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Common Data Science Challenges of 2024 [with Solution]

22nd Dec, 2023
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    Common Data Science Challenges of 2024 [with Solution]

    Data is the new oil for companies. Since then, it has been a standard aspect of every choice made. Increasingly, businesses rely on analytics and data to strengthen their brand's position in the market and boost revenue.

    Information now has more value than physical metals. According to a poll conducted by NewVantage Partners in 2017, 85% of businesses are making an effort to become data-driven, and the worldwide data science platform market is projected to grow to $128.21 billion by 2022, from only $19.75 billion in 2016.

    Data science is not a meaningless term with no practical applications. Yet, many businesses have difficulty reorganizing their decision-making around data and implementing a consistent data strategy. Lack of information is not the issue. 

    Our daily data production has reached 2.5 quintillion bytes, which is so huge that it is impossible to completely understand the breakneck speed at which we produce new data. Ninety percent of all global data was generated in the previous few years. 

    The actual issue is that businesses aren't able to properly use the data they already collect to get useful insights that can be utilized to improve decision-making, counteract risks, and protect against threats. 

    It is vital for businesses to know how to approach a new data science challenge and understand what kinds of questions data science can answer since there is frequently too much data accessible to make a clear choice. One must have a look at Data Science Course Subjects for an outstanding career in Data Science. 

    What is Data Science Challenges?

    Data science is an application of the scientific method that utilizes data and analytics to address issues that are often difficult (or multiple) and unstructured. The phrase "fishing expedition" comes from the field of analytics and refers to a project that was never structured appropriately, to begin with, and entails searching through the data for unanticipated connections. This particular kind of "data fishing" does not adhere to the principles of efficient data science; nonetheless, it is still rather common. Therefore, the first thing that needs to be done is to clearly define the issue. In the past, we put out an idea for 

    "The study of statistics and data is not a kind of witchcraft. They will not, by any means, solve all of the issues that plague a corporation. According to Seattle Data Guy, a data-driven consulting service, "but, they are valuable tools that assist organizations make more accurate judgments and automate repetitious labor and choices that teams need to make." 

    The following are some of the categories that may be used to classify the problems that can be solved with the assistance of data science:

    • Finding patterns in massive data sets: Which of the servers in my server farm need the most maintenance? 
    • Detecting deviations from the norm in huge data sets: Is this particular mix of acquisitions distinct from what this particular consumer has previously ordered? 
    • The process of estimating the possibility of something occurring: What are the chances that this person will click on my video? 
    • illustrating the ways in which things are related to one another: What exactly is the focus of this article that I saw online? 
    • Categorizing specific data points: Which animal do you think this picture depicts a kitty or a mouse? 

    Of course, the aforementioned is in no way a comprehensive list of all the questions that can be answered by data science. Even if it were, the field of data science is advancing at such a breakneck speed that it is quite possible that it would be rendered entirely irrelevant within a year or two of its release. 

    It is time to write out the stages that the majority of data scientists would follow when tackling a new data science challenge now that we have determined the categories of questions that may be fairly anticipated to be solved with the assistance of data science. Data Science Bootcamp review is for people struggling to make a breakthrough in this domain.

    Common Data Science Problems Faced by Data Scientists

    1. Preparation of Data for Smart Enterprise AI

    Finding and cleaning up the proper data is a data scientist's priority. Nearly 80% of a data scientist's day is spent on cleaning, organizing, mining, and gathering data, according to a CrowdFlower poll. In this stage, the data is double-checked before undergoing additional analysis and processing. Most data scientists (76%) agree that this is one of the most tedious elements of their work. As part of the data wrangling process, data scientists must efficiently sort through terabytes of data stored in a wide variety of formats and codes on a wide variety of platforms, all while keeping track of changes to such data to avoid data duplication. 

    Adopting AI-based tools that help data scientists maintain their edge and increase their efficacy is the best method to deal with this issue. Another flexible workplace AI technology that aids in data preparation and sheds light on the topic at hand is augmented learning. 

    2. Generation of Data from Multiple Sources

    Data is obtained by organizations in a broad variety of forms from the many programs, software, and tools that they use. Managing voluminous amounts of data is a significant obstacle for data scientists. This method calls for the manual entering of data and compilation, both of which are time-consuming and have the potential to result in unnecessary repeats or erroneous choices. The data may be most valuable when exploited effectively for maximum usefulness in company artificial intelligence

    Companies now can build up sophisticated virtual data warehouses that are equipped with a centralized platform to combine all of their data sources in a single location. It is possible to modify or manipulate the data that is stored in the central repository to satisfy the needs of a company and increase its efficiency. This easy-to-implement modification has the potential to significantly reduce the amount of time and labor required by data scientists. 

    3. Identification of Business Issues

    Identifying issues is a crucial component of conducting a solid organization. Before constructing data sets and analyzing data, data scientists should concentrate on identifying enterprise-critical challenges. Before establishing the data collection, it is crucial to determine the source of the problem rather than immediately resorting to a mechanical solution. 

    Before commencing analytical operations, data scientists may have a structured workflow in place. The process must consider all company stakeholders and important parties. Using specialized dashboard software that provides an assortment of visualization widgets, the enterprise's data may be rendered more understandable. 

    4. Communication of Results to Non-Technical Stakeholders

    The primary objective of a data scientist is to enhance the organization's capacity for decision-making, which is aligned with the business plan that its function supports. The most difficult obstacle for data scientists to overcome is effectively communicating their findings and interpretations to business leaders and managers. Because the majority of managers or stakeholders are unfamiliar with the tools and technologies used by data scientists, it is vital to provide them with the proper foundation concept to apply the model using business AI. 

    In order to provide an effective narrative for their analysis and visualizations of the notion, data scientists need to incorporate concepts such as "data storytelling." 

    5. Data Security

    Due to the need to scale quickly, businesses have turned to cloud management for the safekeeping of their sensitive information. Cyberattacks and online spoofing have made sensitive data stored in the cloud exposed to the outside world. Strict measures have been enacted to protect data in the central repository against hackers. Data scientists now face additional challenges as they attempt to work around the new restrictions brought forth by the new rules. 

    Organizations must use cutting-edge encryption methods and machine learning security solutions to counteract the security threat. In order to maximize productivity, it is essential that the systems be compliant with all applicable safety regulations and designed to deter lengthy audits. 

    6. Efficient Collaboration

    It is common practice for data scientists and data engineers to collaborate on the same projects for a company. Maintaining strong lines of communication is very necessary to avoid any potential conflicts. To guarantee that the workflows of both teams are comparable, the institution hosting the event should make the necessary efforts to establish clear communication channels. The organization may also choose to establish a Chief Officer position to monitor whether or not both departments are functioning along the same lines. 

    7. Selection of Non-Specific KPI Metrics

    It is a common misunderstanding that data scientists can handle the majority of the job on their own and come prepared with answers to all of the challenges that are encountered by the company. Data scientists are put under a great deal of strain as a result of this, which results in decreased productivity. 

    It is vital for any company to have a certain set of metrics to measure the analyses that a data scientist presents. In addition, they have the responsibility of analyzing the effects that these indicators have on the operation of the company. 

    The many responsibilities and duties of a data scientist make for a demanding work environment. Nevertheless, it is one of the occupations that are in most demand in the market today. The challenges that are experienced by data scientists are simply solvable difficulties that may be used to increase the functionality and efficiency of workplace AI in high-pressure work situations.

    Types of Data Science Challenges/Problems

    1. Data Science Business Challenges

    Listening to important words and phrases is one of the responsibilities of a data scientist during an interview with a line-of-business expert who is discussing a business issue. The data scientist breaks the issue down into a procedural flow that always involves a grasp of the business challenge, a comprehension of the data that is necessary, as well as the many forms of artificial intelligence (AI) and data science approaches that can address the problem. This information, when taken as a whole, serves as the impetus behind an iterative series of thought experiments, modeling methodologies, and assessment of the business objectives. 

    The company itself has to remain the primary focus. When technology is used too early in a process, it may lead to the solution focusing on the technology itself, while the original business challenge may be ignored or only partially addressed. 

    Artificial intelligence and data science demand a degree of accuracy that must be captured from the beginning: 

    • Describe the issue that needs to be addressed. 
    • Provide as much detail as you can on each of the business questions. 
    • Determine any additional business needs, such as maintaining existing client relationships while expanding potential for upselling and cross-selling. 
    • Specify the predicted advantages in terms of how they will affect the company, such as a 10% reduction in the customer turnover rate among high-value clients. 

    2. Real Life Data Science Problems

    Data science is the use of hybrid mathematical and computer science models to address real-world business challenges in order to get actionable insights. It is willing to take the risk of venturing into the unknown domain of 'unstructured' data in order to get significant insights that assist organizations in improving their decision-making. 

    • Managing the placement of digital advertisements using computerized processes. 
    • The search function will be improved by the use of data science and sophisticated analytics. 
    • Using data science for producing data-driven crime predictions 
    • Utilizing data science in order to avoid breaking tax laws 

    3. Data Science Challenges In Healthcare And Example

    It has been calculated that each human being creates around 2 gigabytes of data per day. These measurements include brain activity, tension, heart rate, blood sugar, and many more. These days, we have more sophisticated tools, and Data Science is one among them, to deal with such a massive data volume. This system aids in keeping tabs on a patient's health by recording relevant information. 

    The use of Data Science in medicine has made it feasible to spot the first signs of illness in otherwise healthy people. Doctors may now check up on their patients from afar thanks to a host of cutting-edge technology. 

    Historically, hospitals and their staffs have struggled to care for large numbers of patients simultaneously. The patients' ailments used to worsen because of a lack of adequate care.

    A) Medical Image Analysis: Focusing on the efforts connected to the applications of computer vision, virtual reality, and robotics to biomedical imaging challenges, Medical Image Analysis offers a venue for the dissemination of new research discoveries in the area of medical and biological image analysis. It publishes high-quality, original research articles that advance our understanding of how to best process, analyze, and use medical and biological pictures in these contexts. Methods that make use of molecular/cellular imaging data as well as tissue/organ imaging data are of interest to the journal. Among the most common sources of interest for biomedical image databases are those gathered from: 

    • Magnetic resonance 
    • Ultrasound 
    • Computed tomography 
    • Nuclear medicine 
    • X-ray 
    • Optical and Confocal Microscopy 
    • Video and range data images 

    Procedures such as identifying cancers, artery stenosis, and organ delineation use a variety of different approaches and frameworks like MapReduce to determine ideal parameters for tasks such as lung texture categorization. Examples of these procedures include: 

    • The categorization of solid textures is accomplished by the use of machine learning techniques, support vector machines (SVM), content-based medical picture indexing, and wavelet analysis. 

    B) Drug Research and Development: The ever-increasing human population brings a plethora of new health concerns. Possible causes include insufficient nutrition, stress, environmental hazards, disease, etc. Medical research facilities now under pressure to rapidly discover treatments or vaccinations for many illnesses. It may take millions of test cases to uncover a medicine's formula since scientists need to learn about the properties of the causal agent. Then, once they have a recipe, researchers must put it through its paces in a battery of experiments.

    Previously, it took a team of researchers 10–12 years to sift through the information of the millions of test instances stated above. However, with the aid of Data Science's many medical applications, this process is now simplified. It is possible to process data from millions of test cases in a matter of months, if not weeks. It's useful for analyzing the data that shows how well the medicine works. So, the vaccine or drug may be available to the public in less than a year if all tests go well. Data Science and machine learning make this a reality. Both have been game-changing for the pharmaceutical industry's R&D departments. As we go forward, we shall see Data Science's use in genomics. Data analytics played a crucial part in the rapid development of a vaccine against the global pandemic Corona-virus.

    C) Genomics and Bioinformatics: One of the most fascinating parts of modern medicine is genomics. Human genomics focuses on the sequencing and analysis of genomes, which are made up of the genetic material of living organisms. Genealogical studies pave the way for cutting-edge medical interventions. Investigating DNA for its peculiarities and quirks is what genomics is all about. It also aids in determining the link between a disease's symptoms and the patient's actual health. Drug response analysis for a certain DNA type is also a component of genomics research.

    Before the development of effective data analysis methods, studying genomes was a laborious and unnecessary process. The human body has millions of chromosomes, each of which may code for a unique set of instructions. However, recent Data Science advancements in the fields of medicine and genetics have simplified this process. Analyzing human genomes now takes much less time and energy because to the many Data Science and Big Data techniques available. These methods aid scientists in identifying the underlying genetic problem and the corresponding medication.

    D) Virtual Assistance: One excellent illustration of how Data Science may be put to use is seen in the development of apps with the use of virtual assistants. The work of data scientists has resulted in the creation of complete platforms that provide patients with individualized experiences. The patient's symptoms are analyzed by the medical apps that make use of data science in order to aid in the diagnosis of a condition. Simply having the patient input his or her symptoms into the program will allow it to make an accurate diagnosis of the patient's ailment and current status. According on the state of the patient, it will provide recommendations for any necessary precautions, medications, and treatments.

    In addition, the software does an analysis on the patient's data and generates a checklist of the treatment methods that must be adhered to at all times. After that, it reminds the patient to take their medication at regular intervals. This helps to prevent the scenario of neglect, which might potentially make the illness much worse. 

    Patients suffering from Alzheimer's disease, anxiety, depression, and other psychological problems have also benefited from the usage of virtual aid, since its benefits have been shown to be beneficial. Because the application reminds these patients on a consistent basis to carry out the actions that are necessary, their therapy is beginning to bear fruit. Taking the appropriate medicine, being active, and eating well are all part of these efforts. Woebot, which was created at Stanford University, is an example of a virtual assistant that may help you out. It is a chatbot that assists individuals suffering from psychiatric diseases in obtaining the appropriate therapy in order to improve their mental health. 

    4. Data Science Problems In Retail

    Although the phrase "customer analytics" is relatively new to the retail sector, the practice of analyzing data collected from consumers to provide them with tailored products and services is centuries old. The development of data science has made it simple to manage a growing number of customers. With the use of data science software, reductions and sales may be managed in real-time, which might boost sales of previously discontinued items and generate buzz for forthcoming releases. One further use of data science is to analyze the whole social media ecosystem to foresee which items will be popular in the near future so that they may be promoted to the market at the same time. 

    Data science is far from being complete. loaded with actual uses in the world today. Data science is still in its infancy, but its applications are already being felt throughout the globe. We have a long way to go before we reach saturation.

    Steps on How to Approach and Address a Solution to Data Science Problems

    Step 1: Define the Problem

    First things first, it is essential to precisely characterize the data issue that has to be addressed. The issue at hand need to be comprehensible, succinct, and quantifiable. When identifying data challenges, many businesses are far too general with their language, which makes it difficult, if not impossible, for data scientists to transform such problems into machine code. Below we will discuss a few most common data science problem statements and data science challenges. 

    The following is a list of fundamental qualities that describe a data issue as well-defined: 

    • It seems probable that the solution to the issue will have a sufficient amount of positive effect to warrant the effort. 
    • There is sufficient data accessible in a format that can be used. 
    • The use of data science as a means of resolving the issue has garnered the attention of stakeholders. 

    Step 2: Types of Data Science Problem

    There is a wide variety of data science algorithms that can be implemented on data, and they can be classified, to a certain extent, within the following families, below are the most common data science problems examples: 

    • Two-class classification: Useful for any issue that can only have two responses, the two-class categorization consists of two distinct categories. 
    • Multi-class classification: Providing an answer to a question that might have many different responses is an example of multi-class categorization. 
    • Anomaly detection: The term "anomaly detection" refers to the process of locating data points that deviate from the norm. 
    • Regression: When searching for a number as opposed to a class or category, regression is helpful since it provides an answer with a real-valued result. 
    • Multi-class classification as regression: Useful when questions are posed in the form of rankings or comparisons, multi-class classification may be thought of as regression. 
    • Two-class classification as regression: Useful for binary classification problems that can also be reformulated as regression, the two-class classification method is also referred to as regression analysis. 
    • Clustering: The term "clustering" refers to the process of answering questions regarding the organization of data by attempting to partition a data set into understandable chunks. 
    • Dimensionality reduction: It is the process of acquiring a set of major variables in order to lower the number of random variables that are being taken into account. 
    • Reinforcement learning: The goal of the learning algorithms known as reinforcement learning is to perform actions within an environment in such a way as to maximize some concept of cumulative reward.

    Step 3: Data Collection

    Now that the issue has been articulated in its entirety and an appropriate solution has been chosen, it is time to gather data. It is important to record all of the data that has been gathered in a log, along with the date of each collection and any other pertinent information. 

    It is essential to understand that the data produced are rarely immediately available for analysis. The majority of a data scientist's day is dedicated to cleaning the data, which involves tasks such as eliminating records with missing values, locating records with duplicates, and correcting values that are wrong. It is one of the prominent data scientist problems. 

    Step 4: Data Analysis

    Data analysis comes after data gathering and cleansing. At this point, there is a danger that the chosen data science strategy will fail. This is to be expected and anticipated. In general, it is advisable to begin by experimenting with all of the fundamental machine learning algorithms since they have fewer parameters to adjust. 

    There are several good open source data science libraries available for use in data analysis. The vast majority of data science tools are developed in Python, Java, or C++. Apart from this, many data science practice problems are available for free on web. 

    Step 5: Result Interpretation

    Following the completion of the data analysis, the next step is to interpret the findings. Consideration of whether or not the primary issue has been resolved should take precedence over anything else. It's possible that you'll find out that your model works but generates results that aren't very good. Adding new data and continually retraining the model until one is pleased with it is one strategy for dealing with this situation.

    Finalizing the Problem Statement

    After identifying the precise issue type, you should be able to formulate a refined problem statement that includes the model's predictions. For instance: 

    This is a multi-class classification problem that predicts if a picture belongs to one of four classes: "vehicle," "traffic," "sign," and "human." 

    Additionally, you should be able to provide a desired result or intended use for the model prediction. Making a model accurate is one of the most crucial thing Data Scientists problems. 

    The optimal result is to offer quick notice to end users when a target class is predicted. One may practice such data science hackathon problem statements on Kaggle. 

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    When professionals are working toward their analytics objectives, they may run across a variety of different kinds of data science challenegs, all of which slow down their progress. The stages that we've discussed in this article on how to tackle a new data science issue are designed to highlight the general problem-solving attitude that businesses need to adopt in order to effectively meet the problems of our present data-centric era.

    Not only will a competent data science problem seek to make predictions, but it will also aim to make judgments. Always keep this overarching goal in mind while you think about the many challenges you are facing. You may combat the blues of data science with the aid of a detailed approach. In addition, engaging with professionals in the field of data science enables you to get insights, which ultimately results in the effective execution of the project. Have a look at KnowledgeHut’s Data Science Course Subjects to understand this matter in depth.

    Frequently Asked Questions (FAQs)

    1What problems can data science solve?

    The discipline of data science aims to provide answers to actual challenges faced by businesses by using data in the construction of algorithms and the development of programs that assist in demonstrating that certain issues have ideal solutions. Data science is the use of hybrid mathematical and computer science models to address real-world business challenges in order to get actionable insights. 

    2Where can I practice data science problems?

    There are many other platforms available for the same- Kaggle, KnowledgeHut, HackerEarth, MachineHack, Colab by Google, Datacamp etc.

    3Which topic is best for data science?

    Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, and Algorithms are only few of the primary subjects that are covered in the Data Science curriculum.  

    4Is data science a stressful job?

    Aspects of this profession may be stressful, but I imagine that's true of most jobs. I will provide an excellent illustration of a potential source of "stress" in the field of data science. doubt it's a tough time Data science is R&D, and there's always enough time to get everything done.  

    Stressful? No. Frustrating? Absolute yeah. It's really annoying that we often get trapped on an error for three days or have to ponder the question of what metrics to use a thousand times.


    Ritesh Pratap Arjun Singh

    Blog Author

    RiteshPratap A. Singh is an AI & DeepTech Data Scientist. His research interests include machine vision and cognitive intelligence. He is known for leading innovative AI projects for large corporations and PSUs. Collaborate with him in the fields of AI/ML/DL, machine vision, bioinformatics, molecular genetics, and psychology.

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