Course Overview:

The Python and R for Data Analysis course at MELLA is a comprehensive training program designed to equip individuals with the essential skills and knowledge required to perform data analysis using Python and R programming languages. Whether you're a beginner with no prior coding experience or an experienced data professional looking to expand your analytical toolkit, this course will provide you with the foundation to effectively manipulate, analyze, and visualize data using Python and R.

 

Course Objectives:

- Gain a solid understanding of the Python and R programming languages and their applications in data analysis.

- Learn how to manipulate and clean datasets using Python libraries such as pandas and R packages like dplyr.

- Master the fundamentals of statistical analysis and data visualization techniques using Python's matplotlib, seaborn, and R's ggplot2.

- Explore advanced data manipulation and transformation methods, including data aggregation, grouping, and reshaping.

- Develop proficiency in performing exploratory data analysis (EDA) to uncover patterns, trends, and insights within datasets.

- Understand how to conduct hypothesis testing and statistical inference using Python's scipy and R's stats packages.

- Learn to build predictive models and perform machine learning tasks using Python's scikit-learn and R's caret packages.

- Gain hands-on experience working with real-world datasets and completing data analysis projects from start to finish.

 

Course Curriculum:

1. Introduction to Python and R

   - Overview of Python and R programming languages

   - Installation and setup of Python and R environments

   - Introduction to Jupyter Notebooks for interactive coding and analysis

 

2. Data Manipulation and Cleaning

   - Data import/export using pandas (Python) and readr (R)

   - Data cleaning techniques: handling missing values, data transformation, and normalization

   - Data manipulation and filtering using pandas DataFrame and dplyr's data frames

 

3. Data Visualization

   - Introduction to data visualization principles

   - Creating static and interactive visualizations using matplotlib, seaborn (Python), and ggplot2 (R)

   - Customizing plots and adding annotations for effective data communication

 

4. Exploratory Data Analysis (EDA)

   - Exploratory data analysis techniques and strategies

   - Summary statistics, distribution plots, and correlation analysis

   - Identifying and handling outliers in datasets

 

5. Statistical Analysis and Hypothesis Testing

   - Introduction to statistical analysis concepts

   - Hypothesis testing using Python's scipy and R's stats packages

   - Performing t-tests, chi-square tests, and ANOVA tests for inference

 

6. Predictive Modeling and Machine Learning

   - Overview of predictive modeling and machine learning

   - Building and evaluating predictive models using scikit-learn (Python) and caret (R)

   - Regression, classification, and clustering techniques

 

7. Real-World Data Analysis Projects

   - Applying Python and R skills to real-world datasets

   - Completing data analysis projects from data acquisition to insights generation

   - Presenting findings and insights effectively through data visualization and storytelling

 

Prerequisites:

- No prior programming experience is required, although familiarity with basic programming concepts is beneficial.

- Basic understanding of statistics and mathematics is recommended but not mandatory.

 

Whether you're looking to kickstart a career in data analysis, enhance your data science skills, or leverage data-driven insights in your current role, our Python and R for Data Analysis course will provide you with the knowledge and practical experience needed to succeed in the rapidly growing field of data analytics. Join us and unlock the power of Python and R for data-driven decision-making!