Data Science Insights – Beginner’s Guide
The key difference between data and insight is that data is raw, while insights are derived from it. In other words, you can’t derive insights without first having access to the data.
What is Data?
Data is anything that has been created through human activity. It includes everything from text documents to images to statistics to sensor readings. The term “data” was originally used to describe large amounts of numerical information, but in modern times, it also applies to any type of information that can be organized into structured records.
What are the different forms of data?
There are two main types of data: structured and unstructured. Structured data comes in many forms including spreadsheets, databases, and files. Unstructured data is much more difficult to organize because there is no standard format for how it should be presented. Examples include audio recordings, video footage, and even pictures taken with a smartphone.
What is Data Analytics?
Data analytics is the process of deriving meaningful insights from data. It involves using statistical methods to analyze data and draw conclusions about it. However, this doesn’t mean that all data analysts use advanced mathematical formulas. Some simply rely on their knowledge of business processes to help them understand what they see in the numbers.
What are Data Science Insights?
Data science insights are the results of applying data analysis techniques to solve problems. They are often referred to as “insightful solutions.” For example, if you were trying to determine which product would sell best at your store, then you could run a survey asking customers which products they prefer. You might find out that customers like certain items more than others based on their answers. From these findings, you can create a model that predicts which products will do well. That way, you know exactly which ones to stock so you don’t waste money on products that won’t sell.
An insightful analytics approach uses advanced analytical techniques to uncover patterns in your data. It requires a high level of technical skill as well as domain expertise.
Data-Driven Decision Making
A data-driven decision making approach relies heavily on machine learning algorithms to extract meaningful insights from your data. The goal here is to build predictive models based on historical data.
Machine learning is a subset of artificial intelligence (AI) which allows computers to learn from experience without being explicitly programmed. Machine learning has been around for decades but only recently have we seen its application in business.
What Is Data Mining?
Data mining is the process of finding useful patterns in large amounts of data. Data mining is a broad term used to describe many different approaches to extracting meaning from data. It includes clustering, classification, association rules, regression analysis, among others.
Business intelligence is the use of technology to analyze and interpret data so that businesses can take action. BI tools allow users to explore their data visually, identify trends and patterns, and understand how they affect each other.
Big data is any collection of data sets so large or complex that traditional data processing applications are inadequate.
What is the difference between data, analytics and insights?
The three terms are often used interchangeably. But they actually represent very specific concepts. In fact, the definitions below show just how distinct they are.
Data is anything that can be stored and retrieved later. It may be text, images, videos, sound, etc.
Analytics is the process of deriding meaning from data. There are many ways to accomplish this. One common method is through statistics.
Insights are the results of applying analytics to solve problems. They usually involve some kind of prediction.
Examples of Data Science Insights
Here are some examples of data science insights:
1. Customer behavior modeling – A retailer wants to predict whether a customer will buy something or not. To do this, it runs a survey asking questions about the customer’s demographics and past purchases. Based on this data, it builds a statistical model that predicts what actions will lead to future sales.
2. Fraud detection – An insurance company needs to detect fraudulent claims quickly and accurately. So it collects data from all of its customers and analyzes it using various machine learning methods.
3. Product recommendation – A retail chain wants to recommend products to customers based on their preferences. To do this, they collect data about their customers’ shopping habits and compare it with similar customers who bought the same items. Then they apply machine learning techniques to find correlations between these two groups.
4. Image recognition – A bank wants to recognize handwritten signatures on checks. To do this, the bank uses image recognition software to scan thousands of checks. Then it applies machine learning algorithms to classify the handwriting into one of several categories.
5. Natural language processing – A web search engine wants to parse natural language queries like “I want to go to Paris” into structured data. To do this, first it converts the query into a set of keywords. Then it applies machine translation techniques to translate those words into French. Finally, it searches for relevant websites.
6. Recommendation systems – A social network wants to suggest friends to people who have similar interests. To do this, a computer program scans the user’s profile and finds friends with similar tastes in music, movies, books, etc. The system then suggests them as potential friends.
7. Content analysis – A news site wants to determine which stories are most popular among readers. To do this, machine learning programs examine millions of articles and build models that predict which ones are more likely to be read.
8. Text mining – A publisher wants to extract key facts from a long document. For example, it might look at a patent application and extract the names of inventors, companies, and countries involved in the invention.
9. Sentiment analysis – A movie review website wants to figure out if someone likes a movie or not. It does this by scanning reviews for positive and negative terms and assigning a score to each review.
10. Topic modeling – A newspaper wants to understand how different topics affect reader interest. To do this, an algorithm looks at the frequency of words used in articles and assigns them to topics.
11. Visualization – A financial institution wants to visualize the relationship between different assets. To do this, analysts use visualization tools to create charts showing the correlation between different types of investments.
12. Voice identification – A call center wants to identify the caller’s voice so it can route calls appropriately. To do this, machines analyze the sound waves produced when people speak and assign them to a category.
13. Translation – A company provides machine translation services to its clients. They may provide translations from English to Spanish or German or any other language.
14. Speech recognition – A transcription service listens to audio files and transcribes the speech.
15. Fraud detection – A credit card company analyzes transactions to detect fraudulent activity.
16. Genealogy – An online family tree company allows users to enter details about their ancestors. A genealogist can then use this information to construct a digital family tree.
17. Personal finance management – A personal finance manager tracks all of a person’s expenses and categorizes them into various budgets. These budgets help a person track his spending and plan ahead.
18. Stock prediction – An investment firm predicts what stock prices will be in the future based on historical trends.
19. E-commerce recommendation – An e-commerce retailer recommends products to customers based on previous purchases.
20. Customer segmentation – A marketing department groups together similar customers based on demographics and past purchase behavior.
21. Employee scheduling – An employee scheduling software matches employees to shifts according to skills and availability.
22. Online advertising – Companies use algorithms to place ads on websites. They also use these same algorithms to target specific ads to individuals based on demographic traits such as age, gender, marital status, education level, occupation, income, and location.
23. Credit scoring – A credit scoring model uses information about a person’s payment history, employment, and housing to determine whether they are likely to pay back loans.
24. Recommendation system – A recommendation engine suggests items that a user may like based on prior purchases.
25. Ad targeting – An ad network places advertisements on webpages using keywords associated with certain products.
26. Content curation – An article directory aggregates content from across the internet and organizes it into categories. Users access curated content through search engines.
27. Social media analytics – Social media platforms collect data about how many views videos receive and which social networks people use.
28. Mobile app store optimization – Optimize your mobile apps for the Apple App Store and Android Market.
29. Predictive maintenance – Manufacturers use predictive maintenance to predict when equipment is going to fail before it does.
30. Manufacturing intelligence – Intelligent machines analyze production processes to improve efficiency.
31. Industrial automation – Machines take over repetitive tasks performed by humans.
32. Industry 4.0 – The Internet of Things (IoT) connects physical devices to the internet so that they can communicate and share data.
33. Quality control – Defect detection and prevention
34. Lean manufacturing – A lean manufacturing team removes waste from the manufacturing process.
35. Drug discovery – Pharmaceutical companies use various models to identify new drugs.
36. Clinical trials – Researchers conduct clinical trials to test out new drug treatments.
37. Pharmacovigilance – Pharmacists monitor adverse reactions to medications.
38. Medical device development – Engineers design medical devices such as pacemakers, defibrillators, and insulin pumps.
39. Precision medicine – Medicine focuses on treating each patient individually. It involves collecting detailed health information and using it to develop personalized treatment plans.
40. Patient engagement – Patients actively participate in their own healthcare. They ask questions, provide feedback, and engage with doctors and nurses.
41. Real-time monitoring – Healthcare providers use real-time monitoring to detect changes in patients care.
42. Driverless cars – Autonomous vehicles will eliminate human drivers.
43. Self-driving trucks – Trucking companies will replace truck drivers with self-driving trucks.
44. Telemedicine – Doctors diagnose patients remotely.
45. Wearable technology – Smartwatches and fitness trackers help users keep tabs on their daily activities.
46. Health informatics – Computer systems used in hospitals and doctor’s offices manage medical records.
47. Electronic health record – Physicians use electronic health records to store and organize patient data.
48. Personalized medicine – Medicine targets only those who need it.
How to get Data Science Insights?
1. Define Objectives for the data science project
You need to define objectives for the data science experiment before you begin collecting data. Otherwise, you could end up wasting a lot of time and resources.
Exploratory data science projects often involve statistics, probability, and machine learning.
Explanatory data science projects are different. They’re used to explain why something happened. They’re also called predictive analytics.
Predictive analytics involves modeling, forecasting, and optimization.
When you’re doing a predictive analytics project, you’re trying to predict future events based on past events. For example, you may be able to predict whether a customer will buy a product based on previous purchases.
In order to define objectives for a data science project, you first need to understand the problem you’re trying to solve. Then, you need to think about what questions you’d like answered.
For example, let’s say you’re working on a marketing campaign for a company. You want to see how effective certain ads are at driving traffic to the website.
To do this, you would need to collect data on which ad campaigns were successful and which ones weren’t.
Then, you’d analyze the data to find out which factors contributed to the success or failure of each campaign.
After you’ve collected enough data, you’d then try to answer the question: Which ad campaigns work better than others?
Once you’ve defined the objectives for your data science project, you can start collecting data.
2. Collect Data
Data collection is the first step in getting data science insights.
You’ll need to collect data from different sources.
There are two ways to collect data: manually and automatically.
Manual data collection involves visiting each source and collecting data manually. This method is slow and tedious.
Automatic data collection uses software to gather data from sources. The advantage of automatic data collection is that it saves time and effort.
To get started with automatic data collection, you’ll need to install a tool or develop an automated data collection tool.
3. Integrate & Manage Data
Data integration is the process of combining multiple sources of data together. The goal is to create a single view of the data that makes sense and allows you to analyze it easily.
There are three main types of data integration:
Replication is copying the same data from one location to another.
For example, you might replicate customer data from one database to another.
Ingestion is taking data from one type of storage system and converting it into another type of storage system.
For example, if you have a database storing customer data, you might ingest that data into a separate database where you can analyze it.
Transformation is changing the way data is stored.
For example, when you ingest data into a database, you may decide to change the way the data is stored.
As you can see, data integration is important. You need to think carefully about how you want to organize your data before you start integrating it.
When you integrate data, you can do several things with it.
4. Build a model
Data models are used to organize and analyze large amounts of data. They allow you to create visualizations and reports that help you understand the data better.
When building a data model, you’ll need to decide how much detail you want to go into. The more detailed you go, the more complex the model will become.
You also need to consider how much data you have available. If you have a large amount of data, then it may be easier to create a data warehouse instead of a data mart.
To build a data model, start by defining the structure of the data you want to store. Then, determine how you plan to access the data.
Once you’ve defined the structure of the data, you can begin modeling.
Building Data Visualization and Dashboards
Creating a dashboard is a great way to share data with colleagues and clients.
Dashboards can also help you communicate complex ideas clearly. They’re easy to build and they’re visually appealing.
With a little planning and creativity, you can turn any data into a beautiful dashboard.
Building Augmented Analytics
You may think that AI and machine learning are only useful for automating mundane tasks like data entry. But they can also help you analyze data and suggest insights and analyses for you.
For example, Google Now uses AI and machine learning to predict what you want to see next. It suggests articles based on your interests and location.
There are plenty of ways that AI and machine learning can help you analyze data and make suggestions. So, if you’re thinking about building a business around data analysis, now is the perfect time to start.
Analytics is a powerful tool that helps businesses understand their customers better. Embedding analytics into the apps and workflows that users already use makes it easier for them to access the data they need.
In this article, we discussed four types of data management: ingestion, transformation, integration, and augmentation.
We learned how these different types of data management affect the quality of your data. We also looked at some examples of each type of data management.
Finally, we discussed how to build dashboards and augmented analytics.
Thanks for reading.
Mohan Sangli, MD-India, Intuceo.