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5 Must-have skills in Python for every Data Scientist

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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Here are 5 Must-have skills in Python for every Data Scientist

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.

1. Data Scraping

Gathering data from websites is one of the most logical and easily accessible sources of data.

You'll need to learn how to use Python packages like urllib2, requests, simplejson, regular expression operations, selenium and beautiful soup to make handling web requests and data formats easier.

2. SQL

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.

3. Data Frames

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.

4. Machine Learning

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.

Before you jump into fancier algorithms, try out simpler algorithms like Naive Bayes and regularized, linear regression. In Python, these are implemented in scikit-learn.

5. Data Visualization

Data science is about communicating your findings, and data visualization is incredibly valuable part of that.

Python offers Matlab-like plotting via matplotlib, which is functional, even if it is ascetically lacking and if you are really serious about dynamic visualizations, try d3.

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.

Before You Go

You may also be interested in reading How to Learn Data Science with Python or may want to start with one the Best (and Affordable...) Data Science Courses to learn and upgrade your skills.

If you want to learn Probability and Statistics for Data Science, i've got you covered in this article about the best online classes.

Wishing you the best with your career!

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