Data comes along with a bucket full of new, similar-sounding terms. Data Science, Big Data, Data Analytics, Data Wrangling, Data Technology, Databases, and the list is endless. All these technologies vary from one another but are equally important in this world of ever-growing networks and technology.
In this article, we are going to discuss the difference between Data Science and Data Analytics.
Also read: What is the difference between Big Data and Data Science?
Data Science
The term Data Science was termed in 2002 by Peter Norvig and is assigned to a broad field in which many disciplines come together to collect, analyse, and extract insights from data. It is a rapidly expanding field of study that can significantly impact our society in terms of business, government, economics and more. But what is Data Science? At its core, Data Science is the act of turning raw data into useful information.
Data Science is a subfield of computer science that deals with data extraction, exploration, and modelling. While it has become increasingly popular, there are still relatively few resources on data science—and even fewer geared towards beginners. It focuses on identifying the models and algorithms that produce the best results rather than measuring outcomes or generating insights. In this way, it is closer to mathematics and statistics, where variables are measured independently. Data science also requires strong programming skills, requiring a deep understanding of programming fundamentals, such as Python or R for statistical modelling and machine learning techniques. These models can then be turned into increasingly efficient computer programs that automate tasks ranging from marketing campaigns to recommendation engines.
R is one of the most widely-used programming languages within the field of data science. It was designed specifically for statistical analysis—and it’s free! While there are certainly other options (Python, SAS), R has become the most commonly used programming language for data scientists.
Data science is not the simplest technology, and data scientists require the knowledge and expertise of more than one field. The top three skills a data scientist must exhibit are mathematical expertise, statistics, strong business judgment, and technology skills, including programming languages and complicated algorithms. Alongside, a data scientist should be able to do the following:
- Apply statistics, mathematics and scientific methodologies.
- Use techniques like data mining and integration to prepare and evaluate data
- Extract the essential and important data using Artificial intelligence, Predictive analysis, Machine learning and Deep learning.
- Create applications for data processing.
- Illustrate and convey clearly the results and their meaning and importance.
- Put forward use cases how the results can solve business problems.
Data science lifecycle
The data science pipeline or lifecycle is a tedious process involving multiple steps.
- Planning and defining the project with its potential results.
- Collection of all structured and unstructured data from relevant sources including manual entry, web scraping and data from devices in real-time.
- Using data integration technologies like extraction, transformation and loading to convert all the raw data in a format for analysis.
- Processing the data using analytical methods like predictive analysis, deep learning algorithms among others to examine patterns, ranges or biases.
- Extraction of insights from the data by statistical analysis, regression, machine learning algorithms and more.
- Presenting the model in forms of graphs, reports and charts using languages like Python and R to the decision makers.
- Deployment of the model once fully functional, providing the required results.
- Maintenance of the deployed model to keep clear of hackers and frauds.
Applications of Data Science
Data Science has multiple applications, which makes it one of the most widely expanding fields.
- Fraud and risk detection
- Credit card scoring
- Energy management
- Healthcare
- Targeted advertising
- Recommendation systems
- Image, speech and character recognition
- Airline route planning
- Augmented reality
- Gaming
- Decision-making and automation
- Pattern detection
- Classification and forecasting
Also read: Facebook Page vs Facebook Group: Which one suits your needs more?
Data Analytics
Databases are at the centre of most digital business processes, but have you ever stopped to wonder what is happening in your database? There are many resources on creating a basic database, and every piece of data stored can be used for analysis and insight.
Every day people struggle because they don’t know how their company or business would respond if certain factors were changed. Data analytics allow companies and businesses to use their data and external information such as market changes and customer feedback to make informed decisions. Companies can look for trends in sales volumes, popular products, and other information.
Data analytics is the process of converting raw data into useful information that can be analysed to understand trends and patterns. These patterns could be used to predict demand or the likely cause of the pattern. It is an iterative process; data must be gathered, analysed, and interpreted before being useful. In simpler terms, it turns all the collected data, structured, and unstructured into practical information that can be used to obtain fruitful results.
Data analytics helps with better decision-making, helping companies make rational and more informed decisions by eliminating the element of guesswork. It also gives a better understanding of consumers, hence improving marketing and strategising. This also provides insights into the consumer needs and wants and help tailor the products to their customised requirements, improving relations and sales. These factors affect overall and reduce operational costs while boosting the line of work.
Categories of data analysis
There are three categories of data analysis, and each of these categories contains methods that help analyse data correctly.
- Descriptive
- Predictive
- Prescriptive
The descriptive analysis seeks to describe what has already happened. This is done by summarising the data. The descriptive analysis looks at the present state of things and creates reports using statistics, charts, graphs, etc. It also helps with making decisions regarding future actions by being able to describe what happened during a given period of time. For example, it could tell you how much time visitors spent on your website or how many people out of your entire customer base have visited your website in the past year. The descriptive analysis also answers questions about events in the past. For example, it would tell you what time of the year most traffic came to your website.
Predictive analysis discovers patterns in the data that can be used to predict what will happen or estimate future values. It answers questions about future events or outcomes using statistics and other mathematical models to forecast these future events. It is based on historical data, from which assumptions are made and tested for accuracy and validity. Predictive analytics can be used to predict future sales, stock prices, or which campaign will result in the most significant ROI.
Prescriptive analysis indicates what should be done to create the desired state for which objectives are to be achieved. It is a form of advanced analytics that provides content and data to answer questions such as ‘what needs to be done’ or ‘what can we do?’. It is often expressed in graph analysis, neural networks, simulation, recommendation systems, and complex event processing.
Technologies empowering data analytics
Data Analytics is a powerful tool that is empowered by modern-day technologies.
Data Management is the first and most essential step before we start analysing the data. Data needs to be organised, and data flow from a given system must be systematic to ensure no data loss. A central Data Management Platform (DMP) can be used to collect all the data such that it is kept organised at any point in time.
Data mining is sorting a large amount of data to identify relationships and patterns between different data points. This sorted data eliminates the tedious study of large, unfiltered datasets, saving time and human resources.
Machine Learning (ML), a subset of Artificial Intelligence (AI), is used to develop and use computer systems to simulate and implement human intelligence to complete tasks. ML is a powerful technology used for data analytics. It uses algorithms and allows applications to analyse data and predict its outcome without humans having to code, sort, analyse and comment on the data.
Applications of Data Analytics
Data Analytics finds multiple real-time applications that make it popular.
- Business
- Transportation systems
- Logistics and Delivery
- Maintaining the working of Manufacturing industries
- Web search results
- Security
- Educational purposes
- Military
- Healthcare
- Risk detection and management
- City and town planning
- Energy management
- Accounting
Also read: What is Big Data? Everything you need to know.
Data Science vs Data Analytics
| Parameter | Data Science | Data Analytics |
|---|---|---|
| Aim | To create and innovate new solutions | No creation, only interpretation and analysis |
| Programming languages | Python along with C++, Java, Perl and others | Python and R |
| Coding level | In-depth and advance | Basic |
| Statistical skill | Required | Required in detail |
| Data type | Unstructured | Structured |
| Scope | Macro or large | Micro or small |
| Knowledge of | Knowledge of data mining | Data wrangling and strong database knowledge like SQL |
| Uses | Fraud detection, gaming, advertisements, recommendation systems | Business, transport, security, education, healthcare, military |
| Jobs | Very high paying | High paying |
Data Analytics can be considered an essential part of Data Science. In a way, Data Analytics finds answers to the questions raised by Data Science to accomplish projects. Hence, it can be said that they are slightly correlated but not the same. Nevertheless, they complete each other and help find the best solutions to problems.
Also read: Update vs Upgrade: 5 talking points
