Master Python Essentials, Data Cleaning, Manipulation, Analysis, Transformation, Statistics, Hypothesis Testing and More
What you will 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.
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!
English
language
Content
Setting Up Your Data Analysis Environment
Installing Python and Jupyter Notebook
Setting Up The AI Environment: ChatGPT
Python Programming Fundamentals – Level 1
Why Python?
Your First Python Code: Getting Started
Variables and naming conventions
Data types: integers, float, strings, boolean
Type conversion and casting
Arithmetic operators (+, -, *, /, %, **)
Comparison operators (>, =, <=, ==, !=)
Logical operators (and, or, not)
Python Programming Basics – Level 1
Python Programming Fundamentals – Level 2
Lists: creation, indexing, slicing, modifying
Sets: unique elements, operations
Dictionaries: key-value pairs, methods
Conditional statements (if, elif, else)
Logical expressions in conditions
Looping structures (for loops, while loops)
Defining, Creating and Calling functions
Python Programming Basics – Level 2
What is Data Analysis?
Understanding data analysis
Step-by-step data analysis procedure
Practice dataset and quizz instructions
Clean Dataset for Integrity and Validity
Importing dataset into Jupyter Notebook
Imputing missing values with SimpleImputer
Finding and dealing with inconsistent data
Identify and assign correct dataset
Dealing with duplicate values
Data Cleaning in Python
Manipulate Data to Increase the Functionality
Sorting and arranging dataset
Conditional Filtering of dataset
Merging extra data with the dataset
Concatenating variables within dataset
Data Manipulation
Explore dataset and generate significant insights
What is exploratory data analysis?
Frequency and percentage analysis
Descriptive analysis for numeric data
Grouping analysis – numeric measure by nominal data
Pivot table – a tabulation of insights
Crosstabulation – categorical v/s categorical data
Correlation – numeric v/s numeric data
Exploratory data analysis
What is Statistical Data Analysis?
Various aspects of hypothesis testing
Confidence level, significance level, p-value
Steps in hypothesis testing
Statistical data analysis
Transforming Data into Normal Distribution
Test normality of numeric data
Square root transformation method
Logarithm transformation method
Boxcox transformation method
Yeo-johnson transformation method
Data Transformation
Statistical Analysis and Hypothesis Testing
One sample T-test
Independent sample T-test
One way analysis of variance (ANOVA)
Chi-square test for independence
Pearson correlation analysis
Linear regression analysis
Hypothesis Testing and Analysis
Understanding Python Errors
Module not found error
Syntax error
Key error
Index error
Attribute error
Value error
Type error
Handling Errors in Python
Debugging errors in seconds
Enhancing python codes