Most Data Scientists and Machine Learning Engineers prefer using Python for Data Science and developing artificial intelligence and machine learning apps.
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If you are a data scientist or want to learn data science with Python track, here are five critical skills you need to develop as a beginner.
And to help you develop these skills, we have linked some of the best available resources to help you become a creative data practitioner.
Gathering data from websites is one of the most logical and easily accessible sources of data.
You need to learn how to turn raw data into actionable insights and once you have a large amount of structured data, you will want to store and process it.
To be an effective data scientist or an engineer, you should be able to wrangle and extract data from relational databases using SQL.
SQL is important in data science and great for handling large amounts of data however it lacks Machine Learning and Data Visualization.
So you will have to go through the painful process of enabling Machine Learning services in SQL Server or use MapReduce to get data to a manageable size and then process it using Pandas.
A lot of data science can be done with select, join, and group by (or equivalently, map and reduce) but sometimes you need to do some non-trivial machine-learning.
Data science is about communicating your findings, and data visualization is incredibly valuable part of that.
These skills are taught excellently in Data Scientist with Python Career Track offered by DataCamp.
DataCamp offers over 100+ courses by expert instructors on topics such as Importing Data, Data Visualization, SQL, Machine Learning, Statistical Thinking and more.
You will learn faster through DataCamp's immediate and personalized feedback on every exercise.
Wishing you the best with your career!