Data Science
Data Science Definition
Data science is the practice of mining large data sets of raw data, both structured and unstructured, to identify patterns and extract actionable insights from them. This is an interdisciplinary field, and the foundations of data science include statistics, inference, computer science, predictive analytics, machine learning algorithm development, and new technologies to gain insights from big data.
Data Scientist Role In Industry
Businesses hired data scientists to perform a bulk of work related to artificial intelligence, to improve business performance and create new products. There are 20 data science use cases that many businesses are using today under marketing.
1. Anomaly detection
This involves identifying steep downswings and upswings, and sales data as they occur. Building an anomaly detection system would help a company to quickly address problems that affect its bottom line. This use case could also apply to other business functions, like manufacturing and supply chain management.
2. Image classification
This involves creating algorithms that automatically classify images of products based on attributes of those products, such as colors, product types and so on. In the long run this can be less time intensive and more accurate than manually classifying thousands of images in the company’s database.
3. Recommending Products to Website Visitors
A company could capitalize on cross sale and upsale opportunities by recommending products based on the search behavior, past buying behavior, and attributes of website visitors. A recommendation system would give customers and website visitors a clearer idea on what offerings can best fit their needs. Amazon is the best example that uses this approach. Additionally, some chatbots have this capability to a certain stand and they can work on both websites and social media chat windows.
4. Forecasting
This involves forecasting metrics such as sales, number of orders, profits, product sold and so on. To enable a clear idea of where those metrics could land in the next several months or a couple of years in an organization.
5. Identifying drivers of positive and negative outcomes
Manual techniques to find drivers of sales trends can be inconsistent and time-consuming. Using automatic methods to identify factors that drive positive and negative outcomes can give a company a clear idea on how to maximize their success. Example and metrics this approach could apply to include sales, number of orders, profits and so forth. This approach could also apply to other business functions like manufacturing and supply chain management.
6. Developing chatbots
Customer service reps for companies often invest too much time answering frequently asked questions. Sometimes distributing a fact sheet isn’t convenient from a customer’s point of view. Chatbots can answer many types of questions based on data you feed to it. Additionally they can recommend products and services to customers and direct them towards making optimal buying decisions. Chatbots can integrate with websites, social media accounts, crm software and smartphone texting systems. There are already multiple high quality chatbots on the market that companies can simply buy and customize. But some companies have unusual integrations of software and unique company objectives that may necessitate the need for a data scientist or developer to create a chatbot from scratch.
7. Customer acquisition
on the customer relationship management there’s customer acquisition. This involves tracking the probabilities of prospective customers buying a company’s products based on current and past customers with similar behavior and attributes. This helps company’s calibrate and maximize the success of their customer acquisition strategist.
8. Customer Segmentation & clustering
Many companies use manual methods to segment the customers based on characteristics like price points, survey results, geographic areas, industries and so forth. Using clustering techniques can help companies segment customers based on factors that are more difficult to pinpoint such as behavioral patterns. Using this approach enables companies to apply personalized marketing and sales strategies to each customer segment.
9. Customer sentiment analysis
This involves using modern natural language processing and sentiment analysis approaches to analyze text data. Such as customer feedback, social media post, call center records, product reviews, written survey results, and more. This helps companies understand the customers’ buying preferences and motivation at a deeper level.
10. Customer journey optimization
A typical customer journey has 5 phases: awareness, consideration, purchase, retention, and advocacy. Customer journey optimization involves analyzing the typical sequences of events that take customers from becoming aware of a company and its offerings, and than buying thos offerings. It involves identifying the weak points in the customer journey that prevent customers from moving forward and buying from a company. Those weaknesses can include ineffective marketing campaigns, miscommunication, technology not working properly and so forth.
11. Customer lifetime value prediction
This involves estimating the total amount of money. A customer is likely to spent with a company over the entire customer and company relationship. This is achieved by analyzing the customer lifetime values of customers with similar attributes and behaviors in the past.
12. Customer churn prevention
This involves identifying customers that are most likely to end their relationships with the company. Data science can help companies find behavioral patterns, so that those companies can proactively address their underlying concerns.
13. A/B testing
This is the process of comparing 2 versions of a web page, email, other marketing assets and measuring the difference in performance. Conducting A/B tests can greatly maximize the return of investment from marketing campaigns, product launches, and so forth. They can also help companies understand the preferences of customers at a deeper level. There are already a high number of automated tools that can handle this but some companies deal with unique factors that other companies do not and they may need data scientists to create a custom A/B testing algorithm from scratch.
14. Market basket analysis
Under products there’s market basket analysis, this involves identifying products and skills that are most frequently ordered together. This data can help companies recommend product and skill combinations to customers based on purchases made by other customers in the past.
15. Product life cycle phase prediction
Every product has a life cycle phase associated with it, those phases typically include introduction, growth, maturity, and decline. Predicting product life cycle phases helps companies to predict when a company might stop growing in sales, and when that product might decline in this life cycle. These types of predictions are made based on life cycles of similar products in the past.
16. Prize optimization
In the company under finance there’s price optimization. This involves analyzing multiple price and volume levels of products to determine what prices would most likely maximize sales and profits.
17. Fraud detection
This use case is most commonly used in the financial industry and other industries that require considerable government oversight. This involves using algorithms to detect instances of fraud including fake profiles, unusual financial transactions, theft and so forth.
18. Inventory optimization
Under manufacturing, planning, and supply chain management, there’s inventory optimization, the costs associated with inventory storage can be astronomical. in some companies using modern optimization and demand forecasting techniques could lead to a dramatic reduction of cost. This approach can involve a combination of programming skills, lean six sigma knowledge and applied statistical knowledge.
19. Supply chain optimization
This involves optimizing shipment routes, decreasing process times, and predicting delivery times. Using optimization and demand forecasting techniques can automate this effort considerably and introduce a new level of standardization.
20. Process improvement
Determining factors that most contribute to process time deviations can be inconsistent and labor intensive modern advanced analytics techniques can considerably automate and standardize this effort.
Why am I interested in learning data science?
There are several reasons that made me interested in studying data science, some of which include utilization of machine learning in it, and having good opportunities in industry.
1. Utilization of machine learning in it
Data science and machine learning often complement each other in research and implementation. Data Science is the process of identifying and analyzing patterns from data. Meanwhile, Machine Learning is the process of utilizing these patterns to make more accurate and efficient predictions. The utilization and combination of these two fields can help organizations or individuals make better data-driven decisions.
2. Having good opportunities in industry
A data scientist is one of the professions with significant opportunities on several job portals such as jobstreet.co.id, id.indeed.com, kalibrr.com, and glints.com. At the time this article was written, there were around 800+ results found in the search for “data science” on id.indeed.com. This is what piqued my interest in studying data science.