Exploring Data Science Basics

Data Everywhere

We live in an era with an exploding amount of data surrounding us. Every day or even every second, data is generated non-stop and at a fast pace. Some say that data is gold nowadays; that seems true when we think about what powers we can gain from data and where we can use it.  

You can find data in every industry, from agriculture to healthcare, banking to retail, manufacturing to aerospace. In other words, in every field you can imagine, data is generated or stored somehow. Data can be in any format; in your everyday life, you may send text or audio via social media, take photos or videos with your smartphone, monitor your health condition through wearable devices, search on the internet, and so forth. It can be seen that each of us produces thousands of bytes of data each day; no surprise that big companies produce much more.

What is Data Science?

To deal with such a massive amount of data, a new field of study named Data Science has emerged. As its name suggests, data science uses scientific methods and tools; it also works with data, which means lots of raw, unorganized, and unstructured things that need to be processed. Data science aims to transform somehow not ready-to-use data into useful information. This helps us to uncover hidden patterns of data and gain new insights into our business. In the following, some of the values that data science can add to business are listed:

• Measuring related metrics and performance
• Making better decisions
• Developing data-driven products or services
• Predicting future trends
• Guiding management to take wise actions
• Providing better user experiences  

"The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades."  - Hal Varian, chief economist at Google, 2009 [1]

So far, you learned about what data science is. But let's have a more detailed look with some examples. Data science dominates most industries today, as most operate based on data. Today,  because the value data science can add has been proven, more and more businesses are utilizing the power of data science to make evidence-based decisions, promote employee training, and understand their customers. Manufacturing, retail industry, healthcare, and education are examples of the fields in which data science has strong applications there. For example, it can be used to provide valuable insights to manufacturers aiming at profit maximization, risk minimization, and productivity assessments. In the following, we consider examples of the applications of data science in the retail industry in more detail:

The retail industry is one of the industries that extensively use data science in various fields like product placement, high-demand products on special occasions, inventory management, and customization of offers. This kind of industry always saves various data, including historical sales data, items in shopping carts of each customer, time of purchase, goods in stock of each department, time of presence of each employee in the store, and a lot more informative data. These data help the data scientists to get the information that the company needs, like:

• Frequent shopping times: By detecting peak times for shopping, the company increases the number of employees at that time and manages inventory.
• Loyalty of customers: By using loyalty-enhancing activities, customers return to the store more often and are willing to shop more.
• Best items for sales offer: By Choosing the most appropriate products for sales, the number of visitors will increase.
• Markdown events: Suppose a retail business conducts promotional markdown events before major holidays such as Christmas, Thanksgiving, or the Super Bowl, among others (A markdown is a reduction of the original price of goods to increase sales). Of course, it is crucial for the company to investigate the effects of the markdowns on sales during the holiday weeks. This is where we need data science.

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Who is a Data Scientist?

In simple terms, a data scientist is a person who helps businesses to achieve their goals and find appropriate answers to their questions through data. For this, they need to tackle data, analyze and explore it, and then based on the results, make decisions.  

As we said, data science is a multidisciplinary field, so a data scientist needs to have proper background knowledge in needed tools, technologies, and fields. In the following well-known Drew Conway's Venn diagram, different expertise to be a data scientist is shown. As we can see, a data scientist is someone who develops their skills in computer science, mathematics, and domain expertise. The latter means understanding the overall aspects of the field in which you work. But what is computer science?

Computer science is a broad field that encompasses the theoretical and practical aspects of modern computing. At its core, computer science is concerned with understanding how computers work and how they can be used to solve complex problems. This includes everything from software to the operating systems they run on and to the base hardware that interacts with the OS. One of the key skills that a computer scientist possesses is the ability to code and program. This involves writing instructions that a computer can understand and execute. Remind a data scientist does not necessarily need to be a computer scientist; rather, they need to possess certain skills and knowledge essential for the job.

Mathematics is also a fundamental field for data science. Some of the key areas of math that a data scientist needs to know include:

1. Statistics: This is the branch of math that deals with the collection, analysis, interpretation, presentation, and organization of data

2. Linear Algebra: This is the branch of math that deals with the study of vectors, matrices, and linear transformations.  

3. Calculus: This is the branch of math that deals with the study of rates of change and accumulation.

You might ask what the difference between a statistician and a data scientist is. Indeed data scientists go beyond just analyzing and exploring data; they also use specific algorithms called machine learning algorithms to do more advanced tasks, such as detecting patterns of data and predicting the future trend. In the following some of the important tasks of a data scientist are listed:

4. Asking the right questions to identify the problem or goal of the project.

5. Conducting exploratory data analysis, which may include statistical analysis or other techniques to understand the data.

6. Visualizing and presenting the data to communicate insights or findings to stakeholders.

7. Modeling the data using machine learning algorithms to gain further insights or make predictions.

8. Detecting patterns and anomalies, which can help identify important trends or outliers in the data.

9. Understanding the past and present by analyzing historical data and current trends.

10. Making data-driven decisions based on the insights and predictions generated by the analysis.

11. Predicting the future by using machine learning models to forecast future trends or outcomes.

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References

[1] https://ischoolonline.berkeley.edu/data-science/what-is-data-science/