Data Science: Complete Introduction to Data Science

With the ongoing digital revolution, data science and advanced analytics (AI, Machine Learning, etc.) have become a big part of our lives and society as a whole. Data science is the study of data.

But okay, let’s be a bit more specific; Data science is an interdisciplinary field that extracts knowledge and insights from structured and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data

What this means is that data science combines multiple fields, including statistics, scientific methods, machine learning, and data analysis, to extract value from data.

Data science is about the process of using data to understand many different things and the skill of unfolding the insights and trends that are hiding (or abstract) behind data. It’s when you create your story based on data.

People who practice data science are called data scientists, and they apply the scientific method of observing, recording, analyzing and reporting results to understand information and use it to solve problems.

Additionally, it’s important that a data scientist has non-technical skills such as critical thinking and problem solving, thorough business understanding, curiosity, and communication and storytelling skills. In order to make full use of the insights derived from the data.

I like to think that data science is about combining programming, statistics, machine learning, and AI, computer science, to find interesting insights from large data sets. Then, package it and present it nicely to various colleagues and management within the company, to move from insights to actions.

The main purpose of data science is to find patterns within data and use various statistical methods to analyze and draw insights from the data. Companies need data to help them make better decisions. Data Science converts raw data into meaningful insights.

A skilled Data Scientist will know how to find meaningful information with almost any relevant and quality data that comes across – to help the company frame the proper actions in the right direction.

Some examples of how businesses can use data science

Data-driven decision making is the method of using data to make informed and verified decisions. Data Science helps people to overcome biases and make the best decisions that are aligned with business strategies.

Decision Making could be described as a four-step process

1. Understanding the problem
2. Quantifying with data
3. Implementing tools
4. Translating and sharing insights

Data-driven product development uses the power of data and advanced analytics to reveal what customers want, need, and respond to. In the context of developing new products, information is definitely a required resource.

Customer insights to:

• Understand customer behaviour and how they use and interact with your product and service
• Find churn patterns and work to prevent them. Churn rate, sometimes called attrition rate, is the rate at which customers stop doing business with a company
• Finally, Data Science can support customer segmentation and identify customer groups to target, and how to do it

Companies can use past and current data with predictive analytics to forecast trends and find and exploit patterns within data to detect risks and opportunities.

For example, airlines use predictive analytics to set ticket prices reflecting past travel trends and forecasts

Data Science for cyber security uses machine learning to identify, classify, and neutralize attacks and hacks. Detecting anomalies is a significant feature that machine learning brings to cybersecurity. Attacks are often committed by code that is different from the norm or code that does tasks that are considered abnormal.

A data scientist typically provides the cybersecurity team with information that can better inform them how to counter attacks.

In general, a data scientist requires both technical skills and non-technical skills.

The technical skills include:

• Programming languages such as Python, R, C/C++ and SQL as the main foundations for most data scientists
• Math: with three topics constantly coming up: Calculus, Linear Algebra, and Statistics
• Statistics are at the heart of refined machine learning algorithms in data science, capturing and translating data patterns into actionable evidence

The non-technical skills involve

• Critical thinking and problem solving
• Strong business understanding
• Communication and storytelling skills to share the results in a compelling way
• Curiosity to find out more and dig deeper into the data set

Note that a data scientist doesn’t have to be an expert in all these fields, but preferably have profound knowledge and experience in one or two of them, and some basic working knowledge in the others. The power of data science is to combine these different areas.

Let’s now focus on the programming languages and tools used by a Data Scientist

1. Python

Python has become popular for data scientists because of its wide range of uses, such as machine learning, deep learning, and artificial intelligence. Python can support data collection, modelling, analysis, and visualisation to work with big data.

Python has a large set of available libraries to use and is easy to learn and get started.

2. R

R in data science is used to handle, store and analyse data and can be used for data analysis and statistical modelling. R is used in statistical computing and graphics, is easy to learn, and is a free, open-source software.

3. SQL

SQL (Structured Query Language) is one of the most important data science programming languages, as it’s used for performing various operations on the data stored in the databases. Typical operations include, updating records, deleting records, creating and modifying tables, views, etc.

Data Science is the comprehensive study of data, and to work with data, we need to extract it from the database.

4. Apache Spark

Apache Spark, or just Spark, is among the most popular tools in the big data industry. It’s an open-source framework developed by Apache that is used for big data storage, processing, and analysis. Apache Spark comes with a group of tools that can be used for various features, such as structured data, graph data processing, and Machine Learning analysis.

Spark offers various APIs that are programmable in Python, Java, and R. But the most powerful conjunction of Spark is with Scala programming language.

5. TensorFlow

TensorFlow is an open source framework that has become a standard tool for Machine Learning. TensorFlow has an extensive ecosystem of tools, libraries, and community resources that lets data scientists quickly build and deploy machine learning applications.

The major benefit of using TensorFlow is abstraction – allowing the data scientist to focus on the overall logic of the application rather than going into too much detail.

6. Julia

Julia, an open-source programming language released in 2012 that was created to be as easy to use as languages such as R and Python while also as fast as C. Julia is a high-level and general-purpose language that can be used to write code that is fast to perform and easy to implement for scientific calculations

7. Cloudera

Cloudera is one of the fastest and most secure Big Data Tools available. Cloudera provides the Cloudera Data Platform, a collection of products related to cloud services and data processing. Cloudera can be used on AWS, Google Cloud, and Microsoft Azure, among other cloud platforms.

8. SAS

SAS (Statistical Analysis System) is a tool for analysing statistical data and is considered one of the first Data Science tools on the market. The main purpose of SAS is to retrieve, report and analyse statistical data.

However, SAS is a quite expensive software and is therefore not suited for beginners and independent data science enthusiasts, but rather companies and organisations looking for reliable software for advanced analytics and complex statistical operations

9. MATLAB

MATLAB is built for linear algebra, and data scientists use linear algebra to analyse large datasets with many attributes and samples. More specifically, in data science, MATLAB is used for simulating neural networks, fuzzy logic and image- and signal processing.

10. Scala

Scala is a multi-paradigm programming language as it supports both object-oriented and functional programming. Scala is especially useful for analysing large sets of data without any significant impact on performance.

One of the primary reasons to learn Scala for machine learning is Apache Spark (that we looked at earlier). Scala can be used in conjunction with Apache Spark to deal with a large volume of data. Actually, Apache Spark was built using Scala, so it makes sense that learning it will be a great tool for any Data Scientist.

Curious to learn more about technical skills and non-technical skills for a Data Scientist? Check out our posts

• Technical Skills to become a Data Scientist
• Non-Technical Skills for a Data Scientist
• Top 10 Skills Every Data Scientist Should Have

To give a few examples of some use cases for data science

Finance is one of the most critical sectors in the world, and with the use of data science, companies can now quickly analyze financial related matters and make better decisions. Data Science is used for many finance areas, for example:

• Fraud detection
• Algorithmic trading
• Customer management
• Risk analytics
• And many more….

Let’s look closer at fraud detection. One of the major concerns for financial institutions is fraud, and as the number of transactions increases, so are the possibilities of fraud.

Credit card fraud will remain at the top of the list of financial scams, but with the help of analytical tools to analyze the big data, financial companies can detect anomalies with higher precision and speed. The unusual patterns in data can be identified using various machine learning tools.

Furthermore, another example was algorithmic trading. By understanding massive datasets in a better way, financial institutions can make better predictions for how the market will, which is the analytical engine’s aim, and hence, improve their trading and positioning.

There is even courses at Coursera and Udemy on Data Science for Algorithmic trading

If you are curious to learn more about Data Science can be used Finance, we recommend our post: The Role of Data Science in Finance: A Comprehensive Guide, where we will walk you through the different ways data science is being used in finance and some of the challenges that come with it.

The healthcare sector gets excellent benefits from data science applications. The healthcare industry generates large datasets of valuable information on patient demography, treatment plans, results of medical examinations, data from connected devices (IoT) etc.

Some examples of use cases for data science in healthcare are:

Traditionally, doctors would manually inspect images and find anomalies within them. Data science techniques make it possible to find such microscopic deformations in the scanned images. Image segmentation makes it possible to search for defects in the scanned images and can support the medical staff.

Additionally,  there are other image processing techniques like image recognition, image enhancement and reconstruction, edge detection etc.

Drug development and innovation is a highly complex discipline. Pharmaceutical companies can leverage data from, for example, patient metadata, journals, and clinical research to develop models and find statistical relationships between the attributes.

Data science can help design smarter trials, strengthen the scientific discoveries, shorten the time to develop new and safer medicals, and hopefully help more people

Read more about AstraZenecas work with Data Science here

A predictive model uses historical data, learns from it, finds patterns and generates predictions from it. In healthcare, it can find various correlations and associations of symptoms, find habits and diseases, and then make influential forecasts.

Hospitals may use data science to forecast the deterioration of a patient’s health and give preventative measures and early treatment to help reduce the likelihood of further deterioration of the patient’s health

If you are curious to learn more about Data Science can be used Healthcare, we recommend our post: Data Science in Healthcare: How It Can Help Save Lives, where we will walk you through the different ways data science is being used in healthcare and some of the challenges that come with it.

Data science plays an important role in e-commerce and online marketing.

Some of the ways the leading companies use data science are:

• Make personalised product recommendations: Popular eCommerce giants like Amazon and Netflix are leveraging data science to achieve it
• Optimising pricing structures: Selling a product at the right price is an important task – Quite obvious, right. With the help of data science applications such as machine learning algorithms, companies can use the algorithm to analyse a number of parameters from the data like the flexibility of prices, location of the customer, the buying attitude of an individual customer and competitor pricing
• Identifying styles of popular products and predicting trends: Data science can support in identifying customer behaviour and shopping patterns. This is crucial as it helps marketers to understand what impacts consumers’ buying decisions
• Predictive analytics for forecasting goods and services: Predictive forecasting makes forecasts using various data sources, including sales history, customer searches, economic indicators, and demographic data.
• Identifying and targeting a potential customer base via customised marketing: In general, marketing programs are widely spread, regardless of geography or target. However, if marketers use data science to evaluate their data correctly, they will learn which areas and demographics provide the best return on investment (ROI). In other words, data science can help to determine which channels to use and in which areas and match them with the right customer.

Data science is the process of building, cleaning, and structuring datasets to analyze and extract meaning. Data analytics, on the other hand, refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends.

While Data Science focuses on discovering meaningful correlations between large datasets, Data Analytics focuses on uncovering the specifics of extracted insights. In other words, Data Analytics is a subset of Data Science that focuses on more detailed answers to the questions that Data Science unveils.

I like to think of it as; Data Science seeks to discover new and unique questions that can drive business innovation. On the other hand, Data Analysis aims to find answers to these questions and determine how they can be implemented within an organization to promote data-driven innovation.

While data analysts and data scientists both work with data, the main distinction lies in what they do with it:

• Data analysts: Evaluate big data sets for trends, generate charts, and create visual presentations to assist businesses in making better strategic decisions
• Data scientists: Plan and create new processes for data modelling and production using prototypes, algorithms, predictive models, and custom analysis

In our article on Business Intelligence (BI) we saw that BI can be defined as:

Sounds pretty similar to Data Science right? So what are the differences?

First of all, let’s start with some similarities

• Decision-making: Both Data Science and BI enable business users to make smart decisions based on data
• Share insights within entire company: These fields can analyse data and engage technical experts who translate or transform data-enriched results into friendly insights or competitive intelligence 
• Non-technical skillset: Both Data Science and BI requires critical thinking and problem solving, strong business understanding, having communication and storytelling skills to share the results in a compelling way, and finally, a curios drive to find out more and dig deeper into the data set

On the other hand, some of the differences between Data Science and Business Intelligence (BI)

• Perspective: Data science focuses on the future with predictive and prescriptive analysis (what will happen), while BI looks in terms of the past and present with descriptive analysis (what has happened) 
• Focus: While BI is a simpler version, data science is more complex. BI is about dashboards, data management, arranging data and producing information. In comparison, data science is about using statistics and complex tools on data to forecast or analyze what could happen. 
• Tools and Skills: