Last Updated on December 11, 2019
Data science uses mathematics, statistics, algorithms, and machine learning to uncover patterns in raw data. The ultimate aim of using data science is to generate value for the business from the raw data.
This can be done in two ways. One is to analyze the data and gain an insight that can guide business decisions. Another way is to use the data to develop products that can help the business. Both of these add immense value to the business.
To successfully analyze and process the data, you need to use a programming language to write the algorithms required. While there are many languages like SQL, R, Matlab, Java, etc. that can do this, Python has become very popular, and rightfully so. Python is a simple high-level programming language that is supported across multiple platforms.
Python also has the support of an open source community that is constantly working towards making it better. There are many libraries supported by Python that make it easier to perform the complex computations required for data science. This is why data science with Python training is the way to go.
Before you get started, there are few things that can help you optimize your learning experience and guide you along the way.
Learning Python for Data Science
1. Learn Python First
The first step towards becoming a data scientist is learning Python. You need the tools required to implement the algorithms and Python is this tool. Any data science with Python course will also start by teaching you the basics of Python.
One of the best things about learning Python is that there is no dearth of online resources. And if you encounter any doubts, the very active Python community can help you clear it.
2. Familiarize Yourself with Python Libraries
The reason many data scientists prefer using Python over other languages is that it has many libraries that can help them write the algorithms that do the data manipulation. You need to familiarize yourself with these.
While you always learn about them as you do certain projects, the whole process will take less amount of time if you know which library does what.
Here are the top few to get you started.
NumPy – Numerical Python. This library can do Fourier transform, linear algebra functions, provides n-dimensional arrays and provides tools for integration with other languages such as C, C++, Fortran etc.
SciPy – Scientific Python. This library can do high-level science and engineering computations.
Matplotlib – This library helps with plotting data and comes in handy for data visualisation.
Scikit Learn – This library is built on the above three and contains tools for statistical modelling and machine learning.
Pandas – This library is used for structured data manipulations and operations.
Scrapy – This library can be used for web crawling.
3. Learn by Doing
Data science is not a theoretical field. To fully understand the concepts, you have to be prepared to practice it. Every time you learn a new concept in your data science with Python course, you need to implement it using some data to really see how it works and how the parameters affect the outcome.
You can also create your own projects. This can help you out in two ways. The first is that you get to practice what you learn. The second is that when you are applying for jobs, you can add these projects to your resume to show the employer that you have some practical experience.
4. Pick an Industry
Data science has found application in a lot of industries. In fact, many choose to work in data science precisely for this reason. While the basic concepts of data science remain the same irrespective of the industry, the problems faced by the industry vary widely.
If you haven’t picked out which industry you would like to work in or if you do not mind working in any industry as long as you are a data scientist, then you can practice by creating projects in any industry. However, if you would like to establish a career in one specific industry, then concentrate on it from the very beginning.
Even when you are practicing, pick data sets and problems that are specific to that industry. Not only will it help you understand the intricacies of the field better, it will give you an edge over others when you apply for a job.
Make the Best out of Data Science with Python Training
Becoming an exceptional data scientist is all about understanding the concepts well and practicing until you get it right. Since Python is easy to learn, you will not need a lot of time for it. But learning the concepts of data science and understanding it well will require some time, effort, and determination on your part.
In the end, however, it will really be worth it since you will then enter the job market with a highly sought after skill-set and can easily secure a well-paying job.