What can data science do in the future?

Data science is almost an indefinite pool of diverse data operations by leveraging the power of which a data scientist should be able to accomplish the following in the future.

• AI will become omnipresent: Most of us have seen the power of AI applications in the fields of automation and robotics to some extent. But in the next few years, data science professionals are expected to take AI to such a level that robots will be able to do almost everything – from cleaning the houses to running a business. Probably you’ve seen that Google Assistant has already reached a level that it can make phone calls and book reservations in restaurants and in the upcoming future, its activities will become more specialized and broaden even more.
• Advanced personalization: With billions of users across the globe using smartphones, the amount of data being generated seems to be even more massive in the next few years. Therefore, businesses, with the help of data scientists, will be able to maximize the value of data in an even better way that’ll help them address their user-base more thoroughly.
• More accurate predictions: We can expect big data analytics tools to become so advanced that data science professionals will be able to help in real-time decision making. As a result, many key functions could be predicted with much higher accuracy than they’re being done today.

Apart from the above, we can expect to see more specialized career paths evolve. With advancements in the field, the overall status of data literacy will likely to improve across the workforce where employees other than data science professionals will obtain a better understanding of the usage of data. And thus, the future of data scientists would probably become even more specialized, handling the most complex and business-critical challenges which will help their companies become even more successful in their respective fields.

Conclusion

Today, it can be safely said that data scientists will have a prominent future and the field will stay for years to come. If you’re thinking of pursuing a data scientist career, perhaps this is the best time to start your journey. Magnimind Academy’s data science bootcamp in Silicon Valley helps students to become future-proof data scientists with unique combination skills which will be always be in great demand.

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

The post Why do people see Data Science as part of the future? appeared first on Magnimind Academy.

A significant number of newcomers in data science tend to spend a huge amount of time to develop theoretical knowledge and earn certifications only. While theoretical knowledge is certainly required to become a good data science professional, recruiters don’t put much emphasis on certifications only. Instead, they tend to evaluate the potential of a candidate by going through his/her work.

As a data science professional, you may have worked on lots of crucial problems, but if you fail to present them to the recruiters, getting a good job in the field may become even more difficult. And this is exactly where data science projects come to your rescue. They help you demonstrate your data science skills to prospective employers. Therefore, it’s important to pick your data science projects carefully. The process of picking up data science projects can be overwhelming, especially when you’re planning to mention them in your CV. In this post, we’ve outlined five top data science projects to help you in your endeavor.

1- Movie Recommendation Engine

This is one of the most common data science projects for everyone in the field. Every successful data science professional has developed at least one recommendation engine in the entire career. Personalized recommendation engines are considered highly effective when it comes to demonstrating data science skills.

Problem: To analyze the Movie Lens dataset in order to comprehend patterns and trends that will help the system to recommend new movies to users.

2- Walmart Sales forecasting

Retail is one of those industries where data science is being used extensively and thus, it’s important to have worked on at least one project related to it. There’re a plethora of tasks including inventory management, product placement, product bundling, customized offers etc are being handled efficiently utilizing different types of data science techniques.

Problem: Predict the department-wise sales of the store.

3- Text Mining

A text mining project in your portfolio may dramatically improve your chances of being hired as a data science professional. It involves data mining and advanced analytics that can prove your skills as a professional. Text mining is heavily used in social media monitoring as it helps to obtain an overview of a broader public opinion on specific topics.

Problem: Classify a set of documents according to specific labels.

4- Urban Sound Classification

This is one of those data science projects that will help you demonstrate skills in machine learning. This project is designed to getting you introduced to audio processing in the context of the usual classification scenario.

Problem: Classify the kind of sound from an audio.

5- Chicago Crime Analysis

Law enforcement agencies take help of data science techniques to understand the actual reasons behind crimes and thus, to be able to prevent their repetitions. While the problem may seem easy, data management is the key here.

Problem: Predict the type of crime.

Parting Thoughts

If you’re a complete beginner in the data science field, it’s important to select data science projects with limited variables and data. The above ones may seem a little challenging, but they should be fun to do.

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To learn more about data science, click here and read our another article.[/vc_column_text]

The post What is a good data science project? appeared first on Magnimind Academy.

In this post, we’ve tried to outline the key differences between these two positions to help you make an informed decision. Let’s start the discussion.

1- What’s a data scientist?

Data scientists are the people who’ve got the ability to derive actionable insights from massive datasets to address specific business problems. At their core, these people analyze massive amounts of data to develop applied mathematical models.

2- What’s a data engineer?

A data engineer is a professional who focuses on preparing the data infrastructure for analysis. Their job responsibilities encompass the production readiness of data and various other things like resilience, scaling, formats, and security.

3- Skills required for each position

At their core, data engineers come from a programming background. This usually encompasses Python, Java, or Scala. These people usually have an emphasis on big data and distributed systems.

Data scientists, on the other hand, usually come from a statistics and/or applied mathematics background together with computer science. These people also need to interact with different business domain experts to cultivate the desired insights.

4- Overlapping skills between a data scientist and a data engineer

There’re various skills where both of these positions’ abilities overlap. For instance, both of them overlap on programming. However, a data scientist’s are usually well behind that of a data engineer. They also overlap on analysis. Here, a data scientist’s analytics skills are well beyond the analytics skills of a data engineer. Probably the biggest overlap can be observed when it comes to big data. A data engineer uses his/her systems creation and programming skills to develop big data pipelines. And a data scientist uses his/her advanced math and limited programming skills to develop advanced data products utilizing those existing data pipelines.

At some organizations, data scientists are tasked with doing things that data engineers should. While data scientists aren’t equipped with the skills to become data engineers, they can acquire the skills. On the other hand, it’s far less common when data engineers begin doing data science. In reality, these positions aren’t interchangeable and it may not be completely easy for a data engineer to become a data scientist. However, recently we’re seeing a new breed of engineers who’re proficient in both data science and data engineering. These people have enough experience and knowledge to work in both fields. These people are called machine learning engineers who’re cross-trained to become experts at both fields. As the bar for performing data science is decreasing gradually, we can expect to see the value of these people increasing only.

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

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1- What is machine learning?

Put simply, machine learning is a subset of AI (artificial intelligence) and enables machines to step into a mode of self-learning without being programmed explicitly. Machine learning-enabled programs are able to learn, grow, and change by themselves when exposed to new data. With the help of this technology, computers can find valuable information without being programmed about where to look for specific piece information. Instead, they achieve it by utilizing algorithms which iteratively learn from data. Machine learning is unique within the field of artificial intelligence because it has triggered the largest real-life impacts for business. Due to this, machine learning is often considered separate from AI, which focuses more on developing systems to perform intelligent things. While the core concept of machine learning isn’t a new one, the ability to apply complicated mathematical calculations to big data automatically – quickly and iteratively – is a recent development.

2- Key characteristics of machine learning

In order to understand the actual power of machine learning, you have to consider the characteristics of this technology. There are lots of examples that echo the characteristics of machine learning in today’s data-rich world. Here are seven key characteristics of machine learning for which companies should prefer it over other technologies.

2.1- The ability to perform automated data visualization

A massive amount of data is being generated by businesses and common people on a regular basis. By visualizing notable relationships in data, businesses can not only make better decisions but build confidence as well. Machine learning offers a number of tools that provide rich snippets of data which can be applied to both unstructured and structured data. With the help of user-friendly automated data visualization platforms in machine learning, businesses can obtain a wealth of new insights in an effort to increase productivity in their processes.

2.2- Automation at its best

One of the biggest characteristics of machine learning is its ability to automate repetitive tasks and thus, increasing productivity. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent. The make accounting tasks faster, more insightful, and more accurate. Some aspects that have been already addressed by machine learning include addressing financial queries with the help of chatbots, making predictions, managing expenses, simplifying invoicing, and automating bank reconciliations.

2.3- Customer engagement like never before

For any business, one of the most crucial ways to drive engagement, promote brand loyalty and establish long-lasting customer relationships is by triggering meaningful conversations with its target customer base. Machine learning plays a critical role in enabling businesses and brands to spark more valuable conversations in terms of customer engagement. The technology analyzes particular phrases, words, sentences, idioms, and content formats which resonate with certain audience members. You can think of Pinterest which is successfully using machine learning to personalize suggestions to its users. It uses the technology to source content in which users will be interested, based on objects which they have pinned already.

2.4- The ability to take efficiency to the next level when merged with IoT

Thanks to the huge hype surrounding the IoT, machine learning has experienced a great rise in popularity. IoT is being designated as a strategically significant area by many companies. And many others have launched pilot projects to gauge the potential of IoT in the context of business operations. But attaining financial benefits through IoT isn’t easy. In order to achieve success, companies, which are offering IoT consulting services and platforms, need to clearly determine the areas that will change with the implementation of IoT strategies. Many of these businesses have failed to address it. In this scenario, machine learning is probably the best technology that can be used to attain higher levels of efficiency. By merging machine learning with IoT, businesses can boost the efficiency of their entire production processes.

2.5- The ability to change the mortgage market

It’s a fact that fostering a positive credit score usually takes discipline, time, and lots of financial planning for a lot of consumers. When it comes to the lenders, the consumer credit score is one of the biggest measures of creditworthiness that involve a number of factors including payment history, total debt, length of credit history etc. But wouldn’t it be great if there is a simplified and better measure? With the help of machine learning, lenders can now obtain a more comprehensive consumer picture. They can now predict whether the customer is a low spender or a high spender and understand his/her tipping point of spending. Apart from mortgage lending, financial institutions are using the same techniques for other types of consumer loans.

2.6- Accurate data analysis

Traditionally, data analysis has always been encompassing trial and error method, an approach which becomes impossible when we are working with large and heterogeneous datasets. Machine learning comes as the best solution to all these issues by offering effective alternatives to analyzing massive volumes of data. By developing efficient and fast algorithms, as well as, data-driven models for processing of data in real-time, machine learning is able to generate accurate analysis and results.

2.7- Business intelligence at its best

Machine learning characteristics, when merged with big data analytical work, can generate extreme levels of business intelligence with the help of which several different industries are making strategic initiatives. From retail to financial services to healthcare, and many more – machine learning has already become one of the most effective technologies to boost business operations.

Whether you are convinced or not, the above characteristics of machine learning have contributed heavily toward making it one of the most crucial technology trends – it underlies a huge number of things we use these days without even thinking about them.

3- Why the adoption of machine learning is getting thwarted?

It isn’t possible to predict whether machine learning-enabled systems will replace human workers or not. But it can be said that the biggest factor which is slowing down the advancements of cutting-edge technologies like machine learning is the lack of human skills. A new survey conducted by Cloudera reveals that for 51% of business leaders across Europe, it’s the skills shortage that was holding them back from implementation.

Machine learning, in a similar way like data science, is progressing in a clearly different way. As this technology trend involves capturing, collating, and interpreting data, an effective machine learning professional needs to a master of a huge number of disciplines – from mathematics and statistics to programming – all are required. As you may already imagine, machine learning is pretty complicated stuff and thus, it has become actually difficult for business leaders to find the right candidates who can help them to meet their digital transformation goals.

Those who are interested to become a machine learning professional should choose their learning avenue wisely. Though there are different types of avenues available including self-learning, traditional approach, bootcamps etc, most of them come with their own disadvantages. Given the broad spectrum of machine learning domain and its rapid advancements, aspirants need to understand that no course is actually comprehensive enough. If you too are interested in stepping into this field with real-life knowledge and possess the core skills to some extent, joining a bootcamp like the ones offered by Magnimind Academy would be a good idea.

Final Takeaway

These days, machine learning is gaining serious momentum throughout the world and it has become one of the key responsibilities of senior executives to steer their business in the right direction by leveraging its true characteristics. We are at the verge of entering a world where machines and humans will work in harmony to collaborate, campaign, and market their products/services in an innovative way which is more personal, effective, and informed than ever before. In order to attain this, it is the time for business owners to think about how they can leverage machine learning characteristics, how they want the technology to operate and behave to take the business forward. It’s also important to roll out an effective and transparent strategy encompassing machine learning. It’ll help the teams to understand how they can perform their tasks more effectively by embracing the power of machine learning.

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

The post 7 Characteristics of Machine Learning appeared first on Magnimind Academy.

With all these in mind, it can be concluded that if you’re not satisfied with your income or present job role, learning immersive data science would be your best bet to change the scenario drastically.

1- Why data scientists get paid so much?

You may wonder why data scientists get to earn such a high salary. First of all, these people are extremely difficult to find, given the combination of skills and knowledge necessary to perform data science tasks well.

The key reason is probably that proper data science involves three different fields of expertise. A data scientist blends programming skills, math, and statistics expertise and domain knowledge (the industry they operate in). After all, looking at data from different angles for using it in prescriptive and predictive modeling is the backbone of any business that deals with data. If they fail to do it right, all the data collection and presentation would be meaningless for them. In short, data science is difficult and thus, richly rewarded.

2- Why you should become a data scientist?

Let’s have a look at the benefits, apart from high salary, which you’ll be able to enjoy as a data scientist after completing an immersive data science course.

2.2- You’ll be working on interesting and useful things

A large percentage of data science is about researching human behavior. Whether you’re working on predicting stock prices or creating a chatbot or understanding the online user experience, it’s mostly about how humans think and act. This unique approach of understanding and researching should motivate you enough to keep going.

2.2- It’ll be actually easy to find a higher paying job

Businesses are looking for experienced data scientists desperately and are ready to offer an even more high salary than what’s on offer at present. This scenario will probably change in the future, but will not happen in the near one if you consider the current formal education necessary to pursue data science.

So, if you learn immersive data science now and get a job as a data scientist, in the future, say after 5-10 years from now, you’ll be an experienced data scientist who’ll be in great demand.

2.3- You’ll be able to work across industries

Nearly every business needs data scientists to excel in its field. From the online world to production companies to biology to renewable energies to information technology, you’ll get to enjoy a lot of horizontal mobility in the field of data science.

2.4- You’ll be able to develop your own product

You’ve to learn how to code in order to learn immersive data science. And when you know how to code, you’ll be able to develop your own products, or the prototypes at the least. In short, a good understanding of coding will open a new world for you. If you won’t actually develop your own products, you’ll become a good data scientist for sure.

3- Key things to remember

Hopefully by now, you’re convinced enough and have made up your mind to learn immersive data science to take your income to the next level. Before you start your journey, it’s crucial to get a good understanding of the specific competencies and skills employers generally look for before they offer you such a high salary. Let’s have a look at the typical job responsibilities of a data scientist.

• Data acquisition, collection as well as storage
• Discovery and goal identification
• Access, absorb, and integrate data
• Processing and cleaning of data
• Initial data investigation
• Exploratory data analysis
• Choosing one or multiple algorithms and models
• Applying various data science techniques and methods
• Measuring and improving results

4- Key reason behind the paucity

When it comes to data scientists, there’s a huge gap between the demand and availability of competent ones. One key reason for this shortage is probably because many businesses are looking for people who can bring an array of high-end skills to the table. In short, almost any business, regardless of the industry it’s operating in, can get excellent benefits by hiring data scientists, which makes the demand exponentially high.

5- Getting that high salary job

We’ve already discussed the probable job responsibilities of a data scientist. Perhaps you are already aware of the skills necessary to become a member of that high-income group. If not having the requisite degree is what’s holding you back from switching your career and stepping into the field of data science, there’s still hope for you.

You can still get your dream job quickly enough with some smart decisions, proper planning, and complete dedication. There’re some great data science boot camps that offer immersive data science courses designed to help you gain practical knowledge that’s necessary for working in the field. These boot camps are generally aimed at students who hold a bachelor’s degree and come with an aptitude for math and statistics. Knowledge of a programming language like Python or R is considered a plus. However, some schools require students to have master’s degrees or Ph.D. degrees before taking these courses.

5.1- What’s in a boot camp?

Put simply, if you want to learn immersive data science, a data science boot camp is what you should head straight to. The programs are intensive and usually come with three-to-six-month duration, and prepare students for entry-level and junior data scientist jobs. When pursuing an immersive data science course, you’ll be able to learn an array of languages and frameworks together with various technical skills in data visualization, data analysis, predictive analytics, statistical analysis, and much more. Let’s have a look at the advantages of attending an immersive data science course at a data science boot camp.

• Boot camps typically cost less and are significantly shorter compared to traditional programs.
• Many boot camps provide various career services like networking opportunities, preparation for job interviews, career coaching after graduation etc.

Students who’re looking to get into the field usually pursue immersive data science courses for a number of weeks, which are available at a wide range of prices. It’s important to note that these courses vary in the work expected, time commitments, and topics covered. So, you’ve to be sure about everything before enrolling into one.

Though the cost of an immersive data science course in a boot camp may seem hefty upfront, it’s more reasonable than going back to the college for a master’s degree. In addition, knowing you’d only be away from a paying job for a couple of months instead of for years will surely make you feel much more comfortable, especially when you’re doing it for switching to a high salary job. But you should also remember that boot camps aren’t a golden ticket to a job with a great income opportunity – they demand work and you’ll get back the result based on what level of effort you put into them.

5.2- There’s still something that you should know

Before you enroll with an immersive data science course at a boot camp, you should understand that these programs aren’t structured like traditional programs. So, how much you benefit from them depends on how much effort you put in. Before joining a program, you should think about what actually you want and what skills you actually need to gain. In short, you’ve to really invest in them to make the most out of them. For example, the projects and homework that are often required in such a program can be great ways to get your portfolio started. So, you’ve to make sure that you spend enough time doing those correctly with care and thought. You can learn a lot from an immersive data science program because it’ll pave your road to success, but you’ll actually have to practice hard and invest in the program.

Final thoughts

Joining an immersive data science program brings you extensive opportunities in terms of gaining relevant knowledge, the scope of networking, and hands-on experience, all of which will help you greatly in getting a job as a data scientist. But you should also understand that unlike a traditional program, the structure is entirely different here and your education will solely depend on your own efforts, engagement and dedication.

And finally, your learning won’t end once you complete an immersive data science program and land a job. You’ll need to keep on learning along the way, obtaining new skills and developing your resume, especially if you want to be positioned as a good data scientist. Depending on the actual position you go for, you’ll find that you need to remain competitive by maintaining the required credentials. The boot camp is obviously a great start, but you’ll have to ensure that you’re up to speed on every software, hardware and tools necessary for the job.

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

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Before we delve deeper into the topic, let’s have a look at some probable drawbacks of working as a full-time data scientist. We aren’t trying to say that these won’t happen if you get a part-time job once you’d become a data scientist, but chances will be pretty low for sure. And if you take the freelance route, these factors will hardly be playing a role.

1- Why a significant number of data scientists are dissatisfied with their full-time job?

Before we explore the reasons, it’s important to note that we’re not trying to discourage anyone who’s planning to become a data scientist and get a full-time job because the role can always be fun, rewarding, and stimulating.

1.1- You’re everything with regard to data

A lot of people, even in reputed tech organizations, don’t clearly understand the exact role and responsibilities of a data scientist. It means you’ll be, at least to them, the database expert, the analytics expert, the go-to reporting person, and more. In addition, it’s not just the non-technical persons who make lots of assumptions about your skills. Other persons in the tech department may also assume that you know everything about data.

Though it’s an uncommon phenomenon, sometimes a company that has very limited idea about data strategy may be under the impression that any data person is capable of fixing all the problems associated with data. On top of that, it’s sometimes assumed that because you know everything about data and unquestionably hold access to all data, you’re able to answer all the data-related questions.

1.2- Expectation fails to match reality

Many people want to become a data scientist to get involved into solving complex problems that can help businesses make a huge impact. But sometimes, expectations fail to match the reality. There’re lots of companies that fail to hire experienced data scientists and end up hiring junior ones. As a result, the person who came in to write smart algorithms often ends up creating analytic reports and/or sorting out the data infrastructure. Finally, these data scientists get frustrated because they don’t experience any value being generated by their effort.

Today, it may not be that difficult to become a data scientist, but for many of these professionals, a full-time job may be too tough a task to hold onto. In this scenario, a part-time job as a data scientist can be a great option for those who want to step into the field but don’t want to be burdened by the responsibilities of a full-time job. Introduction to data science is not finite at one thing.

2- Key tips to find a part-time job as a data scientist

Almost every successful company is data-driven today. Data literate employees, who work with data in order to help the business make timely and informed decisions, can surely leave a positive impact on the business’s operations and its bottom-line.

A surge in the adoption of big data has propelled different marketing strategies. Today, it’s of no use to simply make educated guesses or look at global trends to rise above the competition. Instead, businesses need to drill into the data really hard to obtain actually valuable insights. And this is probably the key reason why companies keep on searching for professionals who can add value to their business, be it as a part-time candidate or a full-time one.

So, despite the fact that working for a large company as a data scientist can be stimulating, rewarding and fun, many professionals these days are quitting their full-time jobs and trying to get a part-time job or the position of a freelance data scientist. Also, there’re people who want to become a data scientist but don’t want to take up a full-time offer. The reason is quite simple – freedom. Not everybody loves to get up every morning and head straight to the office with a card around their neck. There’re people who wish to work on their own terms and according to their own sweet convenience. And for all of them, getting a part-time job as a data scientist is the perfect option.

The appeal of higher pay together with a convenient work schedule is bringing a significant number of data scientists in the part-time job domain. To step successfully into the field, you need to have a clear understanding of two things at first: Where do you find part-time jobs as a data scientist? And where can you network with others in your field? Let’s have a look at the steps you’d need to perform to get answers to these questions.

2.1- Build a portfolio

Simply listing yourself as a data scientist looking for a part-time job on LinkedIn may not be enough. You need to publicize yourself by exploring other networks. One of the most useful ways to advertise your ability is by having a personal website that demonstrates your complete portfolio.

Generally, employers use portfolios to gauge aspirants’ ability for a part-time job. You’ll be able to communicate your motivations and strengths by showcasing your portfolio. It’s important to note that in your website, your portfolio section should be easily found. This will make it easier for a potential employer to find your contact information including phone number, email address etc.

Services and projects are two other sections that you need to give special attention to. In the services section, detailed and clear information about all the services that you’re going to provide should be listed. When it comes to pricing, you’ve to perform a detailed research and then decide on your pricing. If you’re a beginner, your prices should be lower than that of your competitors in the market. In the projects section, remember to highlight some of your best previous works to help the employers get a sneak peek of your competence.

2.2- Other avenues to explore

Other than having a personal website, it’s recommended to create a data scientist profile looking for a part-time job on platforms that receive a huge amount of traffic from potential employers. Directories that are frequently browsed by employers are the best places to start. You can start with, for example, Upwork. It’s one of the largest freelance and part-time jobs’ platforms and is a familiar name to companies across the globe. This way, you can be rest assured that your listing as a data scientist looking for a part-time job will get noticed there. Once you’ve signed up for an account with the platform, you’re free to browse the part-time job listings and apply to the ones you’re best suited for.

You can also use non-traditional methods to connect with potential employers to increase your chances of being noticed and get hired as a part-time data scientist. Being proactive is crucial for getting a part-time job as a data scientist.

2.3- Growing your network

Once you’ve secured your presence across various online platforms, it’s time to grow your network as well as your presence on the internet as an expert in the field. The web is filled with forums for data scientists who’re interested in part-time jobs like you to collaborate, share ideas, and learn from one another. Remember to make sure to participate regularly in professional groups to advertise your skills to your peers as well as potential employers. It’ll help you strengthen your position as a data scientist and even offer you valuable insights about the market trend, open positions etc.

Data scientists starting a new project often turn to online communities of experts to deal with difficult problems. If you’ve the ability to help, your peers in other communities will probably reach out to you with offers to join on part-time jobs. And this will get noted by potential employers too. A significant percentage of part-time jobs are found via online professional networks. So, it’s highly recommended to develop your network and enjoy the benefits of increased visibility.

Conclusion

When you’ve become a data scientist, you can always make a living getting a part-time job. But it’s immensely crucial to be proactive and try to make the most of your every available resource. To be master in data science, remember to do more than what your peers do. Implement the above tips, and search for entrepreneurs, startups and other organizations in search of the skill sets that you have, and you may end up getting a part-time job offer from one of them. Also, remember that learning never stops. So, explore every available avenue to read and learn. This way, you’ll not only pick up new skills while honing your old ones but would be able to solidify your position with valuable insight as well.

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

The post Becoming a Data Scientist is a Perfect Match for those Who Work Part Time appeared first on Magnimind Academy.

So, how do you prepare to step into the data science field? Though there isn’t any standard roadmap to follow to become a data scientist, there’re some options used by aspiring candidates like going through the traditional route, becoming a self-taught professional, or attending a data science bootcamp.

Among all these options, the last one i.e. attending a data science bootcamp has become the most preferred option among aspiring candidates. This is because these programs offer a multitude of benefits that are impossible to find if you follow any other option.

However, participating in a data science bootcamp and completing it successfully isn’t as simple as many may think and/or express it through their reviews on the web. In reality, there’re participants who fail to complete the program successfully or fail to make the most out of it.

In this post, we’ve put together five key tips that would help you survive a data science bootcamp and embark on your journey to become a data scientist.

1- Define your learning goals

What exactly motivates you to join a data science bootcamp? To switch into the data science field, expand your present skills, or just your personal interest? Write it down and review it frequently. Because there’re high probabilities that things will get tough and you’ll hit inevitable roadblocks, and it’ll be your goals that’ll help you get through.

You should also understand that participating in a data science bootcamp may sometimes feel like a difficult task. This is because participants of the program come from different backgrounds and with different levels of strengths and weaknesses. Given the wide spectrum of addressed topics, it’s quite normal if you find yourself a bit overwhelmed. Again, it’ll be your goals that’ll help you maintain focus and encourage you to invest your time and effort on what you want and need to learn exclusively.

2- Understand the power of preparation

A data science bootcamp is an intensive, fast-paced course where you get to learn both technical and non-technical skills that are relevant to the present-day data science industry. However, the reason we’re trying to emphasize on preparation is because graduating from a data science bootcamp successfully is difficult. While these programs greatly help aspiring data science professionals to step into the field by eliminating the need of following a complicated degree path, having a little bit of preparation can go a long way in sailing through the program.

Ideally, you should have a good understanding of statistics, probability, linear algebra, and Python, among others, before attending a data science bootcamp. Though there’re some good amount of resources available that would help you to gain this understanding, taking online courses is probably the best one among them. If you can pursue a data science preparatory course from the same institute where you’re planning to do the data science bootcamp from, it’d be even better.

3- Work hard to keep up with the pace

Put simply, a data science bootcamp offers a great deal of information crammed into a comparatively short amount of time. So, it’s quite normal that no participant is able to maintain the pace always. There’ll be lots of assignments, lectures, sessions, discussions etc which can easily make you feel overwhelmed. Here’re the things you should follow at the very beginning of the data science bootcamp.

• Start your assignments from the day they’re given to you. You don’t need to finish them immediately but something like going through them thoroughly will make them much easier and will also help you get an overview of them.
• When you’re participating in a data science bootcamp, falling behind isn’t an uncommon thing. However, when you actually do, it’s important to manage your current assignments. Participants usually fall behind on something they find more difficult. While it’s quite tempting to try to complete the earlier things before working on your present assignments, you shouldn’t do that. Instead, you should focus on the current ones so you don’t fall behind further.

Apart from assignments, you should also try not to be too selective about the things you need to work on. Remember that since you get only a fixed amount of time in a data science bootcamp, being too selective will probably hinder your progress.

Ideally, you should always keep your mind open to more common topics. Even if those subjects aren’t among your preferences, you should focus on mastering them. The program offered at a data science bootcamp is usually well thought out, structured, and aimed to meet the industry requirements. So, you should try to learn everything the program offers to make the most out of it.

4- Be ambitious but understand your limits

We’ve already discussed the importance of defining your goals. However, setting unrealistic goals won’t help you reach anywhere, apart from wasting your time, money, and effort that you invest in the data science bootcamp. If you want to stay within your comfort zone, learning can become difficult.

So, you should expect to get hurt by the program every now and then. If it doesn’t happen, you’re most probably not making much progress and/or aren’t paying complete attention. Ideally, you should try to build a strong foundation with your newly acquired skills in the data science bootcamp when doing assignments during the program. In addition, to make steady progress, you can deal with the highest-risk problems first and then move on to the easier ones.

5- Leave your ego at your home

Full-immersion data science bootcamps are intense and it’s quite easy to try to self-protect, and to get defensive. However, the truth is you’re not attending a data science bootcamp to impress someone. So, you must be open to learning and fully admit if you don’t know something.

As we’ve already discussed earlier, participants in a data science bootcamp can come from a diverse range of backgrounds. So, some may learn faster or already have more experience with the fundamentals than you do. It’s important not to compare yourself to others because you’re not there to win a race against your fellow participants. Also, you shouldn’t feel embarrassed to ask questions. Sometimes, the unasked questions are the key to mastering a concept.

Apart from these tips, you should take some time out to review your progress. So, go back and review earlier lessons from the program once a week at the least. This proactive methodology helps to strengthen your concepts and accelerates your learning. Eventually, your response to certain challenges will become automatic.

6- Key things to keep in mind when selecting a data science bootcamp

The internet is certainly a treasure trove of information. So, it’s obvious that every aspiring data science professional searches it first when s/he plans to attend a data science bootcamp. With the skyrocketing demand of data scientists, there’s a lot of schools that have started organizing data science bootcamps though some of which may not be able to rise up to the expectations of the participants. So, we strongly suggest you to focus on some critical factors before investing your money, time, and effort in a data science bootcamp. Let’s have a quick look at them.

6.1- Cost

While data science bootcamps are significantly more affordable compared to other traditional programs, the cost has to be within your budget. Also, there’re programs where additional days bring additional costs in different ways that you may not have calculated before joining the program. So, it pays to make all these things clear before getting enrolled.

6.2- Success rate

Put simply, data science bootcamps aren’t for everyone and not every participant gets a job after finishing such a program. This happen mainly because the success rate depends on the efforts given by the participants to a great extent.

Also, sometimes data science bootcamps don’t count students who don’t remain in touch with them when calculating the success rate. So, don’t blindly trust the numbers you’re being given. Instead, ask questions like if the figures includes every participant who enrolls for and completes the bootcamp, or if it takes into account just the people who got a job after doing it.

Wrapping up

While a data science bootcamp is intensive and needs a participant’s complete attention to help him/her proceed toward success, they’re really powerful at the same time. So, remember the above tips, try to adopt a growth mindset, work truly hard, and take your time to rejuvenate in-between the information-packed study sessions. This way, your data science bootcamp experience will surely be excellent.

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

The post 5 Tips for Participating in a Data Science Bootcamp appeared first on Magnimind Academy.

But is it actually possible to become a data scientist for anyone? Of course, one can learn some tools used in the data science field and call himself or herself a data scientist, but that’s actually far from the truth. However, it’s possible for everyone to become a data scientist these days, indeed with a robust plan. In this post, we’re going to discuss the data scientist learning path following which anyone can become a part of this sophisticated, smart and glamorous league of professionals.

1- What’s a data scientist?

Before delving deeper into the learning path, let’s have a quick look at what a data scientist actually does. Data science is a complex field and it involves lots of different skills that contribute toward making the position even more important. In its simplest form, a data scientist can be considered as someone who has the ability of capturing and analyzing a massive amount of data in order to reach a conclusion. They perform this through different high-end tools and techniques. Essentially, a data scientist looks for meaning in huge amounts of data.

The key reason behind the emergence of the data scientist role is the need to understand the usually messy and huge amounts of data captured by fast-growing companies. These companies are trying to glean actionable insights about their business as well as customers with this data, but the professionals needed to perform this task are in short supply.

Another factor that contributed heavily toward increasing the demand for data scientists is that business leaders of today not only just want to know what happened, but they also want to know what’s happening, what’ll happen in the future, and how will it impact their business operations.

2- How can anyone become a data scientist?

As we’ve already discussed that it’s almost impossible for companies to manipulate and make sense of the data they capture on their own. As a result, many organizations are more than willing to pay an attractive salary for a good data scientist. With an excellent number of high-paying job opportunities, data science has become the field to be in at the moment. The data science field is growing and it’ll continue to do so for the foreseeable future.

Whether you’re a working professional looking to step into the field, a student planning for your future or are belong to a different background like the non-coders league, there’re ways to become a data scientist and it’s never too late to start your journey. Regardless of your present exposure to data science, here’re the skills you need to have to succeed in your endeavor.

2.1- Mathematics and statistics

Mathematics is a subject of which lots of people are scared of, but if you want to be a successful data scientist, you’ve to get your concepts cleared on things like probability, linear algebra etc. Put simply, probability refers to the measure of how likely something is going to happen.

In the data science field, there’re lots of events that cannot be predicted with complete certainty. So, concepts like Bayes Theorem, probability distribution etc are much needed to perform data science. Linear algebra deals with vector spaces. It’s crucial to understand different ideas behind different techniques of linear algebra like Time Series, Clustering, among others to understand their applicability.

Statistics is a crucial part of analyzing and interpreting the data. A lot of statistical concepts are used to perform data science, so a good understanding of them is essential.

2.2- Coding

More and more employers of data scientists are looking for candidates who’re conversant with programming languages like Python, R, Java etc. A good understanding of these languages is a must to succeed as a data scientist. You should understand that this isn’t about being an excellent coder but it’s all about being comfortable with different programming environments to be able to work with data as and when required. If you can demonstrate the expertise to adapt to the changes in the technological landscape, it’ll surely be considered as a good advantage.

2.3- Machine learning

It’s a field that provides computers with the ability to make decisions based on earlier data or previous experience. It’s a group of algorithms that use machine power to derive insights for you. To become a good data scientist, you should have a good understanding of neural networks, adversarial learning, reinforcement learning, supervised machine learning, logistic regression, decision trees, among others. In the data science field, different machine learning skills are used to perform different activities. So, it’s wise to be familiar with them.

2.4- Data visualization

Once you’ve done working with your data analysis, you’ll need to convince others to adopt your insights. Being visual creatures, it’s typically much easier for humans to consume the information by examining a graph or chart than by going through the numbers.

As a data scientist, you’ve to be able to visualize data with the help of data visualization tools like Tableau, ggplot, D3.js etc. These tools help you to convert complicated results from your findings to an easily consumable format. With data visualizations, organizations can grasp insights quickly to act on different business opportunities.

2.5- Analytical mindset

An overall analytical mindset is required to do well as a data scientist. Essentially, these people need to spend a huge percentage of their time in discovering and preparing data. So, as a part of that league, you’ve to be able to raise questions about data. Keep on updating your knowledge by reading relevant resources to be able to channel your thinking in the right direction.

2.6- Strong business acumen

As a matter of fact, most organizations that work with data depend on their data scientists not only to mine huge datasets but also to communicate the insights to decision makers. An effective data scientist should not only come with the ability to work with complex, massive datasets but with the understanding of the intricacies of the business he/she works for.

Having good business knowledge allows him/her to ask the right questions and come up with actionable solutions which are actually feasible for the business. In the context of data science, being able to understand which problems are crucial to solving for the business plays an extremely important role.

3- Different ways to become a data scientist

There’re different ways to become a data scientist, but it’s completely impossible to become one without a college education. At the very least, you’ll need a Bachelor’s degree to pursue further study. Also, if your goal is to land a leadership position, you should try to earn a Master’s degree or Ph.D.

There’re three main ways to become a data scientist – the traditional way, self-learning, and by attending a reputed school (like Magnimind Academy) that offers data science prep course and data science bootcamp. If you look at the traditional way, it may not be feasible for everyone to go back to school to complete a Master’s degree, both in terms of time and increasing educational cost.

If you consider the self-learning method, you can obviously learn many things but one of the major drawbacks of being self-taught is that your knowledge may not be complete and you may not be aware of that. Also, you won’t be able to measure your learning progress through this method.

Coming to the third option, institutes that offer data science degrees have become quite an obvious choice to aspiring data scientists. Even if you’re coming from a different background, the non-coders group, for example, data science prep courses offered by these schools are sufficient enough to provide you with the necessary skills based on which you can move forward to attend a data science bootcamp. Another major advantage of attending these schools is that they let you step into the field in a much shorter span of time (usually data science bootcamps come with the duration of 6-12 weeks) and around the one-fifth cost of attending a 2-year Master’s program. So, if you have the basics right like having a college degree, analytical bend of mind and mindset to put your best effort in, you can surely become a data scientist by attending one of these institutes.

In conclusion

Regardless of the path you prefer to take to become a data scientist, it’s always crucial to keep some things in mind. For example, finding a mentor, working on increasing your network, visiting data science conferences, meetups etc play important roles in establishing yourself in the industry.

In addition, as different technologies will come and go in the field, it’s important that you keep on learning continuously about new tools and technologies to stay on the same page with industry trends and remain in demand.

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

The post Guide to Becoming a Data Scientist for Everyone appeared first on Magnimind Academy.

In today’s competitive business landscape, consumer social norms have changed a lot and as a result, expectations too have escalated. It compels businesses to leverage their biggest asset i.e. data to rise above the competition, which is the reason behind data science being employed continuously. It has already become clear that data science benefits are simply not possible to overlook for a business or an organization that wants to grow. If you’re new to data science field, you may be wondering what are the other data science benefits for which businesses are constantly looking for data science professionals and offering them a fat pay packets.

Here, we’ve put together seven such benefits to help you get a clear understanding.

1- Surprising benefits of data science

It has become a globally accepted truth that businesses can tremendously benefit from the appropriate use of data and analytics when it comes to driving positive outcomes for expanding and improving various aspects of business. However, there’re also some other aspects where data science benefits can be leveraged to a great extent. Let’s have a look at them.

1.1- Redefined customer success

You’re probably aware that data is being heavily used to identify opportunities, modify marketing strategies, and design marketing campaigns, among others. But did you know that predictive power of data sometimes remains where businesses don’t expect it? It may sound a little different but it does and that’s one of the most surprising data science benefits.

Sometimes, information lies in data beyond what businesses can target and exercise. For, example, data-driven companies usually heavily rely on their audience performance to define marketing messaging in order to maximize results. Sometimes, the attributes are made limited by those companies based on what they consider crucial for defining customer success. In this case, crucial actionable insights can remain beyond those attributes and employing the power of data science methodologies can help more customer attributes to be leveraged and put to use.

1.2- Agricultural sector

You may not be able to believe it immediately, but the agricultural sector is yet another aspect that can reap data science benefits. Farmers use this technology to decide on the amount of fertilizer, water, and other inputs that are required to grow the best crop.

Another surprising thing is that farmers take advantage of solutions to plant the right amount of seeds so that they can gain maximum benefit out of them, even before they start growing the crops. In addition, they also depend on the weather forecast to a good extent just like us. While we use the information to decide what we’ll wear tomorrow, farmers interpret that information to understand whether it’ll affect their harvests’ quality and act accordingly.

1.3- Journalism

Have you ever heard of something like “Data-Driven Journalism” or “Data Journalism”? If you haven’t, it has become a popular trend and is considered as one of the unexpected data science benefits. Here, data heavily influences the jobs of journalists and the entire workflow is being driven by data – from data analysis and visualization to storytelling.

This data-driven approach is being used by media houses to evaluate journalists’ and their articles’ performance by considering likes, click through rates, shares on social media etc to incentivize journalists to develop content completely targeted to the readership of the website, newspaper, or magazine for which they’re writing.

1.4- Education sector

This is one of those data science benefits that may sound futuristic and a bit unusual to many people, but it isn’t. Today, educational institutes are all set to screen applicants with the help of analytic insights. There’re some universities that not only utilize analytics to evaluate students but are also inclined toward developing marketing strategies to enroll a greater number of eligible students.

Implementation of data science has made them enable to make calculated decisions about student admissions based on prospective students’ digital footprints. Advantages such as lower drop out and higher enrollment ratio, which have been made possible with the help of data science, cannot be overlooked.

1.5- Airline industry

One of the surprising data science benefits can be experienced in the airline industry. Across the globe, this industry is known to experience heavy losses. Except for a couple of service providers, most of them are struggling to maintain their operating profits and occupancy ratio.

The situation has worsened in the recent times because of certain factors like steadily increasing air fuel prices and heavy discounts offered to flyers. However, the situation has started to change as the airline companies have decided to employ data science to identify areas of improvements. With the help of this technology, airline service providers can enjoy a multitude of benefits including predicting flight delay, deciding on the class of airplanes to buy, identifying whether to take one or multiple halts or go direct etc.

1.6- Image and speech recognition

You’re most probably familiar with this thing but may not know that this is another of the many surprising data science benefits. You upload an image on a social media platform and start getting recommendations to tag your friends. This feature uses face recognition algorithm.

When it comes to speech recognition, some of the finest examples can be Google Voice, Cortana, Siri etc. Even if you’re not in a position to type something, you can use speech recognition to get the job done. All you need to do is speak out our message and it’ll be converted to text. However, speech recognition may not be able to perform accurately, at times.

1.7- Healthcare industry

You’re probably aware of healthcare providers taking the help of data science to predict patient admission rates but you’ll be surprised to know that doctors also get benefitted from this technology. For example, with the help of data science, doctors can diagnose patients quickly and more accurately, and hence, make faster decisions that play a crucial role in saving lives.

Data science has also helped greatly in the emergence of applications and wearables that can monitor patients on a constant basis to help prevent potential health problems. It also plays a crucial role in the progress of pharmaceutical research when it comes to finding a cure. Here, machine learning algorithms are used to extract and analyze biological samples from patients to develop cures.

2- What’s in it for you?

The above mentioned ones are just some of the key unconventional data science benefits. There’re lots of other benefits that data science has brought to us. In light of the above, it can be said that this is probably the best time to step into the data science field, if you haven’t already.

As modern businesses are getting flooded with data, data science professionals are experiencing a high demand across industries. It has become a universal truth that without the expertise of these professionals who can use cutting-edge technology to turn the gathered data into valuable insights, big data is of no use, and that’s why more and more companies are coming up with positions for data science professionals.

If the above read has motivated you enough, there’s a wide range of courses available in the field of data science that can help you take your career to the next level. Just keep in mind that you need to carefully select the institute from where you’ll be pursuing a course or tow to fast-track your data science career. Consider everything from curriculum, cost, and faculty to success rate and post-program guidance prior to getting enrolled to make the most out of your investment in terms of money, time. and effort.

Final thoughts

Considering the above data science benefits, it can be said that this technology is transforming the way organizations or businesses think, execute, and perform. Today, it has become an almost inherent part of one’s daily life – whether we realize it or not. When it comes to data science, many people may only think of corporate offices, where data science professionals work to deliver solutions to critical business problems. While this is completely true, it’s also a fact that data science comes with far wider reach and is being embraced across industries for a large number of unconventional advantages. Data has been there with companies for a long time. It’s the emergence of data science and advanced technologies and tools in the field that have entirely transformed the entire scenario.

So, if you’re geeky at heart, and love to work with data and face the related challenges, data science is the field you should jump into. Data science has already started transforming the global business landscape and it’ll do so in a much bigger aspect in the future, when we’ll be seeing more advanced technologies and tools in the field. So, once again, this is the ideal to time to put your best foot forward and prepare yourself to join the coveted league of data science professionals.

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

The post 7 Surprising Data Science Benefits appeared first on Magnimind Academy.

In fact, if you consider the cost of a data bootcamp with the big amount of money that you’ll need to spend on an equivalent master’s degree, the former would be just a fraction of the latter. And when you consider two years of education for the latter and weigh it against the fast-paced data bootcamps that can take anywhere from 12 to 24 weeks (or a bit more depending on the curriculum), it may all seem worth your money as well as time and effort.

The key in determining whether you should invest money on a data bootcamp depends on a lot of factors. Unless you have considered them all, making a decision simply on the basis of the cost factor alone wouldn’t be prudent.

But before we talk about the key factors on which the cost of data bootcamps depend, it’s important to ask yourself why you would like to join such a bootcamp. It’s equally important to know why learning new data skills as well as enhancing what you already know makes sense in today’s data-driven world.

So, let’s take a look at some statistics to understand why data science has become such a hot topic today, and the reasons behind the paucity of qualified data scientists despite a rapidly rising market demand.

1- What makes data science an interesting field to enter?

According to a McKinsey Global Institute report, the demand for qualified data scientists was growing yearly at about 12%, which was far above the available supply. Since then, the situation hasn’t improved. According to a LinkedIn report (August 2018), almost every large city in the US faces a severe shortage of data science skills. The national shortage of people with data science skills stands at a staggering 1,51,717. The McKinsey report predicts the US to be reeling from a shortage of 250,000 data scientists by 2024. And such shortage would exist despite the field of data science paying extremely well, much more than its similar counterparts.

You can understand how well paid data science professionals are by considering a few statistics. In 2018, data scientists’ mean base salaries stood at $95,000 for those who had 1 to 3 years of experience (non-managers), while those with over 9 years of experience earned more ($165,000). Those working as managers earned more with their mean base salaries varying between $145,000 (for having 1 to 3 subordinates reporting to them) and $250,000 ((for having more than 10 subordinates reporting to them).

2- What triggers the paucity of data scientists?

You may be wondering – if the field pays so well, why is there a huge shortage of professionals having the requisite skills? The reasons are many. Many aspiring candidates take up full-time data science courses and enroll into data bootcamps driven by false and misleading promises, but discover midway through the program that they don’t like it, aren’t cut out for it, or simply can’t handle the tremendous stress as well as put in the desired efforts to finish it. As a result, they quit or drop out.

Then there are some others who finish their courses but aren’t job reedy, either because they haven’t got the hands-on experience, or lack the skills required to handle real-world problems. In such cases, they have to look for specialized courses or data bootcamps that will help them bridge this skill gap and make them employable.

The ability to adapt quickly to changing technology, methods, and tools is another important skill to excel in the field of data science, which is again found lacking in many professionals that make them fall behind the competition, and ultimately, bow out. So, what’s the answer?

3- Why you should seriously consider attending data bootcamps?

If you have a background in computer science, mathematics, or statistics, you can think of entering the field of data science. Having some programming knowledge, especially in Python and R would be an added advantage for sure, but even if you don’t have it, you can always learn provided you’re serious about improving your chances of being a successful data science professional. And if you’re searching for the fastest and most effective way of getting your brain up to speed and ready for a data science career, hardly anything else can match data bootcamps.

If the word “bootcamp” makes you think about a military-style training camp where you would be rousted out of bed early on in the morning and made to do a two-mile jog, take heart for this isn’t it. The word “bootcamp” signifies an intensive, short-paced, extremely targeted curriculum with the aim of drilling a lot of useful information in your mind, which can be extremely overwhelming and hectic, just like a military camp is.

Yet, you won’t find a more practical and effective way to learn about and use the tools and techniques that data scientists use every day. And as you gain knowledge of the field, you’ll also get to meet and collaborate with a lot of link-minded people, some of whom are already working in the field while others are hoping to enter it soon. You’ll also get to learn from industry experts, who give you useful insights into what works in the real world and what doesn’t. By networking with a varied group of people coming from diverse backgrounds, you can end up making some good connections, which in turn might help you further your own career aspirations.

Unlike two-year data science master’s degree courses or Ph.D. in data science that would take longer, you can learn and master the key concepts of data science much faster by enrolling into a bootcamp that focuses on the field. And the best thing is the lot of choices that you can take your pick from.

When bootcamps took off in the 2014, they were originally all-day long, short-term, and on-site courses. But the scenario has changed a lot since then. Now, as a data bootcamp participant, you have a variety of options – from weekend and evening data bootcamps to the ones that are conducted totally online. Even in terms of duration, you have several options – from short-term ones that run for 8 weeks to the long-term ones that could be 20 weeks or even more. You can even take your pick from full-time programs or their part-time counterparts, the latter being most suitable for those who’re employed and don’t want to quit their jobs while learning new skills or honing the skills they already have.

Irrespective of your skill level and experience, you can be sure of finding matching data bootcamps (provided you can tick the basic requirement checklist) that let you learn your desired techniques and tools of the trade. So, apart from helping you with fast-track, effective learning programs, data bootcamps also offer you the flexibility in term of program choices, duration, mode of learning etc. But what to do if you are concerned about the big amount of money that you need to invest into such data bootcamps?

4- Should the high cost of data bootcamps deter you?

The answer is a resounding “No”. If you’re thinking of attending data bootcamps, you should be already aware that they virtually always cost money, and often, a big amount of money that can go up to $16,000 (or even more) in case of some highly rated and extremely popular ones.

However, you may also find some others that are usually called “fellowships” and could be offered for free to the qualified candidates. But be ready to face a tough and intense competition as typically, some of the brightest minds compete to get an entry to such fellowship programs. You can also search online to find data bootcamps that may offer financing options for candidates who can’t afford to pay the total amount at one go.

And if you’re wondering whether paying a big amount of money for a reliable and reputed data bootcamp is really worth it, we would say it is indeed. After all, if you choose your bootcamp carefully, it would deliver you the results that are a few times more than the cost you pay to get it done.

Final words

With the huge shortfall of data scientists and other data professionals, the need has arisen to get professionals with the right skillsets ready at a fast pace. And this is where data bootcamps are miles ahead of their traditional counterparts. If you have the right background (math/statistics/computer science etc), have the zeal and interest to make it big on the field of data science, are ready to toil hard to learn and master the required tools and techniques, and can’t wait to implement your knowledge for solving real-world problems, you should definitely consider attending a data bootcamp.

And once you’ve decided in favor of a data bootcamp, you shouldn’t defer it just because they involve a big amount of money. If you’re really driven, it won’t be that hard to arrange for the money or look for financing options. After all, once you land that coveted data science job, you would soon repay the loan and debt, if any, and start your journey toward financial independence that comes with a lot of perks, a major one of which is working with a futuristic technology that’s set to only grow bigger and better in the forthcoming years. So, if you’re in a dilemma about attending a data bootcamp, go for it now!

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

The post Do Data Bootcamps Require a Big Amount of Money? appeared first on Magnimind Academy.