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In the Age of AutoML, we are all Data Scientists! | Artificial Intelligence (AI) has become increasingly essential to our daily routine lives.

Theory vs Practical Data Science - For real world problems

Published on Sep 30, 2021 by Dhvani Shah

We hear a lot about how artificial intelligence and machine learning are going to change the world and the internet of things will make everyone’s life easier. But what is the one thing that underpins all of these revolutionary Technologies? The answer is Data. From social media to IoT devices for generating immeasurable amounts of data, consider a cab service provider company, what do you think makes the company that rich?  Is it their availability of cabs or is it their service? Well, the answer is data, data makes them very rich, but is data enough to grow a business? Of course, it is not that you must know how to use the data to draw useful insights and solve problems and this is when Data Science comes in.

At the end of this article, you will be able to differentiate theoretical and practical Data Science and learn how to tackle real-world problems.



This article’s outline:

●      Theoretical aspects of Data Science

●      Practical aspects of Data Science

●      The gap between theory and practice in Data Science

●      Why are these problems more challenging?

●      What about the nature of the data?

●      What are the challenges faced these days?

●      How to tackle real-world problems?


❖    Theoretical aspects:

Data Science is becoming a ubiquitous part of science, engineering industry, technology: it’s a part of all our lives. It was established to find hidden patterns and trends in the data. When we talk about Data Science, well it is not one tool, skill, or method but is more like a scientific approach that uses applied statistical and mathematical theory and computer tools to process big data. Data Science is a detailed process that mainly involves pre-processing, analysis, visualization, and prediction. It is majorly concerned with making use of Data Analysis and Data Analytics. It does not involve a high degree of scientific processing. Using Data Science, complex models can be built for achieving various statistical insights and facts about data.  Therefore, Data Science tends to use patterns in data to arrive at a decision with respect to the data.


❖    Practical aspects:

To see the practical aspects of Data Science, let’s go step by step on what needs to be done when we are working on a Data Science project:


Step 1:  Business Problem

In this step, the data scientist has to make sure that he/she has the answer to all the relevant questions, she has to understand and define objectives for the problem that needs to be tackled. This step requires a very curious soul.


Step 2:  Data Acquisition

In this step, the data scientist has to care for data acquisition to gather and scrape data from multiple sources like web servers, logs, databases, API’s and online repositories. After this, it definitely seems like finding the right data takes both time and effort, doesn’t it?


Step 3:  Data Preparation

After the data is gathered comes data preparation. This step involves data cleaning and data transformation, data cleaning is the most time-consuming, as it involves handling many complex scenarios such as inconsistent data types,  misspelled attributes, missing and duplicate values. Then in data transformation, we have to modify the data based on defined mapping rules.


Step 4:  Exploratory Data Analysis

Understanding what you actually can do with your data is very crucial which is why this step is important. With the help of Exploratory Data Analysis we define and refine the selection of feature variables that will be used in the moral development, but why is this step important? Well if we skip this step, we might end up choosing the wrong variables which will produce an inaccurate model, thus this step becomes the most important step.


Step 5:  Data Modeling

The core activity of a Data Science project is known as Data Modeling. In this step, we repetitively apply type force machine learning techniques like KNL, decision tree, naive Bayes to the data so that we can identify the model that best fits the business requirement. We train the model on the training data set and test them to select the best-performing model.


Step 6:  Visualization and Communication

Well, the trickiest part is not yet over, we have visualization and communication left, in this step the data scientist meets up with the client and communicates the business found in a simple and effective manner to convince the stakeholders.


Step 7:  Deploys and Maintains

And finally, in this step, the data scientist deploys and maintains the model, he/she tests the selected model in a pre-production environment before deploying it in the production environment which is the best practice. After deploying it, we have to get real-time analytics and monitor and maintain the project’s performance.


❖    The gap between theory and practice in Data Science:

We all know that Data Science is a very powerful scientific approach, with all kinds of interesting applications. However, it is also well-known that in Data Science there is a huge gap between theory and practice: when it comes to theory, we know everything but nothing works and when it comes to practice, everything works but nobody knows why. The way to bridge the gap between theory and practice is known as "theory to practice", we can describe this by the following: we use deep learning theory tools to obtain new, better, practical, and efficient algorithms. This way shifts towards more quantitative research that’s analyzing algorithms with mathematical methods and computers. This gap between theory and practice leads to the fact that real-life problems become hard to solve and it becomes more challenging. So basically, we try to translate all these academic insights into practically feasible investment solutions for the clients. Initially, these models were developed, were used by the more traditional portfolio managers doing fundamental analysis and using the model as an idea generator. But we found that these models are so effective that we have built a whole pure quant product line based on this. The human factor is crucial in this research, quantitative research is not a matter of turning on a computer and testing thousands or millions of strategies because if you do that, there is bound to be a lot that looks very attractive historically but it is just due to chance. And understanding where the performance is coming from relating it to investor psychology is a key part of the work. Basically, the top specialists in the field are working and giving their hundred percent when it comes to this quantitative research. And using their knowledge, those data scientists are generating efficient algorithms for their clients.


❖    Why are these problems more challenging?

One of the reasons that these problems are more challenging is as we have trained our machine, our models for a particular scenario, for a particular input but when we have to face a new situation, a completely unknown scenario, it becomes more challenging, more difficult to solve. The second reason why these problems are more challenging is the nature of the data: sometimes we have to deal with a small amount of data whereas sometimes we have to deal with big data, and it becomes really challenging.


❖    What about the nature of the data?

We are living in the age of data. According to a study, every two months the total amount of data produced doubles; 4 billion people use the Internet, every second Google processes more than 40 000 searches, and more than 1000 pictures are shared on Instagram. Our car produces 25 GigaBytes of data daily and each flight generates 8 Terabytes of data.

We need technology and algorithms to collect, store, and most of all process this huge stream of data. In fact, this amount of data is quite a lot for traditional computing systems to handle and this massive amount of data is what we term Big Data.


How do you classify any data as Big Data?

This is possible with the concept of 5 v’s: volume, velocity, variety, veracity, and value.


❖    What are the challenges faced these days?

The challenges faced by most people these days are the adoption of Artificial Intelligence, but at the end of the day, the people are just trying to understand where to start. How am I going to change my organization? How am I going to change the way that I am doing business? All these questions are automatic in people’s minds, in order to become that disrupter in their own sector, in order to move forward and have a competitive advantage within the particular set of competitors.

The main problem is that everyone is trying to replace human capabilities instead of focusing on augmenting approaches.


❖    How to tackle real-world problems?

To tackle real-world problems, to bridge the gap between theory and practice in Data Science, to solve all your problems, there is only one solution: SmartPredict.

SmartPredict is a customer-centered company, we have a very strong and authentic service, our platform is easy to use and anyone can manipulate. We are here to help you to solve real-world problems efficiently and easily.