The Comprehensive Data Analyst Course.

The Comprehensive Data Analyst Course., available at $69.99, has an average rating of 3.95, with 191 lectures, based on 14 reviews, and has 107 subscribers.

You will learn about Basics of Python. Introduction to Numpy package for handling arrays Introduction to Pandas package for cleaning and analysing data Introduction to SQL Basics of Linear Algebra – What is a point, Line, Distance of a point from a line What is a Vector and Vector Operations What is a Matrix and Matrix Operations Visualizing data, including bar graphs, pie charts, histograms Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores Analyzing data, including mean, median, and mode, plus range and IQR and box plots Data Distributions like Normal and Chi Square Probability, including union vs. intersection and independent and dependent events and Bayes' theorem Central Limit Theorem Hypothesis Testing This course is ideal for individuals who are Aspiring Data Analysts or Business Analyst or Business Managers or Anyone wanting to learn basics of story telling through data It is particularly useful for Aspiring Data Analysts or Business Analyst or Business Managers or Anyone wanting to learn basics of story telling through data.

Enroll now: The Comprehensive Data Analyst Course.

Summary

Title: The Comprehensive Data Analyst Course.

Price: $69.99

Average Rating: 3.95

Number of Lectures: 191

Number of Published Lectures: 191

Number of Curriculum Items: 191

Number of Published Curriculum Objects: 191

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of Python.
Introduction to Numpy package for handling arrays
Introduction to Pandas package for cleaning and analysing data
Introduction to SQL
Basics of Linear Algebra – What is a point, Line, Distance of a point from a line
What is a Vector and Vector Operations
What is a Matrix and Matrix Operations
Visualizing data, including bar graphs, pie charts, histograms
Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
Analyzing data, including mean, median, and mode, plus range and IQR and box plots
Data Distributions like Normal and Chi Square
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem
Central Limit Theorem
Hypothesis Testing

Who Should Attend

Aspiring Data Analysts
Business Analyst
Business Managers
Anyone wanting to learn basics of story telling through data

Target Audiences

Aspiring Data Analysts
Business Analyst
Business Managers
Anyone wanting to learn basics of story telling through data

THE COMPREHENSIVE DATA ANALYST COURSE IS SET UP TO MAKE LEARNING FUN AND EASY

This 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Linear Algebra, Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:

Python Basics, Data Structures – List, Tuple, Set, Dictionary, Strings
Pandas and Numpy.
Linear Algebra – Understanding what is a point and equation of a line.
What is a Vector and Vector operations
What is a Matrix and Matrix operations
Data Type – Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data types
Visualizing data, including bar graphs, pie charts, histograms, and box plots
Analyzing data, including mean, median, and mode, IQR and box-and-whisker plots
Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores.
Different types of distributions – Uniform, Log Normal, Pareto, Normal, Binomial, Bernoulli
Chi Square distribution and Goodness of Fit
Central Limit Theorem
Hypothesis Testing
Probability, including union vs. intersection and independent and dependent events and Bayes’ theorem, Total Law of Probability
Hypothesis testing, including inferential statistics, significance levels, test statistics, and p-values.
Permutation with examples
Combination with examples
Expected Value
Donors Choose case study.

AND HERE’S WHAT YOU GET INSIDE OF EVERY SECTION:

We will start with basics and understand the intuition behind each topic.
Video lecture explaining the concept with many real-life examples so that the concept is drilled in.
Walkthrough of worked out examples to see different ways of asking question and solving them.
Logically connected concepts which slowly builds up.

Enroll today! Can’t wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.

YOU’LL ALSO GET:

Lifetime access to the course
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee

Course Curriculum

Chapter 1: Basic Python for Data Analysis

Lecture 1: Keywords, Identifiers and Variables

Lecture 2: Variable Assignment

Lecture 3: Strings & List

Lecture 4: Tuple

Lecture 5: Set

Lecture 6: Dictionary

Lecture 7: Data type conversion

Lecture 8: Python Comments

Lecture 9: Print Statement

Lecture 10: Python Arithmetic and Logical Operators

Lecture 11: Identity & Membership Operators

Lecture 12: For & While loop

Lecture 13: Conditional Statement

Lecture 14: Functions

Lecture 15: Modules

Lecture 16: List – Part 1

Lecture 17: List – Part 2

Lecture 18: List – Part 3

Lecture 19: List – Part 4

Lecture 20: List – Part 5

Lecture 21: Tuple – Part 1

Lecture 22: Tuple – Part 2

Lecture 23: Set – Part 1

Lecture 24: Set – Part 2

Lecture 25: Set – Part 3

Lecture 26: Dictionary

Lecture 27: Strings

Lecture 28: Numpy Introduction

Lecture 29: Creating arrays

Lecture 30: Array Operations – Part 1

Lecture 31: Array Masking

Lecture 32: Array Operations – Part 2

Lecture 33: Array Operations – Part 3

Lecture 34: Array broadcasting

Lecture 35: Array – Shape Manipulation & Sorting

Lecture 36: Pandas – Introduction

Lecture 37: Creating a DataFrame

Lecture 38: Accessing elements in a DataFrame

Lecture 39: DataFrame Filtering

Lecture 40: DataFrame Operations

Chapter 2: Basics of SQL

Lecture 1: SQL Introduction

Lecture 2: Select Command

Lecture 3: Limit Command

Lecture 4: Column Filtering

Lecture 5: DISTINCT command

Lecture 6: WHERE command

Lecture 7: AGGREGATE Functions

Lecture 8: GROUP BY command

Lecture 9: AND, OR, NULL commands

Lecture 10: LIKE command & WILDCARD characters

Lecture 11: JOINS – Part 1

Lecture 12: JOINS – Part 2

Lecture 13: JOINS – Part 3

Lecture 14: IN command

Lecture 15: HAVING Command

Lecture 16: UNION command

Lecture 17: ANY & ALL command

Chapter 3: Basics of Statistics

Lecture 1: Quick Introduction

Lecture 2: What is a random variable

Lecture 3: Nominal and Ordinal Data

Lecture 4: Central tendency – Introduction

Lecture 5: Central tendency – Examples

Lecture 6: Data Visualization

Lecture 7: Types of Quartile, Inter Quartile Range

Lecture 8: Types of Quartile, Inter Quartile Range – Example

Lecture 9: Standard Deviation & Variance

Lecture 10: Sample Standard Deviation

Lecture 11: Co Variance

Lecture 12: Normal Distribution

Lecture 13: Chi Square Distribution

Lecture 14: Chi Square Goodness of Fit

Lecture 15: Association between Categorical variables

Lecture 16: Correlation

Chapter 4: Visualization of Iris Dataset using Seaborn and Matplotlib

Lecture 1: Introduction to EDA

Lecture 2: Iris Dataset

Lecture 3: Scatter Plot

Lecture 4: Two dimensional Scatter plot

Lecture 5: Three dimensional scatter plot

Lecture 6: Pair plots

Lecture 7: One dimensional scatter plot

Lecture 8: Histogram, PDF, CDF

Lecture 9: Kde plots

Lecture 10: Kde plot – Intuition

Lecture 11: PDF and its properties

Lecture 12: CDF – Code snippet

Lecture 13: Mean, Median, Standard deviation, MAD – Code snippet

Lecture 14: Box plots

Lecture 15: Violin plot

Chapter 5: Visualization of Haberman dataset

Lecture 1: Haeberman Data – Introduction

Lecture 2: Data Overview

Lecture 3: Univariate Analysis

Lecture 4: Bivariate Analysis

Chapter 6: Donors Choose

Lecture 1: Donors Choose – Introduction

Lecture 2: Data Understanding

Instructors

Newton Academy
Data and Machine Learning Expert

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 4 votes
5 stars: 6 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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