Getting Started with Data Science and IBM Cloud

Around the year 2012, computer scientists found a breakthrough with AI. Data had been growing at exponential rates but we weren’t able to make the most out of it. The breakthrough that we witnessed was the ability to use GPUs to process complex types of data. All the advanced neural networks that were theoretically developed could finally be run in a reasonable amount of time. This ended the AI winter and began the Data Science era. 

Data Science is indeed a broad concept and has several divisions under it. Machine Learning, Data Analytics, Data exploration, and insights to name a few. Work done in this field for the better part of the last decade has proven what it can do. Today, almost everyone is on board to explore, implement, and reap the benefits this technology has to offer. 

Growth of Data Science

Data Science has been growing at an exponential rate. Here’s a recent study conducted in the growth of data: 

Source: Statista

We can easily infer how data growth is happening and will continue to do so for a wide variety of segments. This only means that there will be more demand to build applications that use this data to derive insights, fine-tune business processes, automate workflows, and more.

This makes it imperative for developers to Learn, Upskill, and be ready to code. Getting started with Data Science can be hard and finding the best resources can be a big challenge. I have learned a lot through the IBM developer program and utilizing the IBM cloud to build applications. I wanted to help other developers by sharing my experience and how you can get started with Data Science as well. 

Why Should you learn Data Science? 

Demand for applications that are powered by machine learning or applications that apply data analytics is on the rise. From small to big businesses and even individuals are generating data like never before and there aren’t enough developers who are working to capitalize on this demand. 

Most of the data being generated is unstructured. For example tweets, product reviews, videos on social media platforms, and more. This makes it far more difficult to have applications that can easily process them to find useful insights. 

Thus, there is a demand and it’s not easy to build applications that meet the demand. These reasons make it the perfect opportunity today to learn the concepts of data science and start solving real-world applications. 

Getting Started with Data Science

As I mentioned earlier, data science is a very vast field, so where to begin is the first question that needs to be answered. Following are the main features that are required from any good resource to learn about data science: 

  1. Industry Relevant Information – This field evolves so rapidly, that only people who are in the industry can develop the resources required to best understand the technology, the methodologies, and the tools that are being used. 
  2. Easy to understand and Follow – Indtorductory courses need to be easy to understand and follow so that a beginner can grasp the concepts correctly and find the correlations on where it can be applied. 
  3. Free to Learn – Many universities and organizations came together to build the best courses on the planet and make it free for all to learn. We call it the Massive Online Open Course ( MOOC ). We need to ensure that the course is free and self-paced so that learning can be done without having to spend a lot of money. 

Keeping these requirements in mind, I have found the IBM Data Science Fundamental courses to be the perfect fit for beginners to get started with Data Science. If you go to the course page, you will notice that it’s not one course but a series of three courses all of them being free to learn. 

The Three courses form a learning path that:

  1. Introduces you to the fundamental concepts that come under the umbrella of data science.
  2. Familiarizes you with the tools we use to execute Data Science tasks from start to finish. 
  3. Takes you through the end-to-end process from handling data to building models and evaluating them. 

This path is fantastic for beginners as it takes you from the very basics to the most widely used techniques in building AI-powered applications. 

Take the Journey, it’s worth it!

I have been working in the data science field for about 5 years now and I genuinely wish that I had resources like this that can streamline learning so that we can go from basics to professionals. This field provides a lot more opportunities than people know. From working on chatbots that respond like humans, to autonomous driving, to generating art, there are lots to learn, discover, and explore. 

I personally like video analysis where we try to extract as much information from video frames and stitch that to extract a larger context. Having the core knowledge, and experience with popular tools like Tensorflow gives you the upper hand at cracking complex tasks such as these. So, wait no longer, get started with your Data Science Journey! 

akshay pai

I am a data science engineer and I love working on machine learning problems. I have experience in computer vision, OCR and NLP. I love writing and sharing my knowledge with others. This is why I created Source Dexter. Here I write about Python, Machine Learning, and Raspberry Pi the most. I also write about technology in general, books and topics related to science. I am also a freelance writer with over 3 years of writing high-quality, SEO optimized content for the web. I have written for startups, websites, and universities all across the globe. Get in Touch! We can discuss more.

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