Learn Data Analytics before diving deep into Data Science
Is it true that to become a data scientist you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more???
The answer of this Simply NO!!!
Data science is the simply process of questioning interesting facts, and then answering those questions using large set of data. So data science, in general can be understand as process which includes following steps
- Design the questionnaire
- Gather data that might help you to answer that question
- Clean the data
- Explore, analyse, and visualize the data
- Build and evaluate a machine learning model
- Determine the required results
All above listed tasks do not necessarily requires knowledge of advanced mathematics, a mastery of deep learning, or many of the other skills as mentioned above.
But it still require , skill to understand the data and ability to work with data with any programming language(R or Python). So Just don’t start with complex concepts, start with Data analytics with Pandas.
Why data analysis with pandas ?
For working with data in Python, you should learn how to use the pandas library.
Pandas provides a high-performance data structure (called a “DataFrame”) that is suitable for tabular data with columns of different types, similar to an Excel spreadsheet or SQL table.
It includes tools for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and so much more. In short, learning pandas will significantly increase your efficiency when working with data.