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

Unlock Data Analysis with Hands-on Python Proficiency. Master Full Work-flow and Become Pro Data Analyst in 30 Days.

What you'll learn

Understand and apply the complete data analysis workflow, from data cleaning to hypothesis testing, mirroring real-life scenarios.

Develop the personal capabilities of critical thinking and problem-solving essential for effective data analysis, decision-making, and recommendation.

Implement various data analysis techniques: value counts, percentage, group by, pivot tables, correlation, and regression, practically and professionally.

Solve more than 12 real-world data analytical questions to gain hands-on experience in applying data analysis skills to diverse situations.

Learn to derive significant insights from data and use them to make informed decisions and recommendations by emphasizing the practical application.

Gain proficiency in using Python for data analysis, covering key libraries and tools commonly employed in the industry.

Learn the principles of statistical inference, understand how to draw meaningful conclusions, and make data-driven decisions based on statistical evidence.

Cultivate the ability to think critically about data, and extract meaningful insights, and deliver actionable recommendations for informed decision-making.

Requirements

No coding Experience is Needed.

Mindset to be Successful.

Dedication to be Data Analyst.

Intention to Learn Python.

Description

Embark on a transformative journey with our course, "Data Analysis A-Z: Become Data Analyst in 30 Days," where you will unravel the intricacies of data analysis from start to finish. Throughout this immersive experience, participants will gain a comprehensive understanding of the complete data analysis workflow, mirroring real-life scenarios encountered by professional data analysts.This course goes beyond technical proficiency, emphasizing the development of personal capabilities crucial for effective data analysis. From critical thinking to problem-solving, participants will acquire the skills essential for making informed decisions and recommendations based on robust data insights.The practical application of various data analysis techniques is a cornerstone of this program. Participants will dive into hands-on exercises covering value counts, percentage calculations, group by operations, pivot tables, correlation, and regression analysis, ensuring a professional mastery of these essential tools.Real-world application takes center stage as participants tackle over 12 data analytical questions, providing practical experience in applying data analysis skills to diverse situations. This emphasis on practicality extends to the extraction of significant insights from data, fostering the ability to make informed decisions and recommendations grounded in real-world application.Moreover, this course equips participants with Python proficiency tailored for data analysis. Covering key libraries and tools widely used in the industry, participants will leverage Python to manipulate and analyze data effectively. The principles of statistical inference are also covered, ensuring participants can draw meaningful conclusions and make data-driven decisions based on sound statistical evidence. Throughout the course, critical thinking remains a focal point, cultivating the ability to think deeply about data, extract meaningful insights, and deliver actionable recommendations for informed decision-making. Join us on this intensive 30-day journey and emerge as a proficient and professional data analyst.

Overview

Section 1: Setting Up Your Data Analysis Platform

Lecture 1 Install Python and Jupyter Notebook

Lecture 2 Setting Up ChatGPT for SMART Analysis

Section 2: What is Data Analysis?

Lecture 3 Day 1: Data analysis and its characteristics

Lecture 4 Day 2: Complete data analysis work-flow

Lecture 5 Practice datasets and instruction for QUIZES

Section 3: Stage 1: Data Cleaning A - Z

Lecture 6 Day 3: Loading dataset in your jupyter notebook

Lecture 7 Day 4: Dealing with missing values

Lecture 8 Day 5: Dealing with inconsistent values

Lecture 9 Day 6: Dealing with miss-identified data types

Lecture 10 Day 7: Dealing with duplicated data

Lecture 11 Solution 1: Data Cleaning A-Z

Section 4: Stage 2: Data Manipulation A-Z

Lecture 12 Day 8: Learn data sorting and arrangement

Lecture 13 Day 9: Learn conditional data filtering

Lecture 14 Day 10: Learn to merge extra variables

Lecture 15 Day 11: Learn to concatenate extra data

Lecture 16 Solution 2: Data Manipulation A-Z

Section 5: Stage 3: Exploratory Data Analysis A-Z

Lecture 17 Day 12: Exploring value counts analysis method

Lecture 18 Day 13: Exploring descriptive statistics analysis method

Lecture 19 Day 14: Exploring group by analysis method

Lecture 20 Day 15: Exploring pivot table analysis method

Lecture 21 Day 16: Exploring crosstabulation analysis method

Lecture 22 Day 17: Exploring correlation analysis method

Lecture 23 Solution 3: Exploratory Data Analysis A-Z

Section 6: Stage 4: Understanding Statistical Data Analysis A-Z

Lecture 24 Day 18: Various aspects of hypothesis testing

Lecture 25 Day 19: Understand confidence level, significance level and p-value

Lecture 26 Day 20: Understand complete steps in hypothesis testing

Section 7: Stage 5: Data Transformation A-Z

Lecture 27 Day 21: Testing normal distribution of numeric variables

Lecture 28 Day 22: Square root transformation for normal distribution

Lecture 29 Day 23: Logarithmic transformation for normal distribution

Lecture 30 Day 24: Box-cox transformation for normal distribution

Lecture 31 Day 25: Yeo-Johnson transformation for normal distribution

Lecture 32 Solution 5: Data Transformation A-Z

Section 8: Stage 6: Hypothesis Testing A-Z

Lecture 33 Day 26: One way between groups ANOVA

Lecture 34 Day 27: Pearson product-moment correlation coefficient

Lecture 35 Day 28: Multiple linear regression analysis with statsmodel.api

Lecture 36 Final Solution: Hypothesis Testing A-Z

Section 9: Tips, Tricks and Resources!

Lecture 37 ChatGPT for smooth python coding in Data Analysis

Lecture 38 Course Resources

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.