Top Key Things to Know for a Data Scientist to work on a Data Scientist Project

An Overview

Data Science Project Cycle Overview: Welcome all.. I am sharing some of my knowledge on Data Science and some interesting facts that I learned in real time data science with python project that I worked on. It’s really fascinating to work on real time data science with python project. The exposure that you get when you collect data from various sources to create your machine learning model as API endpoints. You will feel like you have won a war when your gathered data is used in the real time world to mark or analyze trends. There are various stages of data science project cycle that you should know to work on any Data Science Project.

Data Science teaches you the approaches that you gotta follow to extract data. Companies around the globe badly need a hands from Data Scientist as the analyzed data that they offer is going to be a game changer for the companies if they take wise decisions based on these data which will have great impact in their revenue growth, Sales and Profits.

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1. Data Science Project Cycle Overview

I will just explain the process and keep it simple. But I promise you guys. You would really need a Data Science Expert so that it will be interesting to learn these concepts real time. As you all know, Data Extraction or Data gathering is the first step. This step is the key and you got to collect it from multiple sources and then you got to validate or organise the data in the format that it has to be. Once this data wrangling is done, the most important thing that you need to do is to create a machine learning model. This might sound light but you got to repeat this step often.

The reason why I am saying Iterative. It’s just because you got to try different machine learning models, analyze them and try again until it reaches the desired result. Once you have your model and the analysis that you have done is ready..Thats it..Job Done.. You have to present the results in any formats that companies expect.

But one thing you gotta remember here is, if the data insights that you have presented are not convincing, you have to repeat this process with different models to get meaningful data.

2. Is Python worth for Data Science?

As everyone says, I agree with the fact that Python is easy to learn and its open source tools, libraries are very useful for Data Science Projects. In simple terms , We can say that Python has all the features that are essential for a Data Science Project. Python is fast and Scalable to other languages and frameworks. You will learn the many interesting facts about Data Science and Python when you learn it real time. Python 2 offers stable third party packages and Python 3 is much cleaner and faster than its old versions and a special note on the backward compatibility feature which is amazing. Numpy is great in dealing with similar data types and it offers structure and operations to work on tabular data.

A real world data set looks like a spreadsheet having a tabular structure with a bunch of rows and columns. And moreover, each column or feature can be of different data types. And here comes the Panda which handles such situations so easily. The Situation that we discussed now is what we call as Pandas Dataframe and it offers features for Data Manipulation and other common operations such as joins or groupings on these dataframes. Pandas also offer high level functions on top of another popular python library which is matplotlib which is useful to create various visualisations. Now you will realise some benefits of python which is vital for data scientists.

3. Machine Learning is Important

Machine Learning is nothing but what you learn from data or examples. This helps you to draw patterns and make wise decisions. And there are two vital things that you got to know is supervised and unsupervised machine learning. So Machine Learning as a technology helps to analyse large volumes of data that makes the life of a data scientist easy in much automated fashion and setting great expectations.If there is more data available, you gotta try a lot of models to bring in the best. And going forward, Data Scientist should have good knowledge in machine learning if you want to be successful in their Data Science project.