Our curriculum

1.SQL

• Basic query

• Joins, unions

• Aggregate functions, window functions

• Subqueries/table creation

• Case statements, regex

• Group by

  • Data validation

  • Best practices for large datasets

  • SQL practice questions

2.Coding for Analyst - Part 1

• Overview of Python

• Data Types

• Functions & Dictionaries

• Lambda Functions

• Use of Python with SQL

3.Coding for Analyst - Part 2

• How to use libraries; NumPy, Pandas

• How to clean data

• Overview of Regex

• Conduct EDA in Python

• Business Case Study with a dataset

• Visualization in Python

1.Probability and Statistics

• Basic Probability Rules

• Conditional Probability

• Building a Distribution

• Review of Distributions (Normal, Binomial,xponential, Bernoulli, Poisson)

2.Data Analytics & Business Analytics

• Defining metrics

• Hypothesis testing, p-value, statistical significance, confidence intervals

• Regression (linear & multiple, introduction to logistic, lasso, ridge)

• Parametric Testing: Chi-Square, T-Test, ANOVA

• Non-parametric data/testing: Alternative metrics, sign test, Wilcoxon rank sum test

• Normalizing data, handling outliers

• A/B testing

• Case study with a business problem

3.Understanding Business Needs and Decision-Making

• Understanding your stakeholders

• Scope and Practicality of Business Problems

• Project scoping document (question, impact, timeline, potential roadblocks)

• Threshold of Significance, Assessing Impact, Change behaviors

• Necessary requirements/completion

• Data Analyst & Business Analyst Toolkit

4.Visualization Tools (Tableau)

• Introduction to Tableau

• Connecting and loading data

• Visualization techniques

• Designing dashboard

• Storytelling

5.Advanced Data & Business Analytics

• Introduction to Machine Learning: Supervised, Unsupervised, Reinforcement

• Naive Bayes, Logistic Regression, SVM, Decision Trees, Random Forest

• Bagging, Boosting

• Neural Networks (ANN, CNN, and RNN)

6.Application (Case Studies)

• Best practices while solving Case Studies in interviews

• Case Study 1: Trends/Finance

• Case Study 2: Product Analysis

• Case Study 3: Business Strategy

• Metric definition, ROI, return per unit of investment

• Growth potential assessment

• Opportunity cost

1.Data Platform and Architecture

• Data Warehousing, Data Lake, Unified Data Model (UDM)

• Database Schemas Design

• Data Governance

2.Data Pipeline

• Data Pipelines vs. ETL

• Data Collection: Ingestion, Processing, Batching, Validation, Analysis, etc.