Data science continues to evolve as one of the most promising and in-demand career paths and services. It is a forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. Companies collect a ton of data, and much of the time it’s neglected or underutilized. This data, through meaningful information extraction and discovery of actionable insights, can be used to make critical business decisions and drive significant business change. It can also be used to optimize customer success and subsequent acquisition, retention, and growth. This is achieved with data science and today we are going to discuss what Data Science is and what are the most common Data Science use cases.
What is Data Science?
Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems, extracting knowledge and insights from many structural and unstructured data.
Data science is a “concept to unify statistics, data analysis and their related methods”. Data science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.
Data science lets you:
- Find the leading cause of a problem by asking the right questions
- Perform exploratory study on the data
- Model the data using various algorithms
- Communicate and visualize the results via graphs, dashboards, etc.
Data Science is about identifying relevant questions, collecting data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.
Here is a list of the most common data science deliverables
- Prediction (predict a value based on inputs)
- Classification (e.g., spam or not spam)
- Recommendations (e.g., Amazon and Netflix recommendations)
- Pattern detection and grouping (e.g., classification without known classes)
- Anomaly detection (e.g., fraud detection)
- Recognition (image, text, audio, video, facial, …)
- Actionable insights (via dashboards, reports, visualizations, …)
- Automated processes and decision-making (e.g., credit card approval)
- Scoring and ranking (e.g., FICO score)
- Segmentation (e.g., demographic-based marketing)
- Optimization (e.g., risk management)
- Forecasts (e.g., sales and revenue)
Data Science pillars
1. Machine Learning
Machine learning is the backbone of data science.
2. Modeling
Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
3. Statistics
Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.
4. Programming
Programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.
5. Databases
Understand how databases work, how to manage them, and how to extract data from them.
Data science use cases
Nearly any business process can be made more efficient through data-driven optimization, and nearly every type of customer experience (CX) can be improved with better targeting and personalization.
With Data Science you can understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision.
In Gartner’s recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies, and are investing accordingly.
The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.
“Information is the oil of the 21st century, and analytics is the combustion engine.”
— Peter Sondergaard
Here is the list of 15 best data science tools
This tool is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing. It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.
This tool specializes in statistical operations. It is used by large organizations to analyze data. SAS uses base SAS programming language which for performing statistical modeling. It is widely used by professionals and companies working on reliable commercial software. While SAS is highly reliable and has strong support from the company, it is highly expensive and is only used by larger industries.
BigML, it is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. For example, it can use this one software across for sales forecasting, risk analytics, and product innovation. BigML specializes in predictive modeling.
D3.js, a Javascript library allows you to make interactive visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser. Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.You can combine this with CSS to create illustrious and transitory visualizations that will help you to implement customized graphs on web-pages.
MATLAB facilitates matrix functions, algorithmic implementation and statistical modeling of data. In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing. This makes it a very versatile tool for Data Scientists as they can tackle all the problems, from data cleaning and analysis to more advanced Deep Learning algorithms. It also helps in automating various tasks ranging from extraction of data to re-use of scripts for decision making.
Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence. The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with visualizations, you can also use its analytics tool to analyze data. Tableau comes with an active community and you can share your findings on the online platform. Getting started is as easy as dragging and dropping a dataset onto the application while setting up filters and customizing the dataset is a breeze.
It offers comprehensive end-to-end analytics, advanced data calculations, effortless content discoveries, fully protected system that reduces security risks to the bare minimum.
It lets you consolidate, search, visualize, and analyze all your data sources with just a few clicks.
It is a visual analytics platform that supports a range of use cases such as centrally deployed guided analytics apps and dashboards, custom and embedded analytics, and self-service visualization as well, all within a scalable and governed framework. Users are also allowed to create interactive data visualizations to present the outcome in storytelling form with the help of drag and drop interface. Qlik Sense offers a centralized hub that allows every user to share and find relevant data analyses. The solution is capable of unifying data from various databases, including IBM DB2, Cloudera Impala, Oracle, Microsoft SQL Server, Sybase, and Teradata. Key strengths of Qlik sense are: associative model, interactive analysis, interactive storytelling and reporting, robust security, big and small data integration, centralized sharing and collaboration, hybrid multi-cloud architecture.
Rapid Miner is a data science platform developed mainly for non-programmers and researchers for quick analysis of data. The user has an idea in their mind, and easily creates processes, import data into them, run them over and throw a prediction model. RapidMiner claims to make data science teams more productive through a lightning-fast platform that unifies data prep, machine learning, and model deployment. It is a platform with Code-optional with guided analytics With more than 1500 function, it allows users to automate predefined connections, built-in templates, and repeatable workflows.
DataRobot offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. It aims to automate the end-to-end process of building, deploying and maintaining your AI.
Searching relevant information to be analyzed can be time-consuming and unproductive, resulting in recreating assets that already exist within the organization since they can be challenging to find. Alteryx allows the user to quickly and easily find, manage, and understand all the analytical information that resides inside the organization. The tool accelerates the end-to-end analytic process and dramatically improve analytic productivity and information governance, generating better business decisions for all. The tool allows the user to connect to data resources like Hadoop and Excel, bringing them into Alteryx workflow and joining them together. Regardless of data being structured or unstructured, the tool allows creating the right data set for analysis or visualization by using data quality, integration, and transformation tools.
Alteryx offers a quick-to-implement, end-to-end analytics platform that empowers business analysts and data scientists alike to break data barriers and deliver game-changing insights that are solving big business problems. The Alteryx platform is self-serve, click, drag-and-drop for hundreds of thousands of people in leading enterprises all over the world.
Paxata is the pioneer in intelligently empowering all business consumers to transform raw data into ready information, instantly and automatically, with an intelligent, self-service data preparation application built on a scalable, enterprise-grade platform powered by machine learning.
Trifacta’s mission is to create radical productivity for people who analyze data. They are deeply focused on solving the biggest bottleneck in the data lifecycle, data wrangling, by making it more intuitive and efficient for anyone who works with data. Their main product is the Wrangler. Wrangler helps data analysts clean and prepare messy, diverse data more quickly and accurately. Simply import your datasets to Wrangler and the application will automatically begin to organize and structure your data. Wrangler’s machine learning algorithms will even help you to prepare your data by suggesting common transformations and aggregations. When you’re happy with your wrangled dataset, you can export the file to be used for data initiatives like data visualization or machine learning.
LumenData is a leading provider of Enterprise Information Management solutions with deep expertise in implementing Data persistence layers for data mastering, prediction systems, and data lakes as well as Data Strategy, Data Quality, Data Governance, and Predictive Analytics. Its clients include Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, the University of Texas at Dallas, Weight Watchers, Westpac, and many other data-dependent companies.
The tool is known to yield software solutions for data preparation, integration, and application integration. Real-time statistics, easy scalability, efficient management, early cleansing, faster designing, better collaboration, and native code are the advantages of this tool.
Mozenda is an enterprise cloud-based web-scraping platform. It helps companies collect and organize web data most efficiently and cost-effectively possible. The tool has a point-to-click interface and user-friendly UI. The tool has two parts- an application to build the data extraction project and Web Console to run agents, organize results, and export data. It is easy to integrate and allows users to publish results in CSV, TSV, XML, or JSON format.
If you need any help with Data Science projects , you can count on us, we are here to help!
And if you would like to suggest other Data Science tools, feel free to mention them in the comments section below!
Originally published at https://apiumhub.com on February 25, 2021.
