What is Data Science?
A booming career involves several disciplines with scientific methods, processes, and algorithms to extract information from a big chunk of data, be it categorical or continuous. A definite theory about the data could be formed called Data Science. It includes domain knowledge, statistics, and coding skills as all these are combined to give desired results. The vast set of data science involves applying machine learning and deep learning as the past study to predict the future, or the study of behavioral tracts requires data that could not be analyzed without data science.
Subsets of Data Science
This is a mixture of Mathematics and Statistics, Machine Learning, Domain Knowledge, IT, and software development.
Math and Statistics is the core as everything from Exploratory Data Analysis to Model Building requires dealing with numbers, vectors, probability, and so on.
Machine Learning could be further divided into Deep Learning and Artificial Intelligence, and it is the model-building subset of Data Science. Additionally, essential software development and IT skills are deemed necessary to apply in those fields.
Finally, having business or domain knowledge could go a long way in determining the result’s accuracy as different businesses use different data for prediction. Using the right data is of utmost importance in verifying our output’s credibility.
Understanding Data Science
It is primarily the Science used to uncover hidden patterns from data. Those hidden patterns or insights could go a long way in achieving ground-breaking results in several fields and improving people’s lives. The image above shows the six stages in a Data Science workflow, which helps make predictions and build models to be used in the production. It’s described in detail in the next section.
Working with Data Science
Data Science work would be divided into the following categories.
- Understanding the Problem – The problem statement must be clear before you dive into its implementation. Knowing what to find out is crucial to get the right data and derive the perfect solution.
- Getting the right data – Once the problem is understood, it’s imperative to get the right data to operate.
- Exploratory Data Analysis – It’s said that ninety percent of the work done by a Data Scientist is Data Wrangling. The term data-wrangling refers to cleaning and pre-processing the data before feeding it to the model. The steps involve checking for duplicate data, outliers, NULL values, and several other anomalies that don’t fall under the desired business data convention.
- Data Visualization – Once the data is cleaned and pre-processed, it’s necessary to visualize the data to determine the right features or columns to use for our model.
- Categorical Encoding – This step is applicable for those instances where the input features are categorical and needed to be transformed into numeric (0,1,2, etc.) to be used in our model as the machine cannot work with categories.
- Model Selection– Selecting the right model for a particular problem statement is essential as every model cannot fit in perfectly for every data set.
- Using the right metric– Based on the business domain, the metric that would determine a model’s perfectness should be selected.
- Communication– The businessman, the shareholders often don’t understand the technical know-how of Data Science. Hence, it’s essential to communicate the findings in simple terms to the business, developing measures to mitigate any foreseen risks.
- Deployment– Once the model is built and the business is satisfied with the findings, the model could be deployed to production and used in the product.
What can you do with Data Science?
It is rapidly consuming our daily lives. Starting from waking up in the morning to going to bed, there isn’t a single moment that Data Science’s effects don’t influence us. Let’s look at some of the usages of Data Science which has made our life easy in recent times.
YouTube is the favorite mode of entertainment, knowledge, news in our daily lives. We prefer to watch videos than going through slides of long articles. But how did we become so addicted to YouTube? What has made YouTube so unique and different?
Well, the answer is simple. YouTube uses our data to recommend the videos; we would like to see next. It uses a recommender system algorithm to track our search patterns, and based on that; its intelligence system shows us those videos which are somewhat related to the one we have seen so that we are glued to the channel and continue surfing through the other videos.
Basically, it saves our time and energy to manually look for videos that might help us based on our liking.
Similar to YouTube, the recommender system is also used in e-commerce websites like Netflix, Amazon.
In the case of Netflix, we are shown those TV shows or movies that are somewhat related to the one we have watched and thus saves our time to look for more similar videos.
Additionally, Amazon recommends the products based on our buying pattern. It displays those products that other buyers have bought and that product or what we could buy based on our shopping habits or patterns.
One of the breakthroughs in Data Science is Amazon’s Alexa or Apple’s Siri. Often we find it tedious to surf through our phone for contacts or feel lazy to set up alarm bells or reminders.
In this regard, the virtual assistant systems do all the stuff for us only by listening to our commands. We tell Alexa or Siri about the things we want. The system converts our natural voice to text using the Natural Language Processing topology (we would see that later on) and extract insights from that text to solve our problems.
In layman terms, this Intelligent system uses Speech Voice terminology to save time and solve our problems.
Data Science has eased the life of athletes and people involved in Sports arenas as well. The enormous amount of available data these days could be used to analyze a sportsman’s health and mental conditions to prepare accordingly for a game.
Also, the data could be used to make strategies and outplay the opponent even before the match starts.
Data Science has eased life in the Healthcare sector as well. The medics and the researchers could use Deep Learning to analyze a cell and stop a disease from occurring in the first place.
They could also prescribe adequate medication for a patient based on the prediction from the data.
Top Data Science Companies
It is regarded as the most demanded job of the 21st century, with professionals from different backgrounds embarking on the journey of becoming Data scientists.
Nowadays, almost every company is trying to incorporate Data Science to simplify the process and speed fast the operations to ensure accuracy in optimal time. The list of such companies is enormous, and it would be deemed unfair to pit one against the other in terms of the best as different companies use data for various reasons.
Along with the USA, India’s market is expanding, and it would only benefit professionals in the future. Here are some of the top companies where Data Science has an exhaustive usage:-
JP Morgan, Deloitte, Bitwise, Salesforce, LinkedIn, Flipkart, WNS, Mc Kinsey & Company, IBM, Ola Cabs, Mu Sigma, Stripe, Amazon, Big Basket, Netflix, Wipro, Enterprise Bot, Accenture, Myntra, Manthan, TCS, Cisco, Cartesian Analytics, HCL, EDGE Networks, Walmart labs, Cognizant, 7.ai, Target Corporation, TEG Analytics, Citrix, Sigmoid, Facebook, Twitter, Google Inc., Gobble, Reliance, Square, niki.ai, Dropbox, Airbnb, Khan Academy, Uber, Pinterest, Fractal Analytics.
The sites where you could find several Data Science openings are – LinkedIn, Indeed, Hired, and AngelList.
Who is the right audience for learning Data Science technologies?
It is about working with data, and every field uses data in some way or another. Hence, you don’t need to belong to a specific discipline to be a Data Scientist.
However, what you need to do is have a curious mindset and an eagerness to carve out insights from data.
Advantages of Data Science
- It could help mitigate time and budget allocation constraints and assist in the business’s growth.
- Machine determined results of several manual tasks which could be better than human effects.
- It helps prevent loan default, used in fraud detection and several other use cases in the financial domain.
- Generate insights from raw, unstructured textual data.
- Predicting the future outcome could prevent the financial loss of many big corporations.
Required Data Science skills
The above image indicates the importance of the skills required based on different roles.
Programming, Data Visualization, Communication, Data Intuition, Statistics, Data Wrangling, Machine Learning, Software Engineering, and Mathematics are the required skills for anyone who wants to enter the Data Science space.
Why should we use it?
The usage of Data Science in academia and real life is vastly different. While in academia, It is used to solve several cool projects like image recognition, face detection, etc.
On the other hand, it is used to prevent fraud, fingerprint detection, product recommendation, and so on in daily life.
The opportunities or scope in Data Science is boundless. As shown in the image above, a professional could work in several different roles in Data Science depending on thskillset set and level of expertise.
Why do we need Data Science?
A lot of the work done nowadays is manual and takes a lot of time and resources, which often causes hindrance to the budget allocated for the project. Big companies sometimes look for solutions to optimize such tasks and ensure the budget and resource constraints are mitigated.
It allows automating the tedious processes and produce such outstanding results, which might not have been possible in manual work.
How would this technology help you in career growth?
This survey by Forbes shows that it is the future, and it is here to stay. The days of manual work is over, and Data Science would automate every such task. Hence, if you want to remain relevant in the industry in the future, you must learn the various aspects and increase your chances of always being employed.
If you are a graduate or a working professional, it’s high time that you hope onto the Data Science ship and get yourself involved in the Data Science community.
This has been a guide to What is Data Science. Here we discussed various subsets of data science, its life cycle, advantage, scope, etc. You can also go through our other suggested articles to learn more –