## Data Science Bootcamp 2022 5 Data Science Projects

### What you’ll learn

Fundamentals of Data Science

Data Science Capstone Projects

Grouping and Filtering Operations for Data Analysis

Object Oriented Programming in Python

Hypothesis Testing

Basic and Advanced Data Visualization

Clustering Analysis

### Requirements

No prior knowledge required.

Understanding Programming Language

### Description

Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights.The Data Science field is growing so rapidly, and revolutionizing so many industries.Data Science has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways.Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient. It encompasses a wide range of topics:-Fundamentals of Python.Python Data Structures.Python Functions.Python for Data Science.Data Cleaning.Query Analysis.Data Visualizations using Python.Statistics and Probability.Hypothesis Testing.Data Exploration.Each of these topics are build on the other. You need to acquire all the skills in the right order.You are at the right place!!!Welcome to this online resource to learn Data Science Skills.The Complete Data Science Bootcamp course will really help you to boost your career.This Data Science Course begins with the most basic level and goes up to the most advanced techniques step by step.even if you don’t know anything in advance, this course will make complete sense to you.In this Data Science Course you will learn about the following:-1. The fundamentals of python programming language:- variables, data types, loops and conditionals.2. Python data structures:- lists, tuples, dictionaries, sets, stacks, queues.3. Object-oriented programming in python.4. Regular Expressions.5. Numpy library.6. Pandas library.7. Grouping and filtering operations for data analysis.8. Basic and Advanced visualizations.9. Descriptive statistics.10. Inferential statistics.11. Hypothesis Testing.12. Exploring Dabl and Sweetviz library.13. Linear Regression theory and practical.14. Logistic Regression theory and practical.15. Clustering analysis.There are lots and lots of exercises for you to practice In this Python Data Science Course and also a 5 Bonus Data Science Project “Player’s Performance Reviewer”, “Start-ups Case Study and Analysis”, “Movie Recommender Engine”, “Global Cost of Living Analysis” and “Customer Segmentation Engine”.In this Player’s Performance Reviewer project, you will analyze the performance metrics of players based on their ground positions, skills, nationality, clubs, age, height, weight, and understanding the major factors driving the performance of these players.In this Start-ups Case Study and Analysis project, you will analyze the Indian Startups, and Understand the Startup Ecosystems in India to answer some Interesting Questions. Try to find out the Major Investors and Startups.In this Movie Recommender Engine project, you will get to learn How to analyze a Movie Database to find some useful insights and Recommend Movies.In this Global Cost of Living Analysis project, you will learn how to perform Geospatial Analysis and understand some major factors determining the quality of life in different cities of the world. And also learn to perform Comparative analysis.In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.You will make use of all the topics read in this Python Data Science Course 2021.You will also have access to all the resources used in this Python Data Science Course 2021.Instructor Support – Quick Instructor Support for any queries.Enroll now and become a Data Science professional!!!

### Overview

Section 1: Python Fundamentals

Lecture 1 Why should you learn Python?

Lecture 2 Installing Python and Jupyter Notebook

Lecture 3 Understanding Interface of Jupyter Notebook

Lecture 4 Naming Convention for variables

Lecture 5 Built in Data Types and Type Casting

Lecture 6 Scope of Variables

Lecture 7 Q and A

Lecture 8 Quiz Solution

Lecture 9 Thanks for your support!!

Lecture 10 Arithmetic and Assignment Operators

Lecture 11 Comparison, Logical, and Bitwise Operators

Lecture 12 Identity and Membership Operators

Lecture 13 Quiz Solution

Lecture 14 String Formatting

Lecture 15 String Methods

Lecture 16 User Input

Lecture 17 Quiz Solution

Lecture 18 If, elif, and else

Lecture 19 For and While

Lecture 20 Break and Continue

Lecture 21 Quiz Solution

Section 2: Mastering Python Data Structures

Lecture 22 Differences between Lists and Tuples

Lecture 23 Operations on Lists

Lecture 24 Operations on Tuples

Lecture 25 Quiz Solution

Lecture 26 Introduction to Dictionaries

Lecture 27 Operations on Dictionaries

Lecture 28 Nested Dictionaries

Lecture 29 Introduction to Sets

Lecture 30 Set Operations

Lecture 31 Quiz Solution

Lecture 32 Introduction to Stacks and Queues

Lecture 33 Implementing Stacks and Queues using Lists

Lecture 34 Implementing Stacks andd Queues using Deque

Lecture 35 Quiz Solution

Lecture 36 Time Complexity

Lecture 37 Linear Search

Lecture 38 Binary Search

Lecture 39 Bubble Sort

Lecture 40 Insertion and Selection Sort

Lecture 41 Merge Sort

Lecture 42 Quiz Solution

Section 3: Python Functions Deep Dive

Lecture 43 Introduction to Functions

Lecture 44 Default Parameters in Functions

Lecture 45 Positional Arguments

Lecture 46 Keyword Arguments

Lecture 47 Python Modules

Lecture 48 Quiz Solution

Lecture 49 Lambda Functions

Lecture 50 Filter, Map, and Zip Functions

Lecture 51 List, set, and Dictionary Comprehensions

Lecture 52 Quiz Solution

Lecture 53 Introduction to Aggregate Functions

Lecture 54 Introduction to Analytical Functions

Lecture 55 Quiz Solution

Lecture 56 Solving the Factorial Problem using Recursion

Lecture 57 Solving the Fibonacci Problem using Recursion

Lecture 58 Quiz Solution

Lecture 59 Introduction to Classes and Objects

Lecture 60 Inheritance

Lecture 61 Encapsulation

Lecture 62 Polymorhism

Lecture 63 Quiz Solution

Section 4: Python for Data Science

Lecture 64 Introduction to datetime

Lecture 65 The date and time class

Lecture 66 The datetime class

Lecture 67 The timedelta class

Lecture 68 Quiz Solution

Lecture 69 Meta Characters for Regular Expressions

Lecture 70 Built-in Functions for Regular Expressions

Lecture 71 Special Characters for Regular Expressions

Lecture 72 Sets for Regular Expressions

Lecture 73 Quiz Solution

Lecture 74 Array Creation using Numpy

Lecture 75 Mathematical Operations using Numpy

Lecture 76 Built-in Functions in Numpy

Lecture 77 Quiz Solution

Lecture 78 Reading Datasets using Pandas

Lecture 79 Plotting Data in Pandas

Lecture 80 Indexing, Selecting, and Filtering Data using Pandas

Lecture 81 Merging and Concatenating DataFrames

Lecture 82 Lambda, Map, and Apply Functions

Lecture 83 Quiz Solution

Section 5: Data Cleaning

Lecture 84 Causes and Impact of Missing Values

Lecture 85 Types of Missing Values

Lecture 86 When should we delete the missing values

Lecture 87 Imputing missing values with the business logic

Lecture 88 Imputing missing values with Mean/Median/Mode

Lecture 89 Imputing missing values in a real-time scenario

Lecture 90 Quiz Solution

Lecture 91 How outliers can be harmful for machine learning models

Lecture 92 Finding out outliers from the data

Lecture 93 Using Winsorization to deal with outliers

Lecture 94 Deleting and Capping the outliers

Lecture 95 Dealing with outliers in a real-world scenario

Lecture 96 Quiz Solution

Lecture 97 Introduction to reindex, set_index, reset_index, and sort_index Functions

Lecture 98 Introduction to Replace and Drop level Function

Lecture 99 Introduction to Split and Strip Function

Lecture 100 Introduction to Stack, and Unstack Functions

Lecture 101 Introduction to Melt, Explode, and Squeeze Functions

Lecture 102 Data Cleaning on Big Mart Dataset

Lecture 103 Data Cleaning on Movie Dataset

Lecture 104 Data Cleaning on Melbourne Housing Dataset

Lecture 105 Data Cleaning on Naukri Dataset

Section 6: Query Analysis

Lecture 106 Aggregate functions used for Grouping

Lecture 107 Using Groupby for Grouping Operations

Lecture 108 Groupby with Idxmax and Idxmin functions

Lecture 109 Using Color scales for better visualization

Lecture 110 Visualizing the Groupby Operations

Lecture 111 Using Pivot Tables for Grouping Operations

Lecture 112 Difference between Groupby and Pivot tables

Lecture 113 Performing Cross Tabulation

Lecture 114 Visualizing Cross tabulated Data

Lecture 115 Interactive Grouping Operations

Lecture 116 Quiz Solution

Lecture 117 When to perform Filtering Operations

Lecture 118 Introduction to Simple Filtering Operations

Lecture 119 Advanced Filtering Operations

Lecture 120 Filtering and Grouping Operations

Lecture 121 Interactive Filtering Operations

Lecture 122 Quiz Solution

Section 7: Data Visualizations

Lecture 123 Univariate Analysis

Lecture 124 Bivariate Analysis

Lecture 125 Multivariate Analysis

Lecture 126 Quiz Solution

Lecture 127 Scatter Plots

Lecture 128 Charts with Colorscale

Lecture 129 Bar, Line, and Area Charts

Lecture 130 Facet Grids

Lecture 131 Statistical Charts

Lecture 132 Polar Charts

Lecture 133 Subplots

Lecture 134 3D Charts

Lecture 135 Waffle Charts

Lecture 136 Maps

Lecture 137 Quiz Solution

Lecture 138 Animation with Bubbleplot

Lecture 139 Animation with Facets

Lecture 140 Animation with Scatter Maps

Lecture 141 Animation with Choropleth Maps

Lecture 142 Quiz Solution

Lecture 143 Introduction to Ipywidgets

Lecture 144 Interactive Univariate Analysis

Lecture 145 Interactive Bivariate Analysis

Lecture 146 Interactive Multivariate Analysis

Lecture 147 Quiz Solution

Lecture 148 Sunburst Charts

Lecture 149 Parallel Co-ordinate Charts

Lecture 150 Funnel Charts

Lecture 151 Gantt Charts

Lecture 152 Ternary Charts

Lecture 153 Tree Maps

Lecture 154 Network Charts

Lecture 155 Quiz Solution

Section 8: Statistics and Probability

Lecture 156 Why you should learn Statistics and Probability

Lecture 157 Walking through the course Content

Lecture 158 Applications of Probability in Real Life

Lecture 159 Basic Probability

Lecture 160 Conditional Probability

Lecture 161 Set Theory

Lecture 162 Bayes’ Theorem

Lecture 163 Permutations and Combinations

Lecture 164 Quiz Solution

Lecture 165 Types of Data

Lecture 166 Measures of Central Tendency

Lecture 167 Measures of Spread

Lecture 168 Measures of Dependence

Lecture 169 Quiz Solution

Lecture 170 Continuous vs Discrete Distributions

Lecture 171 Introduction to Normal Distribution

Lecture 172 Concept of Skewness

Lecture 173 Using QQ Plots to check Normal Distribution

Lecture 174 Quiz Solution

Lecture 175 Sample Mean and Population Mean

Lecture 176 Central Limit Theorem

Lecture 177 Bias and Variance

Lecture 178 Maximum Likelihood Estimation

Lecture 179 Confidence Intervals

Lecture 180 Quiz Solution

Section 9: Hypothesis Testing

Lecture 181 What is Hypothesis Testing

Lecture 182 Null Hypothesis and Alternate Hypothesis

Lecture 183 Types of Error

Lecture 184 P-Value and Level of Significance

Lecture 185 Quiz Solution

Lecture 186 One Sampled T Test

Lecture 187 Two Sampled T Test

Lecture 188 Paired Sampled T Test

Lecture 189 Quiz Solution

Lecture 190 One Sampled Z Test

Lecture 191 Two Sampled Z Test

Lecture 192 Quiz Solution

Lecture 193 One Sampled ANOVA Test

Lecture 194 Two Sampled ANOVA Test

Lecture 195 Quiz Solution

Lecture 196 Goodness of Fit Test

Lecture 197 Test of Independence

Lecture 198 Quiz Solution

Section 10: Data Exploration

Lecture 199 Why EDA and how it is useful

Lecture 200 Course curriculum walkthrough

Lecture 201 Data Profiling

Lecture 202 Analyzing Target Data

Lecture 203 Quiz Solution

Lecture 204 Summarizing data

Lecture 205 Exploring the Dabl Library

Lecture 206 Exploring the Sweetviz Library

Lecture 207 Using Color Gradients for better analysis

Lecture 208 Best Practices for Data Exploration

Lecture 209 Quiz Solution

Section 11: Capstone Project 1: Players Performance Analysis

Lecture 210 Understanding the problem statement

Lecture 211 Setting up the Environment

Lecture 212 Data Cleaning

Lecture 213 Feature Engineering

Lecture 214 Data Visualization

Lecture 215 Query Analysis

Lecture 216 Major Learnings from the project

Section 12: Capstone Project 2: Startups Case Study and Analysis

Lecture 217 Understanding the Problem Statement

Lecture 218 Setting up the Environment

Lecture 219 Data Cleaning

Lecture 220 Querying the data using Visualizations Part – 1

Lecture 221 Querying the data using Visualizations Part – 2

Lecture 222 Major learning from the Project

Section 13: Capstone Project 3: Movie Recommender Systems

Lecture 223 Setting up the Environment

Lecture 224 Taking a Deep Dive into the Dataset

Lecture 225 Understanding the Problem Statement

Lecture 226 Missing Values Imputation

Lecture 227 Top 10 Profitable Movies

Lecture 228 Manipulating the Duration and Language Column

Lecture 229 Extracting the Movie Genres

Lecture 230 Top 10 Most Popular Movies on Social Media

Lecture 231 Analysing Which Genre is Most Bankable?

Lecture 232 Loss and Profit Analysis on English and Foreign Movies

Lecture 233 Gross Comparison of Long and Short Movies

Lecture 234 Association between IMDB Rating and Duration

Lecture 235 Comparing Critically acclaimed Actors

Lecture 236 Top Movies based on Gross, and IMDB

Lecture 237 Recommending Movies based on Languages and Actors

Lecture 238 Recommending Similar Genres and Movies

Lecture 239 Key Takeaways from this Project

Section 14: Capstone Project 4: Global Cost of Living

Lecture 240 Setting up Environment

Lecture 241 Understanding the Dataset

Lecture 242 Understanding the Problem Statement

Lecture 243 Extracting Latitude and Longitude from the Location

Lecture 244 Performing Feature Engineering

Lecture 245 Comparing Lifestyle in different Countries

Lecture 246 Top N and Bottom N Analysis

Lecture 247 Performing Geo spatial Analysis

Lecture 248 Comparing different Lifestyle Factors

Lecture 249 Comparing Some of the Most Popular Countries

Lecture 250 Comparing Lifestyle in Indian Cities

Lecture 251 Ranking Places based on their cost of living

Lecture 252 Analysing Cost of Essential Items

Lecture 253 Analysing Quality of Life

Lecture 254 Suggesting Better places to live

Section 15: Capstone Project 5: Customer Segmentation Engine

Lecture 255 Understanding the Problem Statement

Lecture 256 Setting up the Environment

Lecture 257 Data Analysis and Visualization

Lecture 258 KMeans Clustering Analysis

Lecture 259 Major Learnings from the projects

Section 16: Outro Section

Lecture 260 Conclusion

Lecture 261 How to Get Your Certificate of Completion

Section 17: Bonus Section

Lecture 262 Bonus Lecture

Anyone who want to start a career in Data Science.,Anyone who wants to level up their Data Science Knowledge.,Anyone who want to become a Data Analysts.,Anyone who want to become a Data Scientists.,Technologists who are curious about Data Science.,Anyone who wants to level up their Python skills,If you have no prior Python coding or scripting experience, This course is for you. This Course also includes Python Fundamental for beginners,College Graduates.,Job-seekers.,Students and professionals who want to improve the training capabilities.

#### Course Information:

Udemy | English | 15h 33m | 21.00 GB

Created by: Data Is Good Academy

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