Top 10 tech tips and tools that data scientists should know

Data science is one of the fastest growing careers in the world. We have considerably more data than we do scientists capable of mining, processing it and making sense of the data we have already generated and are going to generate in the next few decades.

Businesses and governments need to unlock value from the masses of data we currently have. Studies have found that across 10 sectors, such as healthcare and financial services, there is $3.1 trillion worth in economic value that could be unlocked.

Some of the many challenges preventing us from unlocking the value trapped in data – not unlike creating petrol and diesel from fossil fuels trapped underground – is not enough people with the right skills, technical roadblocks and the work required to make raw data usable. Technology, and not enough people with the right skills mix, makes data science jobs so valuable.

Undertaking data science work also requires a computer with enough power and memory. Although using Python doesn’t take up that much power, if you are already using machine learning programs or into deep learning methodologies, you are going to need all of the processing power available. If you use a Mac for data science work, maybe uninstall apps on MacOS to liberate processing power and hardware space before embarking on a new project.

In this article, we cover ten tech tips for data scientists, including useful tools and apps, that should make data science work somewhat easier.

#1: Amazon Web Service (AWS) Lambada

Part of the Amazon Web Service environment, Lambada is a serverless, event-driven platform that makes it possible for data scientists to put a model into production. Create your theory, and then instead of waiting for developers to implement; a data scientist can test it on live or raw data, in a cloud-based environment. Using Lambada requires S3 access.

#2: Python

A pretty standard part of a data scientists toolkit. You don’t need to be a master programmer, but a working knowledge of Python is always going to make your work easier to handle.

#3: Feature engineering and Featuretools

An advantage in the deep learning field of this profession is the power to turn semi-structured datasets into something useful, into features. For example, text documents may not be every data scientist’s first choice as a source of raw material, but with Featuretools, you can automate a lot of the grunt work. Featuretools gives you the ability to define associations between tables, which then produces the models that the data can generate.

#4: Build micro-services with Flask

Flask is a micro web framework tool created in Python. Flask is useful for writing using Python and turning this information into web calls. It is equally using for building microservices, thereby creating shortcuts when working with large datasets.

#5: PySpark

Providing you are familiar with Python, PySpark is invaluable for scaling huge amounts of data. It can also be used for creating pipelines for machine learning and data platform ETLs. Well worth getting used to this language to simplify work with large tranches of data.

#6: RapidMiner

Preparing data is one of the most time-consuming aspects of data science work. It is one of the main reasons that corporate “big data” projects fail or take too long. A widely read and quoted New York Times article noted that data scientists spend – through no fault of their own – too much time doing “data janitorial work”, just to make raw data useable, useful and transferable. With a tool such as RapidMiner, it can make some of this work easier, quicker and automated.

#7: Amazon Athena

Another useful Amazon (AWS) tool is Athena; ideal as a repository for storing large datasets and tranches of data. Microsoft Azure and Google BigQuery are similar, in a number of ways, and competing platforms with a different suite of tools and capabilities.

#8: KNIME Analytics Platform

Another useful analysis tool for extracting as much useful information as you can from raw data. It is an open source application that makes it easier to create extraction and analysis apps around data sets.

#9: Google Fusion Tables

When it comes to data visualization, Google is onto a winner with Fusion Tables. First launched in 2009, when the sector wasn’t anywhere near as large and vibrant as it is now, it can be used to gather, visualize and share data tables.

#10: Microsoft Power BI

Launched more recently, in 2014, Power BI is Microsoft business analytics solution that creates visualizations and intelligence from raw data. Companies get their own dashboard and can quickly use this to turn raw data into something useful and more valuable.

Data science is a challenging and exciting career. We can do so much now with the knowledge and right tools to make the most of the opportunities data analysis represents.

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