If You Want A Radical Career Change, Expect To Do It All On Your Own But Don't Burn Your Bridges Immediately. This article is mainly geared towards folks who want to learn more about data science with python on their own.
Why Data Science With Python?
Python is a general force in the programming language that is very effective for performing data science with plenty of resources available from books to online courses. It has a significant set of data science libraries one can use. It is a ready-to-use programming language with different packages for loading and playing around with data, visualizing the data, transforming inputs into a numerical matrix, or actual machine learning and assessment.
Critical Skills in Python for Data Science 🤔
If you 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 listed the best available resources in the following sections.
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, re, selenium and beautiful soup to make handling web requests and data formats easier.
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 an 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.
Well Here's a Curriculum Guideline 🍠
Learn Data Science With Python ...
Python is a high-level programming language that is becoming more and more popular for doing Data Science and Machine Learning. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace. Most coders prefer using Python for Data Science and developing artificial intelligence and machine learning apps
In order to begin, you can download anaconda from field ion since it is highly recommended. After downloading, you can start by comprehending the fundamentals of the Python, data scraping, importing data, data form. Also, there is a need to learn Scientific libraries in Python such as Numpy, Matplotlib, Pandas, and SciPy,
We have listed some of the best (and free!!!) available resources in the following sections to help you bootstrap your career in the field of Data Science using Python.
Python for Data Science Courses 📙
Start with a Course or a book and study all the important topics for doing data science with Python. Our brain is similar to a muscle, Keeping your brain “fit” with deliberate practice almost every day will help you find a sweet spot for Python.
Python For Everybody Specialization -University of Michigan
Introduction to Python for Data Science - Microsoft
Python Programming Track - DataCamp
Listen To Python Podcasts 🌯
These podcasts will be of tremendous help while navigating through a forest of abstraction especially when you don't know where you're headed. They are great with consistently interesting guests who give away the best resources and present thoughtful content.
Networking for Nerds 🤓
If you are in the right group of people, you'll get the right kind of support. Find people who you could learn from and create some positive reinforcement. Here are some resources to help you get connected and understand your in-group.
The whole point of joining the online communities or going to conferences and regularly attending a Meetup is not to be liked but to benefit from the high-impact sessions and find someone who you will like because then they'll like you in return and help to you if you are seen around repeatedly.
If you don't find any Meetups around your area, write some Python code to find the right Meetup groups around your location. There is a Meetup API client written in Python with all the documentation that has a complete list of available API methods and their descriptions.
Get Good at Statistics and Maths for Data Science 📊
It's easy to fall into a state of depression when you don't have the know-how-to of Statistics and Maths when learning Numpy, Pandas or Scikit-learn. We hope that the following resources will help you to start building the Data Science skills required today.
Why Statistics for Data Science
A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician. As a data scientist student, You can master the core concepts, probability, Bayesian thinking, and even statistical machine learning from best available books or an online course.
Statistics for Data Science Courses
If you need an introduction to Statistics, start with any of the beginner level course listed below. Try and integrate some of these online courses into your schedule while learning python. You'll feel very confident while learning to work with analytical libraries for Python.
Introduction to Probability and Data - Duke University
Inferential Statistics - University of Amsterdam
Bayesian Statistics: From Concept to Data Analysis - University of California
Statistics Foundations: Understanding Probability and Distributions - Dmitri Nesteruk
MicroMasters Program in Statistics and Data Science - Massachusetts Institute of Technology
If you already have a background in statistics and want to learn about the advanced statistical concepts, you’ll find resources provided by EliteDataScience quite helpful.
Why Learn Maths for Data Science
Mathematics is the bedrock of any contemporary discipline of science. It is no surprise that almost all the techniques of modern data science (including all of the machine learning) have some deep mathematical underpinning or the other.
Maths for Data Science Courses
You don’t need a degree in Mathematics to succeed in data science. Yet, if you do have a math background, you’ll definitely get ahead. Here are some best online classes to master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to a more advanced material.
Introduction to Mathematical Thinking - Stanford University
Data Science Math Skills - Duke University
Introduction to Algebra - SchoolYourself
Algebra I - Khan Academy
Also, If you have little to no background in Maths or need a refresher, we suggest that get a copy of All the Mathematics You Missed: But Need to Know for Graduate School for an overview of mathematics that one should have been exposed to upon reaching Graduate School.
Before You Go
We have made sure that a team of 2 Python Programmers and 3 Content Researchers has put all the wisdom and experience in this article. We hope the resources listed in this article puts you in the fast lane and help you financially bootstrap your career in the field of Data Science with Python.
You may also be interested in reading about The Best (and Affordable!!!) Data Science Courses with a Specialization Certificate.
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Wishing you the best with your career! happy learning! 👇🏾