Why Data Science Matters

Data Science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. An increasing number of enterprises today are realizing the importance of data science, AI, and machine learning. Regardless of their size or industry, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities.

Starting a tech company, building a good product, and gaining traction have become easier thanks to improved connectivity, declining costs of cloud storage and computing, and easy access to distribution platforms for reaching target audience. As a result, the time taken for a product to reach 100 million monthly active users has reduced drastically.

Higher production of digital devices, internet connectivity and increased time spent online – all have contributed to generating huge amounts of data and consequently sparked interest in mining this data to derive key insights for business intelligence. A company’s ability to compete is now measured by how successfully it applies analytics to vast, unstructured data sets across disparate sources to drive product innovation.

Why Data Scientists Are Needed

Every time a person posts a status update on Facebook, tags a friend on Twitter, searches for something on Google or shares snapshots on Instagram, they add to the vast amount of data. With more and more data being captured across the globe than ever before, the demand for expertise to extract valuable and actionable insights from that data is going to only grow by leaps and bounds. But companies are not content with merely collecting the data. They want to wisely sort and study the growing collections of data and learn the context surrounding it to enjoy a greater understanding of their customers. This is where Data Scientists step in to help them get clarity and direction today.

Data Enables Businesses to Be Proactive in Growing Their Profits

Data Scientists can help business leaders become increasingly proactive in connecting data trends to opportunities for higher profits. It is helpful if the professionals working with the data have intimate understanding of the business they cater to. Companies that constantly overlook the trends emphasized by data not only miss out on chances to boost their profits, they could also lose out on chances to augment their brand.

Let us take a look at the use cases that require Data Science knowledge in each of the following domains:

1. Data Science in Marketing: Data Science in Marketing helps extracts meaningful information giving marketers the right insights. These insights may cover customer intent, experience, behavior, etc. that would help the marketers efficiently optimizing their marketing strategies and derive maximum revenue. Following are some examples of how data science is used in Marketing.

• Marketing Budget Optimization
• Marketing to the Right Audience
• Identifying the Right Channels
• Matching Marketing Strategies with Customers
• Lead Targeting
• Advanced Lead Scoring
• Customer Personas and Profiling
• Content Strategy Creation
• Sentiment Analysis
• Pricing Strategy
• Customer Communication
• Social Media Marketing
• Ad Offerings
• Email Campaigns

2. Data Science in Sales: Data science brings growth, improvements, efficiency, and effectiveness in sales. Thus, the collective dream of all those dealing with sales — to sell more with fewer efforts becomes real with the use of Data Science methods and technologies. Following are some examples of how data science is used in Sales:

• Chatbots instead of salespeople
• Customer Sentiment Analysis
• Maximization of Customer Lifetime Value (CLV)
• Future Sales Prediction
• Churn Prevention
• Cross-sell Recommendations
• Price Optimization

3. Data Science in Human Resources (HR): HR departments are met with challenges when integrating intelligent systems into their workflows. They are tasked to manage the organization’s employees — hiring, firing, resolving disputes, payroll, benefits, and more. Many of these tasks seem ripe for automation with data science, however, are often met with subjective and interesting challenges.

Below are some useful ways in which data science can be applied in HR:

• Data-driven Recruitment
• Employee Performance analysis
• Retention Management
• People Analytics
• Organizational Network Analytics
• Chatbots for Recruitment
• Employee Churn Models
• Recommender System for Training Courses

4. Data Science in Finance: Financial institutions are spending huge amounts of money to get exclusive rights to data. By having more information, they can construct better models and get ahead. Thus, the most valuable commodities are no longer the analysts themselves or the quants that help design these algorithms. It’s the data itself. Below are some use cases of Data Science in Banking and Finance:

• Detect and prevent fraud
• Manage customer data more efficiently
• Enable data-driven risk assessment
• Leverage customer analytics and personalization
• Anomaly detection
• Algorithmic trading

Essential Competencies for Data Scientists

To be able to successfully deliver Data Science insights and furthermore support its functions, there are certain skills that a professional must be trained in:

Below are the most essential skills and components of data science:

• Mathematics, probability and statistics
• Programming
• Machine Learning
• Deep learning
• Feature Engineering
• Natural Language Processing
• Data Visualization
• Big Data and Deployment
• Business Acumen

How to Complete a Data Science Project Successfully

Data Science entails a set of processes that gather, analyze, interpret and present data in meaningful ways. It begins by understanding the business, setting goals, collecting the data and then cleaning it to make revelations and business decisions based on refined findings.

These processes come together to make what can be called the ‘Data Science Way’ of solving problems. It is a full circle, as every problem leads to a new discovery that throws up new problems. Ultimately, it is a continuous process of discovery and re-discovery of new insights and challenges in the wake of the insights presented.

In India, nearly 93,000 jobs in Data Science were vacant towards the end of August 2020.
70% of these openings were for positions with under five years of experience. According to analysts, India will have over 11 million vacancies by 2026.

The supply-demand mismatch is costing us thousands of unfilled positions yearly. This gap can be filled with the right corporate training programs to upskill candidates with the specialized skills required.