Step into the world of Data Science with our comprehensive video designed for beginners. We’ll cover everything you need to start your journey, from understanding the Data Science Life Cycle to exploring roles like Data Analyst, Data Scientist, and Data Engineer. Learn essential Python libraries, core statistics concepts, and key Machine Learning algorithms. Discover the role of SQL in data management and get introduced to Generative AI and ChatGPT. We’ll also guide you through practical Data Science projects and prep you for job interviews. Watch now and start building your skills today!
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🏁 Topics Covered:
00:00:00 Introduction
00:01:18 What is Data Science
00:03:07 Data Science Life Cycle
00:08:01 Data Analyst vs Data Scientist vs Data Engineer
00:10:24 Numpy
00:10:51 Creating and Intializing Numpy Array
00:21:21 Numpy Shape
00:23:43 Joinig Numpy arrays
00:27:12 Numpy Intersection and Differences
00:30:49 Numpy Array Mathematics
00:38:17 Numpy Matrix
00:41:32 Numpy Matrix Transpose and Multiplication
00:44:46 Numpy Save and Load
00:46:44 Pandas
00:47:10 Pandas Series Object
00:49:32 Changing Index
00:50:49 Series object from Dictionary
00:53:01 Extracting Individual Elements
00:57:39 Pandas Dataframe
00:58:15 Creating a Dataframe
01:00:36 Dataframe In-build Function
01:03:07 .iloc and loc function
01:07:02 Dropping Columns
01:08:22 Dropping rows
01:11:00 Matplotlib
01:11:12 Line plot
01:25:18 Bar plot
01:31:38 Scatter Plot
01:39:33 Histogram
01:46:15 Seaborn Line Plot
01:54:24 Seaborn Bar Plot
02:02:24 Seaborn Scatter Plot
02:07:38 Seaborn Histogram/ Distplot
02:16:56 Types of Statistical Analysis
02:22:29 Inferential Statistics
02:24:56 Descriptive Statistics
02:29:12 Measures of Central Tendency
02:37:40 Measures of Variability
02:43:03 Measures of Relationship
02:48:30 Measures of Skewness
02:51:33 Analysis of Variance
02:56:56 ANOVA Define
03:06:11 Grand Mean
03:10:12 F-Ratio
03:12:16 Hypothesis Testing
03:13:17 Types of Hypothesis Testing
03:18:25 Important Terms in Hypothesis Testing
03:20:30 ANOVA vs.T-test
03:22:06 Applications of ANOVA Test
03:23:01 Machine Learning
03:23:32 Supervised Learning
03:29:39 Unsupervised Learning
03:33:59 Reinforcement Learning
03:37:45 Clustering
03:39:20 Examples of Clustering
03:39:50 Need of Clustering
03:40:42 Types of Clustering
03:43:47 K-Means Clustering
03:44:51 Applications of K-means
03:51:55 Deciding value of K
03:53:00 Elbow Method
03:54:34 Implementation of K-means Clustering
04:19:16 Regression
04:21:24 Use case of Regression
04:23:05 Linear Regression
04:25:14 Multi Linear Regression
04:27:10 Types of Linear Regression
04:31:54 Demo - Simple Linear Regression
04:42:53 Demo- Multiple Linear Regression
05:25:09 Logistic Regression
05:30:23 Use Cases of Logistic Regression and its Demo
06:07:07 Installing MySQL
06:18:00 DML Command
06:48:39 DCL Command
06:56:50 TCL Commands
06:58:10 Joins in MySQL
07:00:14 INNER Join
07:50:17 Full Join
07:59:22 SELF Join
08:06:15 Subquery and Types
08:09:17 Demo on Subqueries
08:36:14 ALL Operators
09:04:06 Need of Gen AI in Data Science
09:05:54 Projects on Data Science
09:50:53 Top Data Science Interview Questions
10:20:23 Summary
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27 авг 2024
