Module 1: Introduction to Data Analysis and R
- Introduction to Data Analysis
- Understanding Data & types
- Overview of Data Analyst and Data Scientist Roles
- Population & Sample
- Introduction to R and RStudio
- Installing R and RStudio
- Navigating the RStudio Interface
- Basic R Syntax and Operations
- Using R Scripts and R Markdown
- Basic Data Structures in R
- Vectors, Matrices, Lists, Data Frames, Strings
Skills You Will Learn: Running basic R commands.
Hands-On: Setting up R and RStudio, exploring basic R commands and data structures.
Module 2: Descriptive Statistics and Probability Concepts
- Descriptive Statistics
- Measures of Central Tendency
- Measures of Shape
- Measures of Dispersion
- Measures of Association
- Probability Concepts
- Basic Probability, Conditional Probability, Bayes’ Theorem
- Probability Distributions – Binomial, Poisson, Normal, etc
Skills You Will Learn: Summarizing and visualizing data.
Hands-On: Compute descriptive statistics, create basic visualizations using ggplot2.
Module 3: Data Manipulation in R
- Data Manipulation with dplyr and tidyr
- Filtering, Selecting, Mutating, Summarizing
- Reshaping Data with tidyr
- Data Manipulation with data.table
- Efficient Data Operations
- String Manipulation with stringr
- Date and Time Manipulation with lubridate
- Functional Programming with purrr
Skills You Will Learn: Manipulating and cleaning data in R.
Hands-On: Perform data manipulation tasks using dplyr, tidyr, data.table, and other packages.
Module 3: Statistical Inference
- Statistical Inference
- Sampling Techniques
- Central Limit Theorem
- Theory of Large Numbers
- Confidence Intervals
Skills You Will Learn: Understanding probability, calculating confidence intervals.
Hands-On: Calculate probabilities, visualize probability distributions, perform sampling.
Module 4: Hypothesis Testing and ANOVA
- Hypothesis Testing
- Null and Alternative Hypotheses
- P-values and Significance Levels
- One-sample and Two-sample T-tests
- Chi-Square Tests
- Analysis of Variance (ANOVA)
- One-Way and Two-Way ANOVA
- Post-Hoc Tests
Skills You Will Learn: Conducting hypothesis tests and ANOVA.
Hands-On: Perform hypothesis tests and ANOVA, interpret results.
Module 5: Regression Analysis
- Simple Linear Regression
- Fitting a Linear Model
- Interpreting Coefficients
- Multiple Linear Regression
- Adding Multiple Predictors
- Model Diagnostics
- Advanced Regression Techniques
- Logistic Regression
- Regularization Techniques (Ridge Regression, Lasso Regression)
Skills You Will Learn: Building and interpreting regression models.
Hands-On: Fit and evaluate regression models.
Module 6: Time Series Analysis
- Introduction to Time Series Data
- Components of Time Series (Trend, Seasonality)
- Time Series Decomposition
- Additive and Multiplicative Models
- Forecasting Techniques
- Moving Averages, Exponential Smoothing, ARIMA Models
Skills You Will Learn: Analyzing and forecasting time series data.
Hands-On: Decompose and forecast time series data.
Module 8: Data Visualization with Advanced Tools
- Data Visualization in R
- Exploratory Data Analysis (EDA)
- Introduction to ggplot2
- Creating Basic Plots (Histogram, Boxplot, Scatterplot)
- Customizing Plots
- Advanced Data Visualization
- Creating Interactive Visualizations with plotly
- Building Interactive Dashboards with shiny
- Customizing Visualizations
- Themes and Custom Functions in ggplot2
Skills You Will Learn: Creating advanced visualizations, building interactive dashboards.
Hands-On: Create interactive plots and dashboards.
Module 9: Git and GitHub for Data Analysts
- Introduction to Version Control
- Understanding Git and GitHub
- Setting Up Git and GitHub
- Basic Git Commands
- Initializing a Repository, Committing Changes, Branching, Merging
- Collaborating with GitHub
- Pull Requests, Code Reviews, Managing Issues
Skills You Will Learn: Using Git and GitHub for version control.
Hands-On: Set up and use GitHub, collaborate on projects.
Module 10: Advanced Excel for Data Analysis
- Advanced Excel Functions
- Pivot Tables, Advanced Formulas, Conditional Formatting
- What-If Analysis
- Scenario Manager, Goal Seek, Data Tables
- Data Analysis Tools
- Solver, Descriptive Statistics
- Data Visualization in Excel
- Creating Charts and Graphs
- Using Sparklines
Skills You Will Learn: Advanced Excel techniques for data analysis.
Hands-On: Create pivot tables, perform what-if analysis, use data analysis tools.
Module 11: Databases and MySQL
- Introduction to Databases
- Understanding Database Concepts
- Relational Databases
- SQL for Data Analysis
- Basic SQL Queries (SELECT, INSERT, UPDATE, DELETE)
- Advanced SQL Queries (JOINS, Subqueries, Aggregations)
- Working with MySQL
- Setting Up MySQL, Importing Data, Performing Queries
Skills You Will Learn: Using SQL for data analysis, working with MySQL.
Hands-On: Perform SQL queries, set up and use MySQL.
Module 12: Power BI for Data Visualization
- Introduction to Power BI
- Connecting to Data Sources
- Data Transformation and Modeling
- Creating Reports and Dashboards
- Basic and Advanced Visualizations
- Using DAX for Calculations
- Creating Measures and Calculated Columns
- Publishing and Sharing Reports
Skills You Will Learn: Using Power BI for data analysis and visualization.
Hands-On: Create reports and dashboards in Power BI.
Module 13: Data Mining and CRISP-DM
- Introduction to Data Mining
- Understanding CRISP-DM Methodology
- Data Preprocessing
- Handling Missing Values, Outlier Detection
- Building Data Mining Models
- Classification, Clustering, Association Analysis
Skills You Will Learn: Applying CRISP-DM methodology, building data mining models.
Hands-On: Perform data preprocessing, build and evaluate data mining models.
Module 14: Machine Learning for Data Analysts
- Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Supervised Learning Techniques
- Linear Regression, Decision Trees, Random Forests
- Model Evaluation and Tuning
- Unsupervised Learning Techniques
- K-Means Clustering, PCA
- Using Machine Learning Libraries in R
- caret, randomForest, e1071, keras
Skills You Will Learn: Building and evaluating machine learning models.
Hands-On: Implement machine learning models in R.
Module 15: Bonus Module: AI, LLMs & Future Trends
- Introduction to AI and Generative Models
- Large Language Models (LLMs) Overview (GPT, BERT, etc.)
- Applications of AI in Data Science
- The Future of AI and Its Impact on Data Analytics
- Hands-on: AI-powered Data Analysis with R & Python
Hands-On: Application of AI.
Module 16: Final Project and Evaluation
- Capstone Project
- Applying All Learned Concepts
- Data Analysis and Interpretation
- Creating Comprehensive Reports and Dashboards
- Presentation and Feedback
Skills You Will Learn: End-to-end project implementation, presenting insights.
Hands-On: Complete a capstone project involving data analysis and visualization.
Course Duration & Options
1. Full-Time Weekly (3 Days a Week, 4 Hours per Day)
- Total hours per day: 4 hours
- Total days per week: 3 days
- Total hours per week: 4 hours/day * 3 days/week = 12 hours/week
- Total weeks: 20-22 weeks
2. Weekend (6 Hours a Day)
- Total hours per day: 6 hours
- Total days per week: 1 day
- Total hours per week: 6 hours/day * 2 days/week = 12 hours/week
- Total weeks: 20-22 weeks
3. Part-Time Weekly (2 Hours per Day, 3 Days a Week)
- Total hours per day: 2 hours
- Total days per week: 3 days
- Total hours per week: 2 hours/day * 3 days/week = 6 hours/week
- Total weeks: 40-42 weeks
Learning Outcomes
By the end of the program, candidates will:
- Develop a strong foundation in data analysis using R and essential tools.
- Apply statistical inference for data analysis.
- Perform predictive analytics with statistical modeling, time series forecasting, and machine learning.
- Master data manipulation, visualization, and dashboard creation.
- Utilize SQL, Power BI, and advanced Excel for data handling.
- Understand AI concepts, LLMs, and future trends in data science.
- Complete real-world projects and build a strong portfolio.
Who this course is for?
Anyone who aspires to become Data Analysts, Data Scientists, and Business Analysts, working professionals looking to upskill in data science & students and recent graduates seeking industry-ready training to enter the world of data. Anyone interested in statistical analysis and data-driven decision-making with a background/undergraduation in math, statistics, engineering, economics.
Program Highlights
- Weekly Assignments & Quizzes
- Structured Learning with unlimited content access
- Real-World Projects & Portfolio Development
- Live, Real-Time coding Classes with Personalized Guidance
- Small Batch Sizes (Max 6 Students) for Individual Attention
- Cohort-Based Learning for Peer Interaction
- Multi-Language Support (Course in R, Python Codes Included)
- Deep Focus on Applied Statistics & Data Science
- Industry Certification assistance e.g., Microsoft PL-300 for Power BI
- Unlimited classes with no restrictions
- Add-On Supportive Video Lessons for Reinforcement
- Resume Rework & Job Search Assistance
- Internship Opportunities Available
