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Easy Python for A-Z Data Analysis Bootcamp for Beginners

Name: Easy Python for A-Z Data Analysis Bootcamp for Beginners
Rating: 4.6 (46 reviews)

Master Python Essentials, Data Cleaning, Manipulation, Analysis, Transformation, Statistics, Hypothesis Testing and More

Created byAnalytix AI

Last updated 8/2024

English

What you'll learn

Learn Python's syntax, data types, variables, and operators to construct simple programs and execute basic functions.
Learn to regulate program flow, use loops and conditional statements like if, elif, and else.
Acquire skills to use Python lists, dictionaries, tuples, and sets.
Learn NumPy and Pandas the key Python packages for data manipulation and computing.
Learn how to quickly fix NameError, TypeError, IndentationError, and other issues.
Harness the power of ChatGPT for real-time code suggestions, completion, and improvement.
Learn and apply the data analysis methodology, from data cleaning to hypothesis testing, in real-world applications.
Increase your critical thinking and problem-solving skills for data analysis, decision-making, and recommendation.
Use value counts, percentage, group by, pivot tables, correlation, and regression professionally and realistically.
Solve over 60+ real-world 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.
Learn Python for data analysis using industry-standard libraries and tools.
Master statistical inference, draw meaningful findings, and make data-driven decisions.
Develop critical thinking, data analysis, and practical recommendations for informed decision-making.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

14 sections • 62 lectures • 6h 45m total length

Installing Python and Jupyter Notebook0:07
Setting Up The AI Environment: ChatGPT0:04
Connect with my youtube channel0:06
Get special handbooks0:11
Why Python?2:47
Installing necessary python libraries3:08

Your First Python Code: Getting Started4:36
Working with Print()
Variables and naming conventions9:05
Working with variable
Data types: integers, float, strings, boolean7:22
Learn how integers, floats, strings, and booleans behave in Python and how to identify them with type for accurate calculations and data analysis.
Type conversion and casting9:49
Learn how to convert data types in Python using int, float, and str to prepare data for analysis and data frames, with practical casting examples and arithmetic.
Assigning correct data types
Arithmetic operators (+, -, *, /, %, **)6:44
Arithmetic operation
Comparison operators (>, =, <=, ==, !=)7:35
Comparison operation
Logical operators (and, or, not)7:06
Logical operation
Python Programming Basics – Level 1

Lists: creation, indexing, slicing, modifying15:59
Working with lists
Sets: unique elements, operations7:51
Calculating with sets
Dictionaries: key-value pairs, methods9:23
Working with dictionaries
Conditional statements (if, elif, else)7:04
Applying conditional statements
Logical expressions in conditions9:57
Advanced condition with logical expression
Looping structures (for loops, while loops)9:12
Working with loops
Defining, Creating and Calling functions5:11
Define, create, and call custom Python functions using def to compute percentages from total respondents and a category, using male and female examples to demonstrate.
Dealing with functions
Python Programming Basics – Level 2

Importing dataset into Jupyter Notebook7:06
Learn how to load an Excel data set into a Jupyter notebook, install and import pandas, and view the first rows with df.head to begin cleaning data.
Imputing missing values with SimpleImputer12:43
Identify missing values
Impute missing values
Finding and dealing with inconsistent data11:17
Removing inconsistent value
Identify and assign correct dataset7:43
Learn to remove a row with an inconsistent value from a numeric column in pandas, using mask and tilde, and convert data types to float and date time 64.
Assign correct data type
Dealing with duplicate values4:22
Removing duplicated values
Data Cleaning in Python

Sorting and arranging dataset5:17
Learn to sort a dataset by numeric values in ascending or descending order using pandas sort_values, unlocking basic insights from the data.
Sorting dataset
Conditional Filtering of dataset10:34
Conditional filtering
Merging extra data with the dataset3:47
Learn to merge extra data into an existing cleaned dataset using Python, matching on the order_id key to add the refund variable with a simple merge.
Merging datasets
Concatenating variables within dataset3:47
Concatenating datasets
Data Manipulation in Python

What is exploratory data analysis?3:39
Frequency and percentage analysis22:17
Frequency and percentage analysis
Descriptive analysis for numeric data16:18
Descriptive analysis
Grouping analysis - numeric measure by nominal data15:58
Groupby analysis
Pivot table - a tabulation of insights21:59
Pivot table analysis
Crosstabulation - categorical v/s categorical data9:41
Crosstab analysis
Correlation - numeric v/s numeric data5:33
Explore correlation analysis to reveal relationships between numeric variables like order value, cost, and refund amount using Python to compute a correlation matrix and visualize it with a heatmap.
Correlation analysis
Exploratory data analysis

Test normality of numeric data8:29
Assess normality of numeric data before statistical tests, examine order value, costs of goods sold, and refunds, and apply transformations to achieve normal distributions for reliable analysis.
Square root transformation method6:15
Square root transformation
Logarithm transformation method5:57
Logarithmic transformation
Boxcox transformation method5:45
Apply box-cox transformation to order, value, cost, and refund in Python using numpy and scipy.stats, compare with sqrt and log transforms, visualize via KDE, and preview Johansson method next.
Box-cox transformation
Yeo-johnson transformation method5:14
Yeo-johnson transformation
Data Transformation

Requirements

No coding experience is needed
Desire to learn data analysis in python

Description

Unlock the power of Python and dive into the dynamic realm of data analysis with our comprehensive bootcamp tailored for beginners. In the "Python Data Analysis Bootcamp for Beginners: All in One," we guide you through every essential aspect of Python programming and data analysis, equipping you with the skills needed to thrive in today's data-driven world.

Key Course Highlights:

Master Python Essentials:
- Lay a solid foundation with a hands-on approach to mastering Python basics.
- Learn the syntax, data types, and control structures to build a strong programming foundation.
Data Cleaning and Manipulation:
- Explore techniques for cleaning and organizing raw data.
- Gain proficiency in data manipulation using Python libraries, ensuring your data is ready for analysis.
Data Analysis and Transformation:
- Dive into the core of data analysis, learning how to extract meaningful insights.
- Acquire skills to transform and reshape data to derive actionable conclusions.
Statistical Analysis:
- Understand fundamental statistical concepts and their application in data analysis.
- Learn how to interpret and draw conclusions from statistical data.
Hypothesis Testing:
- Master the art of hypothesis testing to make informed decisions based on statistical evidence.
- Apply hypothesis testing techniques to validate assumptions and draw accurate conclusions.
Real-world Projects and Scenarios:
- Immerse yourself in hands-on projects simulating real-world data challenges.
- Apply your knowledge to practical situations, solidifying your skills through experiential learning.

Why Choose Our Bootcamp?

Beginner-Friendly: No prior coding experience? No problem! Our course is designed for beginners, starting from the basics and guiding you step-by-step to becoming a proficient data analyst.
Comprehensive Curriculum: Covering Python essentials to advanced statistical analysis, our all-in-one curriculum ensures you gain a well-rounded understanding of data analysis.
Smart Application of ChatGPT: Experience a unique blend of traditional teaching methods and AI assistance. ChatGPT is intelligently applied to explain complex Python coding in simple layman's terms, enhancing your learning experience.
Hands-On Guidance: Learn not just the 'how' but also the 'why' behind each concept with hands-on guidance, empowering you to tackle real-world data challenges confidently.

Embark on a transformative journey where you'll not only master Python but also emerge as a skilled data analyst. Enroll now in the Python Data Analysis Bootcamp for Beginners: All in One and open doors to a world of possibilities in the field of data analysis. Your data story begins here!

Who this course is for:

Beginner Data Enthusiasts and Aspiring Analysts

Easy Python for A-Z Data Analysis Bootcamp for Beginners

What you'll learn

Explore related topics

Coding Exercises

Course content

Setting Up Your Data Analysis Environment6 lectures • 6min

Python Programming Fundamentals - Level 17 lectures • 52min

Python Programming Fundamentals - Level 27 lectures • 1hr 5min

What is Data Analysis?3 lectures • 7min

Clean Dataset for Integrity and Validity5 lectures • 43min

Manipulate Data to Increase the Functionality4 lectures • 23min

Explore dataset and generate significant insights7 lectures • 1hr 35min

What is Statistical Data Analysis?3 lectures • 21min

Transforming Data into Normal Distribution5 lectures • 32min

Statistical Analysis and Hypothesis Testing4 lectures • 42min

Requirements

Description

Who this course is for: