Course Overview:
This course will guide you through the fundamentals of data analysis, from understanding data structures and tools to performing advanced analytics techniques. You will learn how to use popular data analysis tools like Excel, Python, and SQL, and apply statistical methods to derive meaningful insights. By the end of the course, you’ll be able to analyze, visualize, and present data efficiently for decision-making.
Course Structure:
Week 1: Introduction to Data Analysis
- What is data analysis and why is it important?
- The different types of data: structured and unstructured
- Understanding data types: numerical, categorical, time-series, etc.
- Overview of data analysis tools: Excel, Python, SQL, and others
- Setting up your environment: Installing Python (Anaconda), Jupyter Notebook
- Assignment: Explore a sample dataset and identify its structure and types of data
Week 2: Data Cleaning and Preprocessing
- Understanding the importance of clean data
- Handling missing data: Techniques (mean/mode imputation, interpolation, etc.)
- Dealing with outliers and erroneous data
- Data transformation: Normalization, scaling, and encoding categorical data
- Introduction to pandas in Python for data manipulation
- Assignment: Clean and preprocess a messy dataset using pandas
Week 3: Exploratory Data Analysis (EDA)
- The importance of EDA in data analysis
- Descriptive statistics: Mean, median, mode, standard deviation, variance
- Visualizing data: Histograms, bar charts, scatter plots, and box plots
- Identifying patterns, trends, and correlations in data
- Assignment: Perform an EDA on a dataset and generate visual insights
Week 4: Data Analysis Using Excel
- Excel as a tool for data analysis
- Functions and formulas for data manipulation (SUMIF, VLOOKUP, INDEX, etc.)
- Pivot tables: Summarizing large datasets
- Data visualization in Excel: Charts and graphs
- Using Excel’s built-in data analysis tools (Solver, Data Analysis Toolpak)
- Assignment: Perform data analysis using Excel on a given dataset
Week 5: Data Analysis Using SQL
- Introduction to SQL for data querying
- Understanding databases: Tables, records, and fields
- Writing SQL queries: SELECT, WHERE, JOIN, GROUP BY, and HAVING clauses
- Aggregating data using COUNT, SUM, AVG, MIN, and MAX
- Importing data from databases to Excel or Python for further analysis
- Assignment: Write SQL queries to analyze a dataset from a relational database
Week 6: Data Analysis Using Python
- Introduction to Python for data analysis
- Working with pandas for data manipulation (Series and DataFrames)
- Using numpy for numerical analysis
- Visualizing data using matplotlib and seaborn
- Analyzing time-series data with Python
- Assignment: Analyze a complex dataset using Python and generate insights with visualizations
Week 7: Statistical Data Analysis
- Introduction to statistical methods in data analysis
- Hypothesis testing: T-tests, chi-square tests, and ANOVA
- Correlation vs. causation: Understanding relationships between variables
- Regression analysis: Simple and multiple linear regression
- Interpreting p-values and statistical significance
- Assignment: Perform a statistical analysis on a dataset and report on findings
Week 8: Advanced Analytics and Reporting
- Introduction to machine learning and predictive analytics (optional)
- Building simple predictive models (linear regression, decision trees)
- Evaluating model performance using accuracy, precision, and recall
- Communicating results: How to present your data analysis to stakeholders
- Best practices for data visualization and storytelling with data
- Final Project: Complete a full data analysis process on a given dataset, from cleaning and exploration to reporting insights and building a simple predictive model (optional).
Learning Outcomes: By the end of this course, students will be able to:- Understand and work with different types of data (structured and unstructured).
- Clean and preprocess raw data to prepare it for analysis.
- Perform exploratory data analysis (EDA) to uncover insights.
- Use Excel for basic to intermediate-level data analysis tasks.
- Write SQL queries to extract and analyze data from relational databases.
- Use Python (pandas, numpy, matplotlib) for advanced data manipulation and analysis.
- Apply statistical methods to analyze and interpret data.
- Present findings through clear and effective data visualization and reporting.
- A computer with internet access
- Microsoft Excel (or equivalent) for data analysis
- Python (with pandas, numpy, matplotlib, seaborn installed)
- Access to a database or database management tool for SQL practice (e.g., MySQL, SQLite)
- Jupyter Notebook for Python-based data analysis