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Kelvin Kiarie Kaira
Kelvin Kiarie Kaira

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Data Science for Absolute Beginners: 2023-2024 Complete Roadmap

Data science is the art and science of extracting valuable insights and knowledge from data. It's like a detective story where we collect, analyze, and interpret data to solve real-world problems and make informed decisions. We use the power of numbers, statistics, and technology to uncover hidden patterns, predict future trends, and create a smarter, data-driven world. Whether it's understanding customer behavior, optimizing operations, or advancing medical research, data science plays a crucial role in today's information-driven society.

steps 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 from here
-->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

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:
-->Excel (& VBA)
-->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--:
-->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.

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