Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka
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- Опубліковано 27 вер 2024
- 🔥 Data Science Course (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): www.edureka.co...
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners course:
00:00 Data Science Full Course Agenda
2:44 Introduction to Data Science
9:55 Data Analysis at Walmart
13:20 What is Data Science?
14:39 Who is a Data Scientist?
16:50 Data Science Skill Set
21:51 Data Science Job Roles
26:58 Data Life Cycle
30:25 Statistics & Probability
34:31 Categories of Data
34:50 Qualitative Data
36:09 Quantitative Data
39:11 What is Statistics?
41:32 Basic Terminologies in Statistics
42:50 Sampling Techniques
45:31 Random Sampling
46:20 Systematic Sampling
46:50 Stratified Sampling
47:54 Types of Statistics
50:38 Descriptive Statistics
55:52 Measures of Spread
55:56 Range
56:44 Inter Quartile Range
58:58 Variance
59:36 Standard Deviation
1:14:25 Confusion Matrix
1:19:16 Probability
1:24:14 What is Probability?
1:27:13 Types of Events
1:27:58 Probability Distribution
1:28:15 Probability Density Function
1:30:02 Normal Distribution
1:30:51 Standard Deviation & Curve
1:31:19 Central Limit Theorem
1:33:12 Types of Probability
1:33:34 Marginal Probability
1:34:06 Joint Probability
1:34:58 Conditional Probability
1:35:56 Use-Case
1:39:46 Bayes Theorem
1:45:44 Inferential Statistics
1:56:40 Hypothesis Testing
2:00:34 Basics of Machine Learning
2:01:41 Need for Machine Learning
2:07:03 What is Machine Learning?
2:09:21 Machine Learning Definitions
2:11:48 Machine Learning Process
2:18:31 Supervised Learning Algorithm
2:19:54 What is Regression?
2:21:23 Linear vs Logistic Regression
2:33:51 Linear Regression
2:25:27 Where is Linear Regression used?
2:27:11 Understanding Linear Regression
2:37:00 What is R-Square?
2:46:35 Logistic Regression
2:51:22 Logistic Regression Curve
2:53:02 Logistic Regression Equation
2:56:21 Logistic Regression Use-Cases
2:58:23 Demo
3:00:57 Implement Logistic Regression
3:02:33 Import Libraries
3:05:28 Analyzing Data
3:11:52 Data Wrangling
3:23:54 Train & Test Data
3:20:44 Implement Logistic Regression
3:31:04 SUV Data Analysis
3:38:44 Decision Trees
3:39:50 What is Classification?
3:42:27 Types of Classification
3:42:27 Decision Tree
3:43:51 Random Forest
3:45:06 Naive Bayes
3:47:12 KNN
3:49:02 What is Decision Tree?
3:55:15 Decision Tree Terminologies
3:56:51 CART Algorithm
3:58:50 Entropy
4:00:15 What is Entropy?
4:23:52 Random Forest
4:27:29 Types of Classifier
4:31:17 Why Random Forest?
4:39:14 What is Random Forest?
4:51:26 How Random Forest Works?
4:51:36 Random Forest Algorithm
5:04:23 K Nearest Neighbour
5:05:33 What is KNN Algorithm?
5:08:50 KNN Algorithm Working
5:24:30 What is Naive Bayes?
5:25:13 Bayes Theorem
5:27:48 Bayes Theorem Proof
5:29:43 Naive Bayes Working
5:39:06 Types of Naive Bayes
5:53:37 Support Vector Machine
5:57:40 What is SVM?
5:59:46 How does SVM work?
6:03:00 Introduction to Non-Linear SVM
6:04:48 SVM Example
6:06:12 Unsupervised Learning Algorithms - KMeans
6:06:18 What is Unsupervised Learning?
6:06:45 Unsupervised Learning: Process Flow
6:07:17 What is Clustering?
6:09:15 Types of Clustering
6:10:15 K-Means Clustering
6:10:40 K-Means Algorithm Working
6:16:17 K-Means Algorithm
6:19:16 Fuzzy C-Means Clustering
6:21:22 Hierarchical Clustering
6:22:53 Association Clustering
6:24:57 Association Rule Mining
6:30:35 Apriori Algorithm
6:37:45 Apriori Demo
6:40:49 What is Reinforcement Learning?
6:42:48 Reinforcement Learning Process
6:51:10 Markov Decision Process
6:54:53 Understanding Q - Learning
7:13:12 Q-Learning Demo
7:25:34 The Bellman Equation
7:48:39 What is Deep Learning?
7:52:53 Why we need Artificial Neuron?
7:54:33 Perceptron Learning Algorithm
7:57:57 Activation Function
8:03:14 Single Layer Perceptron
8:04:04 What is Tensorflow?
8:07:25 Demo
8:21:03 What is a Computational Graph?
8:49:18 Limitations of Single Layer Perceptron
8:50:08 Multi-Layer Perceptron
8:51:24 What is Backpropagation?
8:52:26 Backpropagation Learning Algorithm
8:59:31 Multi-layer Perceptron Demo
9:01:23 Data Science Interview Questions
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Masters Certification Curriculum, Visit our Website: bit.ly/3sw3tJj (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
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Hi is this 10hr video enough to get into data science jobs?
edureka! Can you share all the presentation slides with us...
Hi Sourabh, You will find the presentations on our slideshare channel: www.slideshare.net/EdurekaIN
Please how can I join edureka and get certification
Hi Ololade, kindly share your contact details, our team will reach you out with the details.
I love her teaching style...I'm gonna finish this in 2 days, it's so good and easy to learn...wasted 6 months in college learning this and they taught in much detail in 10 hours... it's amazing.
2:44 Introduction to Data Science
9:55 Data Analysis at Walmart
13:20 What is Data Science?
14:39 Who is a Data Scientist?
16:50 Data Science Skill Set
21:51 Data Science Job Roles
26:58 Data Life Cycle
30:25 Statistics & Probability
34:31 Categories of Data
34:50 Qualitative Data
36:09 Quantitative Data
39:11 What is Statistics?
41:32 Basic Terminologies in Statistics
42:50 Sampling Techniques
45:31 Random Sampling
46:20 Systematic Sampling
46:50 Stratified Sampling
47:54 Types of Statistics
50:38 Descriptive Statistics
55:52 Measures of Spread
55:56 Range
56:44 Inter Quartile Range
58:58 Variance
59:36 Standard Deviation
1:14:25 Confusion Matrix
1:19:16 Probability
1:24:14 What is Probability?
1:27:13 Types of Events
1:27:58 Probability Distribution
1:28:15 Probability Density Function
1:30:02 Normal Distribution
1:30:51 Standard Deviation & Curve
1:31:19 Central Limit Theorem
1:33:12 Types of Probablity
1:33:34 Marginal Probablity
1:34:06 Joint Probablity
1:34:58 Conditional Probablity
1:35:56 Use-Case
1:39:46 Bayes Theorem
1:45:44 Inferential Statistics
1:56:40 Hypothesis Testing
2:00:34 Basics of Machine Learning
2:01:41 Need for Machine Learning
2:07:03 What is Machine Learning?
2:09:21 Machine Learning Definitions
2:!1:48 Machine Learning Process
2:18:31 Supervised Learning Algorithm
2:19:54 What is Regression?
2:21:23 Linear vs Logistic Regression
2:33:51 Linear Regression
2:25:27 Where is Linear Regression used?
2:27:11 Understanding Linear Regression
2:37:00 What is R-Square?
2:46:35 Logistic Regression
2:51:22 Logistic Regression Curve
2:53:02 Logistic Regression Equation
2:56:21 Logistic Regression Use-Cases
2:58:23 Demo
3:00:57 Implement Logistic Regression
3:02:33 Import Libraries
3:05:28 Analyzing Data
3:11:52 Data Wrangling
3:23:54 Train & Test Data
3:20:44 Implement Logistic Regression
3:31:04 SUV Data Analysis
3:38:44 Decision Trees
3:39:50 What is Classification?
3:42:27 Types of Classification
3:42:27 Decision Tree
3:43:51 Random Forest
3:45:06 Naive Bayes
3:47:12 KNN
3:49:02 What is Decision Tree?
3:55:15 Decision Tree Terminologies
3:56:51 CART Algorithm
3:58:50 Entropy
4:00:15 What is Entropy?
4:23:52 Random Forest
4:27:29 Types of Classifier
4:31:17 Why Random Forest?
4:39:14 What is Random Forest?
4:51:26 How Random Forest Works?
4:51:36 Random Forest Algorithm
5:04:23 K Nearest Neighbour
5:05:33 What is KNN Algorithm?
5:08:50 KNN Algorithm Working
5:14:55 kNN Example
5:24:30 What is Naive Bayes?
5:25:13 Bayes Theorem
5:27:48 Bayes Theorem Proof
5:29:43 Naive Bayes Working
5:39:06 Types of Naive Bayes
5:53:37 Support Vector Machine
5:57:40 What is SVM?
5:59:46 How does SVM work?
6:03:00 Introduction to Non-Linear SVM
6:04:48 SVM Example
6:06:12 Unsupervised Learning Algorithms - KMeans
6:06:18 What is Unsupervised Learning?
6:06:45 Unsupervised Learning: Process Flow
6:07:17 What is Clustering?
6:09:15 Types of Clustering
6:10:15 K-Means Clustering
6:10:40 K-Means Algorithm Working
6:16:17 K-Means Algorithm
6:19:16 Fuzzy C-Means Clustering
6:21:22 Hierarchical Clustering
6:22:53 Association Clustering
6:24:57 Association Rule Mining
6:30:35 Apriori Algorithm
6:37:45 Apriori Demo
6:40:49 What is Reinforcement Learning?
6:42:48 Reinforcement Learning Process
6:51:10 Markov Decision Process
6:54:53 Understanding Q - Learning
7:13:12 Q-Learning Demo
7:25:34 The Bellman Equation
7:48:39 What is Deep Learning?
7:52:53 Why we need Artificial Neuron?
7:54:33 Perceptron Learning Algorithm
7:57:57 Activation Function
8:03:14 Single Layer Perceptron
8:04:04 What is Tensorflow?
8:07:25 Demo
8:21:03 What is a Computational Graph?
8:49:18 Limitations of Single Layer Perceptron
8:50:08 Multi-Layer Perceptron
8:51:24 What is Backpropagation?
8:52:26 Backpropagation Learning Algorithm
8:59:31 Multi-layer Perceptron Demo
9:01:23 Data Science Interview Questions
00:00 Agenda
2:44 Introduction to Data Science
9:55 Data Analysis at Walmart
13:20 What is Data Science?
14:39 Who is a Data Scientist?
16:50 Data Science Skill Set
21:51 Data Science Job Roles
26:58 Data Life Cycle
30:25 Statistics & Probability
34:31 Categories of Data
34:50 Qualitative Data
36:09 Quantitative Data
39:11 What is Statistics?
41:32 Basic Terminologies in Statistics
42:50 Sampling Techniques
45:31 Random Sampling
46:20 Systematic Sampling
46:50 Stratified Sampling
47:54 Types of Statistics
50:38 Descriptive Statistics
55:52 Measures of Spread
55:56 Range
56:44 Inter Quartile Range
58:58 Variance
59:36 Standard Deviation
1:14:25 Confusion Matrix
1:19:16 Probability
1:24:14 What is Probability?
1:27:13 Types of Events
1:27:58 Probability Distribution
1:28:15 Probability Density Function
1:30:02 Normal Distribution
1:30:51 Standard Deviation & Curve
1:31:19 Central Limit Theorem
1:33:12 Types of Probablity
1:33:34 Marginal Probablity
1:34:06 Joint Probablity
1:34:58 Conditional Probablity
1:35:56 Use-Case
1:39:46 Bayes Theorem
1:45:44 Inferential Statistics
1:56:40 Hypothesis Testing
2:00:34 Basics of Machine Learning
2:01:41 Need for Machine Learning
2:07:03 What is Machine Learning?
2:09:21 Machine Learning Definitions
2:!1:48 Machine Learning Process
2:18:31 Supervised Learning Algorithm
2:19:54 What is Regression?
2:21:23 Linear vs Logistic Regression
2:33:51 Linear Regression
2:25:27 Where is Linear Regression used?
2:27:11 Understanding Linear Regression
2:37:00 What is R-Square?
2:46:35 Logistic Regression
2:51:22 Logistic Regression Curve
2:53:02 Logistic Regression Equation
2:56:21 Logistic Regression Use-Cases
2:58:23 Demo
3:00:57 Implement Logistic Regression
3:02:33 Import Libraries
3:05:28 Analyzing Data
3:11:52 Data Wrangling
3:23:54 Train & Test Data
3:20:44 Implement Logistic Regression
3:31:04 SUV Data Analysis
3:38:44 Decision Trees
3:39:50 What is Classification?
3:42:27 Types of Classification
3:42:27 Decision Tree
3:43:51 Random Forest
3:45:06 Naive Bayes
3:47:12 KNN
3:49:02 What is Decision Tree?
3:55:15 Decision Tree Terminologies
3:56:51 CART Algorithm
3:58:50 Entropy
4:00:15 What is Entropy?
4:23:52 Random Forest
4:27:29 Types of Classifier
4:31:17 Why Random Forest?
4:39:14 What is Random Forest?
4:51:26 How Random Forest Works?
4:51:36 Random Forest Algorithm
5:04:23 K Nearest Neighbour
5:05:33 What is KNN Algorithm?
5:08:50 KNN Algorithm Working
5:14:55 kNN Example
5:24:30 What is Naive Bayes?
5:25:13 Bayes Theorem
5:27:48 Bayes Theorem Proof
5:29:43 Naive Bayes Working
5:39:06 Types of Naive Bayes
5:53:37 Support Vector Machine
5:57:40 What is SVM?
5:59:46 How does SVM work?
6:03:00 Introduction to Non-Linear SVM
6:04:48 SVM Example
6:06:12 Unsupervised Learning Algorithms - KMeans
6:06:18 What is Unsupervised Learning?
6:06:45 Unsupervised Learning: Process Flow
6:07:17 What is Clustering?
6:09:15 Types of Clustering
6:10:15 K-Means Clustering
6:10:40 K-Means Algorithm Working
6:16:17 K-Means Algorithm
6:19:16 Fuzzy C-Means Clustering
6:21:22 Hierarchical Clustering
6:22:53 Association Clustering
6:24:57 Association Rule Mining
6:30:35 Apriori Algorithm
6:37:45 Apriori Demo
6:40:49 What is Reinforcement Learning?
6:42:48 Reinforcement Learning Process
6:51:10 Markov Decision Process
6:54:53 Understanding Q - Learning
7:13:12 Q-Learning Demo
7:25:34 The Bellman Equation
7:48:39 What is Deep Learning?
7:52:53 Why we need Artificial Neuron?
7:54:33 Perceptron Learning Algorithm
7:57:57 Activation Function
8:03:14 Single Layer Perceptron
8:04:04 What is Tensorflow?
8:07:25 Demo
8:21:03 What is a Computational Graph?
8:49:18 Limitations of Single Layer Perceptron
8:50:08 Multi-Layer Perceptron
8:51:24 What is Backpropagation?
8:52:26 Backpropagation Learning Algorithm
8:59:31 Multi-layer Perceptron Demo
9:01:23 Data Science Interview Questions
Im just 12 years old and excited about data science
Ohh wow great
2:44 Introduction to Data Science
9:55 Data Analysis at Walmart
13:20 What is Data Science?
14:39 Who is a Data Scientist?
16:50 Data Science Skill Set
21:51 Data Science Job Roles
26:58 Data Life Cycle
30:25 Statistics & Probability
34:31 Categories of Data
34:50 Qualitative Data
36:09 Quantitative Data
39:11 What is Statistics?
41:32 Basic Terminologies in Statistics
42:50 Sampling Techniques
45:31 Random Sampling
46:20 Systematic Sampling
46:50 Stratified Sampling
47:54 Types of Statistics
50:38 Descriptive Statistics
55:52 Measures of Spread
55:56 Range
56:44 Inter Quartile Range
58:58 Variance
59:36 Standard Deviation
1:14:25 Confusion Matrix
1:19:16 Probability
1:24:14 What is Probability?
1:27:13 Types of Events
1:27:58 Probability Distribution
1:28:15 Probability Density Function
1:30:02 Normal Distribution
1:30:51 Standard Deviation & Curve
1:31:19 Central Limit Theorem
1:33:12 Types of Probability
1:33:34 Marginal Probability
1:34:06 Joint Probability
1:34:58 Conditional Probability
1:35:56 Use-Case
1:39:46 Bayes Theorem
1:45:44 Inferential Statistics
1:56:40 Hypothesis Testing
2:00:34 Basics of Machine Learning
2:01:41 Need for Machine Learning
2:07:03 What is Machine Learning?
2:09:21 Machine Learning Definitions
2:11:48 Machine Learning Process
2:18:31 Supervised Learning Algorithm
2:19:54 What is Regression?
2:21:23 Linear vs Logistic Regression
2:33:51 Linear Regression
2:25:27 Where is Linear Regression used?
2:27:11 Understanding Linear Regression
2:37:00 What is R-Square?
2:46:35 Logistic Regression
2:51:22 Logistic Regression Curve
2:53:02 Logistic Regression Equation
2:56:21 Logistic Regression Use-Cases
2:58:23 Demo
3:00:57 Implement Logistic Regression
3:02:33 Import Libraries
3:05:28 Analyzing Data
3:11:52 Data Wrangling
3:23:54 Train & Test Data
3:20:44 Implement Logistic Regression
3:31:04 SUV Data Analysis
3:38:44 Decision Trees
3:39:50 What is Classification?
3:42:27 Types of Classification
3:42:27 Decision Tree
3:43:51 Random Forest
3:45:06 Naive Bayes
3:47:12 KNN
3:49:02 What is Decision Tree?
3:55:15 Decision Tree Terminologies
3:56:51 CART Algorithm
3:58:50 Entropy
4:00:15 What is Entropy?
4:23:52 Random Forest
4:27:29 Types of Classifier
4:31:17 Why Random Forest?
4:39:14 What is Random Forest?
4:51:26 How Random Forest Works?
4:51:36 Random Forest Algorithm
5:04:23 K Nearest Neighbour
5:05:33 What is KNN Algorithm?
5:08:50 KNN Algorithm Working
5:24:30 What is Naive Bayes?
5:25:13 Bayes Theorem
5:27:48 Bayes Theorem Proof
5:29:43 Naive Bayes Working
5:39:06 Types of Naive Bayes
5:53:37 Support Vector Machine
5:57:40 What is SVM?
5:59:46 How does SVM work?
6:03:00 Introduction to Non-Linear SVM
6:04:48 SVM Example
6:06:12 Unsupervised Learning Algorithms - KMeans
6:06:18 What is Unsupervised Learning?
6:06:45 Unsupervised Learning: Process Flow
6:07:17 What is Clustering?
6:09:15 Types of Clustering
6:10:15 K-Means Clustering
6:10:40 K-Means Algorithm Working
6:16:17 K-Means Algorithm
6:19:16 Fuzzy C-Means Clustering
6:21:22 Hierarchical Clustering
6:22:53 Association Clustering
6:24:57 Association Rule Mining
6:30:35 Apriori Algorithm
6:37:45 Apriori Demo
6:40:49 What is Reinforcement Learning?
6:42:48 Reinforcement Learning Process
6:51:10 Markov Decision Process
6:54:53 Understanding Q - Learning
7:13:12 Q-Learning Demo
7:25:34 The Bellman Equation
7:48:39 What is Deep Learning?
7:52:53 Why we need Artificial Neuron?
7:54:33 Perceptron Learning Algorithm
7:57:57 Activation Function
8:03:14 Single Layer Perceptron
8:04:04 What is Tensorflow?
8:07:25 Demo
8:21:03 What is a Computational Graph?
8:49:18 Limitations of Single Layer Perceptron
8:50:08 Multi-Layer Perceptron
8:51:24 What is Backpropagation?
8:52:26 Backpropagation Learning Algorithm
8:59:31 Multi-layer Perceptron Demo
9:01:23 Data Science Interview Questions
Even a beginner also learn easily to watch this video with free of cost, I really thankful to edureka. I started learning data science, now iam getting confidence on date science, because I have knowledge on DS is zero. From basics to insane level is available in this video. Tq for providing such a adequate information about a course
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
2 hours.... continuously watching it and hopefully going to complete in 1-2 days only. Already in love with it. 😍 Very intuitive teaching. Thanks for providing this knowledge for free.
Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
First of all, thank U so much Edureka for helping me out in my studies. It was such an amazing video. ❤ Secondly, want to know why U don't have any link to the power point of the lecture. Personally it's very difficult for me to write down everything and make notes. Could U plz share the PPP as well ? 🙏🙏🙏🌷🌷🌷
world level clear concept platform thanku so muchhh edureka
Very very thankful to edureka for making it available on UA-cam, absolutely free🙏😇👍
Thank you for watching our video. We are glad you loved the video. Do subscribe, like and share to stay connected with us. Cheers :)
This is an awesome tutorial! I could not believe you gave a 10hr tutorial for free! Wow! I am currently enrolled in a Data Science certification program & this tutorial will complement my school course. I plug in my headphones at work & listen away all day lol 😁👍Thank you!
Thanks for the compliment! We are glad that our video was of great help to you. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Thank you Edureka for making this course free on UA-cam
You are welcome Issac, Subscribe to our channel and stay tuned for more videos.
Edureka team gets 5 star room in heaven
Thanks for the amazing video I love this video a lot
Great, eagerly waiting for this, I will be back ASAP with my exy
Thanks a lot Edurika , I am in 9th for now and I am looking at data science as a career option . I have finished 10 minutes of the video and the topic really interested me , I will be finishing the video in 2 weeks and hopefully I get a fair idea of how Data science works . Again , thanks a lot 😄
It's a great beginners course. Loved it, thank you for getting me started off.
Thank you Edureka!!
I just love it data science now.
Amo esses cursos! Vocês são awesome!!!!!! Muito obrigada! Greetings from Brazil =)
Thanks for such a great course! However, can we confirm if categorical and discrete variables are the same thing? Isn't categorical the same as Qualitative like Good, Bad, Average, or Gender?
Great Job Edureka. Thank You. Appreciated. 🙌👏
I would like to take up the course for Data science with python.
Here's a link to our course: bit.ly/2IhxgQa
Amazing lecture!!Thank you!
Looks brilliant. gonna start in a while..
This was awesome! Thank you! I will be that much more ready for when I begin my masters in data science next Fall. ❤️🧡💛 💚💙 💜 🖤
Amazing video for beginner
I'm from Pakistan
Love u edureka!
Really u just not made us understood concept but coding also.
❣️🙏
Appreciate your team on creating such detailed video about Data Science!
Thank you for your review : ) We are glad that you found our videos /contents useful . We are also trying our best to further fulfill your requirements and enhance your expirence :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Guys this is really an awesome tutorial, you won't find out on another channel.
Like if u r watching this video
Thank you Edureka for making us understand complex thing in simplest manner.
Heeee hii...I'm just starting data science courses but after seen this video..I'm full intrested to this course . Really it is very wonderful and also helpful video for me. Thanks a lot edureka .plz share me note or PDF
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
Very insightful and thank you for keeping it free. I really liked the content it's very well structured. It's worth your 10 hours guys! kudos to you Ma'am keep the good work👍
Glad you enjoyed it! Happy Learning!😊
Thanku 🤩 So Much
Ｅｄｕｒｅｋａ!
It's really useful course ..thanks for providing it's for free
i am a commerecde student and want to learn data science skill for mt Mba will it be helpful or should I go in depth for more ?
I love the way you guys teach. Could I please get the presentation and the materials via which you have taught? They are absolutely the best!
Hi Kanupriya, you will find the presentations on our slideshare channel: www.slideshare.net/EdurekaIN
Thanks for making it simple and clear.
Edureka is great 👍👏😊
Much love to you guys..
How do I install all these packages on my MacBook, I mean R, python, tableau, SQL and Matlab
Is discrete data categorical? Really because according to my knowledge Qualitative data is called as categorical. Please explain
Thank you so much edureka! for this video. This is so beneficial for me as a beginner. 😊😊
You are most welcome. We are glad we could help. Do subscribe to our channel to stay posted on upcoming tutorials.
i have an exam in 6 hours you saved my life thank you!!
You are best bro
Amazing content, loved it!
Thanks for the awesome tutorial. A little fast and would be better if there were more problems to solve, but otherwise good.👌
Thankyou so much for this great course 🔥.
Please if you can share the notes of this course in pdf format !
I am from non IT background.. today m starting..let c how much I will grasp this .
1) ultimate teaching skills, u wouldn't like to quit session....
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thankyou for the course. Would you please share the datasheet so it would be easier to refer?
Hi please share your mail id to share the data sheet :) We'll Update you soon ! Do Subscribe the channel to keep updated
Thanks for sharing this information Edureka. Really Helpful
it might be possible.
Thanks for such a rich content on DataScience... Can you please share datasets with me used in video tutorial!!
Hi ! Good to know that our videos are helping you to learn better :) Please share your mail id to share the data sheets :)We’ll update you soon . Do subscribe the channel for more updates : )
Thank You sooooo much..... from malaysia ❤❤❤
Can't stop me to comment here.. Thanks you edureka... Superb course.
Thank you so much :) Thanks for your supporting
Thanks for this awesome video
Can you share the ppt and datasheets for revision and practice?
saw till 1hr . it's great . thanks a lot to the creators .
please give complete and full course ....👏👏👏👏
Thank you Edureka for this informative course. Can you share the course contents - slides, python code notebooks, and datasets?
Hi great to hear from you :) please share your mail id ! so that we can share the data sheet with you :)Do subscribe the channel for more updates : )
Awesome course Layout
Keep Going Edureka 👍
very nice video. I followed by edureka videos .. I had so much learned in this site like python,java and AI
Thanks Nahar, don't forget to subscribe our channel.
0.45 answer of bayes' theorem question
Thank you Edureka for this wonderful course. From where can we get the datasets used in this course? It would be great if you could share them through a link.
Yes sure, kindly drop your mail id
Hi I am not related to any of the branches neither have degree or certification of any kind still I find it interesting and want to learn it as these are the skills of future
Can anyone please suggest if I have persued BA Maths then what should I do
Hi Satyendra, you can definitely learn Data Science. Also, if you wish to gain in-depth knowledge with certification to enhance your skills, kindly visit: bit.ly/39jzdcY and avail exclusive discounts using the code "UA-cam20". Cheers :)
Good video thx to edureka team, i hope i can read modul .pdf
Can you provide me in pdf or ppt or whatever please? That will be convention for me to understand
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Great and simple way to understand the whole course ,thanku so much ma'am for bringing this video in such a beautiful way to youtube, kudos❤️
Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Great Short DS Course!! :)
It's very helpful video ...
U ppl are doing amazing job...keep it up...
This is amazing or awesome youtube channel for computer science and technology or coding language or programming language 🙏🙏 I also want to started teeching about this topic. Nobody don't miss any video from this channel
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
thanks edureka for providing free education 😍😍😍😍
from Pak
You are most welcome, Tasadduq. Don't forget to subscribe our channel.
could you please clear me the dataset at 1:11:00 of video, from where did you get 6 instances true & 8 instances false in the humidity dataset. I am getting 9 yes and 5 no. Please help me out.
Hi Anish, we have got your request covered. Kindly drop in your email id to help us assist you with the required datasets. Cheers :)
Thank you very much for this great videos.
Thank you, it helps me lot
great teaching motive is clear
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks edureka for this video 💗💗
Hello,
Can we please get the csv files
Thanks!
Hi can you please send me the solution of example which in the bayes theorem? and also pdf of this course. It would be a great help.
Hi great to hear from you :) please share your mail id ! so that we can share the data sheet with you :)Do subscribe the channel for more updates : )
Pls can we Get pdf of this whole Lecture. pls provide the download link 🙏🏻
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Please can you sharee the dataset, for more practice. Thank you.
Hey Edureka. I have a doubt, if I attend this whole course. then what will be the certification process?? Will I get or not? Please reply
Hi Thanks for showing interest in edureka ! Yes we provide certification for all the courses for more information you can visit our web site :www.edureka.co/all-courses
this is very useful, if possible please make a video on pallel programming
tq edureka it is very useful to all
You are welcome👍
We are glad we could help. Do subscribe to our channel to stay posted on upcoming tutorials: ua-cam.com/users/edurekaIN.
From where we will get this datasets
Just Completed the 10hrs theory.
Thanks for that....
When starting to learn Data Science, which programming language should I start with before embarking on Data Science?
The best option is to go with python because its the most used programming language in this field
Thanks so much for your work...is this all a data scientist and a Data Analyst needs to know?
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you very much.
It helps me to gain a lot of knowledge
I am just a beginner will this course be useful for me.please explain sir
Hey edureka!
Can you please provide the datasets so that i can practice more...
Btw amazing course 🌸
1:44:00 Probability Question= Ans: 9/17
subtitle would be helpful.
Excellent course for beginners, please share the datasheet for these and slides
Thanks for showing interest in Edureka! Kindly share your mail id for us to share the datasheet/ source code :) Do subscribe for more videos & updates
good explanation...thanku so much
Awesome, this video is great. Special thanks to all the Edureka community. Bless you guys.
Thank you for your review : ) We are glad that you found our videos /contents useful . We are also trying our best to further fulfill your requirements and enhance your expirence :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you so much for this great course. Could you provide us the whole content in pdf or any other format ?
We are glad to have learners like you. Please do share your mail id so that we can send the notes or source codes. Do subscribe our channel and hit that bell icon to never miss an video from our channel
Good course expert in teaching the course .bravo
Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Could you help me with the datasets and python code notebooks used here?
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
How can I achieved as early as possible to become a data scientist in 1 years. I'm science backgrounded. Please help me to achieve this target
The course was great .Good Tutorial
Amazing work, very professional. Can I please get these data sets used?
We are happy that Edureka is helping you learn better ! We are happy to have learners like you :) Please share your mail id to share the data sheets :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )