Data Science Coding Schools to help to launch a career in Data Science

In recent years, the scope for data science has been gaining momentum with large amounts of data being produced across the globe. This also increases the opportunities for data scientists and enthusiasts who can process and analyze data for new insights. The Bureau of Labour Statistics of the U.S estimates a 15% increase in the demand for trained data scientists over the next decade due to meet the data requirements. This means completing a data science course could benefit you to break into this highly in-demand field which can boost your career to a great extent. Data science courses can not only help you in getting a job but also ensures you stay in the field for a longer time. Here are the top 10 data science coding schools to help you launch a career in data science. Let’s quickly jump into the article to know more about them.

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1. Propulsion Academy

Learning mode: Online and Offline (Munish, Zurich)

Propulsion Academy provides opportunities to learn about the new technologies, tools, and techniques that are necessary to master all areas of data science from scratch. This academy offers data science courses such as Data Science Bootcamp full-time, Data Science Bootcamp part-time dealing with subjects like Back-End Development, Blockchain, Cryptography, CSS, R, Python, Machine Learning, Coding, and natural language processing. Students can apply this knowledge and skills in the real world with its industrial-based projects. The academy also offers career assistance in terms of jobs and careers.

2. NYC Data Science Academy

Learning mode: Online and Offline (NYC)

The NYC Data science academy promotes three courses: online Bootcamp, a Remote-Live Bootcamp, and a professional development course. The Online Bootcamp is of both full-time and part-time, remote Bootcamp is a full-time course and professional development is also a full-time course. The subjects of the academy include Data Science, Data Visualization, Git, Hadoop, Linux, Coding, and Python.

3. Data Science Retreat

Learning mode: Offline (Berlin)

Data science retreat offers full-time data science immersive courses. The subjects include Big Data, Data Science, Machine Learning, Python, Tensorflow, and many more. Once the courses are completed, graduates will have projects that are presented to prospective employers. Career coaching services include resume assistance, practice interviews, and LinkedIn updates.

4. Cambridge Spark Ltd

Learning mode: Offline (Cambridge, London)

Cambridge Spark Ltd offers many applied data science coding courses. The subjects include Data Science, Hadoop, Machine Learning, Python, Coding, etc. It has 14-months blended courses in Data Science and AI. Targeted at offices and currently working professionals, the program simulates real-world projects with Cambridge Spark’s K.A.T.E. (Knowledge assessment teaching engine) platform.

5. Hackwagon Academy

Learning mode: Offline (Singapore)

Hackwagon Academy offers many data science courses with unique subjects such as CSS, Cyber Security, Data Science, Data Visualization, HTML, Python, R, etc. There are three intensive courses provided by the academy: Data Science 101, Data Science 102, and Data Science 103. In Data Science 101, students learn about programming concepts, in 102, students learn about machine learning and python coding. In Data Science 103 they learn about end-to-end Analytics.

6. Galvanize

Learning mode: Online and Offline (Austin, Denver, Los Angeles, NYC, San Francisco)

Galvanize offers full-time courses, part-time courses, and online prep data science courses. The full-time courses run for 13 weeks. It also offers online programming courses. The different types of courses are Data Science Immersive, Data science Prep, Data Science Analytics Online immersive. The subjects include Big Data, CSS, Data Analytics, Data Mining, Data Science, Coding, Python, etc.

7. Ubiqum Code Academy

Learning mode: Online and Offline (Barcelona, Geneva, Madrid)

Ubiqum, Code Academy offers many immersive data science courses. They have campuses in Amsterdam, Barcelona, Berlin, Lisbon, and Madrid. The courses contain subjects such as AJAX, Algorithms, AWS, Big Data, Python coding, and many more. Students have both full-time and part-time courses and can learn data mining in both R language and Python.

8. Xccelerate

Learning mode: Offline (Hong Kong)

Xccelerate, offers many data science courses such as data science, machine learning, python crash courses both full time and part time. The subjects include AJAX, artificial intelligence, blockchain, and crypto, CSS Coding, Python, and many more.

9. BrainStation

Learning mode: Online and Offline (Boston, Chicago, London, Miami, NYC)

BrainStation, provides both full-time online and offline courses on the campuses. The courses the academy offers are Data Analytics Certification course, Data Science Diploma, and Coding. The subjects include A/B Testing, Agile, Algorithms, API, iOS Development, Python Programming, and many more. Students will be assisted in the career too after completing their courses such as helping them in adding LinkedIn portfolios and resumes, etc.

10. The Dev Masters

Learning mode: Offline (Irvine, Lacey, Los Angeles, San Diego, Santa Monica)

Dev Masters provides numerous data science courses such as Mastering Applied Data Science, Data Science for Professionals, Mastering Applied Data Science with Deep Learning, Coding, and many more. The subjects include Artificial Intelligence, Big Data, Data Science, Machine Learning, and Python coding. The academy is intensive along with part-time and full-time courses.

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Since the dawn of time, and accelerated through several industrial revolutions, businesses and individuals have aspired to do more with less resources. However, with labour-productivity growth figures at an all-time low, perhaps, for the moment at least, organisations are not maximising the ability of current resources where they could be embracing and harnessing technological change. One of the major ‘resources’ of modern businesses, noted for its value but widely regarded as under-utilised, is data.

Boosting productivity with data is one of the biggest challenges in the modern enterprise, and it all comes down to finding a way to extract and maximise value. In the business world, value is all about achieving the maximum productivity possible. This aligns well with the central purposes of data science, which we at Mango define as “the proactive use of data and advanced analytics to drive better decision making.” That is to say, data scientists take existing data resources, and use these to create “more” using analytical techniques. These can be applied to many use cases, but driving productivity from data often falls into a number of key categories – delivering solutions to business problems, reporting in real-time and using trend data to drive future growth.

The first key of successful data science is that it is using data to solve real business problems. This means increasing integration between data and data science teams by fostering collaboration and creative discussions to help them understand what is needed and what is possible. Clarity of purpose and communication enables better understanding of what is needed, and the resources they need to get there. Meanwhile, the business team will not only be able to explain what they need, how and why, but may also know of department-specific or team-specific data sources which could aid in the creation of a new solution. This allows all teams to work more productively, with data scientists better informed about what the solution is for, and business teams more confident that the solution will work.

However, data science does not always need to be about creating detailed new solutions out of nowhere. Often, given the wealth of data that businesses hold, data scientists can use historical trend data to drive insights in future activities. This can be external facing (such as optimising strategies for engaging with customers) or internal (such as predicting the impact of different hiring patterns or organisational reshuffles). While historical data cannot offer a perfect answer to how a situation may play out in future, previous patterns can help shape models that at least offer a probable explanation of what might happen next. This helps make more informed decisions quicker and more accurately drive company performance.

Thirdly, data scientists can boost productivity both of their own teams and the organisation as a whole by implementing more real-time analytics solutions. These make use of not only historical data, but also look to proactively generate insights alongside action. While data scientists have traditionally been viewed as part of the strategic pipeline – that is, as providers of insight into weighty, considered decisions – the evolution of real-time analytics and streaming data enables data scientists to provide tools that support in a tactical role at great speed in changing conditions. This can support business productivity “on the fly” – such as when dealing with a customer during a complaints call, or with on-tap insights around successes and challenges with existing data science project implementations.

The benefits of data science for boosting productivity are hard to understate. However,  against the grain of our XaaS technology era, data science is more than just a plug-and-play solution.  The productivity of a data science team itself, and the business as a whole, relies on more than just tools or training or the right resources. Instead, it comes down to creating a culture of data science – and this is something championed from the top down. Harnessing data as a resource, and then finding a way to use it effectively within the work environment requires businesses to build a foundation of curiosity and an acceptance of the possibilities around data. Creating a thriving data science culture will be the difference between a vital productivity boom and a state of data overload.

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