We connect with data every day in one way or another, whether directly or indirectly. Your phone book and even how you use daily computations contain data. According to wikipedia, data science is an interdisciplinary academic subject that employs statistics, scientific computers, and scientific methods, procedures, algorithms, and systems to deduce knowledge and insights from noisy, structured, and unstructured data.
Why Learn Data Science?
Data science is recognized as the "sexiest job of the 21st century" by Harvard Business review in 2021. Its capacity to pull, obtain, and derive actionable insights from various data sets
1) Demand for data scientists - As more organizations begin to value data, they will require someone who can extract insights from it.
2) It has Numerous endeavors in the fields of health,disaster relief,and even business use the data that data scientists collect.
3) Data scientists earn competitively high salaries.
What you should Learn to become a data scientist
Programming
Math
Version Control
Communication
1. Programming For Data Science
Learning is a journey with its own curve, so getting your hands dirty to grasp the fundamentals of what you are going into should be your first step as you enter the world of data science. Understanding programming techniques will assist you in performing fundamental tasks as well as efficiently processing massive datasets to get insightful conclusions.
Python
learn Python; it's simple to pick up, and it'll allow you investigate real-world data sets and provide you insights and solutions using its libraries and framework to Finnish tasks. Libraries & Modules like Pandas, a Python module used for data manipulation, will be available for you to utilize and interact with. To understand more visit https://www.codecademy.com/resources/blog/why-you-should-learn-python-for-data-science .
SQL
Yes, It refers back to data and teaches how to handle, analyze, and manage it all in order to gain insightful information for wise decision-making. In order to extract, compute, visualize, and explore
https://emeritus.org/blog/data-science-and-analytics-sql-for-data-science/
2.0 Maths For Data Science
You will learn and need to comprehend the crucial computation of data through math.
Statistics
plays a part in the evaluation of data to make decisions
- Descriptive Statistics - Inferential Statistics
Probabilities
used for machine learning and deep learning that are.
- Conditional Probability and Joint Probability
Calculus
- Deferential and Integral Probability
Linear Algebra
- Vectors, Linear regression, Matrices
3.0 Version control For Data Science
Get familiar with version control systems like Git. Your code can be kept or stored here. It makes peer cooperation and change tracking easier.
Learn how to start a project, commit, branch, push, rebase, merge, and resolve disputes and work around with open source
Work with version control like git and git providers like
- Bitbucket, Github,AWS code commit, and Git Lab.
Also how to use CLI for work around on your pc
4. Data Visualization
Know to present your data
Present your data with Visuals like graph. Understand
- Matplotlib and Seaborn
to share the details of your data.
5. Career Paths For Data Science
Data Scientist
Data Analyst
Data Engineer
Data Architect
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