Best tools and techniques for your Data Science and Advanced Analytics

Best tools and techniques for your Data Science and Advanced Analytics

Data scientists are inquisitive and often seek out new tools that help them find answers. They also need to be proficient in using the tools of the trade, even though there are dozens upon dozens of them. Overall, data scientists should have a working knowledge of statistical programming languages for constructing data processing systems, databases, and visualization tools.

 Here is the list of best data science tools that most of the data scientists used.

1. SAS

It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language for performing statistical modeling. It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you, as a Data Scientist, can use for modeling and organizing their data. While SAS is highly reliable and has strong support from the company, it is expensive and is only used by larger industries. Also, SAS pales in comparison with some of the more modern tools which are open-source. Furthermore, there are several libraries and packages in SAS that are not available in the base pack and can require an expensive up-gradation.

 2. BigML

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. BigML provides a standardized software using cloud computing for industry requirements. Through it, companies can use Machine Learning algorithms across various parts of their company. For example, it can use this one software across for sales forecasting, risk analytics, and product innovation. BigML specializes in predictive modeling. It uses a wide variety of Machine Learning algorithms like clustering, classification, time-series forecasting, etc.

BigML provides an easy to use web-interface using Rest APIs, and you can create a free account or a premium account based on your data needs. It allows interactive visualizations of data and will enable you to export visual charts on your mobile or IoT devices.

Furthermore, BigML comes with various automation methods that can help you automate the tuning of hyperparameter models and even automate reusable scripts.

3. Apache Spark

Apache Spark, or simply Spark, 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. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help data scientists make powerful predictions with the given data.

Spark does better than other Big Data Platforms in its ability to handle streaming data. It means that Spark can process real-time data as compared to other analytical tools that process only historical data in batches. Spark offers various APIs that are programmable in Python, Java, and R. The most powerful conjunction of Spark is with Scala programming language, which is based on Java Virtual Machine and is cross-platform in nature.

Spark is highly efficient in cluster management, which makes it much better than Hadoop as the latter is only used for storage. It is this cluster management system that allows Spark to process application at high speed.

4.TensorFlow

TensorFlow has become a standard tool for Machine Learning. It is widely used for advanced machine learning algorithms like Deep Learning. Developers named TensorFlow after Tensors which are multidimensional arrays. It is an open-source and ever-evolving toolkit which is known for its performance and high computational abilities. TensorFlow can run on both CPUs and GPUs and has recently emerged on more powerful TPU platforms. This gives it an unprecedented edge in terms of the processing power of advanced machine learning algorithms. 

Due to its high processing ability, Tensorflow has a variety of applications such as speech recognition, image classification, drug discovery, image and language generation, etc. For Data Scientists specializing in Machine Learning, Tensorflow is a must-know tool.

5.Tableau                     

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. Tableau's most important aspect is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau can visualize geographical data and for plotting longitudes and latitudes in maps.

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. While Tableau is enterprise software, it comes with a free version called Tableau Public.