As the world gravitates toward a digital economy, big data is becoming the most valuable resource available to any entity. Those who manipulate data to extract useful information are called data scientists, and their craft is data science. This is considered one of the best tech jobs in 2022. Understanding data science itself is key to overcoming data science challenges.
Data science is a field that applies scientific analytics, computational methods, statistics, machine learning, and deep learning to develop predictive models from large amounts of structured or unstructured data. This article highlights common data science problems, some of the challenges data scientists face, and some actionable solutions to them.
Challenges Data Scientists Face Daily
Some of the common challenges data scientists face daily include finding data that is relevant to the question they are trying to answer, filtering data to discard useless information, and understanding it in order to extract useful information that can help businesses make better decisions. Below we will discuss some of the most common data science problems.
Common Data Science Problems
- Finding data
- Securing data
- Filtering and preparing data
- Understanding data
- Misconceptions about data scientists
Common Data Science Challenges and How to Overcome Them
Finding data is an arduous task, despite the abundant data present in this digital age. In fact, it is this very abundance that creates this problem. Data is scattered across multiple sources, and the data acquisition phase is time-consuming. Many companies gather data indiscriminately to solve this problem, but a large percentage of the data becomes useless.
Solution: Integrated Data Centers
There are several affordable cloud storage services that data scientists can convert into centralized data centers. This allows them to store large volumes of data with proper documentation and cataloging. This centralized platform can also serve to integrate multiple data sources. The data available through this platform can then be instantly accessed, saving time for more productive tasks.
Storing massive amounts of data in-house is a costly venture for many enterprises. Hence, it is an intelligent business strategy to adopt cloud platforms for data storage. Unfortunately, poorly installed cloud data centers frequently fall victim to cyber attacks, which can harm a company’s reputation and finances.
In addition, data scientists often mine data from multiple unverified sources. This practice creates the common challenge of sorting through data infected with malware or ransomware. As a result, organizations are at the risk of having entire data centers corrupted by these malicious programs.
Solution: AI-Powered Cyber Security
Data scientists should spare no expense in investing in data security software like antiviruses and firewalls. They can also take full advantage of machine learning technologies that scrutinize incoming data from untrusted sources for security vulnerabilities.
It is a common opinion that the weakest link in a security chain is a human. Therefore, every member of any organization’s data science team should educate themselves on data security practices. Additionally, companies should enforce the confidentiality of proprietary data.
Filtering and Preparing Data
Every day, data scientists collect many terabytes of noisy data from numerous sources. These data sets are present in multiple formats and may produce unreliable or irrelevant results after several hours of filtering. According to Harvard Business Review, filtering, cleaning, and preparing data constitutes 80 percent of a data scientist’s workflow, taking up the bulk of their time and representing one of their biggest challenges.
Solution: Augmented Analytics
Filtering and preparing data, however mundane, is a critical task that every data scientist must perform. Before you get discouraged, it is interesting to note that there are several augmented analytics tools to aid data scientists. These AI-powered tools perform automated analysis of data, saving time and boosting the productivity of data science professionals.
As a data scientist, you need to perform predictive model building immediately after obtaining clean data. However, poor data quality will leave you asking more questions than can be answered. Time and again, data scientists encounter large blobs of data that make sense only to the person that prepared the data.
The data may also lack context, making it subject to multiple and often conflicting interpretations. Understanding the data problem to be solved is imperative to creating accurate solutions, but poor documentation makes that process difficult.
Solution: Documenting Data
The easiest solution to this problem is to document data. The simple task of naming a row or column will save the time otherwise spent trying to understand the given data. Moreover, data scientists can use automated analysis tools to create auto-documentation on related data sets.
It is also vital to identify and understand the business problem so that you can use the provided data effectively. Real-life data is ambiguous and is an aggregate of several contexts. Proper insight into the business problem helps create varying models from the same data set.
Misconceptions About Data Scientists
There is a big misconception about the roles of data scientists in organizations. Data scientists are often mistaken for artificial intelligence experts and are expected to fill that role. As such, employers pressure their data scientists to perform tasks not included in the job description.
An individual data scientist is often presumed to be a jack of all trades and tasked with finding, cleaning, preparing, and analyzing data. This excessive work load will leave them feeling demotivated and burnt out. As a consequence, data scientists may consider resigning or pivoting to other types of data science jobs.
Solution: Educating Stakeholders on the Roles of a Data Scientist
The scope of data science stops at analyzing data to develop an accurate model. It is the responsibility of other professionals to use the model in their respective application fields. Employers should be educated on the roles of a data scientist and understand the job limits.
Furthermore, organizations should make efforts to understand the interests, strengths, and weaknesses of every data scientist they employ. They should also invest in recruiting more professionals. Only then will they be able to divide the workload and assign tasks that each data scientist would enjoy and excel at.
Useful Data Science Tools That Will Make Your Life Easier as a Data Scientist
|Tableau||Understanding data||Data visualization tool for data analytics and ease of understanding||$70 per month|
|Azure Stream Analytics||Filtering and preparing data||Analyzes and processes high volumes of data from multiple sources||$0.11 per hour of streaming data|
|Apache Spark||Filtering and preparing data||Provides fast APIs designed for data analytics, batch processing, and stream processing||Free|
|Weka||Finding data||Provides no-code machine learning models for data mining||Free|
|IBM QRadar Advisor||Data security||Uses artificial intelligence technology to investigate security loopholes||Free for the community version, $800 per month for the cloud version|
Resources to Overcome Data Science Challenges and Become a Better Data Scientist
- Kaggle. This tool provides a development environment, an extensive code repository, and over 50,000 curated data sets for data science teaching and learning.
- Data Science Weekly. Data science weekly sends a spam-free newsletter to any email address specified. This free newsletter contains weekly updates on data science news, articles, and jobs.
- Analytics Vidhya. Analytics Vidhya is a community of analytics and data science professionals. It provides an online learning platform that contains hundreds of free and paid courses for data science students.
- Flowing Data. Run by Nathan Yau, Flowing Data is a repository of free resources, courses, and tutorials focused on data visualization techniques.
- Simply Statistics. This blog website collects data science discussions, inspirational articles, and advice from statistics experts. It aims to use statistics to solve real-life problems.
Is Becoming a Data Scientist a Good Career Choice?
Generally, yes. In recent times, enterprises and industries have become more reliant on big data to shape their business decisions. Several modern businesses consider their data scientists valuable employees, as evidenced by lucrative salaries and attractive working benefits.
If you enjoy working with numbers and possess good analytical skills, you should consider a data science career path. Data science helps solve practical problems in society, and you will be leading the charge.
Data Scientist Salary and Job Outlook
According to ZipRecruiter, in the United States, data scientists earn an estimated $58 per hour. This rate works out to a national average of $119,686 per annum. Data scientists can earn as much as $193,000 annually, depending on their level of experience, location, and job activities.
These salary figures show that data scientists are in high demand, and employers are willing to pay a hefty price for the power of data science. The U.S Bureau of Labor Statistics projects 3,200 job openings yearly for information research scientists over the next eight years. You can rest assured that data scientists will be in business for several years.
Next Steps in Your Data Science Journey
Data science is an evolving field of study, and industry standards are constantly changing to suit user demands. As such, professionals must possess the latest relevant skills and display knowledge of the most current methods. Consequently, employers expect data scientists to provide certification as proof of their expertise.
If you are only just starting your data science journey, data science bootcamps are an excellent source of education. Bootcamps also cater to professionals seeking to update their skills. They provide a cheap, fast, and highly effective training regimen for students. They also make an extensive pool of resources available to learners.
Data Science Challenges FAQ
Not necessarily. Coding is used extensively by data scientists, but it is only one of the tools in their belt. Several coding alternatives are available to data scientists, and the data science community prefers them to coding. The main reason for this is that they allow professionals to perform tasks without worrying about coding intricacies.
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No, but data science maintains a close relationship with artificial intelligence. It’s so close that when people typically start learning about artificial intelligence, data science naturally flows into the mix. However, data science is a different field of study. It purely involves data manipulation and building models. The data generated is used in several applications, including artificial intelligence.
No, you don’t need a college degree to become a data scientist. However, you should note that data science is a complex discipline and requires some form of training. Bootcamps are a cheaper and quicker alternative to a bachelor’s degree and equip you with a more practical set of relevant skills.
Yes. The foundations of data science are mathematics and statistical analysis. Most employers will require you to demonstrate adequate math skills before they will hire you. If you seek a data science career, you have to develop a deep understanding of mathematics.
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