Data Science Full Course with SQL and Python for Beginners

Overview

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Embark on a comprehensive 10-hour journey into the world of Data Science with this beginner-friendly video course. Explore the Data Science Life Cycle, distinguish between roles like Data Analyst, Data Scientist, and Data Engineer, and master essential Python libraries including NumPy, Pandas, Matplotlib, and Seaborn. Dive into core statistical concepts, from measures of central tendency to hypothesis testing and ANOVA. Discover key Machine Learning algorithms, including supervised, unsupervised, and reinforcement learning, with hands-on implementations of clustering and regression techniques. Learn SQL fundamentals, covering DML, DCL, and TCL commands, as well as various join types and subqueries. Get introduced to Generative AI in Data Science, work through practical projects, and prepare for job interviews with top industry questions. By the end, gain a solid foundation in Data Science, equipping you with the skills to start your career in this exciting field.

Syllabus

Introduction
What is Data Science
Data Science Life Cycle
Data Analyst vs Data Scientist vs Data Engineer
Numpy
Creating and Intializing Numpy Array
Numpy Shape
Joinig Numpy arrays
Numpy Intersection and Differences
Numpy Array Mathematics
Numpy Matrix
Numpy Matrix Transpose and Multiplication
Numpy Save and Load
Pandas
Pandas Series Object
Changing Index
Series object from Dictionary
Extracting Individual Elements
Pandas Dataframe
Creating a Dataframe
Dataframe In-build Function
.iloc and loc function
Dropping Columns
Dropping rows
Matplotlib
Line plot
Bar plot
Scatter Plot
Histogram
Seaborn Line Plot
Seaborn Bar Plot
Seaborn Scatter Plot
Seaborn Histogram/ Distplot
Types of Statistical Analysis
Inferential Statistics
Descriptive Statistics
Measures of Central Tendency
Measures of Variability
Measures of Relationship
Measures of Skewness
Analysis of Variance
ANOVA Define
Grand Mean
F-Ratio
Hypothesis Testing
Types of Hypothesis Testing
Important Terms in Hypothesis Testing
ANOVA vs.T-test
Applications of ANOVA Test
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Clustering
Examples of Clustering
Need of Clustering
Types of Clustering
K-Means Clustering
Applications of K-means
Deciding value of K
Elbow Method
Implementation of K-means Clustering
Regression
Use case of Regression
Linear Regression
Multi Linear Regression
Types of Linear Regression
Demo - Simple Linear Regression
Demo- Multiple Linear Regression
Logistic Regression
Use Cases of Logistic Regression and its Demo
Installing MySQL
DML Command
DCL Command
TCL Commands
Joins in MySQL
INNER Join
Full Join
SELF Join
Subquery and Types
Demo on Subqueries
ALL Operators
Need of Gen AI in Data Science
Projects on Data Science
Top Data Science Interview Questions
Summary