Data Analysis Masterclass A–Z Data Analysis In Python

What you'll learn
You will get proficient in Python for thorough data analysis. Prepare for a career as a data analyst by acquiring practical skills and expertise.
You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.
You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.
You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.
You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.
You will pass practical assignments, 85+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire course.
You will accomplish one capstone project on Sport data analysis at the end to get the full view of data analysis workflow in Python.
Requirements
Access to computer and internet
Basic computer literacy
No coding experience required
Description
Welcome to the Data Analysis Bootcamp: A-Z Data Analysis in Python! In this comprehensive course, you'll embark on a journey from Python novice to proficient data analyst, equipped with the essential skills and knowledge to excel in the field.Throughout this course, you will delve deep into the realm of Python programming, focusing on its application in data analysis. Starting from the basics, you'll master fundamental concepts such as variable naming, data types, lists, dictionaries, dataframes, sets, loops, and functions. With a solid foundation in Python, you'll seamlessly transition to advanced topics, including data cleaning, sorting, filtering, manipulation, transformation, and preprocessing.But that's not all. As you progress, you'll learn how to harness the power of Python for data visualization, exploratory data analysis, statistical analysis, hypothesis testing, and even delve into the exciting world of machine learning. Through a combination of theoretical understanding and hands-on practice, you'll gain proficiency in a wide range of methods and techniques essential for data analysis.What sets this course apart is its emphasis on practical application. You won't just learn the theory; you'll put your newfound knowledge to the test through practical data analysis projects and hands-on exercises. With over 85 coding exercises, 10 quizzes featuring 100+ questions, and practical assignments covering all topics, you'll have ample opportunities to reinforce your skills and enhance your problem-solving abilities.As the culmination of your journey, you'll undertake a capstone project focused on sports data analysis. This final project will allow you to apply all the skills you've acquired throughout the course, providing you with a comprehensive understanding of the data analysis workflow in Python.Whether you're a seasoned professional looking to upskill or someone just starting their journey in data analysis, this course is designed to equip you with the expertise and confidence needed to succeed. Join us on this exciting adventure and unlock your potential as a data analyst in Python.
Overview
Section 1: Start Here: MUST Follow the Instructions
Lecture 1 Instructions to accomplish the course
Lecture 2 Python cheatsheet for data analysis
Lecture 3 Resources used in the course
Section 2: Data Analysis and Its Application
Lecture 4 Understanding analyzing data
Lecture 5 Real-world application of data analysis
Section 3: Data Analysis Tools, Techniques and Methods
Lecture 6 Various aspects of data cleaning
Lecture 7 Various aspects of Joining datasets
Lecture 8 Methods of exploratory data analysis Part 1
Lecture 9 Methods of exploratory data analysis Part 2
Lecture 10 Methods of exploratory data analysis Part 3
Section 4: Statistical Analysis Methods and Techniques
Lecture 11 Population v/s sample and its methods
Lecture 12 Types of statistical data analysis
Lecture 13 A Recap on descriptive statistics methods
Lecture 14 Inferential statistics Part 1 – T-tests and ANOVA
Lecture 15 Inferential statistics Part 2 – Relationships measures
Lecture 16 Inferential statistics Part 3 – Linear regression
Section 5: Clarifying the Concept of Hypothesis Testing
Lecture 17 Hypothesis testing for inferential statistics
Lecture 18 Selecting statistical test and assumption testing
Lecture 19 Confidence level, significance level, p-value
Lecture 20 Making decision and conclusion on findings
Lecture 21 A-Z statistical analysis and hypothesis testing
Section 6: Data Transformation and Visualisation Methods
Lecture 22 Techniques for data transformation Part 1
Lecture 23 Techniques for data transformation Part 2
Lecture 24 Several methods of data visualization Part 1
Lecture 25 Several methods of data visualization Part 2
Lecture 26 Several methods of data visualization Part 3
Section 7: Data Modeling with Machine Learning Model
Lecture 27 Importance of ML in data analytics
Lecture 28 Widely used machine learning models
Lecture 29 Steps in developing machine learning model
Section 8: Setting Up Python and Jupyter Notebook
Lecture 30 Installing Python and Jupyter Notebook – Mac
Lecture 31 Installing Python and Jupyter Notebook – Windows
Lecture 32 More alternative methods – Check the article
Section 9: Starting with Variables to Data Types
Lecture 33 Getting started with first python code
Lecture 34 Assigning variable names correctly
Lecture 35 Various data types and data structures
Lecture 36 Converting and casting data types
Lecture 37 Starting with Variables to Data Types
Section 10: Various Operators in Python Programming
Lecture 38 Arithmetic operators (+, -, *, /, %, **)
Lecture 39 Comparison operators (>, <, >=, <=, ==, !=)
Lecture 40 Logical operators (and, or, not)
Lecture 41 Operators in Python Programming
Section 11: Dealing with Data Structures
Lecture 42 Lists: creation, indexing, slicing, modifying
Lecture 43 Sets: unique elements, operations
Lecture 44 Dictionaries: key-value pairs, methods
Lecture 45 Several data structures
Section 12: Conditionals Looping and Functions
Lecture 46 Conditional statements (if, elif, else)
Lecture 47 Nested logical expressions in conditions
Lecture 48 Looping structures (for loops, while loops)
Lecture 49 Defining, creating, and calling functions
Lecture 50 Conditions loops and functions
Section 13: Sequential Cleaning and Modifying Data
Lecture 51 Preparing notebook and loading data
Lecture 52 Identifying missing or null values
Lecture 53 Method of missing value imputation
Lecture 54 Exploring data types in a dataframe
Lecture 55 Dealing with inconsistent values
Lecture 56 Assigning correct data types
Lecture 57 Dealing with duplicated values
Lecture 58 Sequential data cleaning and modifying
Section 14: Various Aspects of Data Manipulation
Lecture 59 Sorting data by column and order
Lecture 60 Filtering data with boolean indexing
Lecture 61 Query method for precise filtering
Lecture 62 Filtering data with isin method
Lecture 63 Slicing dataframe with loc and iloc
Lecture 64 Filtering data for many conditions
Lecture 65 Various aspects of data manipulation
Section 15: Merging and Concatenating Dataframes
Lecture 66 Joining dataframes horizontally
Lecture 67 Concatenate dataframes vertically
Lecture 68 Merging and concatenating dataframes
Section 16: Applied Exploratory Data Analysis Methods
Lecture 69 Frequency and percentage analysis
Lecture 70 Descriptive statistics and analysis
Lecture 71 Group by data analysis method
Lecture 72 Pivot table analysis - all in one
Lecture 73 Cross-tabulation analysis method
Lecture 74 Correlation analysis for numeric data
Lecture 75 Applied exploratory data analysis
Section 17: Exploring Data Visualisations Methods
Lecture 76 Understanding visualisation tools
Lecture 77 Getting started with bar charts
Lecture 78 Stacked and clustered bar charts
Lecture 79 Pie chart for percentage analysis
Lecture 80 Line chart for grouping data analysis
Lecture 81 Exploring distribution with histogram
Lecture 82 Correlation analysis via scatterplot
Lecture 83 Matrix visualisation with heatmap
Lecture 84 Boxplot statistical visualisation method
Lecture 85 Exploring data visualisations methods
Section 18: Several Data Transformation Methods
Lecture 86 Investigating distribution of numeric data
Lecture 87 Shapiro Wilk test of normality
Lecture 88 Starting with square root transformation
Lecture 89 Logarithmic transformation method
Lecture 90 Box-cox power transformation method
Lecture 91 Yeo-Johnson power transformation method
Lecture 92 Practical data transformation methods
Section 19: Statistical Tests and Hypothesis Testing
Lecture 93 One sample t-test
Lecture 94 Independent sample t-test
Lecture 95 One way Analysis of Variance
Lecture 96 Chi square test for independence
Lecture 97 Pearson correlation analysis
Lecture 98 Linear regression analysis
Lecture 99 Statistical tests and hypothesis testing
Section 20: Exploring Feature Engineering Methods
Lecture 100 Generating new features
Lecture 101 Extracting day, month and year
Lecture 102 Encoding features - LabelEncoder
Lecture 103 Categorizing numeric feature
Lecture 104 Manual feature encoding
Lecture 105 Converting features into dummy
Lecture 106 Feature engineering methods
Section 21: Data Preprocessing for Machine Learning
Lecture 107 Selecting features and target
Lecture 108 Scaling features - StandardScaler
Lecture 109 Scaling features - MinMaxScaler
Lecture 110 Dimensionality reduction with PCA
Lecture 111 Splitting into train and test set
Lecture 112 Preprocessing for machine learning
Section 22: Predictive Analytics - Regression Machine Learning
Lecture 113 Linear regression machine learning
Lecture 114 Decision tree regressor machine learning
Lecture 115 Random forest regressor machine learning
Lecture 116 Regression machine learning
Section 23: Predictive Analytics - Classification Machine Learning
Lecture 117 Logistic regression machine learning
Lecture 118 Decision tree classification machine learning
Lecture 119 Random forest classification machine learning
Lecture 120 Classification machine learning
Section 24: Data Segmentation with KMeans Clustering
Lecture 121 Calculating within cluster sum of squares
Lecture 122 Selecting optimal number of clusters
Lecture 123 Application of KMeans machine learning
Lecture 124 Data segmentation with KMeans clustering
Section 25: Final Project - Sports Data Analytics
Those who are highly interested in learning complete data analytics using Python.,This course is NOT for those who are interested to learn data science or advanced machine learning application.
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