Common Tools used in Data Science Spectrum
In the previous article, Common Data Capturing Types and Tools, we got an understanding of different tools and ways of capturing data. Now if we come back again to the Data Science Spectrum, which tools do you think are required to start working on each domain that we’re seeing here? Should you purchase licenses for a tool or opt for an open-source, one for enabling your machine learning project? This is just a microcosm of the questions you will face as a business leader.
Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.
Now we’ll take up each level of the spectrum and see popular tools that are used in the industry to perform those tasks. We should start from the lower end of the spectrum.
Reporting and BI Tools
Reporting and Business intelligence enables an organization to understand trends and patterns. So as to take crucial strategy decisions, the first tool as you might’ve guessed is Excel.
You’d offer a diverse range of options, including pivot tables and charts that let you do analysis in double-quick time.
The next one is Quick view lets you consolidate search, visualize and analyze all your data sources. We just a few clicks it’s easy and intuitive to learn and that has been the primary factor in its rise to popularity.
Tableau is among the most popular data visualization tools in the market today. It’s capable of handling large amounts of data and even offers Excel calculation functions and parameters. Tableau is liked because of its neat dashboard and stories interface. It lets you inculcate multiple reports in one single screen, a very popular feature at the boardroom level.
Micro strategies is another BI tool that supports dashboards, scorecards alerts, and automated report distribution.
PowerBI is a Microsoft offering in the BI space. It was built to integrate with Microsoft’s technology so if your organization is a SharePoint or SQL database user, you and your team will love power BI. And of course, it also connects to non-Microsoft software as well.
And the finer tool we’ll look at in this space is Google analytics.
Now you might be wondering why? because it doesn’t exactly fit well with the previous four tools have you seen so far. Well, digital marketing is playing a major role in transforming businesses and there’s no better tool out there than Google analytics to analyze how your digital efforts are doing. So if you are not using Google Analytics to track your digital efforts, I strongly encourage you to do this.
Predictive Modeling and ML Tools
Now it’s time to turn to Predictive Analytics and Machine Learning tools. This is where the bread and butter of most data science professionals come from. You might see an overlap of tools in these two domains, but this is because their tasks tend to be similar in nature and often performed by the same people. We’ll start off with the most dominant language in the industry these days, Python-
Because of its flexibility, ease of use, and of course, open-source nature. It has gained rapid popularity and acceptance in the ML community. If your organization is just starting your AI and ML journey, we would highly recommend going with Python as the primary language.
Next up is R. It is another well-respected and popular language.
Till recently it was well ahead of Python, but that ground has swiftly been eaten up by the latter. R has a thriving and incredibly useful and supportive community. And it comes with a plethora of packages and libraries that support most machine learning tasks.
And here’s yet another tool from Apache.
Spark was open-sourced by UC Berkeley in 2010 and has since become one of the largest open source communities in big data. It’s commonly called the Swiss army knife of big data analytics because of its multiple advantages, including flexibility, speed, computational power among other things.
Julia is an upcoming language that has been touted as a successor to Python. I personally think it’s way too early to make any judgments, but Julia is a useful option to consider.
Jupiter notebooks are widely used for coding in Python. While these notebooks do of course support other languages as well but python is predominantly the favorite among data scientists. These notebooks are used by data teams in companies like Netflix, Facebook, Uber among others.
Now all their tools in this slide are true Open Source tools that you’ve seen so far. You don’t need to pay for them or purchase any extra licenses. They have active communities which maintain and release updates on a very regular basis. Now let’s check out a few tools that are not open source, but are still acknowledged as industry leaders.
SAS, you must have heard of SAS. It’s a very powerful tool. It’s especially prevalent and commonly used in the banking and financial sector globally. It’s easy to pick up for folks who know SQL and has their own certification, which is not easy to get. So anyone holding that certification will have good experience in knowledge of the tool. SAS has a very high market share in private organizations like American Express, JP Morgan, Mu Sigma, Royal Bank of Scotland, etc.
SPSS or the statistical package for the social sciences was acquired by IBM in 2009. SPSS offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open-source extensibility, integration with big data, and seamless deployment into applications. In short, are a very useful tool.
And the next up here is MATLAB. Even though does not see a lot of adoption in organizations, is very popularly used in Academia and research divisions.
Common frameworks for Deep Learning
Deep learning is another aspect that business leaders should be aware of, because of its expanding usage and applications. But as I mentioned earlier, deep learning requires significant computational resources. And to power and utilize these resources it needs special frameworks. I want to take a moment here to emphasize the advantages of enabling your team with GPUs and TPUs.
GPU or Graphical Processing Unit is a massive upgrade on CPUs or the Central Processing Unit. GPUs help train and execute your models at a much quicker rate than a CPU. On the other side, TPUs even more powerful than GPUs, launched by Google in 2018. They’re still making their way into the deep learning consciousness, but it won’t be long before they see a very, very quick adoption rate.
In order to utilize the part of deep learning, we need certain frameworks without going into too much detail let’s look at a few of them here-
Google’s TensorFlow is easily the most commonly used in the machine learning community.
PyTorch is considered to be an upgrade on TensorFlow and has only recently become a popular research framework. It was developed by researchers at Facebook. And the remaining two famous frameworks are Keras and Caffe.
I want to reiterate here, there, you don’t necessarily need to remember all these names, but it’s always helpful for your team if their leaders are aware of what’s trending in the market. And that brings us to the final section of our data science spectrum, Artificial Intelligence.
If you haven’t heard of Automated Machine Learning or AutoML yet, then there is no better time than now to educate yourself. It’s an entire industry by itself. Thanks to tools like AutoKeras which by the way, is open source:
Google’s Cloud AutoML:
H2O’s Driverless AI:
AutoML is expected to be the next big thing in the AI and ML community. It aims to eliminate or reduce the technical side of things so that business leaders like yourself can use it to make strategic decisions. With each update these tools are now able to automate almost the entire ML Pipeline, thus enabling you to focus on what’s important, making Decisions!
But autoMLl takes care of one aspect of AI. What about the other facets like hardware and cloud solutions? To answer that have a look at this-
As you can see here, there are multiple things to consider when building an AI solution, we’ll use the example of a self-driving car. In order to collect the data generated by the car and integrated it into a mobile application, you will need an app developer. You will also need automobile and mechanical engineering experts for designing and building the car. Further deploying your system in the cloud requires professionals well versed with cloud technology and so on and so forth.
So as you might’ve gathered, building an AI system is no walk in the park. It’s a multidimensional effort. And finally, let’s wrap up this reality of the look at the power of GitHub.
It is the world’s largest open-source platform that brings together developers from around the world to discover and share their code. But more importantly, what this means for your business is that your developers and data scientists can use it to collaborate on projects. You even have the option to make projects private, if you wish not to share them with the world, it really is the ideal companion, your team’s AI project.
In this article, we got an understanding of various tools and technologies used in the various fields of the data science spectrum.
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If you have any questions, let me know in the comments section!