Do You Have These Data Science Tools In Your Arsenal ?

  “If I only had an hour to chop down a tree, I would spend the first 45 minutes sharpening my axe.” 

 Abraham Lincoln

Tools aid us in getting things done faster. This is true when it comes to data science projects as well. Here is a Quick comprehensive and latest guide on which tools are crucial for data scientists to learn. For your convenience, I have divided this guide into multiple stages of a data science project: IDE, Data Pre-Processing, MLDL, Visualisation, and Deployment. 

IDE:
IDEs (Integrated development environments) are the main canvas on which the lines of our code are to be drawn. Hence, it is immensely vital to discuss IDEs first. There are multiple IDEs preferred by data scientists, but the most popular, as you might have guessed already, is the Jupyter notebook. A good grasp of Jupyter notebook and its usage will ensure smooth deployment. Other popular libraries are PyCharm and Spyder. 

Data Pre-Processing:
We all are aware that a major chunk of time is invested in making the data into the right form during the feature engineering stage. A survey in 2018 revealed that feature engineering (or data pre-processing holistically) alone accounts for 30-35% of most data science projects in terms of the total time spent. Fortunately, some libraries can help you perform data pre-processing faster. Most of these libraries are advanced and intriguing to work with. For instance, Seaborn can aid you to visualise the data in a much better and comprehensible manner. Similarly, matplotlib can help you create 2D and 3D diagrams to get a better understanding of what’s really cookin’ with your data! Likewise, there are libraries such as pandas (to read the data), NumPy, SciPy, etc which are a must-know if you want to ace data pre-processing.

For Machine Learning and Deep Learning (MLDL):
Once you have a firm grasp of MLDL concepts (the underlying math of the algorithms and their applications) you can start exploring all the relevant libraries meant for making MLDL utilization much more lucid in your project. Scikit-learn is one such prominent library. It’s a one-stop station for all the major ML operations. Most of the implementation is taken care of, provided that you are proficient with the fundamentals of ML algorithms (i.e., when to apply what). Likewise, TensorFlow can be immensely handy when it comes to DL model creation. It used to be associated with Google before but it has been made open source recently. Keras is an add-on to TensorFlow. It can be utilized for neural networks. And lastly, PyTorch can help you significantly if your project revolves around computer vision or NLP (natural language processing). 

Visualization and deployment:
For visualizing your data effectively, tools like Tableau and PowerBI (either one of these two) can be opted which can help you gain a lot of useful insights via graphs and stats. In addition to that, these visualizations can also be shared directly with the stakeholder, which is a wonderful feature. When it comes to deployment, one can make use of libraries like Flask and Django which can help you create API frameworks. These frameworks can later be deployed to platforms such as AWS(Amazon Web Services), Heroku, Aruze, etc.



Image courtesy: https://www.cnnindonesia.com/internasional/20170504100522-113-212153/banyak-dicari-turis-jepang-akui-defisit-ninja

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