Top 10 Data Science And Machine Learning Tools For Non-Programmers
With the continuous generation of data, the need for Machine Learning and Data Science has increased exponentially. This demand has pulled a lot of non-IT professionals into the field of Data Science. This blog on Data Science and Machine Learning For Non-Programmers is specifically dedicated to non-IT professionals who are trying to make a career in Data Science and Machine Learning without the experience of working on programming languages.
Here’s a list of topics that will be covered in this article:
- Introduction To Data Science And Machine Learning
- Data Science vs Machine Learning
- Data Science and Machine Learning Tools For Non-Programmers
Introduction To Data Science And Machine Learning
Data Science and Machine Learning have drawn professionals from all backgrounds. The reason for this demand is the fact that currently, everything around us runs on data.
Data is the key to grow businesses, solve complex real-world problems and build effective models that will help in risk analysis, sales forecasting and so on. Data Science and Machine Learning is the key to finding solutions and insights from data.
Before we go any further, let’s make one thing clear. Data Science and Machine Learning are not the same. People often tend to get confused between the two. To make things clear let’s understand the difference:
Data Science vs Machine Learning
Data Science is an umbrella term that covers a wide range of domains, including Artificial Intelligence (AI), Machine Learning and Deep Learning.
Let’s break it down:
Artificial Intelligence: Artificial Intelligence is a subset of Data Science which allows machines to simulate human-like behavior.
Machine Learning: Machine learning is a sub-field of Artificial Intelligence which provides machines the ability to learn automatically & improve from experience without being explicitly programmed to do so.
Deep Learning: Deep Learning is a part of Machine learning that uses various computational measure and algorithms inspired by the structure and function of the brain called Artificial Neural Networks (ANN).
Therefore, Data Science revolves around the extraction of insights from data. To do so, it uses a number of different technologies and methods from various disciplines, like Machine Learning, AI and Deep Learning. A point to note here is that Data Science is a very vast field and does not exclusively rely on these techniques.
Now that you know the basics, let’s understand the benefits of using Data Science and ML tools.
Why use Data Science and Machine Learning Tools?
Here’s a list of reasons that will help you understand the benefits of using Data Science tools:
- You don’t require programming skills to use Data Science and Machine Learning Tools. This is especially advantageous to Non-It professionals who don’t have experience with programming in Python, R, etc.
- They provide a very interactive GUI which is very easy to use and learn.
- These tools provide a very constructive way to define the entire Data Science workflow and implement it without worrying about any coding bugs or errors.
- Given the fact that these tools don’t require you to code, it’s faster and easier to process data and build strong Machine Learning models.
- All the processes involved in the workflow are automated and require minimal human intervention.
- Many data-driven companies have adapted to Data Science tools and often look for professionals who are able to handle and manage such tools.
Now that you know the advantages of using Data Science and Machine Learning tools, let’s take a look at the top tools that any non-programmer can use:
Data Science And Machine Learning Tools
In this section, we’ll discuss the best of the Data Science and Machine Learning tools for non-programmers. Please note that this list is in no particular order.
Here’s a list of Data Science and Machine Learning tools that are discussed below:
- RapidMiner
- DataRobot
- BigML
- MLBase
- Google Cloud AutoML
- Auto-WEKA
- IBM Watson Studio
- Tableau
- Trifacta
- KNIME
RapidMiner
It’s no surprise that RapidMiner made it to this list. One of the most widely used Data Science and Machine Learning tools preferred by not only beginners who are not well equipped with programming skills but also by experienced Data Scientists. RapidMiner is the all in one tool that takes care of the entire Data Science workflow, from data processing to data modeling and deployment.
If you’re from a non-technical background, RapidMiner is one of the best tools for you. It provides a strong GUI that only requires to dump the data, there is no coding required. It builds predictive models and Machine Learning models that use convoluted algorithms to achieve precise outputs.
Here’s are some of its key features:
- Provides a powerful visual programming environment.
- Comes with an in-built RapidMiner Radoop that allows you to integrate with Hadoop framework for data mining and analysis.
- It supports any data format and performs top-class predictive analytics by expertly cleaning the data
- Uses programming constructs that automate high-level tasks such as data modeling
DataRobot
DataRobot is an automated Machine Learning platform that builds precise predictive models to perform extensive data analysis. It is one of the best tools for data mining and feature extraction. Professionals with less programming experience go for DataRobot because it is considered to be one of the most simple tools for data analysis.
Like RapidMiner, DataRobot is also a single platform that can be used to build an end to end AI solution. It uses the best practices in creating solutions that can be used to model real-world business cases.
Here are some of its key features:
- Automatically identifies the most significant features and builds a model around these features.
- Runs the data on different Machine Learning models to check which model provides the most accurate outcome
- Extremely fast in building, training, and testing predictive models, performing text mining, data scaling and so on.
- Can run large scale Data Science projects and incorporate model evaluation methods such as parameter tuning and so on.
BigML
BigML eases the process of developing Machine Learning and Data Science models by providing readily available constructs that help in classification, regression and clustering problems. It incorporates a wide range of Machine Learning algorithms and helps to build a strong model without much human intervention, this lets you focus on important tasks such as improving decision making.
Here are some of its key features:
- A comprehensive Machine Learning tool that supports the most complex Machine Learning algorithms, involving full support for Supervised and Unsupervised learning, including anomaly detection, association mining and so on.
- Provides a simple web interface and APIs that can be set up in a fraction of the time it takes for traditional systems.
- Creates visually interactive predictive models that make it easy to find correlations among the features in the data
- Incorporates bindings and libraries of the most popular Data Science languages such as Python, Java, etc
MLBase
MLbase is an open-source tool that is one of the best platforms used to create large scale Machine Learning projects. It addresses the problems faced while hosting complex models that require high-level computations.
MLBase uses three main components:
- ML Optimizer: The main purpose of the optimizer is to automate the Machine Learning pipeline construction.
- MLI: The MLI is an API that is focused on developing algorithms and performing feature extraction for high-level computations
- MLlib: It is Apache Spark’s very own Machine Learning library that is currently supported by the Spark community.
Here are some of its key features:
- Provides a simple GUI for developing Machine Learning models
- It learns and tests the data on different learning algorithms to find out which model gives the best accuracy
- Non-programmers can easily scale Data Science models due to the ease and simplicity of the tool
- It can scale large, convoluted projects much effectively than any traditional system
Google Cloud AutoML
Cloud AutoML is a platform of machine learning products that allows professionals with limited experience in Data Science to train high-end models specific to their business needs. One of the best Machine Learning platforms with over 10 years of trained Google Research constructs to help you build predictive models that out-perform all traditional computational models.
Here are some of its key features:
- Professionals with minimal expertise in the field of ML can easily train and build high-level Machine learning models specific to their business needs.
- A fully-fledged integration with many other Google Cloud services that helps in data mining and data storage.
- Generates REST API while making predictions about the output
- Provides a simple GUI to create custom ML models that can be trained, tested, improved, and deployed through the same platform.
Auto-WEKA
Auto-WEKA is an open-source GUI based tool which is ideal for beginners since it provides a very intuitive interface for performing all Data Science related tasks.
It supports automated data processing, EDA, Supervised and Unsupervised learning algorithms. This tool is perfect for newbies who are just getting started with Data Science and Machine Learning. It has a community of developers, who were kind enough to publish tutorials and research papers about using the tool.
Here are a few features of the tool:
- WEKA provides a huge range of Machine Learning algorithms for classification, regression, clustering, anomaly detection, association mining, data mining and so on.
- Provides an interactive graphical interface to perform data mining tasks, data analysis and so on.
- Allows developers to test their models on a varied set of possible test cases and helps in providing the model that gives the most precise output.
- It also comes with a simple, yet intuitive CLI (Command Line Interface) to run basic commands.
IBM Watson Studio
We’re all aware of how much IBM has contributed to the AI-driven world. Like most services provided by IBM, IBM Watson Studio is an AI-based tool used for extensive data analysis, Machine Learning, Data Science and so on.
It aids organizations to ease the process of data analysis and takes care of the end-to-end workflow, from data processing to deployment. It is one of the most recognized tools for Data Science and Machine Learning in the market.
Here are some key features of IBM Watson Studio:
- Provides support to perform data preparation, exploration and modeling within a span of a few minutes and the entire process is automated.
- Supports multiple Data Science languages and tools such as Python 3 Notebooks, Jython scripting, SPSS Modeler, and Data Refinery
- For coders and Data Scientists, it offers integration with R Studio, Scala, Python and so on.
- Uses the SPSS Modeler that provides the drag-and-drop functionality for exploring data and building strong Machine Learning models.
Tableau
Tableau is the most popular data visualization tool used in the market. It allows you to break down raw, unformatted data into a processable and understandable format. Visualizations created by using Tableau can easily help you understand the dependencies between the predictor variables.
Though Tableau is mainly used for visualization purpose, it can also perform data analysis and exploration.
Here are a few features of Tableau:
- It can be used to connect to multiple data sources, and it can visualize massive data sets to find correlations and patterns.
- The Tableau Desktop feature allows you to create customized reports and dashboards to get real-time updates
- Tableau also provides cross-database join functionality that allows you to create calculated fields and join tables, this helps in solving complex data-driven problems.
- An intuitive tool, that uses the drag-and-drop feature to derive useful insights from data and perform data analysis
Trifacta
Trifacta is an enterprise data wrangling platform for meeting your business needs. Understanding exactly what is in your data and how it will be useful for different analytic explorations is the key to identifying the value of the data. Trifacta is considered the best tool for performing data wrangling, cleaning, and analysis.
Here are a few features of Trifacta:
- Connects to multiple data sources irrespective of where the data lives
- Provides an interactive GUI for understanding the data to not only derive the most significant data but also to remove unnecessary or redundant variables.
- Provides visual guidance, Machine Learning workflows, and feedback that will guide you in assessing the data and performing the needed data transformation.
- Continuously monitors the inconsistencies in data and removes any null values or missing values and makes sure data normalization is performed to avoid any biases in the output.
KNIME
KNIME is an open-source data analytics platform aimed at creating out of the box Data Science and Machine Learning applications. Building Data Science applications involves a series of tasks that are well managed by this fully automated tool. It provides a very interactive and intuitive GUI which makes it easy to understand the whole Data Science methodology.
Here are a few features of KNIME:
- It can be used to build end-to-end Data Science workflows without any coding, you just have to drag-and-drop the modules.
- Provides support to embed tools from different domains, including scripting in R, Python and it also provides APIs to integrate with Apache Hadoop.
- Compatible with various data sourcing formats including simple text formats, such as CSV, PDF, XLS, JSON, and unstructured data formats including images, GIFs, etc.
- Provides full-fledged support for performing data wrangling, feature selection, normalization, data modeling, model evaluation and even allows you to create interactive visualizations.
Now that you know the top tools for Data Science and Machine Learning for non-programmers, I’m sure you’re curious to learn more. If you wish to check out more articles on the market’s most trending technologies like Python, DevOps, Ethical Hacking, then you can refer to Edureka’s official site.
Do look out for other articles in this series which will explain the various other aspects of Data Science.
2.Math And Statistics For Data Science
9.Introduction To Machine Learning
12.How To Create A Perfect Decision Tree?
13.Top 10 Myths Regarding Data Scientists Roles
15.Data Analyst vs Data Engineer vs Data Scientist
16.Types Of Artificial Intelligence
17.R vs Python
18.Artificial Intelligence vs Machine Learning vs Deep Learning
20.Data Analyst Interview Questions And Answers
21.Top 5 Machine Learning Algorithms
22.Top 10 Machine Learning Frameworks
23.Statistics for Machine Learning
25.Breadth-First Search Algorithm
26.Linear Discriminant Analysis in R
27.Prerequisites for Machine Learning
28.Interactive WebApps using R Shiny
29.Top 10 Books for Machine Learning
Originally published at https://www.edureka.co on July 31, 2019.
