Data Analytics

4 Months Course 50% Theory 50% Lab Daily Homework Real time Projects

Topics Covered

Module 1: Foundations of Data Analytics

1.1 Introduction to Data Analytics

• Definition, scope, and significance of data analytics
• Historical perspective and evolution of the field
• Key components of the data analytics ecosystem

1.2 Data Collection and Preprocessing

• Techniques for data acquisition (APIs, web scraping, databases)
• Data cleaning and preprocessing using Pandas
• Exploratory Data Analysis (EDA) and feature engineering

1.3 Descriptive Statistics and Visualization

• Measures of central tendency and dispersion
• Data visualization using Matplotlib, Seaborn, and Plotly
• Dashboard creation and design principles

1.4 Inferential Statistics

• Hypothesis testing, confidence intervals, and p-values
• Regression analysis and correlation
• Advanced statistical methods and experimental design

Module 2: Programming for Data Analytics

2.1 Python Programming Fundamentals

• Variables, data types, and control structures
• Functions, modules, and error handling
• Object-oriented programming concepts

2.2 Data Manipulation and Visualization with Python

• Pandas for data manipulation
• Data visualization libraries (Matplotlib, Seaborn, Plotly)
• Interactive visualizations and geospatial data

2.3 Advanced Topics in Python

• Web scraping using BeautifulSoup and Selenium
• Machine learning libraries (Scikit-Learn, TensorFlow, PyTorch)
• API integration and working with JSON data

2.4 Database Management and SQL

• Relational databases and normalization
• SQL basics and advanced querying
• Connecting Python to databases for seamless integration

Module 3: Machine Learning and Predictive Analytics

3.1 Introduction to Machine Learning

• Supervised learning, unsupervised learning, and reinforcement learning
• Model evaluation, validation, and overfitting
• Ensemble methods and model deployment considerations

3.2 Regression and Classification Models

• Linear regression, logistic regression
• Decision trees, random forests, and support vector machines
• Neural networks and deep learning fundamentals

3.3 Clustering and Dimensionality Reduction

• K-means clustering, hierarchical clustering
• Principal Component Analysis (PCA) and t-SNE
• Anomaly detection and outlier analysis

3.4 Time Series Analysis and Forecasting

• Time series data exploration and preprocessing
• ARIMA models and forecasting techniques
• Advanced time series models and applications

Module 4: Big Data and Advanced Technologies

4.1 Introduction to Big Data

• Characteristics of big data and its challenges
• Distributed computing frameworks (Hadoop, Spark)
• Big data storage solutions (HDFS, S3, Google Cloud Storage)

4.2 Cloud Computing for Data Analytics

• Cloud platforms (AWS, Google Cloud, Azure)
• Setting up cloud-based data analytics environments
• Scalability, security, and cost considerations

4.3 Advanced Analytics with Spark

• Spark architecture and RDDs
• Spark SQL and DataFrame API
• Machine learning with Spark MLlib

Module 5: Capstone Project and Industry Applications

5.1 Capstone Project

• Real-world data analytics project in collaboration with industry partners
• Project planning, execution, and presentation
• Peer review and feedback sessions