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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1- Major reasons to become a machine learning professional

Here’re some major forecasts about machine learning’s future.

1.1- Enhanced personalization

Personalized recommendations can entice users to complete certain actions. With the help of machine learning personalization algorithms, the information in a data can be synthesized to make appropriate conclusions like a user’s interests. For instance, on an e-commerce website, it can be deduced from a user’s browsing activity that he/she is looking for a garden chair.

1.2- Better cognitive services

Cognitive services offered by machine learning professionals all developers to incorporate intelligent capabilities into their applications. Developer can empower those applications to perform various duties including speech detection, vision recognition, speech understanding. As machine learning is evolving continuously, we can expect to see the emergence of highly intelligent applications which can increasingly see, speak, hear, and even reason with surroundings.

1.3- Fraud prevention

Machine learning professionals can analyze transactions of an e-commerce website and the machine will pick out the fraud unlike rules-based, traditional system. In the future, we’re likely to witness machine learning technology becoming more sophisticated and the machines will modify, through self-learning, to prevent fraud.

1.4- Better use of quantum computing

The machine learning spectrum can be transformed through implementation of quantum machine learning algorithms. If machine learning can be integrated into quantum computers, it could result in faster processing of data that could dramatically accelerate the ability of synthesizing information and derive insights – what the future probably holds for us.

Though many people consider machine learning to be in its nascent stage, its future is clearly bright and so is the future of machine learning professionals.

2- Key career opportunities as a machine learning professional

Let’s have a look the major job opportunities as a machine learning professional.

2.1- Data scientist

Professionals, who extract meaning from a massive amount of data and analyze and interpret it.

2.2- Machine learning engineer

Programmers, who develop the machines and systems that can learn and apply the acquired knowledge without requiring any specific direction or lead.

2.3- Deep learning engineer

These professionals specialize in developing tasks related to AI with the help of deep learning platforms.

3- Roadmap to become a machine learning professional

As you’re aware of the reach of machine learning, let’s discuss how you can become a machine learning professional. Though this field involves a broad range of skill sets, there’re plenty of resources like Magnimind Academy that would help you master all these skills. They offer some valuable machine learning mini bootcamps for students with different skill sets. And if you’re interested in becoming a data scientist, you can always join their full-fledged data science bootcamp in Silicon Valley to move toward that direction.

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

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1- What’s data science?

Put simply, data science can be considered as the way of extracting valuable insights from raw data. Stakeholders and business decision-makers use these insights to make critical business decisions. As the world is getting more and more connected, a huge amount of data is being generated by businesses, as well as, common people almost every moment. While a business can always collect that data but it’ll be of no use until that’s analyzed. And this is exactly where data science professionals come into the picture.

Data science involves a huge number of expertise areas and disciplines to develop a thorough, holistic, and refined approach toward raw data. Data science professionals have to be skilled in a plethora of disciplines – from math, statistics, and data engineering to advanced computing and visualizations to perform the roles effectively. While data science is a broad spectrum and there’re lots of jobs associated with it, data science professionals typically follow a workflow. Usually, the steps of such a workflow include capturing data, managing data, performing exploratory analysis, and reporting.

2- How data science can revolutionize the industry?

Applications of data science can be observed in a huge number of industries and the field has already helped us achieve some major goals which weren’t simply possible or needed a great deal of time and effort to accomplish them just a few years ago. Implementation of data science not only presents a great opportunity to make a substantial economic impact but it holds the power to revolutionize the industries as well. Here’re some ways it can do it.

• Businesses can use data science to derive actionable insights from a massive amount of data and can put that to their use. For instance, it can greatly help them to predict the future. With the help of machine learning algorithms, data science professionals can identify patterns in raw data which wouldn’t be possible otherwise and forecast future outcomes with a greater level of accuracy.
• Businesses can implement data science to review risky ideas before putting them into action actually and thus, they become able to avoid potentially costly mistakes and risks.

3- Parting thoughts

There’re lots of other ways through which data science impact almost all the industries. Some of the major ways it can revolutionize the world include bringing precision to public services, stability for businesses, a new degree of operational efficiency, and many more. And we can only expect to see the presence of data science in a more improved manner across more industries in the coming years.

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1- Develop problem-solving skills

While it’s crucial for any data scientist to stay up-to-date with the latest tools and technologies, it’s mandatory to have robust problem-solving skills. Assuming you’ve got the required qualifications to become a data scientist, focus on developing this skill as much as you can. In reality, most businesses value experience much more than traditional educational qualification because with more experience, comes more ability to solve different business problems.

2- Develop a solid focus on business impact

One of the biggest factors that separate an average data scientist from a good one is the latter’s natural curiosity to identify patterns in data. Try to delve into the detailed work of exploring datasets, explore the latest techniques in the data science field, test their effects systematically etc. Try to develop a mindset that would help you identify the larger goal of projects and question the core assumptions. The data science field is still not a standardized field and thus, there’s much room for thinking outside of the box to solve pressing problems.

3- Ability to manage expectation carefully

Data science can be quite a confusing field from the outside. It’s almost impossible for those, who’re not associated with this field, to understand whether a data science project is a machine learning project and things like that. The expectation around what a data scientist can do may greatly vary between different people. Thus, it’s imperative for a data scientist role candidate to be able to consistently and proactively communicate with the stakeholders to identify clear expectations and find out misunderstandings early to get everybody on the same page.

4- Ability to work with cloud services

Cloud computing has already become a core part of the field of data science. There’re lots of cases when a data scientist needs to use cloud services. These may include querying databases for scalable analytics, managing and sharing datasets, etc. The ability to work with major cloud service providers like AWS, GCP, Microsoft Azure etc is steadily becoming one of the preferred skills for businesses looking to hire data scientists.

Conclusion

It’s certainly a great time to start your journey to become a data scientist if you aren’t one already, but you’ve to prove that you stand out of the pack. Focus on developing the skills mentioned above, put in real effort and you should be able to become a data scientist role candidate for hiring managers.

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

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