Data Science for Beginners:

What is Data Science?
Data science can be define in many ways considering that data has been named to be new oil in different business practice. Data Science can be simply defined as the study of data in order to derive valuable business insights. It is a multidisciplinary approach to data analysis that integrates principles and practices from mathematics, statistics, artificial intelligence, and computer engineering.
there are different paths associated with data , namely data analysis data engineering and analytical engineering. They can be dine differently since each can have different technologies to handle.
Data Engineering can be defined as the practice of designing and building systems for collecting,analyzing and storing data at scale and the practitioner of the discipline is known as a data engineer.
Data Analysis is a discipline which involves gathering raw data and then transforming it into information that users may utilize to make decisions.The practitioner of this discipline is known as a data analyst whose work mainly entails reviewing data to identify key insights into a business's customers and ways the data can be used to solve problems.
Analytical engineering is a discipline where it involves optimization of data models. Its practitioner is referred to as analytical engineer whose task mainly involves modeling data to provide clean, accurate datasets so that different users within the company can work with them.

Data Science for Beginners: 2023 - 2024 Complete Road-map.

Data science has emerged as an uncontested practice and debate topic. The key causes for this scenario are the massive amounts of data generated and the importance of data analysis in driving decision-making. Data science employs a variety of approaches for data collecting, preparation, storage, analytics, and interpretation. The advancement of tools, methods, computational speeds, and automation has further cemented data science's dominance.

1. Programming-Programming is a process that creates programs that involve the ratification of codes, For data science there are specific languages where one use. These are Python,R, Java and SQL.
2. Math Fundamentals- There are basic math fundamentals where one is required to use in Data Science. These are, statistics, linear algebra,differential calculus,discrete maths.
3. Data AnalysisThe main concepts in data analysis involve, feature engineering,data wrangling,exploratory data analysis,
4. Machine Learning- one should have deep understanding of the following algorithms.classification, regression,reinforcement learning,deep learning, dimensionaly reduction,clustering
5. Web Scrapping. beautiful Soap,scrappy,URLLIB
6. Visualization
   • PowerBI
   • Excel
   • Matplotlib
   • Tableau
7. Deployment. This can be done by using various cloud technologies such as Azure or AWS.