R and Python for Data Analysis Professional Certification Course
Program Name - R and Python for Data Analysis Professional Certification Course
The only professional certification course you need to learn data analysis using R and Python.
Course Overview
This course is designed to equip learners with essential skills in data analysis using R and Python. It covers foundational concepts, such as data manipulation, visualization, and statistical analysis, progressing to advanced topics like machine learning, text analytics, and big data integration. Through hands-on projects and case studies, students will gain practical experience in exploring, cleaning, and analyzing data, making informed decisions based on insights. The course emphasizes both the technical and strategic aspects of data analysis, ensuring learners are well-prepared for real-world applications in diverse industries.
Course Type
Entry to Intermediate Level
Course Objectives
– To understand the fundamentals of data analysis and its applications.
– To gain proficiency in using R and Python for data manipulation, visualization, and statistical analysis.
– To explore advanced data analysis techniques, including machine learning and text analytics.
– To develop hands-on experience with data analysis tools and techniques through projects and case studies.
– To prepare learners for solving real-world business problems with data-driven insights.
What You'll Learn?
In this course, you will learn the basics of R and Python, focusing on their application in data analysis. You’ll start with data manipulation, statistical analysis, and visualization in both languages. Advanced topics include working with machine learning models, handling big data, and text analytics. Practical projects, such as stock market analysis and customer segmentation, will provide hands-on experience. By the end of the course, you’ll have the confidence to use R and Python for data-driven decision-making and business problem-solving.
Duration
– Total Hours: 60-70 hours
– Introduction: 5 hours
– R for Data Analysis: 20 hours
– Python for Data Analysis: 20 hours
– Advanced Topics: 10 hours
– Projects and Tips: 5-10 hours
Requirements
– A computer with an internet connection and basic software (RStudio, Anaconda).
– Willingness to learn programming and data analysis techniques.
Prerequisites
– Basic familiarity with programming concepts is helpful but not required.
– Fundamental understanding of mathematics and statistics is recommended.
Target Audience
– Aspiring data analysts and data scientists looking to learn R and Python.
– Professionals transitioning into data-driven roles.
– Students and researchers in need of data analysis skills for projects.
– Business analysts seeking to enhance data-driven decision-making abilities.
– Anyone interested in exploring the field of data analytics.
Batch Details
Batch Start Date
Batch Timing
Batch End Date
Batch Days
Curriculum
1. Understanding Data Analysis
– Importance and Applications of Data Analysis
– Overview of Data Analysis Lifecycle
2. R and Python for Data Analysis
– Key Features and Use Cases
– Comparison: R vs. Python
1. Getting Started with R
– Installing R and RStudio
– Understanding R Environment
– Basic R Syntax and Commands
2. Data Structures in R
– Vectors, Lists, Matrices, Arrays
– Data Frames and Tibbles
3. Data Manipulation with R
– Importing and Exporting Data (CSV, Excel, Databases)
– Data Cleaning and Transformation using `dplyr`
– Data Wrangling with `tidyr`
4. Data Visualization with R
– Introduction to `ggplot2`
– Creating Scatterplots, Histograms, and Boxplots
– Customizing Visualizations
5. Statistical Analysis in R
– Descriptive Statistics
– Hypothesis Testing (t-tests, ANOVA)
– Regression Analysis
1. Getting Started with Python
– Installing Python and IDEs (Jupyter, Anaconda)
– Python Basics: Syntax, Variables, and Data Types
2. Python Libraries for Data Analysis
– Overview of `NumPy`, `Pandas`, `Matplotlib`, `Seaborn`, and `Scikit-learn`
3. Data Structures in Python
– Lists, Tuples, Sets, and Dictionaries
– DataFrames with `Pandas`
4. Data Manipulation with Python
– Reading and Writing Data (CSV, Excel, Databases)
– Cleaning and Reshaping Data with `Pandas`
– Handling Missing Values and Duplicates
5. Data Visualization with Python
– Creating Charts and Plots with `Matplotlib` and `Seaborn`
– Advanced Visualizations (Heatmaps, Pairplots)
– Customizing and Exporting Visualizations
6. Statistical Analysis in Python
– Basic Descriptive and Inferential Statistics
– Correlation and Regression Analysis
– Time Series Analysis
1. Working with Big Data
– Overview of Big Data Technologies (Hadoop, Spark)
– Using Python and R with Big Data
2. Machine Learning Basics
– Supervised vs. Unsupervised Learning
– Simple Predictive Modeling in R and Python
3. Text Analytics
– Text Preprocessing and Sentiment Analysis
– Word Clouds and NLP Basics
4. Working with APIs and Web Scraping
– Fetching Data from APIs
– Basics of Web Scraping with `rvest` (R) and `BeautifulSoup` (Python)
1. Case Studies
– Exploratory Data Analysis on a Real Dataset (R and Python)
– Visualization and Insights Generation
2. Capstone Projects
– Analyzing Stock Market Trends
– Customer Segmentation and Sales Analysis
– Predictive Modeling for Housing Prices
1. Debugging and Error Handling in R and Python
2. Code Optimization and Performance Tuning
3. Best Practices for Data Analysis and Reporting
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