20th August 2023
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Curriculum for Data Analyst
Module 1: Exploratory Data Analysis and Visualization (10 hours)
- Introduction to data exploration techniques and data profiling.
- Principles and best practices of data visualization.
- Chart types and graphs for different data types.
- Exploratory data analysis using Python or R.
- Interactive data visualization tools (e.g., Tableau, Power BI).
- Hands-on project: Performing data exploration and creating interactive visualizations.
Module 2: Statistical Analysis and Hypothesis Testing (10 hours)
- Descriptive statistics and data distribution analysis.
- Formulating hypotheses and conducting significance testing.
- Parametric and non-parametric tests for data analysis.
- Correlation and regression analysis.
- Statistical analysis using Python or R.
- Hands-on project: Applying statistical techniques to draw insights from data.
Module 3: SQL and Database Querying (10 hours)
- Introduction to relational databases and SQL.
- Data manipulation using SQL (e.g., SELECT, JOIN, GROUP BY).
- Filtering and sorting data in SQL.
- Aggregate functions and subqueries in SQL.
- Database management using SQL.
- Hands-on project: Writing SQL queries to retrieve and manipulate data.
Module 4: Business Intelligence and Reporting (10 hours)
- Introduction to business intelligence tools (e.g., Power BI, Tableau).
- Designing interactive dashboards and reports.
- Data modeling and preparation for reporting.
- Creating calculated measures and KPIs.
- Dashboard publishing and sharing.
- Hands-on project: Creating interactive dashboards and reports using a business intelligence tool.
For admission to this Professional Certificate course in Data Analyst Course, candidates should have:
- Basic Programming Knowledge
- Database Fundamentals
- Data Analytics Basics
- Mathematics and Statistics (recommended but not mandatory)
- Data Analysis Tools (e.g., Pandas, NumPy, SQL) (recommended but not mandatory)
Upon completion of this course, students will be able to:
- Conduct exploratory data analysis and visualize data using appropriate charts and graphs.
- Apply statistical analysis techniques, such as descriptive statistics and inferential statistics, to gain insights from data.
- Utilize SQL queries to retrieve and manipulate data from databases efficiently.
- Create interactive dashboards and reports using business intelligence tools.
- Present data analysis findings and insights in a clear and concise manner to stakeholders.