Data Analyst

Syllabus

Course Duration: 150hrs

1. Advanced MS Excel

Master industry-level spreadsheet techniques to clean, transform, and analyze business data efficiently. This module focuses on:

  • Advanced Formulas: Complex lookups (XLOOKUP, INDEX-MATCH), nested logicals (IF, AND, OR), and text manipulation.
  • Data Management: Conditional formatting, advanced data validation, sorting, dynamic filtering, and handling large data tables.
  • Summarization & Visuals: Building interactive dynamic Pivot Tables, Slicers, Pivot Charts, and executive KPI dashboards.
  • Automation Basics: Using Power Query for data cleaning and an introduction to Macros and VBA for streamlining repetitive tasks.

2. Power BI

Learn how to transform raw, disconnected data into dynamic visual reports and business intelligence dashboards for executive decision-making. Key areas include:

  • Data Extraction & Transformation: Connecting to multiple data sources (Excel, SQL, CSV) and shaping data using Power Query Editor.
  • Data Modeling: Designing relational schemas (Star Schema and Snowflake Schema) and managing table relationships.
  • DAX (Data Analysis Expressions): Writing custom calculated columns, measures, time-intelligence functions, and aggregations.
  • Dashboarding & Publishing: Designing custom visuals, configuring drill-through actions, applying tooltips, setting up Row-Level Security (RLS), and publishing reports to Power BI Service.

3. MySQL

Understand relational database management systems (RDBMS) to extract, aggregate, and manipulate enterprise-scale data using SQL queries. Topics include:

  • SQL Fundamentals: Database architecture, Data Definition Language (DDL), Data Manipulation Language (DML), and CRUD operations.
  • Querying & Filtering: Utilizing SELECT, WHERE, ORDER BY, GROUP BY, HAVING, and built-in aggregate functions (SUM, AVG, COUNT).
  • Advanced Query Techniques: Combining tables using Joins (INNER, LEFT, RIGHT, FULL), implementing Subqueries, Views, and Common Table Expressions (CTEs).
  • Analytical Tools: Applying Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG) for complex transactional reporting.

4. Advanced Python

Develop a solid programming foundation in Python tailored explicitly for automating analytics workflows and data handling. Highlights include:

  • Core Concepts: Data types, data structures (Lists, Tuples, Dictionaries, Sets), control flow statements, and custom functions.
  • Object-Oriented & Modular Code: Fundamentals of OOP (Classes, Objects, Inheritance) and modularizing scripts for scalable data pipelines.
  • Data Ingestion & APIs: File handling (reading/writing CSV, JSON, and Excel) and using HTTP libraries (requests) to pull unstructured live data from web APIs.
  • Error & Exception Handling: Writing robust scripts using try-except blocks to automate data extraction without pipeline failure.

5. NumPy

Dive into high-performance numerical computing using Python’s NumPy library. Key skills developed:

  • Array Operations: Working with $N$-dimensional arrays (ndarray), vectorization, indexing, and slicing matrices.
  • Broadcasting & Math Operations: Executing fast, memory-efficient mathematical calculations across arrays without slow standard Python loops.
  • Statistical Methods: Performing matrix operations, linear algebra computations, linear transformations, and generating random distribution samples for data simulation.

6. Pandas

Master tabular data manipulation using Pandas, the primary library for data exploration, cleaning, and preprocessing. Core learning points:

  • Data Structures: Working seamlessly with Pandas Series and DataFrames.
  • Data Preprocessing & Wrangling: Importing and exporting multi-format data, detecting and imputing missing values, removing duplicates, and casting data types.
  • Data Transformation: Combining datasets via merges, joins, and concatenations; applying conditional filters; using groupby() operations; and pivoting tables.
  • Exploratory Data Analysis (EDA): Detecting outliers, discovering structural patterns, calculating correlations, and preparing dataset profiles for downstream visualization.

7. Project (Capstone & Portfolio Building)

Synthesize technical skills by executing end-to-end analytics projects that replicate real-world industry problems. Key components:

  • End-to-End Workflow: Defining business problems, ingesting raw datasets, performing data cleaning (SQL/Python), and conducting Exploratory Data Analysis (Pandas).
  • Dashboard Integration: Generating interactive Power BI dashboards to present key findings, trends, and business performance metrics.
  • Deliverables: Translating technical output into strategic business recommendations, documenting technical code on GitHub, and hosting interactive analytical reports.

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