Data Analyst All-in-1: Excel, Python, PowerBI and ChatGPT

Course Curriculum

Chapter 1: Data Analysis, Statistics & Machine Learning Basics

Lecture 1: Data analysis definition, types and examples

Lecture 2: Key components of data analysis

Lecture 3: Connect with my youtube channel

Lecture 4: Get my special handbooks

Lecture 5: Various sources of collecting data

Lecture 6: Population v/s sample and its methods

Lecture 7: Why you cannot ignore cleaning your data

Lecture 8: Various aspects of data cleaning

Lecture 9: Various aspects of Joining datasets

Lecture 10: Adding extra data with concatenation

Lecture 11: EDA for generating significant insights

Lecture 12: Methods of exploratory data analysis Part 1

Lecture 13: Methods of exploratory data analysis Part 2

Lecture 14: Methods of exploratory data analysis Part 3

Lecture 15: The application of statistical test

Lecture 16: Types of statistical data analysis

Lecture 17: Inferential statistics Part 1 – T-tests and ANOVA

Lecture 18: Inferential statistics Part 2 – Relationships measures

Lecture 19: Inferential statistics Part 3 – Linear regression

Lecture 20: Hypothesis testing for inferential statistics

Lecture 21: Selecting statistical test and assumption testing

Lecture 22: Confidence level, significance level, p-value

Lecture 23: Making decision and conclusion on findings

Lecture 24: Complete statistical analysis and hypothesis testing

Lecture 25: Transforming data for improved analysis

Lecture 26: Techniques for data transformation Part 1

Lecture 27: Techniques for data transformation Part 2

Lecture 28: ML for data analysis and decision-making

Lecture 29: Widely used ML methods in the data analytics

Lecture 30: Steps in developing machine learning model

Lecture 31: Visualizing data for the best insight delivery

Lecture 32: Several methods of data visualization Part 1

Lecture 33: Several methods of data visualization Part 2

Lecture 34: Several methods of data visualization Part 3

Chapter 2: Python for A-Z Data Analytics & Machine Learning

Lecture 1: Extra note on python data analysis

Lecture 2: Resources used in the course

Lecture 3: Installing Python and Jupyter Notebook – Mac

Lecture 4: Installing Python and Jupyter Notebook – Windows

Lecture 5: More alternative methods – Check the article

Lecture 6: Getting started with first python code

Lecture 7: Assigning variable names correctly

Lecture 8: Various data types and data structures

Lecture 9: Converting and casting data types

Lecture 10: Starting with Variables to Data Types

Lecture 11: Arithmetic operators (+, -, *, /, %, **)

Lecture 12: Comparison operators (>, <, >=, <=, ==, !=)

Lecture 13: Logical operators (and, or, not)

Lecture 14: Operators in Python Programming

Lecture 15: Lists: creation, indexing, slicing, modifying

Lecture 16: Sets: unique elements, operations

Lecture 17: Dictionaries: key-value pairs, methods

Lecture 18: Several data structures

Lecture 19: Conditional statements (if, elif, else)

Lecture 20: Nested logical expressions in conditions

Lecture 21: Looping structures (for loops, while loops)

Lecture 22: Defining, creating, and calling functions

Lecture 23: Conditionals Looping and Functions

Lecture 24: Preparing notebook and loading data

Lecture 25: Identifying missing or null values

Lecture 26: Method of missing value imputation

Lecture 27: Exploring data types in a dataframe

Lecture 28: Dealing with inconsistent values