Description
Overview
Data science is an interdisciplinary field that uses various techniques, algorithms, processes, and systems to extract valuable insights and knowledge from data. It combines aspects of statistics, computer science, and domain expertise to make data-driven decisions and solve complex problems. Remember that data science is a dynamic field, and continuous learning and practice are crucial. Building a portfolio of projects and gaining practical experience is key to becoming a proficient data scientist.
Program Content Introduction to Data Science
- Data Science lifecycle and process.
- Role of a Data Scientist.
Data Collection and Preprocessing
- Data sources and types.
- Data acquisition and cleaning.
- Data transformation and normalization.
- Exploratory Data Analysis (EDA).
Statistics and Probability
- Descriptive and inferential statistics.
- Probability distributions.
- Hypothesis testing.
- Regression analysis.
Big Data and Cloud Computing
- Introduction to big data concepts.
- Distributed computing frameworks (e.g., Hadoop, Spark).
- Cloud platforms for data storage and processing.
Time Series Analysis
- Time series data and applications.
- Time series decomposition.
- Forecasting techniques.
Ethics and Privacy in Data Science
- Data privacy and security.
- Bias and fairness in machine learning.
- Ethical considerations in data collection and analysis.
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