Certified Data Scientist (CDS)

Beginner 5(2 Ratings) 964 Students enrolled
Created by Ashok Veda Last updated Mon, 28-Dec-2020 English
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Curriculum for this course
146 Lessons 24:32:23 Hours
COURSE #1 DATA SCIENCE FOUNDATION
2 Lessons 00:01:10 Hours
  • 1.0 Course 1 Data Science Foundation Introduction 00:01:10 Preview
  • Data Science Foundation Deck
  • 1.1.1 What is Data Science 00:10:51 Preview
  • 1.1.2 Evolution of Data Science 00:14:48
  • 1.1.3 Big Data Vs Data Science 00:04:36
  • 1.1.4 Data Science vs Business Analytics 00:02:18
  • 1.1.5 Business Analytics Classification 00:04:48
  • Data Science Basic Terminologies Assessment 00:00:00
  • 2.1.1 Machine Learning Introduction 00:14:43 Preview
  • 2.1.2 Machine Learning Amazon Returns Demo 00:09:52 Preview
  • 2.1.3 Types of Machine Learning 00:10:30 Preview
  • 2.1.4 Machine Learning Industry Applications 00:14:55 Preview
  • 2.1.5 Data Science Project Workflow 00:06:16 Preview
  • 2.1.6 Data Science Project Roles 00:10:12 Preview
  • 1. Statistics Overview - Introduction 00:02:22 Preview
  • Statistics for Data Science Deck
  • 2. Statistics Basic Terminology 00:13:09 Preview
  • 3. Types of Data 00:20:28 Preview
  • 4. Operations Of Various Data Types 00:05:03 Preview
  • Basic Data Types Assessment 00:00:00
  • 1. Introduction - Sampling Methods 00:07:23 Preview
  • 2. Sampling Methods 00:07:08 Preview
  • 3. Cluster Sampling 00:02:06 Preview
  • 4. Systematic Sampling 00:05:57 Preview
  • 5. Biased Sampling 00:03:39 Preview
  • 6. Sampling Error 00:01:39 Preview
  • Statistics Assessment 1 00:00:00
  • 1. EDA - Central Tendencies 00:16:45 Preview
  • 2. EDA - Variability 00:16:25 Preview
  • 3. EDA - Histogram, Z-Value, Normal Distribution 00:22:22 Preview
  • 4. EDA - Normality Testing 00:17:00 Preview
  • Statistics Assessment 2 00:00:00
  • 1. Hypo- Introduction, One Sample T-Test 00:22:18 Preview
  • 2. Hypo - Two Sample T-Test 00:08:02 Preview
  • 3. Hypo - Relational T-Test 00:09:16 Preview
  • 4. Hypo-Annova 00:12:27 Preview
  • 5. Type1 & Type2 00:05:12 Preview
  • 6. Correlation 00:08:11 Preview
  • Statistics Assessment 3 00:00:00
  • 1. What is Vector 00:09:36 Preview
  • 2. Length of Vector 00:03:32 Preview
  • 3. Scalar Vector Multiplication 00:04:25 Preview
  • 4. Matrix Introduction 00:05:02 Preview
  • 5. Matrix Behaviour 00:08:49 Preview
  • 6. Determinant 00:14:26 Preview
  • 7. Matrix Cofactor, Adjoint and Inverse 00:13:16 Preview
  • 8. Solving Matrix 00:11:15 Preview
  • 9. Eigen Vector and Eigen Value 00:21:07 Preview
  • 10. Special Matrices 00:07:04 Preview
  • 11. Idea of Derivatives 00:05:12 Preview
  • 12. Partial Derivates 00:03:00 Preview
  • 13. Helful Derivatives Rules 00:07:10 Preview
  • Google Colab 00:10:08 Preview
  • Python Essentials Module1 Part1 00:18:43 Preview
  • Python Essentials Module1 Part2 00:31:26 Preview
  • Python Essentials Module1 Part3 00:39:28 Preview
  • Python Numpy Module2 Part1 00:23:55 Preview
  • Python Essentials Assessment 00:00:00
  • Python Pandas Essentials1 Module3 Part1 00:23:00 Preview
  • Python Pandas Essentials2 Module3 Part2 00:25:30 Preview
  • Python Pandas Essentials3 Module3 Part3 00:10:39 Preview
  • Python Visualization with Matplotlib 00:28:25 Preview
  • Visualization Assessment 00:00:00
  • 1 Machine Learning Introduction 00:30:58 Preview
  • 2 Supervised Vs Unsupervised 00:04:49 Preview
  • Data Science Modelling Process Flow Assessment 00:00:00
  • 3. Linear Regression Concepts 00:10:58 Preview
  • 4. Linear Regression Modeling 00:35:00 Preview
  • 5. R2 Score - Regression Metrics 00:17:31 Preview
  • Linear Regression Assessment 00:00:00
  • 6. Logistic Regression Concepts 00:24:08 Preview
  • 7. Logistic Regression Modeling 1 00:13:28 Preview
  • 8. Logistic Regression Modeling 2 00:14:09 Preview
  • 9. Classfication model evaluation 00:10:23 Preview
  • Logistic Regression Assessment 00:00:00
  • 10. KMeans Concepts 00:14:33 Preview
  • 11. KMeans Modeling 00:22:17 Preview
  • 12. KMeans Elbow Technique 00:12:43 Preview
  • K-Means Assessment 00:00:00
  • 13. K Nearest Neighbour Concepts 00:09:47 Preview
  • 14. K Nearest Neighbour Modeling 00:11:25 Preview
  • KNN Assessment 00:00:00
  • 1. Decision Tree Concepts 00:16:17 Preview
  • 2. Decision Tree Modeling 00:25:56 Preview
  • 12. Principal Component Analysis (PCA) Concepts 00:13:07 Preview
  • 13. Principal Component Analysis (PCA) Modeling 00:10:02 Preview
  • 14. Imbalanced Dataset - SMOTE Concepts 00:12:18 Preview
  • 15. Imbalanced Dataset - SMOTE Modeling 00:10:38 Preview
  • 16. Kfold Cross Validatation Concepts 00:05:54 Preview
  • 17. KFold Cross Validation Modeling 00:06:10 Preview
  • 18. Automating Hyperparameter Tuning -Grid Search 00:13:35 Preview
  • Decision Tree Assessment 00:00:00
  • 3. Random_Forest_Concepts 00:08:47 Preview
  • 4. Random_Forest_Modeling 00:18:24 Preview
  • Random Forest Assessment 00:00:00
  • 5. Bayes Theorem 00:06:01 Preview
  • 6. Naive Bayes Concepts 00:17:03 Preview
  • 7. Naive Bayes Modeling 00:19:45 Preview
  • Naive Bayes Assessment 00:00:00
  • 8. Artificial Neural Networks (ANN) Concepts 00:22:59 Preview
  • 9. Artificial Neural Networks (ANN) Modeling 00:25:22 Preview
  • ANN Assessment 00:00:00
  • 10. Support Vector Machine (SVM) Concepts 00:12:19 Preview
  • 11. Support Vector Machine (SVM) Modeling 00:19:22 Preview
  • SVM Assessment 00:00:00
  • 100-d array 00:06:09 Preview
  • ADAM Optimizer.mp4 00:01:51 Preview
  • Bagging 00:04:14 Preview
  • Bias Variance 00:06:42 Preview
  • Bias Variance Tradeoff2 00:06:06 Preview
  • Binomial Distribution 00:03:38 Preview
  • Boosting 00:03:51 Preview
  • Cross Entropy 00:14:01 Preview
  • Generative Discriminative Models 00:02:37 Preview
  • Heteroscedasticity - Machine Learning and Data Science Terminologies 00:02:13 Preview
  • Histogram 00:04:26 Preview
  • lin2non-lin 00:06:11 Preview
  • Markov Chain - Machine Learning , Data Science Terminologies 00:07:35 Preview
  • Objective Function - Machine Learning and Data Science Terminologies.mp4 00:07:07 Preview
  • Objective Function 00:05:49 Preview
  • One Hot Encoding 00:06:40 Preview
  • Pandas Limitations - Pandas vs Dask vs Pyspark 00:14:48 Preview
  • R2_score 00:11:54 Preview
  • ReLU 00:02:49 Preview
  • Sparse Matrix 00:04:40 Preview
  • Transfer-Learning 00:03:25 Preview
  • TypeI and TypeII errors 00:04:26 Preview
  • XG Boost-Hyper 00:31:10 Preview
  • 1. Machine Learning vs Deep Learning 00:03:44 Preview
  • 2. Deep Learning Evolution 00:04:54 Preview
  • 3. Computer Vision Introduction 00:06:36 Preview
  • 4. Natural Language Processing(NLP) Introduction 00:03:09 Preview
  • 5. Computer Vision - Image Basics 00:10:33 Preview
  • 6. Convolution Neural Network (CNN) 00:19:13 Preview
  • 7. CNN with Tensorflow Keras 00:17:37 Preview
  • Tableau-part1 00:20:14 Preview
  • Tableau-part2 00:09:58 Preview
  • Tableau-part3 00:09:17 Preview
  • Tableau-part4 00:06:24 Preview
  • Tableau-part5 00:06:32 Preview
  • Class 1 00:14:19 Preview
  • Context Managers In Python 1 00:04:58 Preview
  • Decorator.mov_1 00:10:13 Preview
  • Inheritance_1 00:09:33 Preview
  • Magic Methods 1 00:12:30 Preview
  • Underscore Python 1 00:07:10 Preview
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About the instructor
  • 19 Reviews
  • 3870 Students
  • 38 Courses
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Ashok Veda is the Founder-CEO of Rubixe, an AI company.

Ashok has 15 years of experience in the field of analytics and data science, guiding more than 10,000 data science aspirants, delivering numerous key speeches on AI and Data Science in various prestigious forums.

Ashok is a Ph.D. scholar in the field of AI in Marketing, holds an MBA from the University of Amsterdam (UVA), and IIM Ahmedabad, Engineering degree from NIT Warangal.

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  • Sat, 07-Nov-2020
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Includes:
  • 24:32:23 Hours On demand videos
  • 146 Lessons
  • Access on mobile and tv