1-Month Data Analytics Internship

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1-Month Data Analytics Internship Program

Module 1: Introduction to Data Analytics

• Welcome and Orientation
• Overview of data analytics and its importance
• Tools and software to be used during the internship
• Data Analytics Fundamentals: data types, structures, and statistical concepts
• Data collection methods

Module 2: Data Cleaning and Preprocessing

• Handling missing values
• Data transformation techniques
• Data normalization and standardization
• Introduction to Python for Data Analytics
• Basic Python programming concepts
• Libraries for data analytics: NumPy, Pandas

Module 3: Exploratory Data Analysis (EDA)

• Data visualization techniques
• Identifying patterns and insights
• Using Pandas and Matplotlib for EDA
• Introduction to SQL: Basic SQL queries and data manipulation

Module 4: Advanced Data Analysis

• Descriptive and inferential statistics
• Hypothesis testing
• Correlation and regression analysis
• Advanced data visualization tools: Seaborn, Plotly
• Creating interactive dashboards

Module 5: Machine Learning Basics

• Introduction to machine learning concepts
• Supervised vs. unsupervised learning
• Introduction to popular algorithms: Linear regression, decision trees, clustering
• Using Scikit-Learn for basic machine learning tasks

Module 6: Applied Machine Learning

• Data preparation for machine learning
• Model training and evaluation
• Cross-validation and hyperparameter tuning
• Real-world machine learning project

Module 7: Big Data and Cloud Computing

• Introduction to big data concepts
• Overview of Hadoop and Spark
• Using cloud platforms for data analytics: AWS, Google Cloud, Azure
• Hands-on exercises with cloud-based data analytics tools

Module 8: Final Project With codec technologies developers