Data Analysis Masterclass: A-Z Data Analysis In Python

Master Python for A-Z Data Analysis and Become Pro Data Analyst with Basics to Hands-on Coding Exercises and Assignments

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.