Data science is a multi disciplinary course that uses various techniques, algorithms, processes, and systems to extract  insights and knowledge from structured and unstructured data.

Data science combines  of computer science, statistics, mathematics, domain knowledge, and data engineering to analyze the complex data sets and make informed decisions. 

1. Data Collection: Data scientists first collects the data from available sources, including databases, websites,sensors and more. This data can be unstructured or structured .

2. Data Cleaning and Preprocessing:Raw data obtained by data collection is   messy and incomplete. Data scientists clean and preprocess the data to  remove  any outliers,errors, and inconsistencies, making it suitable for analysis.

3.Exploratory Data Analysis (EDA): EDA involves in  using statistical and visual techniques helps to  understand the data’s characteristics, like  distributions, correlations, and patterns. 

4. Feature Engineering: Feature engineering is  process of creating the new features or transforming the existing ones to improve the performance of the machine learning models. 

5. Machine Learning:Machine learning is  a data science which always  focuse on  algorithms to build the predictive models. which includes in  unsupervised learning , supervised learning  and reinforcement learning.

6. Model Training and Evaluation: Data scientists  train machine learning models in historical data and evaluate their performance using various measures. Cross-validation techniques are  most used to assess model generalization

7.Data science is applied in the real life in  various domains,  marketing, healthcare,including finance, e-commerce, and more. To solve the complex problems and  make decisions and gain a competitive advantage.

8.Data scientists plays  a crucial role in extracting  the insights of data to drive innovation in business growth.