Life-cycle of a Data Science Project

Cover

Are you wondering how would the life-cycle of a data science project be? Here you go..
Problem Identification:

1 identify-the-problem

Have you ever heard the phrase “Here’s the data, can you do some analysis find some insights?” Often, management approach Data Scientists with vague or even undefined goals. Understanding the goal is important and sets up the rest of the project for success.

This step consumes up about 10% of the time in the project life-cycle

Data Preparation:

2 data prep

So far, everybody’s least favorite stage, but possibly the most important one. Data can come from different sources, be in the ugly format, and have errors and a myriad of other problems. A single error in this stage can render the rest of the analysis useless.

That’s why typically, up to 70% of the time is spent here.

Analyse the data:

3 Data-Analysis

Creating models, performing data mining, setting up simulations etc. This is the most exciting part and if the previous stages were done correctly, analyzing the data and getting insights will feel like a good.

Time needed here would be 10%

Visualization of the insights:

4 Visual

Visualizing comes hand-in-hand with analyzing. This is a powerful technique as looking at the data in various forms and shapes can help reveal insights that are otherwise not evident. Also several projects such as BI dashboards don’t need much analysis but rely on visualization instead.

Time needed here would be 10%

Presentation of the findings:

5 data-presentation

We’ve reached 100% the project is over! Actually, No. Presenting findings is a whole separate “Additional” stage. You need to not only convey the insights in your audience’s language but also get buy-in from them to take action based on those insights. This is an art.

Time needed: extra 80% 🙂

Hope you benefited ! Enjoy learning!

Advertisements

Data Engineer vs Data Scientist (Infographic)

This Infographic will assist us to understand better about the skills and responsibilities of Data Engineer and Data Scientist. Also, it helps us to compare salaries, popular software and tools used by each. Hope this helps!

data-engineer-vs-data-scientist

Data Viz ! Cheat sheet for R Data Analyst

Data visualization has become a vital slice of data science arena. Hence, our key tool should have strong capabilities on both the fronts – data analysis as well as data visualization. With this revolution in the landscape, or has extended immense popularity because of its splendid data visualization capabilities. With a few lines of code, you can produce beautiful charts and data stories. R contains superb libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others. Below is the cheat sheet of widespread visualization for representing data. Thanks to my colleague for sharing this.

Data Viz Cheat Sheet

Eight Steps to become a Data Scientist ! (The Sexiest and the Hot Job of the Decade)

Thinking how to become a Data Scientist? Here we go, the 8 Steps to become a Data Scientist (The Sexiest and the Hot Job of the Decade)

Well, these steps are not so easy but possible if we try. Most of the steps come with no-cost or very low-cost.

https://i0.wp.com/blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg

Thanks for DataCamp for the nice infographic. Is this info useful? Then please share this info with your circle.