There are numerous tools associated with data science. If you are someone who is new to the field, you will be overwhelmed and intimidated by the number of data science tools. This article is for those who are looking to move to a data science field or have just started and want to know which tools can help them start their career as a data scientist.
Data science has become one of the most attractive tech profiles in the present day. You will find various news reports and articles stating how data science is the career that you should go for. However, most articles won’t give you an idea of how you can start your career and just throw some random tool names at you.
To learn more about data science, read our blog on – What is data science?
When you are just starting out to become a data scientist, this question will surely haunt you, “which tools should I learn first?” You will be inundated with various suggestions to learn a long list of tools. But when it comes to most data scientists, they all have a similar learning pattern. If you learn more than four to five of the common tools that we are going to mention here, you should have a good career start, earning a salary near to the industry median. You will also get a good idea of how to start your learning if you are wondering to take up professional data science courses.
Before starting with the tools, I hope you have a good concept of basic statistics and mathematics. Otherwise, just the knowledge of tools won’t be enough.
You don’t require any programming or coding skills to work with these tools. These data science tools offer a constructive way to define the entire Data Science workflow and implement it without any coding bugs or errors.
This article is divided into two segments –
Segment 1 – Basic Data Science and Machine Learning Tools
Segment 2 – Advanced Data Science and Machine Learning Tools
Segment 1 – Basic Data Science and Machine Learning Tools
Let us look at the various types of basic yet important tools used in data science. We will use the tool ecosystem as described in a report by O’Reilly, which will help us to understand the data science environment better.
Cluster 1 (Microsoft-Excel-SQL)
Corresponding preferred role: Business Intelligence
- Excel – The basic database and data analysis tool that most data scientists started out with. As you move to a higher role in data science, this tool won’t find any significance but it will still remain core to your work profile.
- VBA – It is essential for those who work with Excel most of the time. With VBA, you can move one step into the field of automation. You can create your own complex functions that otherwise cannot be done simply through Excel’s library functions.
- MS SQL Server/SQL – As your data grows, it might be difficult to store in Excel files as it can become slow and difficult to manage. MS SQL Server is an RDBMS (regional database management system) that offers good functionality in storing and retrieving data. SQL is the language that is used to manage data in an RDBMS.
- SAS/SPSS – SAS and SPSS are both analytical software suites with proprietary licenses. SAS has been developed by SAS Institute while SPSS has been developed by IBM. Due to the popularity of R and Python, SAS and SPSS usage have gone down. However, it is still used by many industries as they are easy to learn and implement.
Cluster 1 is popular among those who have just started their career in data science as it gives them a good learning curve. Excel, SAS, SQL, and VBA are all easy to learn and does not require extensive programming skills.
Cluster 2 and Cluster 3 (Hadoop-Python-R)f
The corresponding preferred role for:
Cluster 2 – Hadoop and Data Engineering
Cluster 3 – Machine Learning and Data Analytics
- Python/R – Both Python and R has been gaining popularity over the years as the go-to programming language for data scientists. R is hugely popular for its complex problem-solving capabilities. Python, on the other hand, is easier to learn if you have basic knowledge of object-oriented programming languages.
- Hadoop – With the growing importance of big data in analytics, Hadoop is an important tool for process huge datasets.
- Cassandra/MongoDB/NoSQL – RDBMS supports structured and predictable data. This becomes a disadvantage while working with big data. Cassandra, MongoDB, and NoSQL are useful for storing and managing unstructured data.
- NumPy/SciPy – They are libraries for Python which can be used for high-level mathematical, scientific, and technical computing. It is one of the widely-used tools in Python programming.
Corresponding preferred role: Data Visualisation
This is a relatively new cluster and is more or less centered on the Mac OS.
- MySQL – It is important for server-side queries and helpful in extracting datasets from a relational database.
Segment 2 – Advanced Data Science and Machine Learning Tools
RapidMiner is a data science tool that offers an integrated environment for various technological processes. This includes machine learning, deep learning, data preparation, predictive analytics, and data mining.
It allows you to clean up your data and then run it through a wide range of statistical algorithms. Suppose you want to use machine learning instead of traditional data science. In that case, the Auto Model will choose from several classification algorithms and search through various parameters until it finds the best fit. The goal of the tool is to produce hundreds of models and then identify the best one.
Once the models are created, the tool can implement them while testing their success rate and explaining how it makes its decisions. Sensitivity to different data fields can be tested and adjusted with the visual workflow editor.
Recent enhancements in RapidMiner include better text analytics, a greater variety of charts for building visual dashboards, and sophisticated algorithms to analyze time-series data.
RapidMiner is used widely in banking, manufacturing, oil & gas, automotive, life sciences, telecommunication, retail, and insurance. Some of the most popular RapidMiner products are –
Studio – Comprehensive data science platform with visual workflow design and full automation. Cost – $7,500 – $15,000 per user per year
Server: RapidMiner Server enables computation, deployment, collaboration, thereby enhancing the productivity of analytics teams
Radoop: RapidMiner Radoop eliminates the complexity of data science on Hadoop and Spark
Cloud: It is a cloud-based repository that enables easy sharing of information among different tools
DataRobot caters to data scientists at all levels and serves as a machine learning platform to help them build and deploy accurate predictive models in reduced time. This platform trains and evaluates 1000’s models in R, Python, Spark MLlib, H2O, and other open-source libraries. It uses multiple combinations of algorithms, pre-processing steps, features, transformations, and tuning parameters to deliver the best models for your datasets.
- Accelerates AI use case throughput by increasing the productivity of data scientists and empowering non-data scientists to build, deploy, and maintain AI
- Provides an intuitive AI experience to the user, helping them understand predictions and forecasts
- Can be deployed in a private or hybrid cloud environment using AWS, Microsoft Azure, or Google Cloud Platform
Tableau is a top-rated data visualization tool that allows you to break down raw data into a processable and understandable format. It has some brilliant features, including a drag and drop interface. It facilitates tasks like sorting, comparing, and analyzing, efficiently.
Tableau is also compatible with multiple sources, including MS Excel, SQL Server, and cloud-based data repositories, making it a popular data science tool for non-programmers.
- It allows connecting multiple data sources and visualizing massive data sets and find correlations and patterns
- The Tableau Desktop feature allows getting real-time updates
- Tableau’s cross-database join functionality allows to create calculated fields and join tables, and solve complex data-driven problems
- Tableau leverages visual analytics enabling users to interact with data and thereby helping them to get insights in less time and make critical business decisions in real-time
Minitab is a software package used in data analysis. It helps input the statistical data, manipulate that data, identify trends and patterns, and extrapolate answers to the existing problems. It is among the most popular software used by the business of all sizes
Minitab has a wizard to choose the most appropriate statistical tests. It is an intuitive tool.
- Simplifies the data input for statistical analysis
- Manipulates the dataset
- Identifies trends and patterns
- Extrapolates the answers to the existed problem with product/services
Trifacta is regarded as the secret weapon of data scientists. It has an intelligent data preparation platform, powered by machine learning, which accelerates the overall data preparation process by around 90%. Trifacta is a free stand-alone software offering an intuitive GUI for data cleaning and wrangling.
Besides, its visual interface surfaces errors, outliers, or missing data without any additional task.
Trifacta takes data as input and evaluates a summary with multiple statistics by column. For every column, it recommends some transformations automatically.
- Seamless data preparation across any cloud, hybrid, or multi-cloud environment
- Automated visual representations of data in a visual profile
- Intelligently assesses the data to recommend a ranked list of transformations
- Enables to deploy and manage self-service data pipelines in minutes
BigML eases the process of developing Machine Learning and Data Science models by providing readily available constructs. These constructs help in the classification, regression, and clustering problems. BigML incorporates a wide range of Machine Learning algorithms. It helps build a robust model without much human intervention, which lets you focus on essential tasks such as improving decision-making.
- Offers multiple ways to load raw data, including most Cloud storage systems, public URLs, or your own CSV/ARFF files
- A gallery of well-organized and free datasets and models
- Clustering algorithms and visualization for accurate data analysis
- Anomaly detection
- Flexible pricing
MLbase is an open-source tool used for creating large-scale Machine Learning projects. It addresses the problems faced while hosting complex models that require high-level computations.
MLBase consists of three main components:
ML Optimizer – It automates the Machine Learning pipeline construction
MLI – An experimental API focused on feature extraction and algorithm development for high-level ML programming abstractions
MLlib – ML library of Apache Spark
- A simple GUI for developing Machine Learning models
- Tests the data on different learning algorithms to detect the best accuracy
- Simple and easy to use, for both programmers and non-programmers
- Efficiently scales large, convoluted projects
Google Cloud AutoML
Google Cloud AutoML is a platform to train high-quality custom machine learning models with minimal effort and limited machine learning expertise. It allows building predictive models that can out-perform all traditional computational models.
Fig – How Google AutoML works?
- Uses simple GUI to train, evaluate, improve, and deploy models based on the available data
- Generates high-quality training data
- Automatically builds and deploys state-of-the-art machine learning models on structured data
- Dynamically detect and translate between languages
Data science has emerged as an advantageous career option for those interested in extracting, manipulating, and generating insights from the enormous data volumes. There is a massive demand for data scientists across industries, which has pulled many non-IT professionals and non-programmers to this field. These machine learning and data science tools will help non-programmers to manage their unstructured and raw data, and draw correct conclusions. To sharpen your data science skills further, you can take up any relevant data science e-course.