Data science has become quite an essential part of businesses today, given the huge amounts of data that is being produced. Its popularity is continuously over the past decade, and businesses have started implementing data science techniques to grow their business and increase customer satisfaction.
In this article, we’ll learn everything about data science, and how one can become a data science professional.
Here is what we’ll cover 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
What is Data Science?
Data science is the field of study that deals with a huge amount of data using various modern tools, techniques, and methodologies from different other fields to find hidden patterns, derive meaningful insights, and make business decisions.
The data for analysis purposes is collected from multiple primary and secondary sources and present in various formats. Now as you are pretty much clear about what is data science, let’s see why data science is essential in the current business world.
Why Data Science?
Data science enables data-driven decision-making, predictive analysis, and pattern discovery. It helps you:
- Find the main cause of a problem by asking the right questions
- Perform investigative study on the data
- Make models out of the data using various algorithms
- Communicate and visualize the results via graphs, dashboards, etc to higher-ups.
Data science is highly applied in the airline industry to predict disruptions in travel. Using data science, airlines companies can optimize their operations in many ways, such as:
- Planning routes and decide whether to schedule direct or connecting flights
- Building predictive models to forecast flight delays
- Offering personalized to the customers based on their booking patterns
- Deciding which assets to purchase for better overall performance
Data science has applications in almost every industry today.
Prerequisites for Data Science
Here are some of the big data concepts that you should be aware of before stepping into data science.
In simple words, machine learning is the backbone of data science. Data Scientists need to have a solid grasp of Machine Learning along with the basic knowledge of statistics.
Mathematical models help us to make quick calculations and predictions on the basis of what we already know from the data. Modeling is also a part of Machine Learning which involves identifying the most suitable algorithms for solving a given problem.
Data science is incomplete without statistics. Having good knowledge of statistics can help you obtain more information and more meaningful results.
Programming is a must to execute any successful data science project. The most common programming languages used in data science are Python and R.
A data scientist must understand how databases work, how to extract data from them, and how to manage them.
What Does a Data Scientist Do?
The work of a data scientist is to analyze the business data with an intention to extract meaningful insights. A data scientist solves the business problems in a series of steps, that includes:
- Asking the right questions to understand the problem
- Collecting data from multiple sources
- Processing raw data and converting it into a suitable format for analysis
- Feeding the data to machine learning algorithms and statistical models
- Preparing reports and visualizations to share with the appropriate stakeholders
Now, you know that machine learning is a vital part of data science. Hence, it is necessary to have a decent knowledge of machine learning as well. So, let us have a look at some of the important machine learning algorithms
Must-Know Machine Learning Algorithms
- Decision Tree
- Support Vector Machines
- Naive Bayes
The Lifecycle of a Data Science Project
To give more clarity on data science, here is a comprehensive summary of stages involved in the lifecycle of a data science project.
#1. Concept Study
The first phase of any data science project is the concept study. The goal is to recognize the problem by doing a study of the business model.
For example, let’s say you are trying to predict the assembling cost of a motorbike. In this case, you will need to understand the terminologies used in the automobile industry and the problems that people face, and then collect enough data surrounding the problem and the industry.
#2. Data Preparation
Raw data has tonnes of missing values and inconsistencies. So, the data has to be cleaned using different techniques under the process called data preparation. It is the most crucial step of the data science lifecycle. You must first check the data to find out the gaps or data that do not add any value. This process includes several steps:
- Data Integration – To resolve any conflicts and eliminate redundancies in the dataset.
- Data Transformation – Normalizing, transforming, and aggregating the data using “Extract, Transform, Load” methods
- Data Reduction – Reducing the size of data without impacting the quality or outcome with the help of various techniques.
- Data Cleaning – Improving inconsistent and noisy data by filling out missing values and smoothing them out.
#3. Model Planning
After cleaning up the data, you must choose a suitable model. The model must match the nature of the problem. This step involves an analysis called “Exploratory Data Analysis” to have in-depth insights into the data and understand the relationship between the variables. Techniques used for Exploratory Data Analysis are box plots, histograms, trend analysis, etc.
Using these techniques, we can quickly discover the relationship between the different variables given in the data. Then, we split the information into training and testing data—training data to train the model, and testing data to validate the model.
The various tools used for model planning are R, Python, Matlab, and SAS
#4. Model Building
The next step is to build the model with the help of various analytical tools and techniques. Then you have to validate that the model— if it is working correctly or not. If it’s not working fine then you gotta retrain it with more data or use a newer model or algorithm.
Related: Time Series Analysis
Applications of Data Science
Data science has its applications in almost every industry. Every industry can reap the benefit of data-driven decisions and can fill the gaps.
Here, let’s discuss the industries where data science has found its crucial place and how it’s helping these industries to prosper.
The healthcare industry has heavily utilized Data Science to build advanced medical tools and instruments to detect and cure diseases at earlier stages. Furthermore, with advancements in medical image analysis, doctors can find out microscopic tumors (that too on early-stage) that were otherwise very hard to find. Data science has transformed the healthcare industry in large ways.
The E-commerce industry was always dependent on data but nowadays it has become more crucial than ever to make good use of customer data. With the help of data science and machine learning, e-commerce companies can perfectly target and recommend their products and services to customers.
#3. Image Recognition
Identifying patterns and detecting objects in an image is called image recognition. Your mobile phone’s face ID unlock uses this very thing. It is one of the most popular data science applications.
Logistics companies take the help of data science to optimize the routes to increase operational efficiency and ensure faster delivery of the products.
#5. Banking & Finance
Banks and financial institutions are now using data science to proactively detect transaction frauds and provide a high level of security to their customers. This is done by analyzing and monitoring the user’s banking behavior and activities to find out if there are any suspicious patterns.
Now, let’s talk about data science as a career, it’s no wonder why data science is such a trending career opportunity in recent times.
Data Science as a Career
Over the last few years, the job vacancies for data science roles have grown immensely. There are various job roles in data science that you can look for.
Some of the important job roles are:
- Data Scientist
- Machine Learning Engineer
- Data Consultant
- Business Intelligence Analyst
- Data Mining Engineer
- Data Architect
With millions of job openings in Big Data, there lie tonnes of opportunities. In today’s data-driven business world, companies are dependent on the insights provided by data scientists to stay ahead of their competition.
Big names like Apple, Oracle, Microsoft, Amazon, and more all have continuous job openings for data scientists.
According to LinkedIn, there are 60,000 Data Scientist jobs available worldwide. Data Scientist is undoubtedly the most promising career in 2021 and ahead.
Data is the most important asset for businesses today and will continue to stay so in the coming decade. By incorporating data science techniques, companies can make good use of the available data and forecast future growth, and analyze upcoming threats. And if you wish to start your career in data science, this is a perfect time.
About the Author!
Ram Tavva, a Senior Data Scientist and Alumnus of IIM-C (Indian Institute of Management – Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning. PMP Certified, ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe Currently authoring a book on ITIL “ITIL MADE EASY”. You can found him on Twitter, Facebook and LinkedIn