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Data Science with Python and R

The Data Science with Python and R course offers a thorough introduction to data science through two widely-used programming languages are Python and R.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Data Science with Python and R course offers a thorough introduction to data science through two widely-used programming languages: Python and R.

  2. Participants will explore data manipulation, visualization, statistical analysis, and machine learning methods.

  3. By leveraging the unique advantages of both Python and R, learners will gain skills in analyzing intricate datasets and constructing predictive models.

  4. This course is suitable for both beginners and those with some experience, focusing on building a solid foundation in data science.

  5. Enrollees will enhance their analytical capabilities and prepare for advanced data science applications.

CERTIFICATION:

  1. Completion certificate issued by the training provider.

  2. Equips participants for certifications such as:

  3. Microsoft Certified: Data Scientist Associate and Cloudera Data Science Certification.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the core principles of data science and its developmental stages.

  2. Utilize Python libraries (Pandas, NumPy) and R packages (dplyr, tidyr) for data manipulation and analysis.

  3. Develop meaningful visual representations with Matplotlib, Seaborn, and ggplot2.

  4. Conduct statistical evaluations and hypothesis testing.

  5. Construct machine learning models using Python’s Scikit-learn and R’s caret.

  6. Address practical challenges through data science methodologies.

  7. Enhance data-driven decision-making skills.

  8. Explore data cleaning and preprocessing techniques.

  9. Implement advanced analytics for deeper insights.

  10. Collaborate on data science projects to apply learned concepts.

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Course Curriculum

Introduction to Data Science
  1. Overview of data science and its applications
  2. Comparing Python and R for data analysis
  3. Setting up the environment: Anaconda, Jupyter Notebook, and RStudio
Data Manipulation and Cleaning
  1. Python: Using Pandas and NumPy for data manipulation
  2. R: Data wrangling with dplyr and tidyr
  3. Handling missing and inconsistent data
Data Visualization
  1. Python: Visualizing data with Matplotlib and Seaborn
  2. R: Creating visualizations using ggplot2
  3. Best practices for presenting data effectively
Statistical Analysis
  1. Descriptive and inferential statistics
  2. Hypothesis testing and p-values
  3. Correlation and regression analysis
Introduction to Machine Learning
  1. Understanding supervised and unsupervised learning
  2. Python: Building models with Scikit-learn
  3. R: Implementing machine learning with caret
  4. Evaluating model performance
Real-World Data Science Projects
  1. End-to-end project using Python and R
  2. Solving business problems with data science
  3. Deploying data science solutions
  1. Time series analysis and forecasting
  2. Natural language processing basics
  3. Overview of deep learning frameworks

Training Features

Hands-On Projects

Work on real-world datasets from various industries like healthcare, finance, and e-commerce.

Dual Language Learning

Gain expertise in both Python and R for comprehensive skill development.

Live Coding Sessions

Interactive coding demonstrations by expert instructors.

Practical Assignments

Practice data manipulation, visualization, and machine learning tasks.

Comprehensive Resources

Downloadable datasets, code templates, and reference guides.

Assessment Quizzes

Reinforce learning with quizzes at the end of each module.

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