What is Data Science: Applications, Tools, Career Prospects, Salary

Data science is not about making complicated models or appealing visualizations. It is also not about coding. In essence, Data Science is about creating an impact for your company. It could be in the form of insights, product feedback recommendations or data. To do so, you need tools like making complicated data models, data visualization or writing code.

As a data scientist, your primary job is to solve real-world problems no matter what tools and techniques you implement.

History of Data Science

Data Mining was a popular term before Data Science. In 2001, William S Cleveland wanted to step up data mining. He did so by combining Computer Science with data mining. He made statistics more technical, he believed that it would expand the possibilities of data mining to discover useful information.

With the evolution of websites like MySpace, Facebook and YouTube, people had the opportunity to share information and interact with people. This created chunks of data which we now call big data. Big data has become too much to handle with traditional tools and technologies. The rise of Big Data around 2010 caused the demand for data science to support the needs of organizations to handle unstructured data and derive insights.

The Journal of Data Science defines data science as:

“Almost everything to do with data – collecting, analyzing, modelling, yet the most important part is the applications – all sorts of applications”

If I ask what kind of applications? I would put Machine Learning first. With the emergence of big data back in 2010, there was a need to train machines with a data-driven approach. So Machine Learning and Artificial Intelligence became the talk of the town and dominated other aspects of data science such as ETL, experimentation, A/B testing, etc.

How Data Science Has Changed Over the Years?

The Data Science job outlook is drastically changing. But first, let me answer one of the most asked questions “Can AI replace data scientists?”. The answer is no. The data scientists’ jobs are not declining or are on the verge of dying, and they’re not close to automation.

Post Covid, more and more data is being generated every day as businesses have transitioned to a digital platform. We are getting insights from this data, and this is not something that a computer can do. Those are machine learning models that are built by humans and we still should know what to do with them.

Data science jobs are still there, but exploratory Data Analysis is not the same anymore. I still believe it is important to know how to code, especially in Python and understand all the libraries so that you can generate ChatGPT prompts effectively.

This is so important right now because coding is something that anyone can learn and practice today. You can stand out from the competition if you have the technical skills.

Till today, getting a data scientist job is about getting some credentials and working on a couple of projects. I believe this is changing after the introduction of AI technologies because anyone can learn to code and build projects now. ChatGPT is great at building code, however what it cannot do is merge things. More like, it can build Lego blocks but cannot build a Lego castle. 

Companies now need data scientists who understand each Lego block and how they can function together to build a Lego castle. To do so, more than coding, you need to know about Google Cloud Platform, AWS and Azure – they help to put actual pieces together.

In conclusion, traditional analytics is getting easier. Your data scientist job is now getting easier, so it demands multi-tasking. Getting into data science is not just limited to coding and building a couple of projects anymore. There is still demand for traditional data analysts, who can do data analysis and visualization, but the number of data analysts required to do one particular task would be less than before. 

Data Science Prerequisites

If I put the data science prerequisites into a pyramid, I would do so according to the hierarchy of data science needs.

1. At the bottom of the Pyramid is ‘collect’. You need to collect some data to use that data. Collecting, storing and transforming – all of the Data Engineering methods, big data and all are put in the bottom of the pyramid. A lot about them is available or talked about in the media.
2. Next are the things that are the most important for a company i.e. analytics, metrics, A/B testing, experimentation, training data, etc. These things are way more important as they tell a company what to do with their product, but they are not covered much in the media.
3. What’s covered in the media are AI and Deep Learning, hence, I have put them on the top of the pyramid.

But when you think from a company’s perspective, let’s say, Facebook, Netflix, Google and Microsoft, AI and Deep Learning are not the top priority or something that yields the highest results. As a data scientist, your primary tasks revolve somewhere in between – analytics, metrics, A/B testing, experimentation, training data, etc.

Data Science and AI: How They Co-exist?

Artificial Intelligence (AI) is like a digital superhero. AI is evolving to perform tasks similarly to humans, transforming practices in fields such as medicine, finance, transportation, and education. Employing unique computer techniques, AI enhances our daily lives and work processes, making them more efficient and effective.

In the business world, AI and Data Science have been big game-changers. They’ve made companies work smarter, make better choices, and find important information from tons of data. More than half of the data consumed by AI is synthetic. This type of data helps in training computers and preparing them for real-world problems, ultimately enhancing the capabilities of AI.

As we look ahead, several noteworthy trends are poised to shape the landscape of AI and Data Science in 2024:

1. Data as the Foundation: Data science encompasses the crucial stages of data collection, cleaning, and preparation, aiming to ensure high-quality data essential for training AI models. Additionally, data scientists are involved in feature engineering, where they identify and craft relevant features from the data. This process significantly influences the performance of AI models by extracting meaningful information and patterns that contribute to the overall effectiveness of the models.
2. AI Models and Data Science: The integration of AI models, including advanced technologies like deep learning neural networks, play a pivotal role in handling intricate tasks such as image recognition, natural language processing, and speech recognition. These AI models bring a heightened level of sophistication to data science applications. By leveraging these models, data science achieves enhanced predictive capabilities, leading to more accurate insights and predictions. This synergy between AI models and data science contributes to the development of advanced and effective solutions across various domains
3. Working together: AI algorithms and data scientists make a great team. AI algorithms work better when they have clear and organised data to learn from. Data scientists help by getting the data ready and understanding what it means. On the other side, AI models can make things easier by automating tasks and finding patterns in the data that might not be obvious at first. They each have their strengths, and when they work together, they can do some cool things! Solving tough problems involves dealing with complex challenges that demand both the ability to understand data (data science) and the use of intelligent systems (AI). This collaboration is crucial in addressing issues like personalised healthcare or analysing climate change.
4. Cross-Training: Data scientists can acquire AI techniques to automate tasks and create more robust models. Meanwhile, those specialising in AI can gain expertise in data wrangling and analysis to enhance the quality of the data they work with.

Uses of Data Science

1. Identifying Patterns in Diverse Data: Data science can discern patterns in seemingly disorganised or unrelated data, enabling the extraction of valuable insights and predictive analysis.
2. Transforming User Data into Strategic Insights: Technology companies collecting user data can employ strategies to convert this data into valuable and profitable insights.
3. Revolutionizing Transportation with Driverless Cars: The impact of Data Science extends to transportation, notably in the realm of driverless cars. This technology simplifies the reduction of accidents by supplying training data to algorithms, which are then analyzed using Data Science methods, considering factors like speed limits and busy streets.
4. Advancing Therapeutic Customization through Genetics and Genomics Research: Applications of Data Science contribute to an enhanced level of personalised therapy through research in genetics and genomics.

Data Science Job Roles

Understanding the applications of Data Science and its general concept, let’s explore the diverse job roles within this rapidly expanding field. Consider specialising in one of these roles:

1. Data Scientist:

• Responsibility: Identifying problems, framing questions, and locating relevant data. Additionally, involved in data mining, cleaning, and presenting.
• Skills Required: Programming (SAS, R, Python), storytelling, data visualisation, statistical and mathematical skills, knowledge of Hadoop, SQL, and Machine Learning.

2. Data Analyst:

• Responsibility: Bridging the gap between data scientists and Business Analysts, organizing and analyzing data to address organizational questions. Transforms technical analyses into actionable insights.
• Skills Required: Statistical and mathematical skills, programming (SAS, R, Python), experience in data wrangling and data visualization.

3. Data Engineer:

• Responsibility: Focusing on developing, deploying, managing, and optimising the organisation’s data infrastructure and pipelines. Supports data scientists by facilitating data transfer and transformation for queries.
• Skills Required: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages (Java, Scala), and frameworks (Apache Hadoop).

Data Science Tools

Navigating the challenges of the data science profession becomes more manageable with an array of tools designed to support the data scientist in their role. Now that we’ve explored what data science entails, its lifecycle, and its general role, let’s delve into some key data science tools:

1. Data Analysis: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
2. Data Warehousing: Informatica/Talend, AWS Redshift
3. Data Visualization: Jupyter, Tableau, Cognos, RAW
4. Machine Learning: Spark MLib, Mahout, Azure ML Studio

Applications of Data Science

Data science finds diverse applications in various fields such as:

1. Healthcare: Data science helps to create advanced medical instruments for disease detection and treatment in healthcare by analyzing old data. It uses big sets of information to predict diseases and make personalized treatment plans. This helps in preventing diseases early. Also, it assists doctors by giving them quick and smart suggestions for treating patients. This speeds up the detection process and gets more accurate results.
2. Gaming: Data science in gaming means using big sets of information from previous data from various sources to make games better. It helps in understanding what players like and how they play. By looking at this data, game developers can create more fun and engaging games. They can also fix problems in the games quickly. 
3. Fraud Detection: Data science plays a crucial role in preventing dishonest activities, such as cheating or stealing, especially in areas involving money. For instance, in banking it examines a lot of information about transactions to detect any unusual or dishonest behaviour. Essentially, data science acts as a safeguard, ensuring the protection of people’s money by identifying and stopping fraudulent activities.
4. Internet Search: Data science helps make internet searches better. When you type something in a search engine like Google, data science is at work. It looks at what people usually search for, and by analyzing that data, it tries to understand what you might be looking for. This way, it can show you more helpful and relevant results. In simple terms, data science makes searching the internet easier and gives you the information you want faster.
5. Airline Route Planning: In planning airline routes, data science helps by looking at past and present info to predict how many people want to fly to different places. It also figures out the best and most cost-effective routes for planes, considering things like fuel and airport rules. Data science helps make good flight schedules and adjusts ticket prices in real-time to get the most money. So, it helps airlines work smartly, fly efficiently, and give passengers what they want.

Conclusion

Looking ahead, data is set to play a crucial role in the business world. If you are well-versed in statistics and programming languages and are looking for a career change, data scientist is a suitable option for you. Every organization or startup now understands the significance of data, especially practical insights derived from data. 

When companies use data science techniques, it’s like having a superpower – they can predict how they will grow, see problems coming, and plan smart strategies for success. This means they can make informed decisions that give them a better chance of doing well in the business world.