Top 10 Machine Learning Applications

In this technology-driven world, machine learning has become a key skill to make intelligent machines or electronic devices. One can ace the machine learning skills only with tremendous practice and research. Having theoretical knowledge surely helps but it’s the hands-on practise that matters the most. There are numerous machine learning projects that one can work on. However, we have narrowed down the top 10 machine learning applications that you can start working on.

In this blog post, you will learn about different real-world machine learning case studies that will help you make great progress in your career. Let’s get started!

Machine Learning Applications

1. Stock Price Predictions

If finance excites you, then predicting stock prices using machine learning might be interesting for you.  In these kinds of projects, you have different types of data to choose from such as prices, global macroeconomic indicators, fundamentals, volatility indices, and much more. These data can be very granular. Typically, financial markets have short feedback cycles, so you can validate your predictions on new data.

Points to Note

• You can download stock market machine learning datasets from Quandl.com or Quantopian.com.
• If you are a beginner, you can limit the project to predict six-month price movements based on your quarterly organisation report.

2. Disease Prediction

With machine learning on the rise, the medical science industry is undergoing drastic changes. Based in Seattle, Washington, KenSci is a healthtech company that employs machine learning to predict illness to help physicians intervene earlier. They predict health risk by identifying patterns and surfacing high-risk markers and model disease progression and more. The company is harnessing AI and machine learning for improving health records and workflow.

What do they do?

• KenSci collects the health system’s data from sources like wearables, EMR records, medical devices, Claims, and ERP systems.
• Using machine learning, they carry out data integration, format conversion, data scrubbing, feature engineering, censoring and more.

3. Fake News Detection

In this digitally connected world, fake news is spreading like wildfire and affecting millions of people on a daily basis. Countless articles are published every day on the internet – how do you distinguish between real and fake? How to deal with such a sensitive issue? This is where fake news detection project comes up.

Points to Note

• Using the machine learning applications of natural language processing (NLP), fake news can be identified.
• For instance, Facebook is employing AI algorithms to filter fake news out of users’ feeds.

4. Road Accidents Analysis

The number of road accidents is becoming unavoidable and growing day by day. With the help of machine learning, you can analyze the patterns in different situations by building appropriate prediction models that are capable of automatically differentiating various accidental scenarios. These clusters help in preventing accidents and developing safety measures.

5. Sentiment Analysis

As social media platforms like Facebook, Instagram, and Twitter are generating tremendous big data, analyzing data is critical to understand users’ sentiments. Sentiment analysis is one of the most interesting machine learning applications and it proves beneficial for digital marketers.

This project allows digital marketing and branding companies to understand the customers’ responses toward a product or a service. Beginners in the machine learning domain can also undertake this project.

Points to Note

• If you are a beginner, you can start this project in Python.
• You can use Twitter to collect data as the platform consists of countless tweets.
• Tweets are simple to pre-process as they comprise text, URLs, and hashtags.
• You can also integrate Twitter API libraries into your project.

6. Sales Forecasting

Sales forecasting is one of the easiest and interesting machine learning applications. One such project is the Walmart sales forecasting project. The aim of this project was to predict sales for every department in every outlet. This project helped Walmart in creating better knowledge-driven choices for inventory designing and channel improvement.

What you can learn

• Throughout this project, you can learn data manipulation in R.
• By developing this project, you can understand data visualization in sales.
• Also, you can learn how to apply machine learning techniques in sales prediction in Python.

7. Human Activities Recognition

The goal of the human activities recognition project is to develop a classification model that can accurately identify human fitness activities using machine learning. Working on this project will help you solve multi-classification problems.

Points to Note

• To develop this project, you can use a smartphone dataset, which consists of fitness activities of 30 people recorded through smartphones.

• 70% of the dataset has been segregated for the training phase and the remaining 30% is for testing.
• If you are a beginner, then this project will help you polish your machine learning skills.

8. Product Bundles Identification

Identifying product bundles from sales data is one of the most interesting machine learning projects in R. To develop this project, you need to employ a clustering technique, which is a subjective segmentation to identify the product bundles from sales data.

Points to Note

• To develop this project, you must have knowledge of data science and R programming language (read about what data scientists do here).
• Also, you need to be familiar with machine learning approaches such as an unsupervised technique for clustering.
• To identify bundles, you need to use market basket analysis.

9. Recommender Systems

Google, Amazon, Netflix, and many others have been using recommender systems to curate content and products for its consumers. Today, millennials prefer watching a movie online rather than on TV. With growing online platforms such as Netflix, the need for an efficient recommender system has gained popularity among today’s generation. Netflix recommends movies and TV shows due to its highly efficient recommender system. Building a well-organized recommender system has become an innovative and exciting project idea. If you want to learn to build recommender systems, then you can use the Movielens dataset, which is one of the popular machine learning datasets available on the web.

What you can Learn

• Movielens dataset contains 1,000,209 movie ratings of about 3,900 movies made by 6,040 Movielens users.
• These recommender systems can be developed using R and Python.

10. TensorFlow

TensorFlow is one of the most popular open-source machine learning projects which help professionals to improve their machine learning skills. It offers a flexible ecosystem of tools, libraries, and community resources that allows developers to easily build and deploy machine learning-powered applications.

What you can learn

• With TensorFlow, project developers can create data flow graphs, arrays of applications, and projects using Java.

• Working on this project, you can easily build and train machine learning models using high-level APIs like Keras, which supports immediate model iteration and debugging.
• You can deploy machine learning models in the cloud, on-premise, in the browser, or on-device no matter what language you use.

Getting your hands on real-world machine learning case studies can be exciting. If you are a beginner, then working on the above-mentioned projects will help you shape your career in the machine learning field. Also, mentioning these projects in your portfolio will land you in a good machine learning job with rewarding perks. But before you get started with these kinds of projects, it’s important for you to understand the concepts of machine learning. Don’t worry, we have got you covered.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!