Data Science Predictive Analytics

INTRODUCTION

Data science encompasses all things data. It is the study of data, which includes collecting, storing, and analyzing data to gather business insights, extract meaningful information, and forecast future trends. Knowledge from this analysis helps businesses in timely decision making, which leads to quick business actions. Decoding humongous chunks of data is no easy task; however, data science is the tool that can help in demystifying it.

Predictive analytics, on the other hand, deals with the creation of predictive models to retrieve historical and current data for predicting future events, trends, or unknown outcomes. This is done using statistical methods such as data mining or machine learning.

Today, the terms data science and predictive analytics are being used interchangeably as the lines of distinction are blurring. Data science includes predictive analytics as one of its analytical methods, whereas predictive models are studied using data science.

Trip planning or travel tools often use data science and predictive models to provide a unique experience to customers. A case in point is Airbnb, which uses these techniques to solve a lot of rental issues and get information on pricing, customer preferences in terms of location or property features, and so on.

ALL ABOUT DATA

Data is generated so rapidly everywhere and is massive. This raw data can be classified as structured, unstructured, or semi-structured.

According to Webopedia, structured data refers to any data that is stored in a record or a file, such as relational databases (in rows and columns) or spreadsheets.

Semi-structured data is not stored in a database but does have some structure. They can be stored in a database by using some processes. For example, XML.

Unstructured data is not stored in a database nor has a predefined structure and hence, alternative ways or platforms are used for storing and managing them. For example, Word, PDF, Text, Video or Audio.

Big data deals with huge data sets that are too large and complex using conventional methods; and so, there are challenges in capturing, storing, querying, analyzing or visualizing data. Big data’s characteristics include the 4 Vs: velocity, veracity, volume, and variety.

Given the complexities of such different types of data, it is imperative to employ robust mechanisms to store, manage, and most importantly, analyze them to extract meaningful information. Data science and analytics provide the solution to this ever-growing problem of data.

Data science includes various technologies such as data mining, data storing, data archival, and data transformation, predictive analytics, and so on, which are used to make raw data more structured and meaningful.

WHAT IS DATA SCIENCE

Data science is multidisciplinary that includes a variety of techniques to gather insights from huge sets of raw data such as structured, semi-structured or unstructured data. Data science incorporates computer science, statistics, machine learning, data mining, predictive analytics and others to analyze massive data sets and determine solutions or predict future trends.

Data science focuses on asking the right questions and locating potential fields of analysis rather than specific trends.

Data science typically involves:

  1. Collecting data from various sources of raw data
  2. Analyzing data
  3. Extracting information
  4. Using extracted information to gather business insights, and build predictive models

WHAT IS PREDICTIVE ANALYTICS

Predictive analytics is derived from statistical sciences and is used to extract information from raw data and predict future trends or outcomes. These statistical methods include data mining, machine learning, and predictive modeling methods that process current and historical events.

Predictive analytics focuses on processing and statistically analyzing data sets after creating and organizing data.

A typical workflow of predictive analytics includes:

  1. Project definition: Project is defined in terms of scope, data sets, deliverables, and outcomes.
  2. Data collection: Data is collected from various sources, prepared, and cleaned.
  3. Data analysis: Includes analysis, testing, and validation of test models.
  4. Data modeling: Machine learning and statistical methods are employed to generate predictive models.
  5. Model deployment: The final accurate model is deployed and data collected on its performance.
  6. Model monitoring: Model is continuously monitored for its performance in real-time

DATA SCIENCE VS PREDICTIVE ANALYTICS

Now that we understand data science and predictive analytics, let’s compare the two technologies. Data science is a foundation for understanding and analyzing data, creating initial observations, and providing potential key insights. This is useful in the case of data modeling, machine learning, and artificial intelligence where the understanding of information is key. The addition of data analytics or predictive analytics to data science provides greater insights into trends, patterns, or outcomes that we don’t know which can be turned into quick business actions. So, in effect, they are 2 sides of the same coin.

TYPES OF ANALYTICAL MODELS

Data science models are commonly based on statistical models such as logistic or linear regression, neural networks, data visualization, and machine learning algorithms.

Regression analysis models include:

  • Cluster models: Used for customer segmentation, product or brand segmentation
  • Propensity models: Used for predictions such as customer behavior patterns
  • Collaborative filtering: Used for recommending products and services. For example, Amazon or Netflix use this kind of model There are 2 types of predictive models: parametric, and non-parametric. Parametric models provide more specific assumptions or predictions than non-parametric ones.

Some predictive models used in the industry are:

  • Random Forests
  • Neural Networks
  • Decision Trees
  • Generalized Linear Models (GLM)
  • Ordinary Least Squares
  • Multivariate Adaptive Regression Splines (MARS)
  • Logistic Regression

ADVANTAGES OF DATA SCIENCE AND PREDICTIVE ANALYTICS

There are many benefits of using data science and analytical models. Many businesses, companies, and industries are using these techniques to improve their operational efficiency and
provide better customer experience.

Advantages of data science techniques are:

  • Identifying abnormalities
  • Optimizing business processes
  • Increasing business efficiency
  • Customer targeting to provide better services
  • Predicting trends

Advantages of predictive analytics:

  • Planning workforce and preventing employee churn
  • Demand and business forecasting
  • Business intelligence gathering including market competition
  • Predicting product or customer behavior
  • Risk modeling

DATA SCIENCE AND ANALYTICS: MARKET TRENDS

Data science combined with analytics is growing rapidly and is the future. IBM predicts a growth of 28% by 2020 in data science, which is because of the increasing demand.

Another report by Market Research Future states that the global analytics market is expected to grow at a rate of 30.08% and will touch US$77.64 billion by 2023.

Predictive analytics tools are becoming more robust and many are open-source, spurring the need for hiring qualified individuals to handle such tools. The market for data science and analytics is highly competitive with major players such as SAP, Microsoft, Oracle, Amazon, and IBM leading the pack.

CONCLUSION

Technology is growing by leaps and bounds and so is data. To make sense of all the chaos surrounding data, two techniques or methods have been employed since some time but are now gaining immense popularity. Data science combined with analytics, specifically predictive models, will go a long way in ushering in business efficiency along with intelligent and actionable business decision making. The rise of AI has spurred the growth of highly niche analytical models coupled with machine learning. The potential of these techniques is exciting in the years to come with solutions driven by technology, rather than humans.

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