Data Science With Python And R

by Infoem Solution Claim Listing

Data Science with Python and R course is offered by Infoem Solution. Infoem Solutions, your trusted partner in IT education and professional development in Namakkal and beyond. With over a decade of experience, we specialize in comprehensive software training.

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Course Details

Data Science with Python and R course is offered by Infoem Solution. Infoem Solutions, your trusted partner in IT education and professional development in Namakkal and beyond. With over a decade of experience, we specialize in comprehensive software training, app and website development, internships, and college projects.

 

Content:

  • 1. Introduction to Data Science
  • Overview of Data Science
  • Roles and Responsibilities of a Data Scientist
  • Tools and Technologies Used in Data Science
  • 2. Data Handling and Manipulation
  • Python
  • Introduction to Python for Data Science
  • Data Structures in Python (Lists, Dictionaries, Tuples, Sets)
  • Data Manipulation with Pandas
  • Data Cleaning and Preparation
  • Data Visualization with Matplotlib and Seaborn
  • R
  • Introduction to R for Data Science
  • Data Structures in R (Vectors, Matrices, Data Frames)
  • Data Manipulation with dplyr and tidyr
  • Data Cleaning and Preparation in R
  • Data Visualization with ggplot2
  • 3. Statistical Analysis and Modeling
  • Descriptive Statistics
  • Measures of Central Tendency
  • Measures of Dispersion
  • Correlation and Covariance
  • Inferential Statistics
  • Hypothesis Testing
  • Confidence Intervals
  • ANOVA and Chi-Square Tests
  • Regression Analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Time Series Analysis
  • Time Series Decomposition
  • ARIMA Modeling
  • Forecasting Techniques
  • 4. Machine Learning
  • Python
  • Supervised Learning Algorithms (Linear Regression, Decision Trees, Random Forests)
  • Unsupervised Learning Algorithms (K-Means, PCA)
  • Model Evaluation and Tuning with Scikit-Learn
  • R
  • Supervised Learning Algorithms (Linear Regression, Decision Trees, Random Forests)
  • Unsupervised Learning Algorithms (K-Means, PCA)
  • Model Evaluation and Tuning in R
  • 5. Data Science Project Lifecycle
  • Problem Definition and Data Collection
  • Data Exploration and Visualization
  • Feature Engineering
  • Model Building and Evaluation
  • Model Deployment
  • 6. Big Data and Cloud Computing
  • Introduction to Big Data and Hadoop
  • Data Processing with Spark
  • Cloud Computing Basics (AWS, Google Cloud, Azure)
  • Data Science on Cloud Platforms
  • 7. Project Work
  • End-to-End Data Science Project with Python
  • End-to-End Data Science Project with R
  • Model Deployment and Monitoring
  • 8. Soft Skills and Interview Preparation
  • Problem-Solving Techniques
  • System Design Concepts
  • Coding Practice with Data Structures and Algorithms
  • Mock Interviews and Resume Building
  • 9. Optional Topics
  • Deep Learning with TensorFlow and Keras
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Data Ethics and Privacy
  • Salem Branch

    2nd Floor, Lmr shopping arcade, Salem

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