If you’re an aspiring data scientist, you’re inquisitive – always exploring, learning, and asking questions. Online tutorials and videos can help you prepare you for your first role, but the best way to ensure that you’re ready to be a data scientist is by making sure you’re fluent in the tools people use in the industry.
I asked our data science faculty to put together seven python tools that they think all data scientists should know how to use. The Nearelarn Data Science program focuses on making sure students spend ample time immersed in these technologies, investing the time to gain a deep understanding of these tools will give you a major advantage when you apply for your first job. Check them out below:
1. IPython
IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history. IPython provides the following features:
Powerful interactive shells (terminal and Qt-based)
A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media
Support for interactive data visualization and use of GUI toolkits
Flexible, embeddable interpreters to load into one’s own projects
Easy to use, high performance tools for parallel computing
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2. 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 to make powerful predictions with the given data.
3.Pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.
Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate. pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.
4. 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 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 provides you with the ability to export visual charts on your mobile or IOT devices.
Furthermore, BigML comes with various automation methods that can help you to automate the tuning of hyperparameter models and even automate the workflow of reusable scripts.
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5. MATLAB
MATLAB is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modeling of data. MATLAB is most widely used in several scientific disciplines.
In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing.
This makes it a very versatile tool for Data Scientists as they can tackle all the problems, from data cleaning and analysis to more advanced Deep Learning algorithms.
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