data science with Python

Why data science and why Python?

Data grants us the power of predictions, especially during these troubled times this ability comes in real handy. Predicting a safer course through time is everything that keeps a new venture alive. The slightest mistakes during the very first stages of planning can ruin a business to its core. The damage can be so severe that they can never stand up and operate again. Thus utilization of data from the very first days is essential if a business is to operate with ease and comfort. Data science is thus very much in demand among businesses and IT, CSE and ECE professionals are looking for a quick diversification.

There is no easier way to master data science than with Python. Python is inexpensive, almost free to use and features a large and helpful community of users. In addition to that, python features a plethora of free to use libraries specifically maintained and run for data science-related operations. Thus data science with Python is the standard choice for most data science enthusiasts and data engineers.

How to start?

It is wise to start with the guidance of a teacher or an institute. An ample amount of time should be invested in this endeavour. And a strong networking effort must be put in while seeking out the right institute. The right institute in this case is expected to be bold, transparent and free of any intentions of hiding anything. In addition to that, there should be ample room for conducting extensive networking. And getting in touch with relevant people should be encouraged by the institute. And after the right institute is identified, one should disembark on learning the technicalities, while following these three steps.

Step 1

It’s important to learn the basics of Python and get the setup ready for an extensive and long duration of work. Python comes preinstalled with Linux systems and for windows, it is free to download. In addition to that, the free IDE for Python like Intellij IDE must be installed properly to run python.

Jupyter notebook is the fresher’s companion when it comes down to mastering data science with the help of Python. This tool can provide introductory ideas for both the language and field and help a fresher immensely. A Kaggle account also helps in learning and understanding data science. It is a platform for uniting local data enthusiasts and helps in the flourishment of ideas.

 

Step 2

After getting in touch with the technicalities of python, and going through the fundamentals, a fresher, budding data scientist should work on freely available data science-related practice projects.

A fresher in this case can take up guided projects and get a wonderful internship experience. These are interactive python projects for all levels of skill and expertise.

Open survey data sources are abundant these days and these easily accessible databases can provide the perfect practice before a student embarks on professional grounds. This practice is expected to train a student regarding the mining approaches and a deep down insight into how to make sense of large databases.

Tracking personal spending habits on online platforms is another way of enriching one’s skills with data science and Python. Analysis of personal data is ethical by all means and analysis such as this one will grant a student experience that is useful in modern-day marketing campaign planning.

Step 3

It is important to create a data science portfolio while learning python so that the relevance of the python training remains intact. This creation of a portfolio while learning Python helps immensely and saves valuable time.

To build such a portfolio, a student can take up projects relevant to each stage of learning.

  • Data cleaning projects

These projects involve cleaning unstructured data and transforming them into something very much usable with machine learning algorithms. These projects are important and if anything goes wrong very little risk is involved as well.

  • Data visualization projects

Visualization of data in a professional field should be seamless and easy to understand. As everyone is not expected to understand data as a data scientist, a data scientist must learn to present data as everyone is interested to encounter.

  • Machine learning projects

Machine learning tools are an integral part of modern data science ventures and data analytics. It is humanly impossible to analyse and make sense of the large sums of data we generate every day. Thus in order to show off one’s adeptness with machine learning, a data scientist should take up one or two machine learning projects throughout the learning process.

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