Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.

Here is what we’ll look into in this article:

What is Data Science? Why Data Science? Prerequisites for Data Science Data Science Skills Who is a Data Scientist? Must-know Machine Learning algorithms Difference between Business Intelligence and Data Science Data Science Lifecycle Applications of Data Science Skills to Become a Data Scientist Data Science as a Career Are you considering a profession in the field of Data Science? Then get certified with the Data Science Bootcamp Program today! What is Data Science? Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can be from multiple sources and present in various formats.

Now that you know what is data science, let’s see why data science is essential in the current scenario.

Why Data Science? Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:

Find the leading cause of a problem by asking the right questions Perform exploratory study on the data Model the data using various algorithms Communicate and visualize the results via graphs, dashboards, etc. In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:

Plan routes and decide whether to schedule direct or connecting flights Build predictive analytics models to forecast flight delays Offer personalized promotional offers based on customers booking patterns Decide which class of planes to purchase for better overall performance In another example, let’s say you want to buy new furniture for your office. When looking online for the best option and deal, you should answer some critical questions before making your decision.

Desicion tree

Using this sample decision tree, you can narrow down your selection to a few websites and, ultimately, make a more informed final decision.

FREE Data Science With Python Course Gain Mastery in Data Science with Python NowSTART LEARNINGFREE Data Science With Python Course Prerequisites for Data Science Here are some of the technical concepts you should know about before starting to learn what is data science.

1. Machine Learning Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics.

2. Modeling Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Databases A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.

Data Science Skills This section of ‘What is Data Science?’ article gives you an idea of the skills and tools used by people in different fields of data science.

Field

Skills

Tools

Data Analysis

R, Python, Statistics

SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner

Data Warehousing

ETL, SQL, Hadoop, Apache Spark,

Informatica/ Talend, AWS Redshift

Data Visualization

R, Python libraries

Jupyter, Tableau, Cognos, RAW

Machine Learning

Python, Algebra, ML Algorithms, Statistics

Spark MLib, Mahout, Azure ML studio

Let us understand what does a data scientist does in the next section of the What is Data Science article.

What Does a Data Scientist Do? A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:

Ask the right questions to understand the problem Gather data from multiple sources—enterprise data, public data, etc Process raw data and convert it into a format suitable for analysis Feed the data into the analytic system—ML algorithm or a statistical model Prepare the results and insights to share with the appropriate stakeholders Now we should be aware of some machine learning algorithms which are beneficial in understanding data science clearly.

Must-Know Machine Learning Algorithms The most basic and essential ML algorithms a data scientist use include:

1. Regression Regression is an ML algorithm based on supervised learning techniques. The output of regression is a real or continuous value. For example, predicting the temperature of a room.

2. Clustering Clustering is an ML algorithm based on unsupervised learning techniques. It works on a set of unlabeled data points and groups each data point into a cluster.

3. Decision Tree A decision tree refers to a supervised learning method used primarily for classification. The algorithm classifies the various inputs according to a specific parameter. The most significant advantage of a decision tree is that it is easy to understand, and it clearly shows the reason for its classification.

4. Support Vector Machines Support vector machines (SVMs) is also a supervised learning method used primarily for classification. SVMs can perform both linear and non-linear classifications.

5. Naive Bayes Naive Bayes is a statistical probability-based classification method best used for binary and multi-class classification problems.

People who are willing to know what is data science should also be aware of how data science differs from business intelligence.