Data Visualization with Python

Check out

https://www.coursera.org/learn/python-for-data-visualization#about

My suggestion is to search for a course on Udemy because a lot of them teach concepts by building an app from start to finish.

Or if you need the structure of a classroom, you could try Coursera. I dunno what language you wanna use, but did a quick search and found this one with Python.

This course was pretty fun:

Data Visualization with Python - https://www.coursera.org/learn/python-for-data-visualization

Hi there! Great question! First of all, Data Science has become a buzzword and an umbrella term. Data Science is a combination of 3 fields - Computer Science, Math and Statistics, and the Business Domain. If you use use scientific methods, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain insights from data, I'd consider you a Data Scientist.

Topics to learn:

1. Programming (Python is extremely popular in DS)
   Python for Everybody Specialization (course)
   Automate the Boring Stuff with Python (book)
2. Math/Statistics (linear algebra, calculus, probability)
   Mathematics for Machine Learning Specialization (course)
   The Elements of Statistical Learning (book)
3. SQL
   SQL course on Udemy (course)
4. Pandas
   Kaggle - Learn Pandas (course)
5. Exploratory Data Analysis (EDA)
   Data Visualization with Python (course)
6. Machine Learning
   Machine Learning by Stanford on Coursera (course)
   DeepLearning.AI Specialization (course)
   fast.ai (course)
7. GitHub - to host your projects (more of that below)
   Git & GitHub - The Complete Git & GitHub Course (course)

Framework of learning:

1. Learn just enough to get you started
2. Do a project
3. Iterate
4. Be consistent/accountable

Practice makes it permanent! The best way to learn Data Science is by doing Data Science. And projects are a great way to learn faster, more deeply, retain information for a longer period, and showcase your skills. Learning is a never-ending process. I know this list might feel overwhelming because there's too much to learn. Break it down to small steps, learn something new everyday, and apply it to a personal project. It can be anything you're interested in! You will get a sense of accomplishment when you finish a project and will see amazing results in the long run.

Also, you don't have to wait to finish one topic to start the next one. I'd recommend learning different topics at the same time and applying them to your projects. Once you're done learning something, iterate over steps 1 and 2 by learning something new and starting a new project or improving the old one. It's a great way to stay excited and motivated throughout this journey. Once you're comfortable, you can start adding more topics.

Lastly, hold yourself accountable. Set deadlines to your learning process and be consistent. Don't try to learn everything at once or you might get overwhelmed and burnout. Accountability is key to doing anything and will be important to not give up.

Book recommendations/extra material:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Naked Statistics
• The Hundred-Page Machine Learning Book
• Practical Statistics for Data Scientists

This is not a perfect roadmap, there are tons of extra materials to learn. But hopefully I covered the basics to get you started. Data Science is HARD, so I'll finish this with a cliché motivational quote:

“The beginning is perhaps more difficult than anything else, but keep heart, it will turn out all right.” — Vincent van Gogh

Good luck :)

No gonna lie mate its a long journey to get to the stage where you pick up a jupyter notebook over an excel spread sheet. You need a basic understanding of python to start which can be found anywhere for free on youtube. Then look into data analysis with Pandas, numpy and some of the graphing library like matplotlib / plotly. Plenty of course online about these core libraries in python -

https://www.datacamp.com/courses/pandas-foundations

https://www.kaggle.com/learn/pandas

https://www.coursera.org/learn/data-analysis-with-python

https://www.coursera.org/learn/python-for-data-visualization

So after a good understanding of python and its libraries you can start making jupyter notebooks. These are something like a extremely customisable but bare-bones word document that has python programming all through it.

They take longer to do analysis than excel, painful to make graphs and all round time sink. But my god if you take the time setting them up they output some of the most beautiful documents. The impact of handing a client something that looks like a research paper with formatted equations and code throughout over an excel spreadsheet is unbelievable. Its has become a defining thing my team does now for deliverables and makes us look top shit.

As you slowly get better with them you build up a personal library of reusable modelling notebooks just like you would in excel. You get to a stage where you have all these customised notebooks where you switch the new data in add the new company logos and hit run.

Here are a couple examples of notebooks: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks#statistics-machine-learning-and-data-science