Machine Learning, Deep Learning, Big Data, Artificial Intelligence, Data Analytics, and Data Statistics are sub-disciplines of Data Science. This discipline has been rising since the first quarter of this century. The main concepts of Data Science lie within increasing data collection abilities and an exponential increase in computational power to evaluate data as a whole.

This field draws interest from many engineers, mathematicians, cyber researchers, statisticians, and increasingly demanding creative, multi-faceted approaches for successful execution. In fact, no engineering, science, or business branch is far from the touch of analytics in any industry. Perhaps you, too, are interested in being a data scientist or using data science for your business. 

Knowing about technology or methodology is one thing and using it in practice is entirely different. That’s why experts always say that academics and business fields are two other worlds. Once you start your journey with data science/analytics, some truths start becoming evident over time. While none is ground-shattering, they often surprise the novices in the field. So, in this article, we will uncover the top 5 facts about Data Science that many people don’t know. 

Data is Never Clean

With the rise of smartphones and IoT, over 2.5 quintillion bytes of data are being created every single day. But this data is neither clean nor usable for analytics. So, analyzing this data will produce a mere collection of misleading hypotheses and theories. From the accumulated data, getting the specific information with proper context requires algorithmically testing, sequencing, crunching, sorting, etc. So, you can conclude that the data sets you have collected from the market, your customer base, or competitors’ consumer base is never going to be clean. Even organizations incorporating data science in the last decade don’t claim their data to be clean for use. 

Apart from missing or wrong values, one of the biggest problems is joining multiple datasets into a coherent whole. So, encoding and extracting accurate contextual data becomes a more significant real-life challenge than most could imagine. 

There are various Machine learning algorithms available and under development, which artificially help in the data analysis jobs. To run and develop those algorithms on huge datasets requires immense computation power. This is where devices like HP Z8 G4 Data Science Workstations become extremely important. They come with Up to 56 cores with 2x Intel Xeon Scalable CPUs, Up to 2x Nvidia RTX A6000 GPUs, and Up to 1.5 TB DDR4 ECC RAM. 

Automated Data Science Is Just a Concept

As discussed above, clean data sets are a myth and there are no ready-made scripts or applications available that will do it for you at the push of a button. Each data set comes with its own problems and requires creative solutions to extract useful information from it.

As there is no substitute for exploring data and testing models, you have to get your hands dirty most of the time. Suppose you want insightful data that can be validated against business sense and used by domain experts. Then you’ll first have to create a process that can spot and remove unwanted data.

This process is very complex and requires immense computational power. As you develop and test unoptimized algorithms, your system needs to be ready to face the unexpected. To keep experimenting and trying out new methods, you must always be active. For this, you need the computation power of a device like the HP ZBook Firefly 14-inch G8 Mobile Workstation PC, which comes with Intel Core i7, NVIDIA Quadro T500, and 32 GB ECC RAM. It will help you experiment with data science models with lesser effort. 

Big Data is just a Tool

The hype around Big Data is getting louder every day, so I won’t blame anyone if they develop misconceptions about the technology. While most people consider big data as a solution, it is not the whole truth. Big Data can ease your pain, but it is not a one-stop solution. Remember that Big Data is just a collection of tools to work with large volumes of data sets in a reasonable manner & time and with industry-grade computer hardware.

You still have to use traditional methods like analytic problem design, identifying & modeling best practices, scrutinizing data with a contextual view, etc. So, you need to understand that tools will come and go, but the fundamental understanding of technology and expertise will persist. So, working with unoptimized methodologies will never be replaced, and that’s complete human work. No AI can replace human methods and viewpoints, and learning the skill over the tools is more important. 

If you are comfortable with remote working and need a laptop to work with Big Data, you can check out the HP ZBook G8 Data science Workstation. This device is powered with the Intel Core i9-11950H, Nvidia RTX A5000 laptop GPU 32 GB ECC SDRAM, and runs on Linux Ubuntu 20.04. 

There Is No Thumb Rule 

The end-users of your data science models are decision-makers and executives, who are more focused on the workability and usefulness of the model and the data within. None of them is interested in knowing how you got the data and the output. So, it would be considered counterproductive to show off your analytic rigor to them. They are interested in the results, and not in tech talks and complex jumbo-mumbo. 

Many industry leaders often say, “as a data scientist, your job is to provide a solution, not give a presentation.” So, you can try out any method you think is feasible to get the result. Data Science is ultimately a trial and error method. Just go with your experiments in search of the solution and save your expert talk about technicalities among your data science peers. 

Hardware Is a Key Factor

Less than 0.5% of all data we create annually is ever analyzed and used. A 10% increase in data accessibility will result in more than $65 million in additional net income for a typical Fortune 1000 company. So, you already know the importance of identifying that critical percentage of the data, which is helpful. While Google uses about 1,000 computers to answer every single search query, you can just guess what type of hardware juice you need. While individuals create 70% of data, enterprises are responsible for storing and managing 80% of that. Just imagine the volume of data you will handle in the coming years to uncover tiny details like some product’s performance in a single region. 

You must create a graphical presentation using real-time data at the end of the hustle. Most people think if that’s the primary job, then why invest in hefty PCs, as a PowerPoint presentation and crunching some excel sheets can be done on any computer. That’s where people are wrong. To reach the final stage, there are countless steps to take, and most of the time, you have to define each step as there are no preset rules. Running and testing algorithms on thousands of megabytes of data calls for incredible processing power, and your casual Intel i3, i5, or Ryzen CPUs cannot handle that. So to reach the storytelling part at the end, you need to have the “ring of power” in your arsenal from the beginning. 

Verdict

Data Science itself is a never-ending work. Any model you prepare is technically wrong, but some are useful. In terms of physics, the world is infinitely complex (think Quantum Mechanics!), so models are approximations of reality. Some models are more wrong than others, but all are wrong at some point. So, any model has a limitation, and that’s how it is. So, you must work out and run multiple models to make decisions based on real-time data. Ultimately, realizing what we aim for and compete against can be crucial in shaping our analytic design process. 

So yes, Data science is the need of the hour, and you also need to invest in increasing your computation power for advanced analytics. If you don’t, your competitor will have an advantage over you. 

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