What is Data Science?
Data Science is the study of data to extract meaningful insights for business. It combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.

Why study Data Science?
Data scientists use their programming, analytical, and statistical skills to collect, analyze, and interpret data. Therefore, these insights help them to develop data-driven solutions that can be applied to various business demands. Data scientists should have many additional technical skills, from reporting technologies to machine learning, database creation, knowledge of programming languages, and machine and statistics learning.

Data Science is a job that is on demand right now because data has become a huge backbone in many industries.

Key Tools For Data Science
Data science relies on various tools and techniques to extract insights from data, including:

1. Programming languages: Python, R, and SQL.
2. Machine learning libraries: TensorFlow, Keras, and Scikit-learn.
3. Data visualization tools: Visualization tools like Tableau, Power BI, and Matplotlib.
4. Data storage and management systems: Databases like MySQL, MongoDB, and PostgreSQL.
5. Cloud computing platforms: AWS, Azure, and Google Cloud Platform.

Programming Languages
You must have a solid foundation. The data science field requires skill and experience in either software engineering or programming.

You should learn a minimum of one programming language such as Python, SQL, Scala, Java, or R.

Machine Learning
You'll need to learn the various algorithms and how they work on datasets. It is important to evaluate which algorithms to use and understanding how to evaluate the effectiveness of algorithms.

What is Big Data
Big data refers to larger, more complex data sets, derived from new data sources. One of the primary concerns of a data scientist is efficiently capturing, storing, extracting, processing, and analyzing information from these enormous data sets.

Data Visualization
Data visualization is the graphical representation of information and data by using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

There are various tools such as PowerBI, Tableau, and QlikSense and Open-source Python libraries such as Matplotlib and Seaborn.

Differences between Data Science, Data Analytics, Data Engineer and Analytical Engineer

*Data Science *
Involves analyzing and visualizing existing data and implementing algorithms to build predictive models for making future decisions.

*Data Analysis *
Involves analyzing and interpreting numeric data which is used to help companies make better and crucial decisions.

*Data Engineering *
Involves building infrastructure and scalable pipelines to manage the flow of data for it to be analyzed.

*Analytical Engineer *
Deals with the data itself as well as moving the data. It is their job to make sure data is ingested, transformed, scheduled and ready to be used for analytics.

Lastly, a data scientist has to learn about the data lifecycle and has to understand each process.