Python Data Analysis Bootcamp For Beginners: All In One

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

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.

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!

Overview

Section 1: Setting Up Your Data Analysis Environment

Lecture 1 Installing Python and Jupyter Notebook

Lecture 2 Setting Up The AI Environment: ChatGPT

Section 2: Python Programming Fundamentals - Level 1

Lecture 3 Why Python?

Lecture 4 Your First Python Code: Getting Started

Lecture 5 Variables and naming conventions

Lecture 6 Data types: integers, float, strings, boolean

Lecture 7 Type conversion and casting

Lecture 8 Arithmetic operators (+, -, *, /, %, **)

Lecture 9 Comparison operators (>, =, <=, ==, !=)

Lecture 10 Logical operators (and, or, not)

Section 3: Python Programming Fundamentals - Level 2

Lecture 11 Lists: creation, indexing, slicing, modifying

Lecture 12 Sets: unique elements, operations

Lecture 13 Dictionaries: key-value pairs, methods

Lecture 14 Conditional statements (if, elif, else)

Lecture 15 Logical expressions in conditions

Lecture 16 Looping structures (for loops, while loops)

Lecture 17 Defining, Creating and Calling functions

Section 4: What is Data Analysis?

Lecture 18 Understanding data analysis

Lecture 19 Step-by-step data analysis procedure

Lecture 20 Practice dataset and quizz instructions

Section 5: Clean Dataset for Integrity and Validity

Lecture 21 Importing dataset into Jupyter Notebook

Lecture 22 Imputing missing values with SimpleImputer

Lecture 23 Finding and dealing with inconsistent data

Lecture 24 Identify and assign correct dataset

Lecture 25 Dealing with duplicate values

Section 6: Manipulate Data to Increase the Functionality

Lecture 26 Sorting and arranging dataset

Lecture 27 Conditional Filtering of dataset

Lecture 28 Merging extra data with the dataset

Lecture 29 Concatenating variables within dataset

Section 7: Explore dataset and generate significant insights

Lecture 30 What is exploratory data analysis?

Lecture 31 Frequency and percentage analysis

Lecture 32 Descriptive analysis for numeric data

Lecture 33 Grouping analysis - numeric measure by nominal data

Lecture 34 Pivot table - a tabulation of insights

Lecture 35 Crosstabulation - categorical v/s categorical data

Lecture 36 Correlation - numeric v/s numeric data

Section 8: What is Statistical Data Analysis?

Lecture 37 Various aspects of hypothesis testing

Lecture 38 Confidence level, significance level, p-value

Lecture 39 Steps in hypothesis testing

Section 9: Transforming Data into Normal Distribution

Lecture 40 Test normality of numeric data

Lecture 41 Square root transformation method

Lecture 42 Logarithm transformation method

Lecture 43 Boxcox transformation method

Lecture 44 Yeo-johnson transformation method

Section 10: Statistical Analysis and Hypothesis Testing

Lecture 45 One sample T-test

Lecture 46 Independent sample T-test

Lecture 47 One way analysis of variance (ANOVA)

Lecture 48 Chi-square test for independence

Lecture 49 Pearson correlation analysis

Lecture 50 Linear regression analysis

Section 11: Understanding Python Errors

Lecture 51 Module not found error

Lecture 52 Syntax error

Lecture 53 Key error

Lecture 54 Index error

Lecture 55 Attribute error

Lecture 56 Value error

Lecture 57 Type error

Section 12: Handling Errors in Python

Lecture 58 Debugging errors in seconds

Lecture 59 Enhancing python codes

Beginner Data Enthusiasts and Aspiring Analysts