Data Science Full Course: All In One

Master the Data Science Universe: A Comprehensive Hands-On Course Covering the A-Z of Data Science Workflow in Python

What you'll learn

Gain a profound understanding of the core concepts and principles of data science, including its role, importance, and applications in various industries.

Acquire the skills to clean raw data effectively, covering techniques for handling missing values, addressing different data types, and managing outliers etc.

Master data manipulation by learning essential techniques such as sorting, filtering, merging, concatenating, and others using Python's pandas library.

Learn exploratory data analysis techniques include frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships.

Dive into the world of data preprocessing with hands-on experience in feature engineering, selection, and scaling to prepare datasets for ML models.

Apply your knowledge through a series of practical projects, reinforcing your understanding of each step in the data science workflow.

Develop expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more.

Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm.

Explore the world of unsupervised learning by understanding and implementing clustering models like KMeans and DBSCAN for uncovering hidden patterns in data.

Become proficient in using essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy.

Test your knowledge and reinforce your learning through a series of seven-layered quizzes that cover various aspects of the data science workflow.

Experience the integration of ChatGPT to rise your understanding of data science applications through interactive conversations and real-world problem-solving.

Learn how to communicate your findings effectively, translating complex data science results into clear and actionable insights for stakeholders.

Kickstart a career in data science, with a well-rounded understanding of the complete data science workflow and the ability to tackle real-world challenges.

Requirements

No Coding Experience is Needed.

Desire to Learn Data Science

Description

Welcome to "Master the Data Science Universe: A Comprehensive Hands-On Course Covering the A-Z of Data Science Workflow in Python." This transformative program is meticulously crafted to cater to diverse learners, including aspiring data scientists, professionals seeking a career transition, and anyone intrigued by the immense potential of data. Embark on a journey that demystifies the complexities of data science, providing you with not only theoretical knowledge but, more crucially, practical skills through immersive hands-on projects.Foundations of Data Science Excellence: Delve into the fundamental principles that underpin data science. Understand its significance, applications across industries, and the pivotal role it plays in decision-making processes. From the very basics to real-world applications, this course ensures you grasp the foundations, setting the stage for a comprehensive exploration of the data science landscape.Hands-On Data Mastery and Practical Projects: Gain a profound understanding of data manipulation, cleaning, and preprocessing through hands-on projects. Tackle real-world data challenges and reinforce your skills with seven layers of quizzes. This practical approach ensures that you not only understand the concepts but can also apply them confidently in real-world scenarios.Model Building Excellence Across Domains: Master the art of building and evaluating both supervised regression and classification models. Dive deep into algorithms such as linear regression, random forest, logistic regression, KNN, and more. Uncover the power of unsupervised learning through clustering models like KMeans and DBSCAN, allowing you to extract valuable insights from unstructured data.Python Libraries Demystified: Navigate the rich Python data science ecosystem effortlessly. From data manipulation using pandas to machine learning with scikit-learn, and visualization with seaborn and matplotlib, you'll gain proficiency in utilizing essential libraries that form the backbone of data science workflows.Interactive Learning with ChatGPT Integration: Experience a unique learning journey with the integration of ChatGPT. Engage in interactive conversations, troubleshoot real-world problems, and enhance your problem-solving skills. This dynamic learning environment not only imparts knowledge but also fosters a deep understanding of the material through practical, interactive experiences."Master the Data Science Universe" goes beyond a traditional course—it's your gateway to becoming a proficient data scientist. As you unravel the intricacies of data science, you'll not only gain theoretical knowledge but also the confidence and skills needed to tackle complex challenges in the real world. Are you ready to unlock the full potential of data? Enroll now and take the first step towards a rewarding journey in data science.

Overview

Section 1: Setting Up Your Data Analysis Platform

Lecture 1 Install Python and Jupyter Notebook

Lecture 2 Setting Up ChatGPT for SMART Analysis

Section 2: What is Data Science?

Lecture 3 Data Science and its characteristics

Lecture 4 Data Science v/s Data Analysis

Lecture 5 Complete Data Science work-flow

Lecture 6 Download datasets for practice and quizzes

Lecture 7 Instructions for Quizzes: IMPORTANT

Section 3: Full Data Cleaning Process

Lecture 8 Getting started: loading your data into jupyter notebook

Lecture 9 Impute missing values with Simple-Imputer

Lecture 10 Rectify inconsistent variables and values

Lecture 11 Identify and assign correct data types

Lecture 12 Abolish duplicated data from the dataset

Lecture 13 Solution 1: Full Data Cleaning

Section 4: Full Data Manipulation Process

Lecture 14 Sorting and arranging dataset

Lecture 15 Conditional filtering (and, or, not etc.)

Lecture 16 Merging dataset with extra features

Lecture 17 Concatenating data with extra data

Lecture 18 Solution 2: Full Data Manipulation

Section 5: Full Exploratory Data Analysis Process

Lecture 19 Understanding exploratory data analysis

Lecture 20 Investigating Value Counts Analysis Technique

Lecture 21 Delving into Descriptive Statistics Analysis Technique

Lecture 22 Understanding Group By Analysis Method

Lecture 23 Mastering Pivot Table Analysis Method

Lecture 24 Unpacking Crosstabulation Analysis Method

Lecture 25 Exploring Correlation Analysis Method

Lecture 26 Solution 3: Full Exploratory Data Analysis

Section 6: Understanding Statistical Data Analysis Concepts

Lecture 27 Various aspects of hypothesis testing

Lecture 28 Understand confidence, significance level and p-value

Lecture 29 Statistical data analysis and hypothesis testing

Section 7: Various Data Transformation Techniques

Lecture 30 Testing normal distribution of numeric variables

Lecture 31 Square root data transformation method

Lecture 32 Logarithm data transformation method

Lecture 33 Box-cox data transformation method

Lecture 34 Yeo-Johnson data transformation method

Lecture 35 Solution 5: Data Transformation Methods

Section 8: Hypothesis Testing (ANOVA, Pearson Correlation, Regression)

Lecture 36 One way between groups ANOVA: Checking the difference

Lecture 37 Pearson correlation test: Checking the relationship

Lecture 38 Regression test: Checking the influence

Lecture 39 Solution 6: Hypothesis Testing

Section 9: Full Data Preprocessing Process

Lecture 40 Feature engineering to generate significant variable

Lecture 41 Feature encoding to assign numeric values

Lecture 42 Techniques to create dummy variables

Lecture 43 Feature scaling for standardization and normalization

Lecture 44 Splitting data into training and testing set

Lecture 45 Solution 7: Full Data Preprocessing

Section 10: Supervised Machine Learning Part 1: Regression

Lecture 46 **Read It: IMPORTANT**

Lecture 47 Getting started: Linear regression ML model

Lecture 48 Decision Tree regressior ML model

Lecture 49 Random Forest regressor ML model

Lecture 50 Support Vector regressor ML model

Lecture 51 XGBoost regressor ML model

Lecture 52 Solution 8: Supervised ML model Part 1

Section 11: Supervised Machine Learning Part 2: Classification

Lecture 53 Getting started: Logistic regression ML model

Lecture 54 Decision Tree classification ML model

Lecture 55 Random Forest classification ML model

Lecture 56 K Nearest Neighbours classification ML model

Lecture 57 LightGBM classification ML model

Lecture 58 Solution 9: Supervised ML model Part 2

Section 12: Unsupervised Machine Learning: Cluster Analysis

Lecture 59 KMeans clustering ML model

Lecture 60 DBSCAN clustering ML model

Lecture 61 Final Solution 10: Complete & Unsupervised ML model

Section 13: Tips, Tricks and Resources

Lecture 62 Kaggle for vast practice resources & portfolio

Lecture 63 ChatGPT: Your Fastest Code Companion

Lecture 64 Course resources

Beginners Data Scientists,Anyone Curious About Data Science,Python and Data Enthusiast