivation

Another thing that will determine if a master’s in data science is worth it is your motivation for pursuing the program. If your motivation is because you are looking to increase your skill and knowledge level and have the time and money for the same, then a master’s degree is worth it. However, if your motivation is so that you can get a better paying job, probably because you are unhappy with your current job, then it may not be worth it and you may be better off just looking for another job as per the subject matter experts over at runrex.com.

You may end up learning the same content you had learned before

As per discussions on the same over at runrex.com, the content tackled at the master’s level when it comes to data science is very similar to the one learned at the basic degree level. Java Script won’t change just because it is being taught at the master’s level. The only thing that may change is the complexity of examples taught and you will be exposed to more complicated scenarios. From this point of view, particularly since you don’t need a master’s degree to get a job in data science, then a master’s in the same may not be worth it.

The time could be better used to gain experience practicing data science

Rather than spend 2 years pursuing a master’s degree, you could use that time to sharpen your skills and earn more experience through mentorship or an internship, which may be more useful for you in the long run. By actually practicing data science, you will learn crucial soft skills such as communication and problem-solving skills, which will help make you a better data scientist as per the gurus over at runrex.com. This is another tip as to why it may not be worth it pursuing a master’s in data science.

It may be better to get a job first

If you have a bachelor’s degree, instead of pursuing a master’s degree, you might want to consider getting a job first, and then later on you can apply for your master’s. Don’t substitute your job as mentioned above, or the job search, which can be tough as explained over at runrex.com, for further education. Experience is usually more valued than education level, and if you don’t have any, you might consider looking for a job first, even as a volunteer or intern, before you consider a master’s degree. Hiring managers will rarely want to take a risk on inexperienced candidates, which means that in certain cases, a master’s degree may not be worth it.

You will need to put your skills into practice or risk forgetting them

If you have already acquired skills and traits in your undergraduate studies that qualify you for a role in data science, then, as per the gurus over at runrex.com, you need to put them to practice or you will forget them. Therefore, a master’s degree may not be worth it in such a situation given that skills in technical fields like computing and statistics will be unlearnt if not practiced. This is why it is better to look for a job rather than pursue a master’s degree as discussed in the point above.

You may not know your strengths and weakness if you don’t put your skills to practice

Another tip that shows that a master’s degree in data science may not be worth it is because it will prevent you from getting an idea of what your strengths and weaknesses are. This is because, when learning, you rarely get to put your knowledge and skills to practice in a practical setting. However, if you seek employment as mentioned earlier on, as you work, you will be able to recognize your weaknesses and put more effort to improve on them, as well as your strengths, according to the folks over at runrex.com.

From the discussion above, it is probably not worth it to pursue a master’s degree in data science, although the decision is very much up to you, based on your situation. There is more on this and other related topics over at runrex.com.

10 Tips. Are Data Science Jobs in Demand?

10 Tips. Are Data Science Jobs in Demand?

Data scientists, as discussed in detail over at runrex.com, are the professionals who sift through the enormous amounts of numerical information that companies are generating daily, and with their training, they make sense of the numbers and data, revealing insights which the company can use to their benefit. Due to the Big Data trend, which is discussed over at runrex.com, data science has gained tremendous attention and traction which means that data science jobs are in high demand. This article will look to highlight 10 reasons why data science jobs are in demand.

The explosion of data

One of the direct consequences of the business world moving online, as discussed over at runrex.com, has been the explosion of the data being generated. This is because, with billions of connected devices all around the world, data getting generated has exploded as these devices are generating millions of terabytes of data daily, as tackled over at runrex.com. This is one of the reasons why data science jobs are in demand, as all these terabytes of data are available, waiting to be tapped, with data scientists being the professionals with the skills to analyze and derive useful conclusions from said data.

Applications of data science are ever-increasing

According to the gurus over at runrex.com, there is no single industry out there today that doesn’t generate or rely on data. From healthcare to agriculture and every other industry in between, data is an important part of their operations. This means that data science is relevant in practically all industries out there, which is yet another reason that has fueled the demand in data science jobs since data scientists can work in practically any industry and sector, as explained over at runrex.com.

Data science is now more affordable due to advancements in technology

The advancements in technology, which we have seen in recent times have also contributed to the fact that data science jobs are now in demand. Computers are now faster with greater processing power, and are now cheaper and more readily available. The internet is also spreading quickly, with more and more people being connected to the internet as discussed over at runrex.com. This has meant that companies are now investing in data scientists which have fueled demand for data science jobs.

Salaries

The fact that data science jobs are among the highest-paying jobs out there today, as revealed in discussions over at runrex.com, is yet another reason why data science jobs are in demand. Given the benefits that are there to be had by leveraging data science, as well as the fact that there are, is limited talent available, companies are willing to pay highly for data scientists, sometimes even 50% more than what is paid for other conventional technical roles, which has contributed to data science jobs being in demand.

Shortage in talent

As mentioned in the point above, and discussed over at runrex.com, there is a shortage of qualified data science professionals on the market today, which is yet another reason that has led to data science jobs being in demand. Studies have shown that most companies have reported that there is a lack of appropriate analytical skills when looking for data science professionals, which is why the few who are qualified and available are highly sort-after. The widening gap between the needs of companies and the abilities of job candidates to fulfill said needs has contributed to data science jibs being in demand.

An increase in reliance on data-driven insights

Another reason that has led to data science jobs being in demand is the fact that companies and organizations are increasingly relying on data-driven insights, as explained over at runrex.com. Data plays an important role in the decision making of companies now as they have realized the power of the data which they collect has in improving operations and increasing profit margins. Every decision companies make now is highly considered and driven by data in one way or another, leading to the demand in data science jobs.

The changing face of the consumer

As is revealed in discussions over at runrex.com, the consumer today is more empowered than ever before and can make informed decisions on what they want without relying too much on what they are told by brands. Companies now have to gather insights into their data to offer their consumers personalized services and experiences, which has also fueled demand for data scientists and consequently, for data science jobs. Data scientists play an important role in turning the large amount of data companies collect into action and actionable intel.

The rise of machine learning and AI

According to the subject matter experts over at runrex.com, the rise of machine learning and AI has also played a role in the demand for data scientists hence the demand for data science jobs. Many companies see data scientists as being crucial if they are to embrace and leverage AI and machine learning and stay on the cutting-edge of technology, keeping up with the latest trends. This has played a key role in data science jobs being in demand.

The mix of skills required for one to be a data scientist

Another reason that explains why data scientists and data science jobs are in demand is due to the mix of skills required for one to become a data scientist. As explained over at runrex.com, to make sense of the large amounts of data gathered daily and come up with useful insights, companies need people with a mix of skills including skills in statistics, database management, data visualization, machine learning, coding, and many others. This mix of skills means that data scientists are a rare breed and explains why data science jobs are in demand.

The power resides with data scientists

The fact that the power remains with data scientists when it comes to recruitment makes the job very attractive and explains why data science jobs are in demand. As covered over at runrex.com, with a shortage of talent in the market, many applicants can be selective on the jobs they want and the terms they are looking for, which has contributed to data science jobs being in great demand as companies go above and beyond to recruit the best talent around.

The above are some of the reasons why data science jobs are in demand, with more on this broad topic to be found over at runrex.com.

10 Tips. Are Data Science Jobs Competitive?

10 Tips. Are Data Science Jobs Competitive?

As is revealed in discussions over at runrex.com, data science is one of the most attractive career paths out there right now, with some of the highest paying jobs and tremendous job satisfaction. The question that most people have been asking is if data science jobs are competitive. This is something this article, with the help of the subject matter experts over at runrex.com, will look to help with through the following 10 tips.

There is a greater pool of talent

One of the reasons why data science jobs are competitive is because there is a greater pool of talent now than it was before as discussed over at runrex.com. There was a time when there was an extreme lack of talent in the industry and as such data science jobs weren’t as competitive with a high demand for data scientists and little supply. However, nowadays, more and more people are pursuing a career in data science, as covered over at runrex.com, and, therefore, data science jobs are more competitive.

Better university programs, online courses, and bootcamps

The fact that there are better university degree programs, as well as better online courses and bootcamps, has also led to a spike of qualified talent, making data science jobs more competitive. As revealed in discussions over at runrex.com, there was a time when most students graduating from universities didn’t have the skills companies were looking for as far as data science jobs are concerned. However, most universities have now built impressive programs that are churning out better-prepared students, as per the gurus over at runrex.com, making data science jobs more competitive.

Companies are more informed

When data science was in its infancy, there was a lot of misinformation out there in subjects such as AI, which played into the hands of candidates for data science positions, and as such data science jobs weren’t as competitive. However, companies are more informed today and know exactly what they are looking for in terms of skills in candidates for positions, as explained over at runrex.com, making data science jobs more competitive than before. 

Companies are aware of the impact on the revenue

According to the gurus over at runrex.com, data science can have a measurable and long-term impact on business revenues, something many companies are now all too aware of. Therefore, firms hiring for data science positions are only looking for the cream of the crop and the most highly skilled professionals. This has made data science jobs extremely competitive as candidates have to work hard to separate themselves from the crowd and show that they are the best candidates for the job given that data science plays a critical role in the fortunes of businesses.

It depends on the level of the job position

Another thing worth noting is that senior-level data science jobs are highly competitive as compared to junior-level roles. As revealed over at runrex.com, given the crucial role data science plays in business operations, as mentioned above, these senior-level data science positions are extremely competitive, with candidates having to push the envelope and show that they are the best candidate for the job as companies will not just hire anyone for such a crucial role.

Competition for citizen data scientists

Another thing that has made data science jobs more competitive is because citizen data scientists, software power users who can do moderate data analysis tasks as explained over at runrex.com, are also beginning to compete for the data science jobs in the lower rungs. Companies still bring in expert data scientists for more senior positions, but most are utilizing citizen data scientist for the lower-rung positions, increasing competition for data science jobs.

It depends on the company

Just as is the case for most industries, not all job openings are the same when it comes to data science. This means that there are certain job openings, depending on the company, which will attract more candidates and will be more competitive than others. As is covered over at runrex.com, there are certain companies and organizations which are a dream to work in and, therefore, every data scientist will be looking to apply for positions in the company when they become available. In such a situation, the data science job will be extremely competitive.

It depends on the role

It is also worth pointing out that certain roles are more competitive than others when it comes to data science jobs. For example, as the subject matter experts over at runrex.com will tell you, machine learning data science jobs will have tougher requirements and will, therefore, be more competitive as you will need to outshine your fellow candidates to show that you are the better candidate for the particular position.

There is no one particular route to becoming a data scientist

The fact that there is no particular route to becoming a data scientist has also contributed to making data science jobs more competitive than they were a few years back. As per discussions on the same over at runrex.com, you don’t need a bachelor’s degree in computer science, mathematics or statistics to become a data scientist as we have people with other degrees pursuing a career in data science, with the help of bootcamps, certification, and online courses. This means that there is increased competition, increasing competition for data science jobs.

The focus is shifting towards domain knowledge

Another aspect that has made data science jobs competitive is that the focus is slowly shifting towards domain knowledge. This means that, as discussed over at runrex.com, companies are now looking for data scientists with the ability to interpret and apply their knowledge from a business and domain standpoint, which has made getting data science jobs more competitive.

The above discussion is only the tip of a large iceberg, and you can uncover more information by visiting the ever-reliable runrex.com.

10 Tips. Are Data Science Bootcamps Worth It?

10 Tips. Are Data Science Bootcamps Worth It?

As is covered in detail over at runrex.com, data science is a complex field, requiring one to master a myriad of skills such as mathematics, business operations, statistics, process understanding, algorithms, databases and data management, programming, and many others. Given that you will need to master all these skills at a high level to give yourself a chance of landing a data science job, and that bootcamps usually last about 10-16 weeks, are data science bootcamps worth it? Well, this article, with the help of the subject matter experts over at runrex.com, will look to answer this question with the following 10 tips.

Costs

The first consideration that will give us an idea of if data science bootcamps are worth it is the cost of the Bootcamp. Here, as is discussed over at runrex.com, you will find that data science bootcamps are significantly more affordable than the master’s program alternatives. When you approach things from a cost point of things, then, as the gurus over at runrex.com will tell you, data science bootcamps look pretty worth it as they are substantially cheaper as compared to a master’s degree.

Time

On a related note, and in yet another tip that should help you decide if data science bootcamps are worth it, you can also consider time as a factor. As is also covered over at runrex.com, while data science bootcamps will last anywhere between 10-16 weeks, with 13 weeks usually being the sweet spot, their master’s program counterparts will require you to commit more than 2 years for you to graduate. The time factor is key, since, as revealed in discussions over at runrex.com, data science bootcamps allow gradates to start earning money as a data scientist sooner as compared to those who choose to go for master’s degree programs. From this standpoint, data science bootcamps are worth it.

Contact hours

Given that they take a relatively short amount of time as compared to master’s degree programs, data science bootcamps, as is the case for most Bootcamp programs, are intensive and immersive programs with a large number of contact hours per day as discussed over at runrex.com. You will have over 6 hours each day of contact hours in a good data science Bootcamp, which will include both teaching time and practice time, with this schedule being followed most days of the week or every weekend for those who have chosen a weekend program. This allows for the Bootcamp to cover a lot of material in a short period, which shows that it is worth it from this point of view, as it highlights that, though data science bootcamps are relatively short, they cover enough material to give you the skills and knowledge you require

Your learning style

On a related note from the point made above, another consideration that will govern if data science bootcamps are worth it for you is your learning style. Given their intensive and fast-paced nature, not to mention that they are extremely hands-on as discussed over at runrex.com, data science bootcamps are worth it for those who like to learn by doing or by practice. If this is not your learning style, then they may not be worth it for you as you may be left behind in class.

Your level of past qualifications and experience

Another main consideration when looking to answer if data science bootcamps are worth it is when it comes to one’s level of past qualifications and experience. According to the gurus over at runrex.com, here, a data Bootcamp science would make sense if one is a relative novice or a fresher on matters data science since a data science Bootcamp will cover enough material and equip them with enough skills and knowledge for an entry-level job. However, if you are not a fresher, then a data science Bootcamp may not be worth it, and you may want to consider how your experience can help you in your data science career. For example, if you have database management skills, you could use these skills to get roles that need extensive data processing requirements.

Your level of expertise in mathematics and coding

Other than your level of past qualifications and experience, your level of expertise in math and coding will also determine if data science bootcamps are worth it for you. This is because, as discussed over at runrex.com, data science bootcamps are generally meant for students with some sort of expertise in mathematics and coding. If you are, therefore, a coding novice, or don’t have some sort of functional mathematics knowledge, then a data science Bootcamp may not be worth it for you just yet.

What data science role are you aiming for?

As mentioned earlier on, and as discussed over at runrex.com, data science is a broad and complex field, which means that, no matter how well a short program like a Bootcamp is designed, it won’t help you master data science. So, to make your data science Bootcamp worth it, make sure you know the data science role you are aiming for so that you can attend the right Bootcamp. For instance, if you are aiming for a machine learning developer role, you should look for a Bootcamp program that will equip you with programming expertise as well as expertise in machine learning to make it worth it.

Outcomes after the Bootcamp

The outcomes after the Bootcamp will also determine if the data science Bootcamp was worth it for you. If you can’t get hired after a data science bootcamp, then I’m sure you will conclude that data science bootcamps are not worth it. As per the gurus over at runrex.com, you should, therefore, look for a good data science bootcamp program, one with a high percentage placement rate, so that you can give yourself an excellent chance of getting hired after the program.

Your passion for the field

It is no surprise to find people pursuing careers that they are not passionate about, just because they have heard that the particular career path is what is now popular and will guarantee them a shot at a well-paying job. If this is your motivation, then maybe data science bootcamps are not for you. This is because, as highlighted by the folks over at runrex.com, if you get a job afterward and dislike it, then, was the data science bootcamp worth it? If you lack passion for the field, you might find that you dislike coding, and if this is the case, then data science bootcamps are not worth it for you since you will be doing lots of coding in whatever program you choose to pursue.

An opportunity to network and get job opportunities

If you attend a good data science bootcamp, then you will get an opportunity to network and get job opportunities which will make it worth it. Such bootcamps, as explained over at runrex.com, will allow learners to collaborate with a private community of alumni who will not only offer great insights and resources but will also post job openings when available helping one get employment after their bootcamp program has ended. From this point of view, data science bootcamps are worth it.

The above discussion only scratches the surface as far as this topic is concerned, and you can learn more about data science and data science bootcamps by visiting the highly-rated runrex.com.

10 Tips. Are Data Science and Machine Learning the Same Thing?

10 Tips. Are Data Science and Machine Learning the Same Thing?

The terms data science and machine learning are often thrown around together, but as the gurus over at runrex.com will tell you, these two are not the same. One of the reasons why most people have been asking if data science and machine learning are the same thing is because data science includes machine learning, and, therefore, whenever data science is brought up, machine learning is never far behind. However, the two are not the same thing, and this article, with the help of the subject matter experts over at runrex.com, will look to highlight why with the following 10 tips highlighting differences between the two.

Definitions

It is important to differentiate the two by defining them individually, after which you will realize they are not the same thing. As discussed over at runrex.com, data science is a field of study that aims to make use of a scientific approach to get meaning and insights from data. On the other hand, with the same also being discussed over at runrex.com, machine learning refers to a group of techniques used by data scientists that allow computers to learn from data and produce results that perform well without the programming of explicit rules.

Differences in the measurement of performance

Another aspect that shows that the two are not the same thing is when it comes to the measurement of performance, with a detailed write-up on the same to be found over at runrex.com. Here, in data science, performance measures are not standardized, which means that they change from one case to another. On the other hand, in machine learning models, performance measures are clear and set in stone and as such, each algorithm will have a measure to show how well or bad the model describes the training data given.

Differences in visualization

Visualization of data is another area where data science and machine learning differ, something discussed in detail over at runrex.com. In data science, generally, data is represented using any of the popular graphs such as bar graphs, pie charts, line graphs among others. On the other hand, in machine learning, as highlighted over at runrex.com, visualizations also involve a mathematical model of training data.

Differences in development methodology

As the subject matter experts over at runrex.com will tell you, data science projects have clearly defined milestones, and resemble engineering projects in this regard, with established milestones being ticked off along the way until the project is completed. This is another area where data science and machine learning differ because, in machine learning, projects take a more research-like approach, where they start with a hypothesis that you will try to prove with the data available to you. The differences in development methodology show that the two are not the same thing.

Differences in languages used

Another area that shows that data science and machine learning are not the same thing is when it comes to the programming languages used. When it comes to data science, the most commonly used programming languages are SQL and SQL-like syntax languages such as HiveQL among others covered over at runrex.com. On the other hand, Python and R are the most used programming languages in the machine learning world.

Skillset required

Given the differences between the two, particularly in the programming languages used, another aspect showing that the two are not the same thing is in the skillset required in each of them. When it comes to data science, as explained over at runrex.com, some of the skills required include domain expertise, strong SQL knowledge as well as knowledge on data profiling, and many others. On the other hand, some of the skills required when it comes to machine learning include a strong understanding of mathematics as well as an understanding of Python or R programming among others.

Differences in system complexities

The differences in system complexities when it comes to the two also goes to show that they are not the same thing. On one hand, in data science, the complexity comes in the fact that there are lots of moving components as well as due to the components for handling unstructured raw data coming in. On the other hand, in machine learning, as discussed over at runrex.com, the major complexity is with the algorithms and mathematical concepts behind it as well as the fact that ensemble models usually have more than one machine learning model, with each having weighted contribution on the final output.

Differences in scope

There is also a clear difference in scope as far as data science and machine learning are concerned, enough to show that the two are not the same thing. In data science, as the gurus over at runrex.com will tell you, the scope is to create insights from data dealing with all manner of real-world complexities. In machine learning, however, the scope is to accurately classify or predict outcomes for new data points by learning patterns from historical data with the help of mathematical models. The two have two very different scopes.

Differences in hardware specifications

This is yet another area showing clearly that data science and machine learning are not the same thing. This is because, in data science, horizontally scalable systems are preferred. After all, they are needed to handle massive amounts of data as explained over at runrex.com. When it comes to hardware, data science also places a bigger importance on the RAM and SSDs used to help overcome I/O bottlenecks. On the flipside, in machine learning, GPUs are preferred because of the intensive vector operations that come into play here.

Differences in input data

This is another aspect that shows data science and machine learning are not the same thing. This is because, in data science, input data is generated as human consumable data, which means that it is to be read and analyzed by humans, like say images for example. In machine learning, however, input data will be transformed specifically for algorithm use, with the addition of polynomial features being an example, with a more detailed write-up on this to be found over at runrex.com.

From the above discussion, it is clear that data science and machine learning are not the same thing, and you can get more information on this broad topic by visiting the amazing runrex.com.