Companies can now make data useful to elevate decision making and to optimise products and processes. But what organizational capabilities are necessary and how to get started? It's currently easy to acquire data strategically. First, consider that smartphones function like questionnaires that customers are frequently filling out in a passive or active manner [,1]. The average smartphone owner uses around 10 apps per day and 30 apps each month [,2]. Since 2017, more than 1.5 billion smartphones are being sold to end-users worldwide every year [,3]. Then, add to this the ubiquitous usage of cu ..read more

Data science encompasses a range of fields, like data analysis, machine learning, statistics, computer science, infrastructure, and data architecture, and looking at how businesses are transforming on a day-to-day basis, we may infer that some data science jobs will be in high demand within the next ten years, there is a strong need for experts who understand the market demands, who can formulate a data-driven approach and then execute the way out. Job change within Data Science is definitely possible Well, it is possible to switch from one profession to another if only you can learn the funda ..read more

Big Data has become the dominant innovation in all high-performing companies. Notable businesses today focus their decision-making capabilities on knowledge gained from the study of big data. Big Data is a collection of large data sets, particularly from new sources, providing an array of possibilities for those who want to work with data and are enthusiastic about unraveling trends in rows of new, unstructured data. Big Data gives you an advantage in competition as true for businesses as it is for professionals working in the area of analytics. The purpose of this post is to expose you to the ..read more

Organizations in several industries such as banking, healthcare, and automobiles are now acknowledging the value of data science in their mode of operation. Thus, an ideal and efficacious data science team are therefore expected to manage numerous volume of tasks. Even then, developing a team to successfully manage AI tasks is essential to tackle any challenges faced by organizations as regard data. While studies over the last few years have shown that data engineering, a vital phase forward to Data Science and AI, it is still a phase that many businesses completely skip, misunderstand, or neg ..read more

As a data scientist, I always felt a missing link between my developed models and putting them in the production process. Yes, I can create a pipeline, write a model, get results, and interpret the results, but if I cannot scale it, these all will sit on my Jupiter notebooks. This thought led me to my data engineering adventure. I am confident that learning data engineering will make me a better data scientist. Where did I start? I joined Andreas's coaching sessions, and I am a member of his sessions from the November 2020 cohort. Our cohort just finished the first week, and the most importan ..read more

Currently, the big buzz about big data is probably apt with the number of technologies and tools available to build products and services. Uber, Google, Microsoft, and now Apple are implementing AI to their core business operations to provide real-time AI services in their ecosystem. I personally believe once due to this success of big data companies, the hype behind AI has blown out of proportions. All businesses are keen to implement some sort of AI technologies to automate processes or at least keen to test the litmus paper. Andrew Ng, one of the leading pioneers of AI has suggested that th ..read more

Week 2: 10/16/20 - 10/23/20 Week 2 of the course consists of Modules 3 & 4. If you have not read my first blog go here. Module 3 focuses on creating a professional LinkedIn profile. Your LinkedIn profile is the world's access to you and how you want to be seen professionally. Below is a screenshot. So here, I have a professionally taken photograph, what I am interested in below, and the 'About' section that summarizes Me...in a professional sense. I do have other details as you scroll down but the header is what the world will see first when they land on your page. I think you have to mak ..read more

Week 1: 10/9/20 - 10/16/20 In my quest to further improve my overall data science skills, I pulled the trigger on October 9th, 2020, and enrolled in a Data Engineering boot camp lead by Andreas Kretz. First a little bit about myself. I have a background in Aerospace Engineering and have been in the industry for close to 15 years now. A little more than a year ago, I decided to pivot to Machine Learning and Data Science. The world itself is changing rapidly and has been so for quite some time. However, I have seen the exponential growth of tools gaining insights from data as far back as 6 year ..read more

"Where can I start if I want to become a Data Engineer?" This is a question I have heard many times before. My answer to it is actually always the same: Start doing a Data Engineering project! Choose a tool Your first step here should be to select a tool. Then start with that tool and then build the whole thing up. So you get some data and then start with a tool. For example a processing tool or a database. It does not matter. Build it up You then build another tool on top of that. And you also build something on top of that again. And so on - so, just start building the whole thing. Of course ..read more

"Since I started exploring Data Engineering, it has been overwhelming. In the end I have the feeling of giving up." This is a message that reached me from a viewer on YouTube. And that's exactly how I feel sometimes! Sometimes I feel a bit overwhelmed by the whole thing. Because there is so much going on. All the technology and Data Science hype. There is always something new on the horizon. And there's always something going on. You always have new tools - for example, you have Spark, then you have Spark 2.0 and then all of a sudden you have Spark 3, or Kafka - Kafka Streaming, Kafka Connect ..read more