If you want to start your career in Data Science then you are in a right place. This is a complete Data Science series where you will learn each and everything related to data Science and if you will follow sincerely then no one can stop you from becoming Data Scientist. At last of every module I will give you some task kindly go through it and see the differenceâ€¦

This diagram is showing some of the common disciplines that a data scientist may draw upon.

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data that is helpful for solving some kind of problem. If we are not able to solve any problems from data then that data is of no use.

Data science practitioners apply some kind of techniques like machine learning, deep learning, etc. to numbers, text, images, video, audio, and more to produce artificial intelligence(AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value. More than applying this techniques there is certain flow in the process from data extraction to deployment as shown in below diagram.

- Statistics
- Python
- Data Analysis: Pandas
- Numpy
- Data Visualization: Matplotlib, Seaborn, Plotly, Cufflinks
- Rest Api
- Machine Learning
- Time Series
- Hardware Setup- GPU
- Deep Learning
- Advanced Computer Vision
- Natural Language Processing( NLP)
- Deployment
- Projects
- Chatbot

These topics are sufficient to start as a Data Science career. Consistency and dedication is really very important to cover these all modules, so practice daily and best of luck to start new journey.

Task1: You have to explore different phases of Data Science and find in which phase data Scientist spends more time.