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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1- Guiding actions based on trends

One of the key responsibilities of a data scientist is to examine and explore the data captured by the organization. Once these processes are done, he/she can recommend different types of actions which bring a huge scope of improvement in the business performance of the company. And after these improvements are made, it can leave a significant impact on the organization in terms of increased profit.

2- Informed decision making

With the help of a data scientist, it has become possible for business owners to predict effective measures and different trends for the success of their businesses. One of the biggest data science benefits is that it has eliminated the possibilities of upper-level risks. By reviewing different types of models created by data scientists based on already existing data, business owners can easily understand which road will lead them to success.

3- Testing the decisions

Once you’ve implemented the changes based on the insights discovered by a data scientist, it’s time to observe how these changes are impacting your business. And this is exactly where the expertise of a data scientist becomes evident again. He/she would be able to measure the key metrics which are related to those changes and quantify their true impact.

4- Real-time data

Once there was a time when marketers used to collect the info about their consumers in bulk after every campaign and analyze that information to track the progress of the campaign. But the emergence of data science has opened up a whole new field of digital marketing. Now you can build your present and future digital marketing campaigns based on real-time data, which means you don’t need to analyze distant past behavior anymore. Instead, you can focus on the present market patterns to make your campaigns highly effective. A data scientist can tell you everything about your target market trends, customer response, their buying patterns, the effectiveness of timing, and much more, helping you target your consumer base at the right time.

Parting Thoughts

These are only some of the major data science benefits that any business would be able to experience by hiring a data scientist. It’s also safe to say that the importance of these professionals will only increase over time, thanks to the increasingly connected world. If you’re an aspiring data scientist and looking for a great start, this data science bootcamp in Silicon Valley offered by Magnimind Academy would be worth checking out.

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1- The three main data science skill sets you need

Even if you don’t have a computer science background, you will need the three main data science skill sets namely programming, statistics, and business knowledge if you aim to have a successful data scientist career.

If you plan to become a data scientist, you’ll need to use programming skills to handle data at scale that can fill terabytes of space. You’ll also need a solid grasp on statistics and mathematics to evaluate patterns in data and manipulate it using different methods. Understanding business fundamentals is an equally important skill to ensure you’re capable of communicating your findings to the concerned teams or management people and encourage them to make informed decisions based on such data-driven insights.

Though you’ll need a diverse skill set to excel in the field of data science, you don’t need to worry as most data scientists won’t have picked up all of these skills in an academic environment. This indicates there’s often a lot of self-learning involved in the process, which would be advantageous for you, especially if you don’t have a computer science or statistics/math background.

You should remember that if you can prove through project work that you’ve got serious data science skills, it won’t matter whether you acquired them on your own, through a formal degree program, or via a data science bootcamp in Silicon Valley.

2- The fast-track to becoming a data scientist

Online and offline bootcamps typically offer a mentor-guided curriculum tailored to get you working with data from day one. With industry experts and experienced data scientists as mentors, who use real-world data to teach you, you’ll get your hands on real-world data from the beginning of the program. You’ll even get hands-on, project-focused classes that prepare you for data science employment by the time you end the training.

Unlike lengthy traditional degree courses, these bootcamps offer extremely targeted learning that demands you stay committed to your studies and invest 15-20 hours (or even more, at times!) regularly right from the beginning. With a hands-on learning approach, these bootcamps make you work with real data sets to analyze interesting problems and even give you additional opportunities for guided real-life projects.

When you join a leading data science bootcamp in Silicon Valley, you’ll not only get real-world experts as your mentors but even get the chance to learn and grow via peer interaction. When working on group projects with other aspiring data scientists, you’ll be able to ask questions, brainstorm to find solutions, and even learn from your peers.

If you plan to become a data scientist but don’t have a computer science background, find a data science bootcamp in Silicon Valley to make your dream come true.

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

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

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

The working process of a data scientist

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

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

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

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

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

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

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

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1- Projects come first

Having a good amount of diverse data science projects can dramatically improve the quality of your portfolio. Projects demonstrate that you have the skills and expertise to work on real-life business problems. If you’re pursuing some sort of data science program from a reputable institute, you shouldn’t have to face any problem in having projects to be solved. If you have opted for the self-learning method, you should focus on carrying out some personal data science projects to build up your portfolio.

2- Explore blogging platforms

While having your own website can surely help you develop your online presence, you should focus on getting some visibility as well. And popular blogging platforms are simply excellent for this purpose. Look for a couple of blogging platforms that get a decent amount of footfalls and come with a good tagging system that would help you reach greater audiences. Once you have your profile set up, post the successful assignments you have completed so far.

3- Have a GitHub profile

Today, Github is one of the most effective online platforms targeted at tech enthusiasts. Over the years, the platform has gained immense popularity. When you have solved a critical problem and truly want people to see the way you have done it, GitHub should be your best bet. Whether it’s a write-up or a code, drop it on the platform and share it with others. There’re lots of companies across the globe keep on looking at GitHub profiles to identify competent and genuine data science professionals.

4- Focus on social media

Having a strong presence on popular social media platforms like Twitter, LinkedIn etc can greatly help you in building a strong data science portfolio. On those platforms, you not only get chances to interact with other data science professionals and go through their inputs but can also share your insights and articles to people who may be your future employer.

Final Thoughts

When you have a strong data science portfolio, it’s up to you to opt for the way to demonstrate it to prospective employers. Depending on the data science position you’re looking it should be decided. Apart from the above tips, there’s one thing you should never overlook – the importance of practice. When people see your work and provide feedback or praise, you can rest assured of getting a bit closer to what the world calls an “expert”.

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Before we delve deeper into how should you learn programming, we should give you an overview of why you should learn Python and R.

1- Key reasons to learn Python and R

If you’re planning to learn programming and start with Python, here’re the reasons you should dive in without any hesitance.

• Easy to learn: Though Python is a highly powerful language, its simplicity makes it one of the most preferred languages among aspiring programmers.
• Data science: Python is the language of choice of a huge number of data scientists. Though private researchers and academic scholars have been using the MATLAB language, the scenario started to change with the emergence of Python numerical engines like Matplotlib.
• Machine learning: This is one of the biggest reasons to start with Python when you’re planning to learn programming to step into the field of machine learning. If you want a pet project in machine learning or just wish to play around it, Python is the only prominent language that makes it easy.
• Widely used: Python is used in a wide range of software industries and packages. For example, it powers Reddit, Quora, Google’s search engine, Dropbox, just name a few. As a skilled Python programmer, you may be able to grab a job at one of these giant companies.
• An object-oriented language: A solid grasp of the basics of Python can help you migrate to any object-oriented language of your choice as you’d only need to learn the new language’s syntax.
• Huge community: Whenever you’re trying to learn programming, a robust community becomes your biggest asset. Thanks to its vast community, you’re most likely to find the answers to any of your Python related queries in minutes.
• Professional growth: Python is growing pretty fast and it makes sense to start your journey to learn programming with this language. Once you become an expert, you can not only get a job quickly but will be able to accelerate your career growth. And Python developers belong to the league of highest paid developers, especially in the fields like web development, data science, and machine learning.

R programming language is specialized for statistical computing and was designed by statisticians. With the advancements in technology, the data captured by businesses has become highly complex, and R has become the most preferred language among professionals need to analyze data. If you’re still thinking of why you should learn R, here’re some key reasons that you can consider.

• A powerful scripting language: R can handle complex, large datasets and is a great language to use for resource-intensive simulations.
• Data visualization: Data visualization is considered to be easier with R as it comes with a lot of useful tools and built-in functionalities which make performing certain tasks more straightforward.
• Statistical packages: When it comes to statistical packages, R is absolutely free. All you need to do is download it and get started. On the other hand, software like SAS, STATA, and SPSS can cost you heavily for a license.
• Robust statistical community: R is supported by a solid widespread community which maintains lots of user groups that allow you to learn and exchange ideas.

Since R is extremely flexible, both as a programming language and a statistical package, its usages continue to increase as a reliable tool for an array of statistical computations.

If you hold limited programming language or are planning to learn programming, familiarizing yourself with languages like Python and R can prove to be the best initial step in advancing your career.

2- Different approaches to learn programming

If you’re planning to learn programming, both Python and R both can greatly help you in taking your career to the next level. When it comes to learning these languages, there’s a wide range of resources available. It actually comes down to the learning method that suits you the best. Here’re the most popular resources from which you can take your pick based on your preferences.

2.1- Online resources

Whether you like a hands-on approach or a textbook approach or a lecture hall approach to learn programming, you can find it online. Courses revolving around hands-on approach usually include projects, lessons, and quizzes to help you learn Python and R. In general, with a free account you can access interactive lessons and exercises, but a pro account is required to practice with project challenges and quizzes. There’re some websites that are considered goldmines of articles, tutorials, and documentation on programming languages. These are usually free resources and can greatly help you in learning Python and R, regardless of whether you’re a beginner or looking to learn complex topics. You can also learn programming by opting for MOOCs (massive open online course). Usually, there’re no entry requirements for MOOCs and you can participate regardless of your financial circumstances or where you live. It’s important to note that because of the large number of people attending MOOCs, you’ll usually receive support from different communities of educators, as well as other learners. And your progress on learning Python and/or R might be assessed through computer-marked tests or peer-reviewed written assignments, rather than by tutors. You can also use GitHub to learn programming through an exploratory approach. However, this method is ideal for people who’ve some experience in languages. Anyone can use this centralized repository to house and maintain code. And you can find lots of Python and R projects which are available for exploration. GitHub can be a great resource if you’re looking for a guide that offers a comprehensive overview of different Python and R concepts, from debugging to installation to writing documentation.

2.2- A programming bootcamp

If you don’t want to take the online or self-learning route, joining a programming bootcamp seems to be your best bet. These bootcamps enable participants to focus on the most crucial aspects of programming and apply the new skills to handle real-world problems. Let’s have a look at why programming bootcamps have become one of the most sought after avenues to learn programming.

• High-impact learning: Unlike other approaches to learn programming where you some of the aspects you learn may not be relevant in the present business world, programming bootcamps only teach the skills that are most relevant in the real world. These programs exclusively focus on the skills that the learners will need to become successful programmers in the professional world. In addition, the duration of these bootcamps is relatively shorter than other avenues to learn programming, which is ideal for people who want to gain programming skills to become employable within a short time frame.
• Secure Return on Investment: When it comes to attending a bootcamp to learn programming, it’s important to remember the old adage – you get what you pay for. Programming bootcamps may seem to be a little expensive but then you need to consider that you’re likely to get a nice job soon after you complete the program. In addition, most of the bootcamp organizers offer job assistance, so your investment remains secure to a great extent.
• Immersive learning: People often get stuck along the way without great support and give up eventually. In a bootcamp, if you get stuck somewhere, instructors are readily available to help you out. In addition, if you choose online or self-learning approaches to learn programming, you’ll be missing out on the additional features offered by a bootcamp. These include presenting in front of a group, pitching ideas, interviews with potential employers etc.

Regardless of whether you want to learn Python or R in a bootcamp, you should always keep in mind that the more effort you invest in the program, the more you’ll get out of it. Most of the students who do well in a bootcamp keep programming outside class hours, and thus you’re expected to. You should also understand that importance of programming languages in the professional world will keep on changing. The crucial part is adapting how programmers look at problems and think about those. Once you learn this, you’ll be able to learn any programming language you want.

Conclusion

Both Python and R can be learned relatively easily as long as you’re motivated and have a clear set of goals in mind based on your background. For instance, if you’re coming from a statistical background, R should be the best option for you. If your goal is to become a data science professional, starting your journey to learn programming with Python should be ideal for you. At the rate the demand for skilled programmers is increasing, now seems to be the best time to learn programming. So, assess the above-mentioned approaches, pick one that suits you the best, and start learning to gain a competitive edge over others.

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Thanks to hyper-connected people, devices and gadgets, vehicles etc, more data was created in the last two years than in the preceding 5,000 years – combined. Almost everyone and everything is sharing and broadcasting data today. Along with efficient machine learning algorithms and systems to run these algorithms, data scientists can get interesting insights from such large data sets today.

According to Burtch Works Study (2018), an entry-level data scientist’s median starting salary stands at $95,000. For a mid-level data scientist, the median salary is $128,750, but it could rise up to $185,000 in case the person also plays a managerial role. For experienced data science professionals, the median salary stands at $165,000, but those working at the manager-level could earn considerably higher at $250,000. All these make the domain of data science an extremely alluring one.

Before talking about a hot dip topic relevant to data science, it’s important to have a clear idea of what the field is all about. Common people like us may often come across data sets that share no information, no story, and no insight to our untrained eyes. But for a data scientist, the same set of data would open up a world of information. Yes, that’s exactly what a data scientist’s job is – to discover the hidden meaning in seemingly innocuous numbers like these. In other words, you can call a data scientist is someone who pulls out meaning from data. Thus, in a way, a data scientist is a data detective, an investigator with a knack for statistics, or a messenger of meaning. By bringing out interesting stories and insights from inanimate numbers, a data scientist helps businesses spot trends, make informed decisions, and do a lot more.

Before proceeding further, let’s take a simple example of how data science can help you analyze large datasets to make data-driven decisions. Say, you love good Indian food and are searching yelp reviews to find the nearby restaurants where you can satiate your palate with your favorite Indian food. But what happens if you’re in search of a particular type of cuisine and there are several restaurants with the same rating within a small radius? If you have a data science background, you can use it to arrive at a solution. You can consider two things:

• The number of restaurant reviews submitted by a single person of a specific cuisine (Indian food in this case). You can rationalize this parameter by looking at other cuisines’ reviewers, like Italian or Chinese food.
• The reviewer’s apparent ethnicity. In case the reviewer in question has an Indian name, you could deduce that s/he might be of Indian ethnicity, and thus, more knowledgeable about what constitutes good Indian food.

Based on these parameters, you can modify the data gleaned from Yelp. You’re likely to find reviewers with Indian names giving good reviews to some specific restaurants in your vicinity. This way, you’ll be able to arrive at a clear choice of which restaurant you should head out to. So, if you think data science is only for the big corporations, think again. You can even use it to make simpler choices and decisions with greater efficiency similar to the one we’ve talked about in this example.

Now that you’ve got a clear idea of what the field of data science entails, let’s take a look at some hot dip topics relevant to it.

1- Data is an important business asset – much more than you could ever imagine

If you don’t believe this, just consider how Facebook’s data breach that affected its 87 million users impacted the 2016 US Elections and ultimately played a huge role in who bagged the coveted post of the US president. So, even if you think your data isn’t useful, many out there think it is cash in their books. In case you haven’t thought about or used the data you collect to the utmost effect, it’s time you do so by engaging a data scientist to spot the insights that such huge pile of data offer for your business.

Let’s take a quick look at the hot dip reason why data is such a valuable asset your business. It lets you understand your website audience types and behaviors better, thus facilitating targeted campaigns to bring in the best results. With proper data analysis, you can understand your audience types better based on their demographics (gender and age group), geographical locations, types of devices they use, other interests etc.

You can even find which customer acquisition channels are working, notice how your website visitors are interacting with/moving around your website, and track your conversion goals (newsletter sign-ups, download of free e-books/reports/whitepapers, buying or short listing products etc). Such insights can help you to fine-tune your business goals as well as marketing and sales campaigns. By knowing what works and what don’t you can create targeted content and strategies to ensure optimum effect and conversions. At the same time, with the help of a data science professional, you can even discover unexploited areas of your business.

Say, you have an online e-commerce store that could expand into a physical store, but where would your brick-and-mortar store be located? An experienced data science professional take a deep dive into your data to reveal the locations that are best aligned with your products, thus empowering your data-driven and informed decision-making process.

2- Data accessibility and proper data collection are just as important 

Did you know 74% of firms say they desire to be data-driven, but a mere 29% say they are good at linking analytics to action? Yes, that’s what Forrester (a US-based market research company) found out in 2016. If you fail to easily and quickly get actionable insights from your data, make your company come together around that insight, and are able to take and implement product or business decisions based on it, you aren’t data-driven.

Businesses may not yet consider easy and fast data accessibility across their organization important but it surely encourages and helps in making better decisions. That’s because with better data accessibility for all your employees, you can iterate, improve, and move quickly – much faster than your competition, focus on the product, and create a data-informed culture where data triggers better decisions and actions. In case you’re wondering how you can build a data-informed culture across your company, you can try implementing these basics correctly:

• Everyone is aligned around a solitary core metric that leads toward a common goal
• For everyone, it’s possible to access the data he or she requires easily and quickly
• Everyone gets to know about the relevant insights from their data

To achieve all these, you need a data science professional as your partner. At the same time, you need to acquire relevant information because gathering whatever you (and your employees) can find or lay their hands upon would mean sitting atop a pile of data, most of which is useless. So, when businesses innovate to create connected digital services and devices that generate relevant usage data, they need to focus on acquiring relevant information.

3- AI and its industrialization challenge

This is another hot dip topic relevant to data science. Artificial Intelligence (AI) has emerged to be as impactful an element as the internet. Machine learning (ML) is an approach to achieve AI and deep learning, which is one of the most advanced forms of ML, is finding a place of importance in several organizations.

Deep learning applications, which are powered by data, are diverse – from natural language processing and image analysis to task automation, and much more. But despite the growing importance of AI as well as approaches and applications connected and relevant to it, the question of AI industrialization challenge has come to the forefront. It has become mandatory for information systems to develop in order to support the diverse uses of artificial intelligence. No wonder why this is a hot dip subject that many are talking about and pondering upon.

When you consider the technical challenges, real-time maintenance, learning, or even scaling of multifaceted algorithms are just a handful of them. To deal with them, the DevOps fundamentals like continuous integration and testing, or containerization need to be applied. The necessary elements required to meet AI industrialization challenges include NoSQL-based new architectures, as well as cloud and data virtualization.

Final words

The year of 2019 looks promising for the domain of data science and is likely to be driven much more by data than its predecessors. For businesses, this would mean a growing emphasis on finding actionable meanings and insights from such data by engaging data science professionals. Quite naturally, the hot dip topics these days are revolving around the field of data science and how best the vast amount of data acquired from multiple sources can be put to the best use.

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