Data Science vs Data Analytics vs Machine Learning

Data Science

Data science is all about data, and I’m pretty sure you already knew that. But did you know that we use data science to make business decisions? I’m pretty sure you knew that as well. So what else is new here? Well, do you know how data science is used to make business decisions? No? Let’s look at that then.

We all know that every single tech company out there is collecting huge amounts of data. And data is revenue. Why is that? That’s because of data science. The more data you have, the more business insights you can generate. Using data science, you can uncover patterns in data that you didn’t even know existed. For example, you can discover that some guy who went to New York City for a vacation is most likely to splurge on a luxury trip to Venice in the next three weeks. That’s an example that I just made up, might not be true in the real world. But if you’re a company offering luxury tours to exotic destinations, you might be interested in getting this guy’s contact number.

Data science is being used extensively in such scenarios. Companies are using data science to build recommendation engines, and predicting user behaviour, and much more. All of this is only possible when you have enough amount of data so that various algorithms could be applied on that data to give you more accurate results.

There is also something called as prescriptive analytics in data science, which does pretty much the same predictions that we talked about in the rich tourist example above. But as an added benefit, prescriptive analytics will also tell you what kind of luxury tours to Venice a person might be interested in. For example, one person might want to fly first class but would be fine with a three star accommodation, whereas another person could be ready to fly economy but definitely needs the most luxurious stay and cultural experience. So even though both these people will be your rich clients, both of them have different requirements. So you can use prescriptive analytics for this.

You might be wondering, hey, that sounds a lot like artificial intelligence. And you’re not entirely wrong, actually. Because running these machine learning algorithms on huge datasets is again a part of data science. Machine learning is used in data science to make predictions and also to discover patterns in the data. This again sounds like we’re adding intelligence to our system. That must be artificial intelligence. Right? Let’s see.

Data Analyst

A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the necessary level of data science. 

Machine Learning

Let’s talk about machine learning now. Machine Learning (ML) is considered a sub-set of AI. You can even say that ML is an implementation of AI. So whenever you think AI, you can think of applying ML there. As the name makes it pretty clear, ML is used in situations where we want the machine to learn from the huge amounts of data we give it, and then apply that knowledge on new pieces of data that streams into the system. But how does a machine learn, you might ask.

There are different ways of making a machine learn. Different methods of machine learning are supervised learning, non-supervised learning, semi-supervised learning, and reinforced machine learning. In some of these methods, a user tells the machine what are the features or independent variables (input) and which is the dependent variable (output). So the machine learns the relationship between the independent and dependent variables present in the data that is provided to the machine. This data which is provided is called the training set. And once the learning phase or the training is complete, the machine, or the ML model, is tested on a piece of data which the model has not encountered before. This new dataset is called the test dataset. There are different ways in which you can split your existing dataset between the training and the test dataset. Once the model is mature enough to give reliable and high accuracy results, the model will be deployed to a production setup where it will be used against absolutely new datasets for problems such as predictions or classification.

There are various algorithms in ML which could be used for prediction problems, classification problems, regression problems, and more. You might have heard of algorithms such as simple linear regression, polynomial regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbours, and the like. These are some of the common regression and clustering algorithms used in ML. There are many more as well. And there are a lot of data preparation or pre-processing steps you need to take care of even before training your model. But ML libraries such as SciKit Learn have evolved so much that even an app developer without any background in mathematics or statistics, or even a formal AI education, can start using these libraries to build, train, test, deploy, and use ML models in the real world. But it always helps to know how these algorithms work, so that you can make informed decisions when you are to select an algorithm for your problem statement. With this knowledge of ML, let’s talk a bit about deep learning now.

Machine Learning modeling starts with the data exist and typical components are as follows :

• Understand problem – Make sure an efficient way to solve the problem is ML. Note that not all problems solvable using ML.
• Explore Data – To get an intuition of features to be used in ML model. This might need more than one iteration. Data visualization plays a critical role here.
• Prepare data – This is an important stage with a high impact on the accuracy of ML model. It deals data issue like what to do with missing data for a feature? Replace with dummy value like zero, or mean of other values or drop the feature from model?. Scaling features, which make sure values of all features are in same range, is critical for many ML models. A lot of other techniques like polynomial feature generation is also used here to derive new features.
• Select a model and train – Model is selected based on a type of problem ( Prediction or classification etc. ) and type of feature set ( some algorithms works with a small number of instances with a large number of features and some others in other cases).
• Performance Measure – In Data Science, performance measures are not standardized, it will change case by case. Typically it will be an indication of Data Timeliness, Data Quality, Querying Capability, Concurrency limits in data access, Interactive visualization capability etc

In ML models, performance measures are crystal clear. Each algorithm will have a measure to indicate how well or bad the model describe the training data given. For example, RME(Root Mean Square Error) is used in Linear Regression as an indication of an error in model.

• Development methodology – Data Science projects are aligned more like an engineering project with clearly defined milestones. But ML projects are more of research like, which start with a hypothesis and trying to get it proved with data available.
• Visualization – Visualisation in general Data Science represents data directly using any popular graphs like bar, pie etc. But in ML, visualizations also used represents a mathematical model of training data. For example, visualizing confusion matrix of a multiclass classification helps to quickly identify false positives and negatives.
• Languages – SQL and SQL like syntax languages (HiveQL, Spark SQL etc) are the most used language in Data Science world. Popular data processing scripting languages like Perl, awk, sed are also in use.Framework-specific well-supported languages are another widely (Java for Hadoop, Scala for Spark etc) used category.

Data Science vs Data Analytics

• Data science is a broader term, much wider in its scope as compared to data analytics. While data science constitutes fields that mine large sets of data, data analytics is much more specific and basically a part of the bigger process. 
• Data science aims to uncover insights and find patterns from large datasets. Unlike data science, data analytics is concerned with finding answers and gaining insights to existing questions. 
• Data science focuses on asking the right and relevant questions while data analysis focuses on questions that require answers.
• Data science is concerned with predicting the future based on the past patterns while data analysis is about curating relevant and meaningful insights from the data.
• Data science precisely revolves around estimating the unknown whereas data analysis deals with exploring new perspectives of the known.
• Data scientists deal with problems whose solutions will have business value while data analysts deal with business problems. 

Data Science vs Artificial Intelligence

• Data science deals with pre-processing, analysing, visualizing, and predicting the data. Whereas, AI implements a predictive model used for forecasting future events.
• Data science banks on statistical techniques while AI leverages computer algorithms.
• The tools used in data science are much more in quantity than the ones used in AI. The reason for this is – there are multiple steps for analyzing data and extracting insights from it.
• In data science, the focus remains on building models that use statistical insights, whereas, for AI, the aim is to build models that can emulate human intelligence. 
• Data science strives to find hidden patterns in the raw and unstructured data while AI is about assigning autonomy to data models.

Data Analytics vs Artificial Intelligence

• Data analytics deals with finding patterns based on past data to predict future events while AI involves data analysis, making assumptions, and aims to make predictions that are beyond human capabilities.
• Data analytics is about finding patterns in the given data while AI aims to automate the process by giving machines human intelligence. 

Data Analytics vs Machine Learning

• Analytics relies on existing information to find patterns that ultimately shape decisions. Whereas machine learning leverages existing data that provides the base for the machine to learn for itself.
• Analytics reveals patterns through the process of classification and analysis while ML uses the algorithms to do the same as analytics but in addition, learns from the collected data.
• Data analytics ultimately aims to find patterns whereas ML aims to learn from data and make estimates and predictions. 

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Data Science vs Machine Learning

• To be precise, Machine Learning fits within the purview of data science. 
• The main difference between data science and machine learning lies in the fact that data science is much broader in its scope and while focussing on algorithms and statistics (like machine learning) also deals with entire data processing.
• Data science is essentially used to extract insights from data while Machine learning is about techniques that data scientists use so that machines learn from data.
• Data Science actually banks on tools such as machine learning and data analytics. 

That marks the end to the Data Science vs Data Analytics vs Machine Learning vs Artificial Intelligence debate and their relationship with AI and Machine Learning. Now that you know about it, it’s time to take the right actions – through leveraging the existing and upcoming opportunities. But how do you gain expertise in the field? Well, Let me help you with that! 

There’s a whole range of e-learning courses at your disposal. For example, Springboard’s 1:1 mentoring-led, project-based data science, data analytics and AI/ML career track are industry-focussed job-oriented online learning programs, designed to prepare you for a meaningful and successful career in future technologies. You can browse through the website to know more about it.

Data Science vs. Data Analytics: Two sides of the same coin

Data Science and Data Analytics deal with Big Data, each taking a unique approach. Data Science is an umbrella that encompasses Data Analytics. Data Science is a combination of multiple disciplines – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence.

It includes concepts like data mining, data inference, predictive modeling, and ML algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. On the other hand, data analytics is mainly concerned with Statistics, Mathematics, and Statistical Analysis. 

While Data Science focuses on finding meaningful correlations between large datasets, Data Analytics is designed to uncover the specifics of extracted insights. In other words, Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science brings forth. 

Data Science seeks to discover new and unique questions that can drive business innovation. In contrast, Data Analysis aims to find solutions to these questions and determine how they can be implemented within an organization to foster data-driven innovation. 

Data Science vs. Data Analytics: Job roles of Data Scientist and Data Analyst

Data Scientists and Data Analysts utilize data in different ways. Data Scientists use a combination of Mathematical, Statistical, and Machine Learning techniques to clean, process, and interpret data to extract insights from it. They design advanced data modeling processes using prototypes, ML algorithms, predictive models, and custom analysis. 

While data analysts examine data sets to identify trends and draw conclusions, Data Analysts collect large volumes of data, organize it, and analyze it to identify relevant patterns. After the analysis part is done, they strive to present their findings through data visualization methods like charts, graphs, etc. Thus, Data Analysts transform the complex insights into business-savvy language that both technical and non-technical members of an organization can understand. 

Both the roles perform varying degrees of data collection, cleaning, and analysis to gain actionable insights for data-driven decision making. Hence, the responsibilities of Data Scientists and Data Analysts often overlap. 

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Responsibilities of Data Scientists:

• To process, clean, and validate the integrity of data.
• To perform Exploratory Data Analysis on large datasets. 
• To perform data mining by creating ETL pipelines. 
• To perform statistical analysis using ML algorithms like logistic regression, KNN, Random Forest, Decision Trees, etc.
• To write code for automation and build resourceful ML libraries. 
• To glean business insights using ML tools and algorithms.
• To identify new trends in data for making business predictions.

Responsibilities of Data Analysts

• To collect and interpret data.
• To identify relevant patterns in a dataset. 
• To perform data querying using SQL.
• To experiment with different analytical tools like predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics.
• To use data visualization tools like Tableau, IBM Cognos Analytics, etc., for presenting the extracted information. 

Data Science vs. Data Analytics: Core Skills 

Data Scientists must be proficient in Mathematics and statistics and expertise in programming (Python, R, SQL), Predictive Modelling, and Machine Learning. Data Analysts must be skilled in data mining, data modeling, data warehousing, data analysis, statistical analysis, and database management & visualization. Data Scientists and Data Analysts must be excellent problem solvers and critical thinkers. 

A Data Analyst must be:

• Well-versed in Excel and SQL database.
• Proficient in using tools like SAS, Tableau, Power BI, to name a few. 
• Proficient in R or Python programming. 
• Adept in data visualization.

A Data Scientist must be:

• Well-versed in Probability & Statistics and Multivariate Calculus & Linear Algebra. 
• Proficient in programming in R, Python, Java, Scala, Julia, SQL, and MATLAB.
• Adept in database management, data wrangling, and Machine Learning.
• Experienced in using Big Data platforms like Apache Spark, Hadoop, etc.

Data Science vs. Data Analytics: Career Perspective

The career pathway for Data Science and Data Analytics is quite similar. Data Science aspirants must have a strong educational foundation in Computer Science, or Software Engineering, or Data Science. Similarly, Data Analysts can pursue an undergraduate degree in Computer Science, or Information Technology, or Mathematics, or Statistics.

Typically, Data scientists are much more technical, requiring a mathematical mindset, and Data Analysts take on a statistical and analytical approach. From a career perspective, the role of a Data Analyst is more of an entry-level position. Aspirants with a strong background in statistics and programming can bag Data Analyst jobs in companies.

Usually, when hiring Data Analysts, recruiters prefer candidates who have 2-5 years of industry experience. On the contrary, Data Scientists are seasoned experts having more than ten years of experience. 

When talking about the salary, both Data Science and Data Analytics pay extremely well. The average salary of Data Scientists in India ranges between Rs. 8,13,500 – 9,00,000, while that of a Data Analyst is Rs. 4,24,400 – 5,04,000. And the best part about choosing to build a career in Data Science or Data Analytics is that their career trajectory is positive, continually scaling up. Read more on data scientist salary in India.

Conclusion

To conclude, even though Data Science and Data Analytics tread on similar lines, here’s a fair share of differences between Data Analyst and Data Scientist job roles. And the choice between these two largely depends on your interests and career goals.