Contributed by: Ramachandran
LinkedIn Profile: https://www.linkedin.com/in/ramachandran-murugan-481010102/
In this article, we cover top Data Science tools which every Data Scientist should know in order to become a successful Data Scientist in their career. This article is divided into seven sections. Each section contains the top tools in the respective section.
First of all, we must know the different Data Science tools, libraries and languages. Tools are software or hardware items used to carry out a specific purpose. Libraries are a set of already compiled programs to be used whenever necessary in our programs to write code faster and optimized way. Languages are a way to interact with computers to get things done. We use language by writing our code, and a set of libraries with the help of data science tools.
To write any programming language, we need an environment to write, compile and debug the code. This environment is called Integrated Development Environment. There are some most popular data science IDEs available in industry and they are programming language-specific.
Jupyter is a browser-based local python notebook environment where we can write code in a snippet of block(cell) and run the code in that block to see the output. We can store the python file <<filename..ipynb>>. The biggest advantage is the neat and clean comment section written in MarkDown. It supports all python ML libraries and stores variable values till we close the environment.
This is the python IDE provided by Google. All it is required to have this IDE is to have the Google account and Google Drive. It comes as an extension of google apps in the name of “collab”. We can easily search and install in our google apps. This is much more similar to Jupyter. It creates the separate Virtual Machine to run our python code in this browser-based IDE. Also the run-time machine config set up can be changed. Available run-time is None(default), GPU and TPU. For deep learning or advanced machine learning algorithms , we can choose GPU or TPU.
It is the exclusive IDE for R Programming Language. R has the capabilities of handling large datasets, maintaining workspace, compatible to install and to use the built-in plug-able libraries. It provides the clear data summary console for quick interpretation. Provides the sophisticated window to see all the objects and its data such as scalar, vectors and data frame values. Has the graphical console to see the visual output.
4. Cloud – Storage
The soil or the source for any data products is the data. In recent trends, most of the data is stored in the cloud-based storage for easy access and fast computing power.
Google Storage Bucket
This is offered by Google. Google Storage Bucket stores any kind of data and any format of data. It provides the rich set of API to upload, download and perform the general file operations in programming languages. There is a browser-based Google Cloud environment from which we can access the cloud files easily. The File objects can be made public, private and user-level right permission can be granted to access the file objects. Since this comes as part of Google Cloud Ecosystem Services, other google services can seamlessly access the Google Bucket.
Read also: Amazon Web Services Certification Roadmap
Amazon S3 Bucket
As a default option for cloud storage, Amazon S3 bucket is a highly secured data storage system which only owners of the credentials can access. Each file is considered as an object. Amazon provides an API to manipulate files. These APIs can be used with other programming languages to access the files in the cloud system. The scaling up of the system size is default, we do not need to monitor the disk size, it will be automatically scaled up.
Azure Blob Storage
Microsoft Azure has provided its own version of storing unstructured data in the scalable cloud environment. The data object files could be anything such as images, audios, log files, data files such as CSV, gzip and zip. There is a provision of taking backup and recovery and archiving. It follows the standard storage hierarchy. The top one is a storage account. A storage account can have any number of containers(consider containers as folders in a computer system). Each container can have any number of blobs(assume blobs as files in the hard-disk system). We can also access the files in three ways. One is from standard API, other is from the Azure Cloud Browser-Based Environment, the last one is installing Azure Storage Explorer in a local machine and accessing the blobs from it.
One of the primary roles of any data science role is to show his findings and to protect the data of the analytic to make better decision making in visual format such as using charts, graphs and tabular formatted etc. The Visualization tool plays a vital role in storytelling using data analysis or the output of machine learning models.
Tableau is a very visualization tool for the Business Intelligence stack. This Data Science tool has the ability to connect to more than 40 data sources such as ranging from CSV, excel to RDBMS servers like Mysql, Oracle through connectors. The most widely used functions are made as drag and drop functionality which makes the job pretty easier to build up the visualization product. Once the visual product is completed, it provides the means to securely share the visual data product to other authenticated users. It provides the predictive analytic function to find the linear prediction or time series forecasting.
This powerful Microsoft Visualization tool comes in cloud-based service, desktop version and mobile app version such iOS or Android devices. The visual product developed in Power BI can be deployed in an on-cloud or on-premises environment for the end-users. One considerable feature is the Natural Language Search which enables the users to question the Power BI to search for data in the Power BI to render in visualization reports in the form of charts, bars, and graphs. It encompasses the real-time data analysis capability of any events such as user clicks in particular websites or DB Spike in DB server.
This is another popular Visualization Tool which has its own unique features to stand separately from others. Data is fed and QlikView easily finds the association and relationships in the data entities. All data is stored in a memory slot, so many users can access the in-memory data objects noticeably faster giving the end-users smooth experience. The data manipulation tasks such as rolling up data, summarizing data are possible in run time. Apart from generic functionalities, it provides the business-specific use cases solution such as web analysis, campaign analysis, Asset Management and Geographical maps.
6. Big Data
Big data is the environment where the huge data with different formats of data such structured, unstructured and semi-structured data is stored. It also provides the computational engine to process this massive amount of data in a quick manner with fault tolerance. As a data scientist, we need to focus only on the data retrieving part from Big Data using SparkSQL, machine learning part using SparkML and the implementation part using any BigData languages such as PySpark or Scala.
Spark is the big data cluster computing framework. There are many entities in the Spark Ecosystem. The SparkSQL is used to retrieve and process structured data in a uniformed way irrespective of any data formats. Since SparkSql runs on top of Spark Environment, it takes advantage of query optimization plans, parallel processing, in-memory data processing. The data generally can be stored in a data frame. It also provides Spark SQL API which can be used directly by many languages such as java, scala or python.
For a big data environment, to process the data and gain insights out of data is possible only because of the clustered high processing distributed computing engine. Machine Learning is one step ahead where the computing speed and supported module are highly required. The Spark ML solves this issue. org.apache.spark.ml is the common library to do machine learning tasks inside a big data spark environment.
All data science operations such as-
- Extracting features(columns) from data
- Transforming the data from scale to other (such as MinMaxScalar, StandardScalar)
- Modifying features (like OneHotEncoder, Normalizer)
- Selecting important features from the original data frame
- Running statistical summary(correlations, hypothesis testing) and,
- Implementing the machine learning algorithms(regression, classification, clustering and dimensionality reduction) on top the refined and cleaned dataset
- Evaluating the algorithms performance using machine learning error metrics
- Model selection with tuning the hyper-parameters of machine learning algorithms(such as cross-validation, train -validation split)
- Providing NLP techniques such as Tokenizer, StopWordsRemover, N-Grams, TF-IDF, CountVectorizer
- Since it provides API, we can consume SparkML in PySpark or scala for implementation.
7. Automated Machine Learning
There are certain tasks in the Data Science pipeline such as data prepossessing, feature selection and extraction, feature engineering, algorithm selection, model selection and hyper-parameter tuning, which can be automated. The recent trend is to facilitate most of data science tasks so that data scientist can focus more on domain and addressing business problems
It provides easy access to non-data scientists to experiment machine learning in a step by step manner from data pre-processing to model deployment. H2O automates the entire Data Science projects steps in the form of automated workflow. Has the automatic exploratory data analysis and visualization capabilities, best-in-class automatic machine learning for transnational, text, time-series and image data, automatic model debugging – documentation, transparency, interpretation and explanation, automatic pipelines for production deployment in Python and Java, ready to set up and install on-premise or on cloud.
H2O contains the GPU based ensemble algorithms for fast computation with performance. Supports TensorFlow deep learning models, IBM Power support, HDFS, S3, Excel, Snowflake, Azure, GCP, BigQuery, Minio connectors and Automatic Documentation. Enables multi-node, multi-user support, model lifetime management and project management. The usability improvements made are visual model debugging and manual control of feature engineering pipelines. The Rest API based interface can be consumed by python, R or WebUI
The AutoML with the best application interface has the ability to load data from multiple data sources, analyzing the statistics summary and vital information, code access control, validating the model performance, suggestions for next steps and recommender for parameter selection and change. This Data Science tool supports third-party libraries and tools such as H2O and Weka. It provides seamless access to NoSql DB such as MongoDB and Cassandra and access to Cloud storage like Dropbox and Amazon S3. The deep learning implementation is very much easier and it supports Neural Network and Multi-layer Perceptron . The key feature in deep learning is its automatic optimization of both learning rate and size adjustment of neural networks during training.
This is one of the AutoML tools which provides real-time prediction such as Stock prediction, anomaly detection of faults in the system. The rich and sophisticated API can be easily integrated with any other applications or languages. Offers tailor-made customization for the users such as combined predictions capabilities and voice processing abilities. BigML can be deployed in cloud-hosted or on-premises. BigML supports the windows, Mac and browser-based device compatibility. We can easily plug models into web, mobile or IoT applications or services such as Google Sheets, Amazon Echo. The company claims that it is the first API company providing a rich set of bindings & libraries available for all popular languages, including Python, Node.js, Ruby, Java, Swift. If a company has more secure data, BigML offers two private deployment options that can run on cloud providers of our choice, or own infrastructure on commodity servers.
Here were the top Data Science Tools that you must remember! If you found this interesting and wish to learn more about Data Science tools or how to implement them, you can upskill with Great Learning’s PGP- Data Science and Engineering Course today!0