Machine learning, a subset of AI, is a core part of data science training and data science professionals are able to leverage this training to create models that make predictions and lead to better decision-making. AI is especially useful when working with large datasets as AI algorithms can help automate the tedious tasks of data cleaning and transformation, analyze and update data quickly in real time, and sift through complex datasets to draw out patterns, trends, and actionable insights.
While AI can often find hard-to-see patterns and accomplish routine work quicker than humans, AI algorithms and models need human intelligence to function correctly. Prospective data science professionals learn how to use AI to solve real-world problems and to help businesses from a variety of industries make informed decisions, devise strategic plans, and become more efficient. Data Scientists themselves are tasked with writing the algorithms and building the machine learning models that underlie the field and it is these professionals who will often have to decide what information is most crucial and useful to pass on to different stakeholders.
Best AI Classes for Data Science
Noble Desktop offers a variety of AI and data science courses for those looking to get started in data science or to add to their current knowledge and skillset. All Noble Desktop classes provide hands-on training from expert instructors, one-on-one mentoring, time-tested course materials, small class sizes, a free retake option, and a guarantee that you will learn the skills in the course syllabus. Python is a commonly used programming language in data science and data science beginners should consider the 30-hour Python for Data Science Bootcamp, a class that teaches students how to clean, manipulate, and visualize data using Python. Intermediate-level Python users can instead take the 30-hour Python Machine Learning Bootcamp and dive into creating machine learning models to solve real-world problems.
In addition to these Python bootcamps, the AI & Data Science Certificate is a comprehensive course of study for those looking to become data science professionals or AI specialists. This two-month course is great for beginners and covers topics necessary for success in data science such as Python, AI, machine learning, automation, data visualization, and SQL. Enrolling in this career-oriented certificate program will prepare someone for entry-level data science jobs and jobs that require AI specialization. The Data Science Certificate will cover many of the same topics without going into depth on advanced uses of Python like web scraping or integrating AI models into web applications. Prospective Data Analysts or Business Analysts can instead consider the Data Analytics Certificate which offers similar training in Python and machine learning principles but includes specific units on data analytics, Excel, and Tableau.
While the AI & Data Science Certificate and other Noble Desktop certificate programs all aim to be comprehensive and all-inclusive, the topics, tools, and techniques covered can also be learned in singular bootcamps and classes. Taking an individual class or bootcamp is best for someone who wants to learn data science more slowly, only needs to learn about certain specific concepts or tools, or someone who cannot commit to a two-month-long class. Someone with Python and data science training who is looking to specialize in AI can consider taking the 30-hour AI for Python class, for instance, which teaches students how to use Flask to build AI-powered applications.
Intermediate or advanced Python users should also consider the short, 6-hour class Python for Automation, which teaches students web scraping and automating routine tasks using Python. The Python Data Visualization & Interactive Dashboards class is another option for those with Python experience. This 30-hour course teaches learners how to collect and manipulate data and find data stories. As a more comprehensive data science class, this one will task students with working on a project of their own choice to test their skills and develop their data science portfolio.
What is Data Science?
Data science is the practice of using mathematics, statistics, computer programming, and machine learning to extract useful information from datasets. Data science professionals, such as Data Scientists, Data Analysts, or Machine Learning Engineers find themselves collecting, cleaning, interpreting, and visualizing data to solve problems and aid the decision-making processes of the companies and organizations they work for. Data science can help automate routine tasks performed by employees and can uncover hard-to-see information that humans often struggle to find on their own. Working with data can help businesses make important decisions, build better strategic plans, and become more efficient.
Many industries use data science, including business, construction, education, finance, healthcare, manufacturing, and retail. Those in manufacturing, for instance, might use data science to optimize employee workflows, assess the efficiency of supply chains, or even detect product defects during the manufacturing process. Someone in advertising, by contrast, can find themselves using data science to analyze consumer behavior and create tailored consumer ad profiles. Data science has been called the hottest career field of the 21st century and those with data science training can find high-paying jobs in a variety of industries. Continued innovations in AI promise to make data science education even more useful for those seeking in-demand career fields.
What Industries Use Data Science?
One can find data science professionals working in most industries and in companies and organizations both large and small. While these professionals are often considered tech workers and many will find employment in the technology industry, some of the top industries for data science professionals include finance, healthcare, and retail.
Finance
Data science has had a major impact on finance and banking, where it is used to do everything from risk management to helping individuals create a household budget. Data science professionals working in finance often aid banks and other financial institutions in creating machine learning algorithms to detect fraudulent transitions or building machine learning models that can predict the outcome of risky investments. It is also becoming more common for financial institutions, Accountants, and Financial Managers to tailor recommendations and services to individuals based on their financial history and plans for the future.
Healthcare
Healthcare, like finance, is another industry being transformed by data science. In healthcare, it is becoming more common for healthcare professionals to use AI models when looking at medical images as studies have shown that many models can detect disease and abnormalities with a high degree of accuracy. Predictive models can be used for everything from assessing the potential for disease outbreaks to predicting the success of a specific treatment plan for a patient. Additionally, data science can be used to tailor treatment plans to individual patients, as models can take into account a patient’s history, genetics, and lifestyle factors to offer a treatment plan personalized for a specific patient’s needs.
Retail
Most people are already familiar with some of the ways that data science is being implemented in the retail industry. One of the most common ways we encounter retailers relying on data science is through personalized recommendation algorithms for music, books, movies, and products that consumers might be interested in buying. How data science professionals use data science in retail is more expansive than this, as it can also be used to forecast demand for certain products which can ensure that one has the right inventory levels. Data science can also be used to create dynamic pricing models which allows retailers to adjust the price of certain products or services based on the factors that they find are most important.
What is AI?
AI is short for artificial intelligence and broadly refers to using technology to do things normally associated with human strength, intelligence, or creativity. AI is not a recent innovation. In fact, research into AI started in the 1900s when early researchers and computer scientists looked into creating machines that could imitate the actions and movements of humans. These scientists successfully created machines that could be programmed to move and interact with objects and physical spaces similar to how humans do. Today, machines like this are used widely in factories and industrial settings.
Current AI research is more focused on developing technologies that can think, reason, plan, create, and solve problems. The newest AI developments are generative and creative and are being widely used across many industries to automate routine tasks and optimize workplace productivity. These technologies do not replace human ingenuity and data science professionals are using AI not only to move through tedious tasks quickly but to also handle large, complex datasets, uncover hard-to-see patterns or trends, or make predictions. Learning how to wield AI tools allows someone to gain highly marketable skills and specializing in AI, machine learning, and deep learning can be a way for data science professionals to make their skills stand out to future employers.
Why is AI a Useful Skill to Learn?
AI is useful to learn both for aspiring data science professionals and for one’s own personal benefit. Not only do those in data science work with AI, but professionals in a variety of businesses and organizations are turning to AI to automate routine tasks. This can include using AI to send emails, manage appointments, or do manual data entry tasks. Recent developments in generative AI have also made it broadly accessible for anyone to use AI to complete a variety of tasks like automating scheduling or developing a realistic personal household budget.
Those pursuing data science will find AI particularly important and useful for their daily work. It is standard for data science courses to teach students how to build predictive machine learning models. Models can be built to make predictions based on one’s inputs or to extract insights from data based on new patterns or trends that the algorithm identifies itself. Many comprehensive data science courses will also teach students how to automate data cleaning using AI and some will even cover how to work with cloud computing platforms, which often require AI use as it can involve dealing with larger and more complex datasets.
Additionally, learning about AI now will prepare someone for future AI developments and innovations. One of the challenges of working in data science is that you will need to stay up-to-date on new techniques for working with data and new developments in AI fields. Companies and organizations will often look to data science professionals to implement the latest technological innovations and to keep them on trend or even ahead of the competition. This can make it difficult to master data science fully but it also means you will have the ability to work on new things and implement new ideas so that your job never becomes too rote.
How Can AI Assist with Data Science Projects?
AI is useful at all levels of working with data in various data science projects. It is common for data science professionals to learn web scraping, which allows you to collect large amounts of data from different sources. Once you have this data, AI can then help to automate the cleaning process, by noting missing or corrupt data and flagging outliers and other issues that need to be addressed before analyzing the data or adding it to a dataset.
A common use of AI in data science is building predictive models using machine learning, a subset of AI. Machine learning models are ways to extract insights from data and make useful predictions and they are used in a variety of industries. In finance, one might use a machine learning model to assess the potential outcomes of making a risky investment, for instance. Data science professionals will need to determine the model they want to use and train and test their machine learning algorithm to make sure it is working effectively. Deep learning can be especially useful for working with large and complex datasets, as this type of advanced machine learning can automatically classify data, a necessary aspect of building a model, making this task less tedious for Data Scientists.
Data science professionals are also working to integrate generative AI into web applications. This is something that you have probably encountered yourself if you have ever chatted online with an AI customer service agent about a product or technical issue. Many companies and organizations are starting to employ AI customer service bots and other digital assistants as they can often aid consumers with finding products or information or resolving easy customer service issues. AI also underlies personalized recommendations that many receive for products, books, music, TV shows, and films, and it is data science professionals who write, implement, and maintain the code that generates these tailored recommendations. Deep learning is also useful for these contexts, as it can help to perform complex functions automatically.
What are the Limitations of AI for Data Science Projects?
Artificial intelligence cannot do everything and doesn’t do a Data Scientist’s job for them. While the newest AI is trained to think like a human, its real value lies in its non-human abilities to work quickly, without becoming tired, and to process large, complex amounts of data often in real-time. A key limitation of AI is that it lacks the creativity and complex problem-solving abilities of humans. AI cannot do the work of a Data Scientist which will involve not only working with data in creative ways but also working to implement the vision of one’s company or organization. It is humans who have to decide which problems to solve and at all steps of a project, a data science professional needs to monitor and refine the work of AI algorithms.
AI can be a great way to make your job easier and allow you to work more efficiently and accurately with data, but you must recognize AI’s limitations and try not to apply AI models to problems that are ill-defined, too multi-faced, or that cannot be solved with the data your organization has access to. Some problems are too large and too complex for even the more up-to-date technologies. These problems will require real human thought, effort, and ingenuity.
What Other Skills Will You Need for Data Science Projects?
Most comprehensive data science certificates provide students with detailed instruction in Python, machine learning, and SQL. These may not be the only tools and skills that you will need to succeed in a data science profession. Data Scientists need the right mix of hard and soft skills and this can include learning Business Intelligence (BI) and Excel and enhancing one’s communication, collaboration, and problem-solving skills.
Data science professionals working in a variety of industries, including finance, healthcare, retail, and manufacturing should consider learning BI software, like Tableau or Microsoft’s Power BI. Noble Desktop’s 12-hour Tableau Bootcamp will teach learners comprehensive Tableau skills and students should expect to leave the course knowing how to create customized visualizations by exploring, analyzing, filtering, and sorting data. It is common for those in data science careers to learn Excel, as well, and Noble Desktop’s 6-hour class AI for Excel will teach interested students how to manipulate, analyze, and visualize data in Excel using AI. This class does require basic Excel knowledge which you can gain by enrolling in the 6-hour Excel Level 1: Fundamentals course.
Data Scientists are often seen as problem solvers. Indeed, a core part of a data science professional’s job will be using mathematics, statistics, machine learning, and AI to solve real, everyday problems specific to their industry. Data science training will naturally enhance one’s problem-solving skills as it will give learners tools to solve and address issues. Another set of skills that those in data science need are communication and collaboration. Data Scientists collaborate with a wide variety of stakeholders to assess what problems to address and need strong communication skills to share the insights they have derived from a company or organization’s datasets.