Data Analysis A-Z: Become Data Analyst in 30 Days

Data Analysis A-Z: Become Data Analyst in 30 Days, available at $74.99, has an average rating of 4.23, with 88 lectures, 69 quizzes, based on 126 reviews, and has 3575 subscribers.

You will learn about Gain understanding of Python's basic syntax, data types, variables, and operators, enabling you to write simple programs and perform basic operations. Learn to utilize control structures like loops and conditional statements such as use if, elif and else to manage program flow effectively. Acquire skills in working with fundamental data structures in Python, such as lists, dictionaries, tuples, and sets. Learn how to manipulate, access, and modify these structures for diverse programming needs. Employ metrics such as counts, percentages, group by, pivot tables, correlation, and regression professionally and realistically. Solve more than 20 data analytical questions to practice applying data analysis to various circumstances. Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions. Master Python for data analysis using industry-standard libraries and tools: pandas, numpy, scipy, scikit-learn etc. Master statistical inference (e.g., ANOVA, correlation, regression), draw meaningful findings, and make data-driven decisions. Understand and apply techniques for cleaning and preparing raw data in Excel. Learn to identify and handle missing data, outliers, and inconsistencies. Utilize Excel functions and tools for data validation and transformation. Explore fundamental statistical concepts and their application in Excel. Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools. Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards. Explore various data visualization techniques available in Excel, including charts, graphs. Design and build interactive dashboards in Excel for effective data visualization. This course is ideal for individuals who are Individuals looking to kickstart their career in data analysis. or Students and recent grads in data analysis or related fields. or Professionals aiming to boost their analytical skills. or Decision-makers want to understand and leverage data analysis. It is particularly useful for Individuals looking to kickstart their career in data analysis. or Students and recent grads in data analysis or related fields. or Professionals aiming to boost their analytical skills. or Decision-makers want to understand and leverage data analysis.

Enroll now: Data Analysis A-Z: Become Data Analyst in 30 Days

Summary

Title: Data Analysis A-Z: Become Data Analyst in 30 Days

Price: $74.99

Average Rating: 4.23

Number of Lectures: 88

Number of Quizzes: 69

Number of Published Lectures: 88

Number of Published Quizzes: 69

Number of Curriculum Items: 158

Number of Published Curriculum Objects: 158

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Gain understanding of Python's basic syntax, data types, variables, and operators, enabling you to write simple programs and perform basic operations.
Learn to utilize control structures like loops and conditional statements such as use if, elif and else to manage program flow effectively.
Acquire skills in working with fundamental data structures in Python, such as lists, dictionaries, tuples, and sets.
Learn how to manipulate, access, and modify these structures for diverse programming needs.
Employ metrics such as counts, percentages, group by, pivot tables, correlation, and regression professionally and realistically.
Solve more than 20 data analytical questions to practice applying data analysis to various circumstances.
Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions.
Master Python for data analysis using industry-standard libraries and tools: pandas, numpy, scipy, scikit-learn etc.
Master statistical inference (e.g., ANOVA, correlation, regression), draw meaningful findings, and make data-driven decisions.
Understand and apply techniques for cleaning and preparing raw data in Excel. Learn to identify and handle missing data, outliers, and inconsistencies.
Utilize Excel functions and tools for data validation and transformation. Explore fundamental statistical concepts and their application in Excel.
Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools.
Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards.
Explore various data visualization techniques available in Excel, including charts, graphs.
Design and build interactive dashboards in Excel for effective data visualization.

Who Should Attend

Individuals looking to kickstart their career in data analysis.
Students and recent grads in data analysis or related fields.
Professionals aiming to boost their analytical skills.
Decision-makers want to understand and leverage data analysis.

Target Audiences

Individuals looking to kickstart their career in data analysis.
Students and recent grads in data analysis or related fields.
Professionals aiming to boost their analytical skills.
Decision-makers want to understand and leverage data analysis.

Data Analysis A-Z: Become Data Analyst in 30 Days is an intensive training program designed to equip participants with the essential skills and knowledge required to excel as a data analyst. This comprehensive course covers a wide range of topics, from basic Python programming to advanced statistical analysis techniques using industry-standard tools such as pandas, numpy, and Excel.

Day 1 – 7: Data Analysis with Excel

The week of the bootcamp focuses on data analysis using Microsoft Excel. Participants will learn how to clean and prepare raw data, perform descriptive and inferential statistics, and create dynamic dashboards and visualizations using Excel functions and tools. Topics covered include:

– Cleaning and preparing raw data in Excel

– Handling missing data, outliers, and inconsistencies

– Descriptive and inferential statistics in Excel

– Creating dynamic dashboards with PivotTables and PivotCharts

– Data visualization techniques in Excel (charts, graphs, slicers)

Day 9 – 17: Python Fundamentals

In this week, participants will gain a solid understanding of Python’s basic syntax, data types, variables, and operators. They will learn how to write simple programs and perform basic operations using Python. Topics covered include:

– Introduction to Python programming language

– Understanding data types (integers, floats, strings, booleans)

– Working with variables and operators

– Utilizing control structures like loops and conditional statements (if, elif, else)

– Managing program flow effectively with control structures

Day 18 – 21: Working with Data Structures

During this week, participants will delve into fundamental data structures in Python, including lists, dictionaries, tuples, and sets. They will learn how to manipulate, access, and modify these structures for diverse programming needs. Topics covered include:

– Introduction to data structures in Python

– Working with lists, dictionaries, tuples, and sets

– Accessing and modifying elements in data structures

– Applying data structures to solve practical programming problems

Day 22 – 30: Data Analysis with Python

In this week, participants will learn how to perform data analysis tasks using Python and industry-standard libraries such as pandas, numpy, and scipy. They will acquire skills in working with dataframes, performing data manipulation, and employing metrics such as counts, percentages, group by, pivot tables, correlation, and regression. Topics covered include:

– Introduction to data analysis with Python

– Working with pandas dataframes

– Data manipulation and cleaning

– Exploratory data analysis techniques

– Statistical inference techniques (ANOVA, correlation, regression)

Throughout the bootcamp, participants will engage in hands-on exercises and real-world data analysis projects to reinforce their learning and apply their newfound skills in practical scenarios. By the end of the program, participants will have the confidence and proficiency to work as data analysts and make data-driven decisions effectively.

Course Curriculum

Chapter 1: Day 1: Getting Ready by Understanding Data Analysis

Lecture 1: Data analysis and its characteristics

Lecture 2: Complete data analysis work-flow

Lecture 3: Connect with my youtube channel

Lecture 4: Get special handbooks

Lecture 5: Practice datasets and instruction

Chapter 2: Day 2: Necessary Foundations on Statistical Data Analysis

Lecture 1: Various aspects of hypothesis testing

Lecture 2: Understand confidence level, significance level and p-value

Lecture 3: Understand complete steps in hypothesis testing

Chapter 3: Day 3: Excel – Data Cleaning Step-by-step Process

Lecture 1: Dealing with missing values in Excel

Lecture 2: Practice File – Missing values

Lecture 3: Dealing with inconsistent values in Excel

Lecture 4: Practice File – Inconsistent values

Lecture 5: Dealing with outliers in Excel

Lecture 6: Practice File – Outliers

Lecture 7: Dealing with duplicated values in Excel

Lecture 8: Practice File – Duplicated values

Chapter 4: Day 4: Excel – Exploratory Data Analysis Part 1

Lecture 1: Install Excel Data Analysis Tool pack (If Necessary)

Lecture 2: Frequency and percentage analysis in Excel

Lecture 3: Practice File – Frequency and percentage analysis

Lecture 4: Descriptive analysis in Excel

Lecture 5: Practice File – Descriptive analysis

Chapter 5: Day 5: Excel – Exploratory Data Analysis Part 2

Lecture 1: Group by analysis in pivot table Excel

Lecture 2: Practice File – Group by analysis

Lecture 3: Crosstabulation analysis in pivot table Excel

Lecture 4: Practice File – Crosstabulation analysis

Chapter 6: Day 6: Excel – Statistical Analysis and Hypothesis Testing Part 1

Lecture 1: Independent sample t-test in Excel

Lecture 2: Practice File – Independent sample t-test

Lecture 3: Paired sample t-test in Excel

Lecture 4: Practice File – Paired sample t-test

Lecture 5: Analysis of variance (ANOVA) in Excel

Lecture 6: Practice File – Analysis of variance (ANOVA)

Chapter 7: Day 7: Excel – Statistical Analysis and Hypothesis Testing Part 2

Lecture 1: Pearson correlation analysis in Excel

Lecture 2: Practice File – Pearson correlation analysis

Lecture 3: Multiple linear regression analysis in Excel

Lecture 4: Practice File – Multiple linear regression analysis

Chapter 8: Day 8: Excel – Putting All in One in a Dashboard

Lecture 1: Creating canvas for dashboard

Lecture 2: Creating the final dashboard

Lecture 3: Practice File – Final Dashboard

Chapter 9: Day 9: Setting Up Your Data Analysis Platform

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting Up ChatGPT for SMART Analysis

Lecture 3: Setting Up Jupyter Notebook

Chapter 10: Day 10: Introduction to Variables and Data Types

Lecture 1: Getting started with your first python code

Lecture 2: Assigning correct names into variables

Lecture 3: Various data types and structures in python

Chapter 11: Day 11: Converting and Casting Data Types

Lecture 1: Assigning correct data types in Python

Chapter 12: Day 12: Arithmetic operations (+, -, *, /, %, **)

Lecture 1: Performing arithmetic operations in python

Chapter 13: Day 13: Comparison operations (>, <, >=, <=, ==, !=)

Lecture 1: Performing comparison operations in Python

Chapter 14: Day 14: Lists: creation, indexing, slicing, modifying

Lecture 1: Various action and activities with list

Chapter 15: Day 15: Set, Dictionaries and Conditional Statements

Lecture 1: Sets: unique elements, operations

Lecture 2: Dictionaries: key-value pairs, methods

Lecture 3: Conditional statements (if, elif, else)

Chapter 16: Day 16: Various Aspects of Logical Operation

Lecture 1: Logical operators (and, or, not)

Lecture 2: Logical expressions in conditions

Instructors

Analytix AI
Unleashing the Power of Data with AI for Informed Insights.

Rating Distribution

1 stars: 4 votes
2 stars: 5 votes
3 stars: 10 votes
4 stars: 30 votes
5 stars: 77 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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