Data Science, Artificial Intelligence, and Machine Learning are the top buzzwords in the IT industry these days. They are used in almost all industries and businesses, cutting across the domain. Companies are on the constant lookout for data science or AI experts that can increase their business and later manage the growth. But before we delve into the skills needed for each, let us briefly understand what each term means in the current context.

1. Introduction
2. Top 5 skills needed to excel in the area of Data Science and Artificial Intelligence
3. How to upskill

Introduction

Data Science

In layman’s terms, Data science is an interdisciplinary field of study involving domain expertise, programming skills, and deep knowledge of probability, mathematics, and statistics to crisp and actionable insights from data. For example, assume you are the chief product manager in a mobile operator and are in charge of their prepaid mobile services. You have to launch new prepaid plans. Historical data and market surveys do not give actionable insights, but rather they give broad messages such as new customers will buy the new plan X with a probability of Y%. A better way is to use all historical data as well as the new market survey data, process it, and make an actionable suggestion such as launching a new prepaid plan with XXX details will increase new customer onboarding by a minimum of YY% while stopping customer churn by Z%.

Machine Learning

Machine learning is a branch of data analysis that implements an already designed analytical model for a specific problem. In a layman’s language, it is a branch carved out of artificial intelligence whereby it is designed to learn from data, identify patterns, process these patterns, and make decisions with minimizing chances of errors. All this is achieved with minimal human intervention. Consider you are the same product manager we mentioned above, suppose you have used Data Science tools and got inference to launch a new prepaid plan XXX, but you do not know what should be its price, talking time offered, and validity. To solve this subproblem, you can use machine learning to design and implement an ensemble of 4-5 prediction models, implement them, test them on the data at hand and choose the one which gives more accurate results with the least error. So this way, you can get values for price, talking time, and validity for each plan.

Artificial Intelligence

Technically, Artificial intelligence (AI) is a very wide-ranging branch of computer science, statistics, and Mathematics that primarily aims to build smart machines that are capable of performing tasks that are normally performed by human intelligence and are also capable of outperforming humans in due course of learning. The prime goal for AI is to enhance the speed, precision, and effectiveness of human efforts. Let us extend the same example, suppose you have got a set of new prepaid plans and have launched them in the market. Now you want to know how many customers will likely recommend new plans to their known people. So you can use the customer data collected at the time of purchase, customer’s past data, and design models that can accurately predict referral probabilities with the least error.

Data scientists help companies to make the best out of their business data. However, there is a shortage of expert data scientists in the industry that are familiar with the latest tools and technologies, and the whole IT industry is booming towards this branch.

The top 5 skills needed to excel in the area of Data Science and Artificial Intelligence are as follows:

1. Python or R programming language

Python is the most command programming language used in DS/AI and ML domains. Its easy-to-use and open-source programming language with a wide user base and very detailed and constantly updated documentation. One can program, script, visualize, scientifically compute, and web scrape using Python. The data structures, modularity, and Object Orientation in Python are perfect for application development using data science. Data scientists use Python for various processes like creating financial models, web scraping data, creating simulations, web development, data visualization, and others. There is a well-tested package for almost any problem in Python.  

R is another programming language widely used in the data science industry. R is more useful for data visualization and making decisions using graphical data. It is very easy to learn and is well documented. There are many free online resources to learn R. R is used as a prime data science programming tool in many industries like healthcare, e-commerce, banking, and others.

2. Cloud Computing

Almost all the major industries are moving from in-house servers to some form of cloud solution. Further, the applications are developed as a set of independent microservices that are deployed and run on the cloud. Cloud computing allows organizations to scale their IT framework according to the demands and save both operation cost and capital investment. All major DS programs are designed to build and run on the cloud efficiently. Major players such as Microsoft (Azure), Amazon (AWS), Google (GCP), and IBM (IBM Cloud) have their own commercial DS offerings running over cloud solutions.

Also Read: Top 6 Cloud Computing Projects to get you hired in 2021

3. Statistics and Mathematics

Statistics, Probability, and mathematics are the basis of Data Science, AI, and ML. One cannot design robust ML algorithms without having a strong foundation in these three fields. It is almost impossible to extract meaningful insights from unstructured data sets. Statistics is a must to do data sorting and analysis. Data scientists usually recommend one model from a collection of models after running various statistical tests on the result of each model to choose the best model. Moreover, many existing models such as NaiveBayes or Support Vector Machine (SVM) require knowledge of probability and mathematics to understand the underlying equations.

4. Artificial Intelligence 

Artificial intelligence is usually employed to automate the data analytics systems and forecast more accurately. Data scientists can derive real-time actionable insights with AI that is well backed up with data. The objective behind AI is to permeate machines with human-like attributes to make them think, process, and act faster in a volatile market scenario. The application of AI has already made many manual jobs obsolete. AI finds wide application in Image processing, Natural language processing, computer vision, and numerous other fields.

5. Machine Learning

Machine learning algorithms are used by organizations to predict something or to classify and categorize. Firms need ML experts that can develop robust data analytics algorithms with accurate predictions with the least error. ML helps data scientists to extract meaningful insights based on various data matrices.

How can you upskill?

If you wish to leverage the power of Data Science, you can take up the Practical Decision Making Using Data Science course offered by Great Learning. The course is designed for mid & senior managers and business leaders and is in collaboration with NUS. It is a 6 months comprehensive program that covers a comprehensive curriculum. Upon successful completion, you will also receive a Certificate of Completion from NUS Business School. Register today, and power ahead!

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