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Computers and TechnologyReference and Education

How to do Online Data Science Courses

Before you set into an ambitious path to learn data science for your career, you should consider these points.  Do you have the skills and you enjoy working in the field of Data. Is there a demand for this skill set and if so, Is your skill level above or below average in the current environment. So basically before making a decision to learn, connect the dots, will I love doing this as my job, will this job be in demand, will this job pay me good. If its an affirmative for all these then you should go ahead. While classroom training are always in demand, from a cost and flexibility perspective they have their own limitations. Online courses in Data Science are comparably more practical. This is essentially inspired by the Japanese philosophy of Ikigai. Herein, you are talking of the four fundamentals of passion, vocation, profession, and mission. If we link each of them, be it for our career or for our way of living, success is bound to accede.

So ask your self this question at various phases of your learning and career. Are you passionate about Data Science, would you want to use it as a vocation, Do you want to make it your profession, and ultimately what’s your mission.

Why The Online Data Science Courses?

Modern day data science is all about big data and the insights it offers. At the same time, the amount of data is growing exponentially: much of which is unstructured and uninterpretable. But there is no shortage of raw material. Learning to read and analyze data, and extract value from it, is important. What’s more, statistics has recently gained in popularity and importance, making it a good time to start. These online courses use open source, peer reviewed, Python-based and theory-based content so you can apply your previous training and experiences to a new career. It will enable you to learn how to do statistics in Python, how to use Python libraries, and how to apply key statistical principles to data. Let me list down some good paid courses platform for data science for you as a reference list. Some portion or few modules may be available for free as well.

  1. Data Science Course from Coursera
  2. Online Data Science Courses offered by Harvard University
  3. Data Structures and Algorithms course offered by Udemy
  4. Data Science Course in Online mode by Simplilearn
  5. Online Courses from Harvard, MIT, Microsoft offered through edX
  6. Data Science Online Courses & Programs by Udacity
  7. Learn R, Python & Data Science in Online mode by DataCamp

If you want you can subscribe to Courses in Python, R Programming, Machine Learning and Business Analytics at Imurgence. These courses are completely free. They also include assessments and certifications.

 

 What are the Benefits?

Complete a set of courses in data science in online mode. Certification is awarded online, to gain professional accreditation. Complete the courses at your own pace. Who is This for ? Master Data Scientist (MDSP), Data Scientist, Data Analyst, Data Engineer Employer Requirements. These courses are suitable for job seekers at the very beginning of their career path. At the heart of this course set are fundamental courses in software development, probability, statistics, machine learning and statistics with Python.

Why Take These Courses

These intensive data science course covers everything you need to know to make data science applications in real business scenario. You’ll start by learning foundational data analysis techniques using the Python programming language, and you’ll work on a real data mining project using some real data. It’s fast paced, yet easy to understand, and you’ll have plenty of time to reflect on the material before moving onto the next topic.

What will I learn?

It teaches you how to use advanced statistical techniques to make inferences based on large, unstructured data sets. Most of these courses also cover the basic fundamentals of computer programming, networking, machine learning, data analytics and visualization. This is a one-of-a-kind course that will improve your analytical skills, make you a better problem solver and contribute to the betterment of your work. The methodology will let you solve advanced analytical problems such as: modeling natural language, evaluating content, drawing patterns, visualizing data, predicting events, and manipulating databases to create new analytical techniques.

Course Curriculum

While evaluating a course , make sure the curriculum is well balanced. It should not be completely theoretical nor completely hands-on. Pure hands on courses are good if you are already working in the domain, whereas for new comers the theoretical layer is important as these are typically helpful in interviews. The first 2 layers of interviews are mostly on concepts rather than assignments.

Mentorship

Quality data analysts have distinctively different personalities and are usually drawn to work in sectors where they can shape customer experiences and optimize businesses processes. They need to know the entire data workflow, from data capture and analytics to visualization. Once they get started, they start working alone. The open-source community is full of knowledge. So if you want to learn and keep up with the latest developments in data science, how to solve complex problems and get up to speed in the latest algorithms, then read and follow the web forums and ask on Stack Overflow and Reddit. The vast majority of developers are willing to teach the world that knows them and, quite often, the nature of their work enables this.

There would be some who would feel that there is no need for mentorship. Experience will teach you things which matter the most. But in real Data application, it may make sense sometimes to have a mentor who has made or seen some mistakes which have a long term impact on projects. If we can use their learning to avoid these mistakes, we would be saving enormous amount of time spent in designing and redesigning flows. Mentors guidance will add a lot of value on your profile.

Projects

You should pursue a internship in the Industry for gaining hands-on experience. Many organizations recruit summer and winter interns to get new ideas for product development. They also use this pools as a gateway for future full time role recruitment’s. While Internships are part of academic curriculum, they also help learners connect with Organizations. You should also create some academic projects based on small open data sets and then gradually move on working on real projects. 

Practical Data Mining

A journey from introductory data-mining concepts to practical, real-world work. Understand statistical analysis and learn practical tools in order to discover important trends, uncover relationships and build analytics. Work on a real-world data science project under experienced mentors, which will help you improve your fundamental data science skills. Get certified in Data Science, Machine Learning or Business Analytics. Once you have now learned the tools and techniques you need to apply in-depth knowledge of data science. You must have experience using these tools in actual applications. You need to put your knowledge to use and develop practical models and ideas, to solve real business problems.

Certifications

While certifications are not absolutely necessary, some organizations need their recruits to be certified. Machine Learning certification are available on many learning management system post completion of a mandated hours of curriculum. It makes more sense to also add up some certifications from non training bodies. As such their certifications are considered unbiased primarily because only assessment is considered. A typical training institute would be biased because its their course and they have been monetarily supplemented by the candidates tuition fees.

Conclusion

The rise of artificial intelligence, the launch of technology across the web and more mature industries pushing on technological boundaries means that everyone’s taking note, and data scientists can’t be far behind. But you can’t spend your time preparing for when the machine takes over. All we have is today, so why not learn to be a data scientist today.

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