data analysis

Objectives

Empower students with data wrangling, analysis, and visualization skills, utilizing tools like Python, SQL, and statistical methods to derive actionable insights and make data-driven decisions.

Enable students to master data analysis and visualization techniques using Python, SQL, and Power BI, encompassing data cleaning, creation of data dashboards, visualization of insights, and leveraging Python for comprehensive data analysis and manipulation.

Eligibility

  • No Prior Experience Required: Open to beginners without prior data analytics or coding experience but with a keen interest in data-driven insights.
  • Analytical Mindset: Ideal for students or fresh graduates curious about analyzing data, storytelling with data, and making informed decisions.
  • Transitioning to Tech or Data Roles: Suitable for individuals aspiring to pivot their careers toward tech or data-focused roles, offering foundational skills in data analytics.

Technologies

Excel
Excel
SQL
SQL
Python
Python
Power BI
Power BI

Program Outline

Module 1: Excel

Excel Topics

  • Introduction to Data Analysis and Excel Basics
  • Data Entry and Formatting
  • Basic Formulas and Functions
  • Data Manipulation: Sorting and Filtering
  • PivotTables and PivotCharts
  • Advanced Formulas: VLOOKUP, HLOOKUP, INDEX, and MATCH
  • Data Validation and What-If Analysis
  • Creating and Customizing Charts
  • Using Sparklines
Module 2: SQL

SQL Topics

  • Introduction to Databases and SQL
  • Basic SQL Commands: SELECT, FROM, WHERE
  • Data Retrieval: DISTINCT, ORDER BY, LIMIT
  • Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
  • Writing and Using Subqueries
  • Aggregating Data: GROUP BY, HAVING
  • Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
  • Using CASE Statements
  • Window Functions: ROW_NUMBER, RANK, PARTITION BY
  • Creating and Managing Tables
  • Indexes and Performance Tuning
Module 3: Python

Python Topics

  • Introduction to Python Programming
  • Data Types, Variables, and Basic Operations
  • Data Structures: Lists, Tuples, Dictionaries, and Sets
  • Introduction to Pandas and NumPy
  • DataFrames: Creation, Indexing, and Slicing
  • Data Cleaning and Preprocessing
  • Aggregating and Grouping Data
  • Merging and Joining Data Sets
  • Introduction to Matplotlib and Seaborn
  • Creating Various Types of Plots
Module 4: Power BI

Power BI Topics

  • Overview of Power BI Interface
  • Connecting to Data Sources
  • Using Power Query Editor
  • Data Cleaning and Shaping
  • Creating and Customizing Visuals
  • Different Types of Visuals in Power BI
  • Creating Interactive Dashboards
  • Publishing and Sharing Reports
  • Introduction to DAX
  • Creating Calculated Columns and Measures
  • Using Power BI's Analytics Features
  • Implementing Advanced Data Visualization Techniques
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