“Who is a mason without his tools?” Just like a worker can’t finish his errands without his toolbox, similarly, a data science professional can’t reform numbers without his tools.
Data science tools like Python, Hadoop, Tableau, TensorFlow etc. are in demand for data scientists and other data science professionals. Are you aware about the data tools that you will need for data analysis? Did you know RapidMiner is used for machine learning processes?
A directed and structured study of data science tools will save the day for you and make you industry-ready. The knowledge of these tools will not only help you get into your dream job but also move up the ladder in your present organization.
Let’s know about popular tools for data science in 2020.
The Programmers Arena
Tools for the Coding Geniuses
There are several tools that are used in the various stages of data science lifecycle. Have a look at the data science lifecycle.
Not familiar with coding or programming? Worried if you will become a data scientist? Relax!! Right after this section, we have got something for you as well.
Structured Query Language (SQL) is a tool/language that fetches the data from RDBMS. Want your process to yield actionable results? Start writing your own SQL codes. This querying language is used when a data scientist is presented with relational variables. If you are aware of the nuances of SQL then your life with data will be easy. How? You don’t need to define a method to access a specific record and can easily access multiple records with a single command.
One of the most famous open source RDBMS, MySQL facilitates scalable, high-performance and reliable embedded and web-based database applications. Its most popular use is in web servers. Dynamic websites access real-time data (content produced when the page loads) from the database and these are the ones that use MySQL. Your friendliness with this tool will mean that you are collecting, cleaning, analyzing and visualizing the data.
This good old spreadsheet tool has now gained in stature. What started as a medium for simple spreadsheet operations has now moved up to the complex calculations and data visualization and processing. A mighty data science analytical tool, Excel lets you play with formulae and functions and build your own custom ones.
Link your SQL with Excel and get started with data analysis. Powered with interactive GUI that makes for easy data cleaning, Excel is a preferred tool by data scientists.
Probably one of the most revered languages/tools, Python is the best friend of several data scientists. Why? It is not an intriguing language that will prove to be tough to acquire. This has become an imperative tool in the data science sphere as most of the organizations are opting it. Did you think what makes Python such a critical tool to be mastered?
It houses vast libraries that support data manipulation. Also, this tool can easily integrate with the existing infrastructure in the organizations. Master this programming language, which is fit for multiple platforms and set your foot in the constantly evolving data science field.
Call it Python’s twin. R is also a favorite programming language of data scientists. It is an open-source statistical software package that lets data scientists perform predictive and statistical analysis. Master R to get hold of data visualization and analysis and get numbers related to your analysis; make various types of graphs and develop data sets. What’s the key? You can easily share all your work by exporting it to CSV format.
A must tool for you, if you wish to know the solutions to practically any statistical problem. Perfect platform for statistics, graphical and clustering methods and techniques, this is an imperative data science tool to get to a coveted position.
One of the most practical and famous tools for data visualization and plotting, Matplotlib makes it easy to plot intricate graphs with the help of easy codes. It is easily compatible with Jupyter notebook, IPython and Python scripts. Bar charts, scatterplots, histograms, error charts, plot etc. can be easily created with Matplotlib.
Pyplot is one of its commonly used open-source modules and a substitute to MATLAB’s graphic modules. This module has an interface similar to MATLAB. Just imagine Matplotlib’s importance by a simple fact that NASA used it in data visualization, related to the Phoenix spacecraft’s landing.
#7. Apache Spark
Spark is fast gaining popularity as a useful tool for data processing and analysis. This tool replaces Hadoop’s YARN and MapReduce as a high performing alternative. Enhanced productivity and ease of operations with data to fetch desired results, make it a better alternative to Hadoop. Spark offers agile in-memory data processing and interactive APIs for efficient streaming of SQL or machine learning workloads, requiring speedy repetitive access to data. Being skilled in Spark will serve as an added advantage to your career, together with knowing its counterpart, Hadoop.
A multi-functional numerical computing interface that simplifies your working with statistical data modeling, matrix functions and implementing algorithms, MATLAB primarily finds its application in the scientific disciplines. It is used in simulating neural networks and signal and image processing. Together with this, you can build some powerful visualizations with the help of MATLAB graphics library. This makes MATLAB a resourceful and comprehensive data science tool for data scientists. The key feature of this powerful tool is its capacity to automate multiple processes, from data extraction to using scripts for decision making.
With an aim to experiment swiftly with deep neural networks, Keras library was brought into practice. This open source neural network library is developed in Python. The best part of Keras is its ability to match high dimensional patterns. Providing support to convolutional neural networks and other deep learning analytic models, natural language and image processing are some of its crucial features, apart from it being efficient. Why would you not want to learn a tool that saves your day? It simplifies your life because it unexpectedly reduces the time that goes into building neural networks.
SAS is specially developed for statistical operations and many businesses use it for data analysis. It provides many statistical tools and libraries for data organization and data modeling. SAS mines, alters, manages and retrieves data from different sources for the purpose of statistical analysis. This tool has a GUI interface and thus makes for easy operations. Owing to its user-friendly interface and unrivaled technical support, even newbies can learn it with ease. With SAS, you can sequentially access data with complete efficiency. It also offers perfect database interaction with SQL.
Empowered with interactive dashboards, Qlikview displays all possible relationships, associations and trends of the search term in your data set. This tool is a master at identifying relationships and reduces the time spent on data analysis. Just with a few lines of code, you can do data manipulation and calculation as Qlikview offers scripting interface. Customize the tool and experience the difference and ease of working.
#12. Natural Language Toolkit (NLTK)
Natural Language Processing (NLP) is making machines understand human language. This is done with the help of statistical models built through several machine learning algorithms. Natural Language Toolkit is a group of libraries that Python accompanies for natural language processing. Several language processing operations, such as parsing, stemming, machine learning and tagging is possible with NLTK. This tool supports different applications like machine translation, parts of speech tagging, text to speech recognition and word segmentation etc. The ease to do such a variety of tasks and enable language processing makes it an enviable tool related to language processing.
#13. Sci-kit Learn
A Python-enabled library, Sci-kit Learn finds use in the machine learning algorithm application. This uncomplicated tool is one of the most popular tools for data analysis. Why? Because it supports a wide range of features like dimensionality reduction, data pre-processing, regression clustering etc. Data science professionals prefer this super fast tool for machine learning research and rapid prototyping. Sci-kit learn uses SciPy, Matplotlib and Numpy for data analysis processes.
Used for deep learning, TensorFlow, the mighty tool is famous for its exceptionally precise computational abilities and performance. All your computational work can be displayed as a graph. This robust machine learning platform lets you work on machine learning algorithms with increased processing speed. You can work on several applications like language and image generation, speech recognition and image classification etc. in TensorFlow. It is easy to create graphs in Tensorflow as it has a responsive construct. Tensorflow is a open-source and customizable tool that lets you work without any restrictions on your advanced machine learning projects.
Data visualization is super easy with one of the most loved tools, Tableau. Not just analytical data but also insight can be easily visualized with this amazingly speedy and efficient tool for data visualization and function reporting. This easy to learn tool lets you easily customize your lengthy report, owing to its drag and drop functionality. Yet another feather in the cap of this super easy tool is that it can be easily integrated with any database or spreadsheet. Tableau supports data in multiple formats, such as XML, csv, xls etc. This BI preferred tool can visualize geographical data and plot latitudes and longitudes in a map. Think that Tableau is just for visualization? It is equally preferred as a data science tool for data analytics.
A Bonus For You!!
An IPython based open-source tool that lets you work in an interactive computing environment, Jupyter offers multi-language support for R, Julia and Python. Write live codes, make presentations and complete data visualization with the help of this user-friendly web-application tool. Do you know which is one of the most striking features that differentiates a data analyst from data scientist? It is Storytelling. Jupyter is your friend as it is one of the mightiest tools that facilitates storytelling with its number of presentation features.
There is so much that you can do with a Jupyter Notebook- build predictive machine learning models and do statistical computation and data cleaning etc.
Here’s what a Jupyter Notebook’s dashboard looks like.
The Non-programmers’ Sphere
Tools for non-programmers
So what if you could not master the persuasive Python or compelling C++? This never translates as your incompetency to become a data scientist. Don’t worry! There are a number of tools for you also, just like your programmer friends, which will simplify things for you.
Here are a few of these tools for non-programmers.
Paxata, an application just like MS Excel, aims at making data preparation and data cleaning easy for non-programmers. It’s easy to use and with visually guided approach, diving deep into data and working on it has never been so easy. This tool has ventured into the consumer goods, networking domains and financial services sectors. Handling data is simple with myriad of processes like add data, explore, clean and change, shape etc. Does a major part of your work requires data cleaning? Paxata will be your mate.
#2. Domino Data Lab
Offering one platform for developing, validating, delivering and monitoring models, Domino is a favorite among data science professionals because it supports all the integrated development environments (IDEs). It is created to transform your data into models. Missed the experiment that you did in the first iteration of your model or that revision you did in the third instance? Don’t worry! This tool tracks all your experiments and revisions.
Domino Data Lab tool for data science expedites research, enhances collaboration, boosts speed and eliminates deployment issues that result in effective models. Whether you wish to use Jupyter, R Studio or SAS tools, Domino Lab supports all of them. This robust web-based operating system has a cloud integration, which means that your work will operate on a group of mighty machines. It’s not too pricey; you pay according to the resources you use.
#3. Microsoft Power BI
Working on data sources that aren’t connected or related? Power BI is your go to tool. It is a coherent group of apps, connectors and software services that cohesively transform your data into systematic, aesthetically pleasing and interactive insights. Microsoft Power BI offers you easy connection with different data sources and work and share without any fail.
Not just this, you can perform real-time data analytics and extensive modeling with this robust tool. Reporting, visualization and analytics, everything is possible, be it from your local database or an Excel spreadsheet.
RapidMiner is one tool that encompasses the complete prediction modeling lifecycle, from preparing data to building models and validating and deploying the model. This tool offers GUI with predefined blocks; simply connect these in a correct way and see different algorithms function; no need to write even one line of code. What’s more? You can integrate customized Python and R scripts into this tool. This tool is widely accepted across the industrial spectrum owing to its ease of use and variety of offerings.
#5. Microsoft Azure ML Studio
A robust browser based ML platform, Microsoft Azure ML Studio offers a drag-and-drop interface, wherein you don’t need to code. The process of working in this tool starts from importing your dataset, cleaning it, using built-in ML algorithms to give training to your model and finally getting predictions from it. With this easy to operate tool, you can build, deploy and share your predictions.
With more and more businesses and organizations opting to venture in the field and many already making it big, mastering these tools will make you stand out of the crowd and give an edge to your career.
The list follows no specific order and there might be several other tools too, than the ones mentioned here. Do let us know which tools you prefer and which ones did I miss including in the list. Just drop a comment in the comment box below.