Definition of data science
Importance and applications of data science
Historical background and evolution of data science
Understanding data types (numerical, categorical, text, etc.)
Data sources and acquisition methods
Data formats (CSV, JSON, Excel, etc.)
Data cleaning and preprocessing techniques
Descriptive statistics (mean, median, mode, variance, etc.)
Data visualization (histograms, scatter plots, box plots, etc.)
Detecting outliers and missing values
Correlation analysis
Basics of programming languages (Python or R)
Variables, data types, and operators
Control structures (loops, conditionals)
Functions and libraries
Overview of machine learning concepts
Supervised learning vs. unsupervised learning
Classification and regression algorithms (decision trees, k-nearest neighbors,
etc.)
Data Science is an interdisciplinary field that utilizes scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data.
Key skills include programming (Python, R, SQL), statistics, machine learning, data wrangling, data visualization, domain knowledge, and problem-solving skills.
The typical steps include problem definition, data collection, data cleaning and preprocessing, exploratory data analysis (EDA), feature engineering, model building, model evaluation, and deployment.
Commonly used programming languages include Python, R, and SQL. Python is especially popular due to its versatility, rich ecosystem of libraries, and ease of use.
Supervised learning involves training a model on labeled data, where the algorithm learns the mapping between input features and output labels. In contrast, unsupervised learning deals with unlabeled data, aiming to discover hidden patterns or structures within the data.
Statistics plays a crucial role in Data Science for tasks such as hypothesis testing, estimation, inference, and understanding the underlying distributions within the data.
Data Scientists use various techniques and tools for handling large datasets, including distributed computing frameworks like Apache Hadoop and Apache Spark, as well as data storage solutions like HDFS and cloud-based platforms.
Common tools and libraries include Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn (for machine learning), TensorFlow, PyTorch, and Jupyter Notebooks.
Data Science is a broader field that encompasses techniques for data analysis and extraction of insights, whereas AI focuses on developing systems that can perform tasks that typically require human intelligence. Data Science often utilizes AI techniques such as machine learning and deep learning.
Real-world applications include predictive analytics in finance, personalized recommendation systems in e-commerce, disease prediction and diagnosis in healthcare, fraud detection in banking, sentiment analysis in social media, and optimizing supply chain management in logistics, among many others.