Data Analysis Training Syllabus (45 Days) – KIT Training Point

Total Duration: 45 Days
Total Hours: 90 Hours (2 Hours per Day)

Module 1: Introduction to Data Analysis (Day 1-5 | 10 Hours)

• What is Data Analysis?
  • Introduction to Data Science and Data Analytics
  • Importance and Application of Data Analysis in Various Industries
  • Key Concepts: Data Collection, Data Cleaning, Data Transformation, Data Visualization
• Data Types and Structures
  • Understanding Different Types of Data (Qualitative vs Quantitative)
  • Working with Structured and Unstructured Data
  • Data Formats: CSV, JSON, Excel, SQL
• Tools for Data Analysis
  • Introduction to Excel, Python, R, SQL
  • Setting up the Data Analysis Environment
  • Installing Python, R, and relevant libraries (Pandas, NumPy, Matplotlib)

Module 2: Data Collection and Data Cleaning (Day 6-10 | 10 Hours)

• Data Collection Techniques
  • Sources of Data: Surveys, Web Scraping, APIs
  • Introduction to Data Collection Tools (Google Forms, APIs, Web Scraping Tools)
• Data Cleaning Basics
  • Handling Missing Values, Duplicates, and Outliers
  • Data Transformation and Formatting
  • Dealing with Inconsistent Data and Errors
• Hands-on Practice: Data Cleaning in Excel and Python

Module 3: Exploratory Data Analysis (EDA) (Day 11-15 | 10 Hours)

• What is Exploratory Data Analysis (EDA)?
  • EDA Process: Overview and Importance
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Data Distributions and Skewness
• Visualization Techniques in EDA
  • Introduction to Data Visualization Tools
  • Creating Histograms, Box Plots, and Bar Charts
  • Correlation and Heatmaps
  • Hands-on: EDA with Pandas, Matplotlib, and Seaborn in Python

Module 4: Statistical Analysis and Hypothesis Testing (Day 16-20 | 10 Hours)

• Introduction to Statistics for Data Analysis
  • Probability Distributions (Normal, Poisson, Binomial)
  • Statistical Measures: Mean, Median, Mode, and Range
  • Variability and Dispersion in Data
• Hypothesis Testing
  • Null and Alternative Hypotheses
  • T-tests, Z-tests, Chi-square Tests
  • Confidence Intervals and P-Values
• Hands-on Practice: Statistical Analysis Using Python and R

Module 5: Data Visualization (Day 21-25 | 10 Hours)

• Importance of Data Visualization
  • Principles of Effective Visualization
  • Storytelling with Data
• Visualization Tools and Techniques
  • Introduction to Matplotlib, Seaborn, and Tableau
  • Visualizing Data with Line Charts, Scatter Plots, and Pie Charts
  • Advanced Visualizations: Geographical Maps, Network Graphs
• Hands-on Practice: Creating Visualizations with Python and Tableau

Module 6: Advanced Data Analysis Techniques (Day 26-30 | 8 Hours)

• Regression Analysis
  • Introduction to Linear Regression and Logistic Regression
  • Understanding the Relationship Between Variables
  • Regression Models and Predictions
• Clustering and Classification
  • K-means Clustering, Hierarchical Clustering
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM) and Naive Bayes
• Hands-on Practice: Building Regression and Classification Models in Python

Module 7: SQL for Data Analysis (Day 31-35 | 8 Hours)

• Introduction to SQL for Data Analysis
  • SQL Basics: SELECT, FROM, WHERE, GROUP BY, JOIN
  • Filtering and Aggregating Data in SQL
  • SQL Queries for Data Extraction and Analysis
• SQL for Complex Data Analysis
  • Subqueries, Nested Queries, and Window Functions
  • Joining Multiple Tables and Handling Relationships
  • Using SQL for Data Cleaning and Transformation
• Hands-on Practice: SQL for Data Analysis with Real-World Data

Module 8: Data Analysis with Excel (Day 36-40 | 8 Hours)

• Excel Basics for Data Analysis
  • Introduction to Excel for Data Cleaning and Analysis
  • Functions and Formulas for Data Manipulation
  • Pivot Tables and Pivot Charts
• Advanced Excel for Data Analysis
  • Data Visualization in Excel (Charts, Graphs, Conditional Formatting)
  • Using Excel for Statistical Analysis (ANOVA, Correlation)
  • Working with External Data Sources (Importing, Merging, Filtering)
• Hands-on Practice: Data Analysis Using Excel

Module 9: Machine Learning for Data Analysis (Day 41-45 | 8 Hours)

• Introduction to Machine Learning (ML)
  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
• ML Algorithms for Data Analysis
  • Linear Regression, Logistic Regression, and Decision Trees
  • K-means Clustering and Random Forest
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
• Hands-on Practice: Building ML Models Using Python (Scikit-learn)

Final Exam and Project (Day 45)

• Final Assessment
  • Written Exam on Data Analysis Concepts
  • Practical Exam: Solving a Real-World Data Analysis Problem
• Capstone Project
  • Apply Data Analysis Skills to a Real-World Dataset
  • Create Visualizations and Insights from the Data
  • Present Findings to the Class