Introduction to Data Science

• What is data science? Understanding its scope and applications
• The data science process: data collection, preparation, analysis, and visualization
• Roles and responsibilities of a data scientist

Data Manipulation and Exploration

• Data wrangling and cleaning using Python and Pandas
• Exploratory data analysis (EDA) to discover patterns and insights
• Dealing with missing data and outliers

Data Visualization

• Visualizing data using Matplotlib, Seaborn, and other Python libraries
• Creating dashboards and reports with Tableau or Power BI
• Best practices for effective data storytelling

Statistics for Data Science

• Introduction to probability, distributions, and hypothesis testing
• Descriptive and inferential statistics
• Statistical significance and p-values in data analysis

Machine Learning for Data Science

• Supervised learning: linear regression, decision trees, and random forests
• Unsupervised learning: clustering techniques, PCA
• Evaluating machine learning models with cross-validation and metrics

Working with Big Data Technologies

• Introduction to Hadoop, Spark, and cloud-based big data tools
• Working with NoSQL databases (e.g., MongoDB, Cassandra)
• Data pipeline management and processing large datasets

SQL for Data Science

• Writing queries to extract, manipulate, and analyze data
• Joining tables, filtering, and aggregating data using SQL
• Integrating SQL with Python for seamless data analysis

Natural Language Processing (NLP)

• Basics of NLP and text data analysis
• Text preprocessing techniques: tokenization, stemming, lemmatization
• Building NLP models for sentiment analysis and text classification

Capstone Project: Data Science in Action

• End-to-end data science project with real-world datasets
• Data collection, cleaning, analysis, and presentation of insights
• Model building and evaluation using machine learning techniques