Data Analytics with Python
Course Type : Offline
Data Analytics Course Outline
Module 1: Introduction to Data Analytics
- What is Data Analytics and why is it important?
- Roles and responsibilities of a Data Analyst.
- Types of data (numerical, textual, time-based).
- Data analysis process (gathering, cleaning, analyzing).
Module 2: Proficiency in Excel/Google Sheets
- Basic features of Excel (formulas, functions, pivot tables).
- Data cleaning (removing duplicates, handling missing data).
- Visualization (charts, graphs).
- Advanced features (Macros, VBA).
Module 3: SQL Database Management
- Basics of relational databases and SQL.
- Data retrieval (SELECT, WHERE, JOIN, GROUP BY, ORDER BY).
- Data update and manipulation (INSERT, UPDATE, DELETE).
- Case study: Data analysis from a database.
Module 4: Programming for Data Analysis (Python/R)
- Python basics (loops, conditional statements).
- Libraries:
- NumPy: Matrix and mathematical analysis.
- Pandas: Data manipulation.
- Matplotlib and Seaborn: Data visualization.
- Data cleaning using regular expressions.
Module 5: Data Visualization Tools (Tableau/Power BI)
- Introduction to Tableau/Power BI.
- Creating interactive dashboards.
- Data filtering and highlighting.
- Connecting data from various sources.
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Module 6: Statistics and Data Interpretation
- Basic statistics (mean, median, mode, standard deviation).
- Hypothesis testing.
- Correlation and regression analysis.
- Case study: Decision-making using data.
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Module 7: Domain-Specific Analysis
- Business analysis.
- Market trend analysis.
- Customer behavior analysis.
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Module 8: Projects and Career Preparation
- Project: Analyzing real-world datasets and creating reports.
- Improving CV and LinkedIn profile.
- Mock interviews and freelancing guidelines.
