Data Science Roadmap for Beginners

Step to be a Data Scientist

1) Mathematics & Statistics
→Learn about basic statistics from a book like Hines.
→Learn what dy/dx actually means!
→Learn about Optimization and gradient descent.
→Learn to plot simple functions in excel itself!
→Learn about basic probability distributions with a bit more emphasis on the normal distribution.

2) Programming
→Learn about Python Basics
→Learn about Numpy
→Learn about Pandas
→Learn about Matplotlib/Seaborn
→Learn about the time complexity of algorithms (just the understanding)!
→Learn about storing data in the Database

3) Big Data and External Data Visualization Tools
→Data visualization and big data can be handled in python but some of the external tools are made just for the task in hand. Once you have started to use python to solve a few of the data science problems, you need to look into these tools to understand what they have to offer on the table. These tools include:
→Tableau
→Excel (& VBA)
→Hadoop
→AWS Offerings

4) Machine Learning & Deep Learning
→Once you have mastered data wrangling with python, you need to work on your Machine learning concepts.
Suggested Course — :
(a) https://developers.google.com/machine-learning/crash-course
(b) https://www.kaggle.com/learn
→Learn Sklearn
→Learn to build a neural network in TensorFlow
→Learn to use tensorflow_hub
→Learn how to use the tensorboard

5) Linux & Version Control
→Apart from learning Python and Mathematics, you need to know how to manage and collaborate with others on the software you create.

6) Learn About Some Data Scrapping and Crawling Techniques.
→ learn how to build dashboards with free tools as R Shiny and Streamlit.
→ learn how to extract data using common scrapper tools such as scrapy.