Data science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. 

It combines skills from statistics, mathematics, computer science, and domain-specific knowledge to analyze data and make it useful for decision-making, predictions, and solving complex problems.

1. Data Collection & Cleaning : Gathering data from various sources and preparing it for analysis, which includes cleaning, transforming, and standardizing data.
2. Exploratory Data Analysis (EDA) : Examining data sets to understand their main characteristics, identify patterns, and find relationships. This often involves data visualization techniques.
3. Statistical Analysis : Applying statistical methods to test hypotheses, find trends, and understand the underlying structure of the data.
4. Machine Learning & Predictive Modeling : Building models that can make predictions or identify patterns. Machine learning, a subset of data science, includes supervised, unsupervised, and reinforcement learning techniques for tasks like classification, clustering, and regression.
5. Data Visualization : Presenting data and results through charts, graphs, and interactive visuals to communicate findings effectively.
6. Big Data Technologies : Working with large and complex data sets that traditional tools can’t handle. Big data tools include Hadoop, Spark, and NoSQL databases.
7. Programming Skills : Data scientists often use Python or R for data analysis, SQL for querying databases, and other tools like Jupyter notebooks for interactive data exploration.
8. Ethics and Privacy : Ensuring responsible use of data, protecting privacy, and avoiding biases in data and models are essential in data science.