Course Program

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#1Introduction to Data Science
  • Overview of Data Science---
  • Applications of Data Science---
  • Roles in Data Science Teams---
#2Fundamentals of Python Programming
  • Python Basics---
  • Working with Data Structures---
  • Functions and Modules---
  • File Handling and Operations---
#3Statistical Foundations for Data Science
  • Descriptive Statistics---
  • Probability Theory---
  • Inferential Statistics---
  • Hypothesis Testing---
#4Data Wrangling and Preprocessing
  • Data Cleaning Techniques---
  • Handling Missing Data---
  • Data Transformation---
  • Exploratory Data Analysis---
#5Data Visualization Techniques
  • Principles of Data Visualization---
  • Plotting with Matplotlib---
  • Seaborn for Statistical Plots---
  • Interactive Plots with Plotly---
#6Introduction to Databases
  • Database Concepts---
  • SQL Basics---
  • NoSQL Databases---
  • Data Warehousing---
#7Big Data Concepts and Tools
  • Introduction to Big Data---
  • Hadoop and HDFS---
  • Spark for Big Data Processing---
  • Big Data Ecosystem---
#8Machine Learning Fundamentals
  • Supervised vs Unsupervised Learning---
  • Regression Techniques---
  • Classification Methods---
  • Clustering Algorithms---
#9Advanced Analytics and Deep Learning
  • Introduction to Neural Networks---
  • Building Deep Learning Models---
  • Deep Learning Frameworks---
  • Applications of Deep Learning---
#10Natural Language Processing (NLP)
  • Introduction to NLP---
  • Tokenization and Text Processing---
  • Building NLP Models---
  • NLP Use Cases---
#11Time Series Analysis
  • Components of Time Series Data---
  • Time Series Forecasting Models---
  • ARIMA and SARIMA Models---
  • Validation Methods---
#12Recommender Systems
  • Introduction to Recommender Systems---
  • Collaborative Filtering---
  • Content-Based Filtering---
  • Hybrid Recommender Approaches---
#13Deploying and Integrating Data Science Solutions
  • Model Deployment Strategies---
  • APIs for Integration---
  • Monitoring and Maintaining Models---