Data Analyst
Week 1-2: Introduction to Data Analysis
- Overview of Data Analysis: Role, Importance, and Applications
- Introduction to Data Types: Structured, Semi-Structured, and Unstructured Data
- Basics of Data Visualization: Charts, Graphs, and Dashboards
- Introduction to Excel: Data Entry, Formulas, Functions, and Basic Data Analysis
- Introduction to Data Types: Structured, Semi-Structured, and Unstructured Data
- Basics of Data Visualization: Charts, Graphs, and Dashboards
- Introduction to Excel: Data Entry, Formulas, Functions, and Basic Data Analysis
Week 3-4: Data Wrangling with Python and Pandas
- Introduction to Python for Data Analysis: Syntax, Variables, and Data Types
- Introduction to Pandas Library: Data Structures (Series, DataFrame), Indexing, and Slicing
- Data Cleaning and Preprocessing Techniques: Handling Missing Values, Duplicates, and Outliers
- Data Transformation Techniques: Reshaping, Merging, and Concatenating DataFrames
- Introduction to Pandas Library: Data Structures (Series, DataFrame), Indexing, and Slicing
- Data Cleaning and Preprocessing Techniques: Handling Missing Values, Duplicates, and Outliers
- Data Transformation Techniques: Reshaping, Merging, and Concatenating DataFrames
Week 5-6: Exploratory Data Analysis (EDA)
- Exploratory Data Analysis (EDA) Techniques: Descriptive Statistics, Data Visualization
- Data Visualization with Matplotlib and Seaborn: Scatter Plots, Histograms, Box Plots, Heatmaps
- Correlation Analysis: Pearson Correlation Coefficient, Correlation Heatmaps
- Feature Engineering: Creating New Features, Feature Scaling, and Transformation
- Data Visualization with Matplotlib and Seaborn: Scatter Plots, Histograms, Box Plots, Heatmaps
- Correlation Analysis: Pearson Correlation Coefficient, Correlation Heatmaps
- Feature Engineering: Creating New Features, Feature Scaling, and Transformation
Week 7-8: Convolutional Neural Networks (CNNs)
- Introduction to Statistics: Measures of Central Tendency, Dispersion, and Distribution
- Hypothesis Testing: T-tests, ANOVA, Chi-Square Test
- Regression Analysis: Simple Linear Regression, Multiple Linear Regression
- Time Series Analysis: Decomposition, Seasonality, and Trend Analysis
- Hypothesis Testing: T-tests, ANOVA, Chi-Square Test
- Regression Analysis: Simple Linear Regression, Multiple Linear Regression
- Time Series Analysis: Decomposition, Seasonality, and Trend Analysis
Week 9-10: SQL for Data Analysis
- Introduction to SQL: Basic Queries, Filtering, Sorting, and Aggregation
- Advanced SQL Concepts: Joins, Subqueries, and Window Functions
- Data Manipulation with SQL: Insert, Update, Delete Operations
- Working with Date and Time Functions in SQL
- Advanced SQL Concepts: Joins, Subqueries, and Window Functions
- Data Manipulation with SQL: Insert, Update, Delete Operations
- Working with Date and Time Functions in SQL
Week 11-12: Data Visualization with Advanced Tools
- Introduction to Advanced Data Visualization Tools: Tableau, Power BI
- Creating Interactive Dashboards and Reports
- Advanced Data Visualization Techniques: Geographic Mapping, Treemaps, and Network Graphs
- Storytelling with Data: Effective Communication and Presentation of Insights
- Creating Interactive Dashboards and Reports
- Advanced Data Visualization Techniques: Geographic Mapping, Treemaps, and Network Graphs
- Storytelling with Data: Effective Communication and Presentation of Insights
Week 13-14: Machine Learning for Data Analysis
- Introduction to Machine Learning: Supervised Learning, Unsupervised Learning
- Machine Learning Algorithms: Decision Trees, Random Forests, K-Means Clustering, etc.
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC Curve, Confusion Matrix
- Model Deployment and Integration: Saving and Loading Models, Integration with Applications
- Machine Learning Algorithms: Decision Trees, Random Forests, K-Means Clustering, etc.
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC Curve, Confusion Matrix
- Model Deployment and Integration: Saving and Loading Models, Integration with Applications
Week 15-16: Capstone Project and Career Preparation
- Capstone Project: Design and execute a real-world data analysis project incorporating concepts learned throughout the course
- Project Presentation and Peer Review
- Career Guidance: Resume Building, Interview Preparation, and Job Search Strategies for Data Analyst Roles
- Project Presentation and Peer Review
- Career Guidance: Resume Building, Interview Preparation, and Job Search Strategies for Data Analyst Roles
