What will i learn?
Curriculum for this course
98 Lessons
31:36:27 Hours
ALL RESOURCES
1 Lessons
- jupyter notebook
SECTION 1:INTRODUCTION TO DATA SCIENCE
2 Lessons
00:23:49 Hours
- Data-Science-Foundation 00:23:49
- Data-Science-Foundation
SECTION 2 : STATISTICS
14 Lessons
03:05:49 Hours
- Resources Attachment-Resampling-Methods
- Resources DS-Statistics
- Resources Linear methods and classification
- Resources Univariate Multivariate Statistics
- Statistics Introduction 00:23:47
- Harnessing-Data 00:19:22
- Exploratory-Analysis 00:34:34
- CTL 00:34:11
- Hypothesis-Testing 00:37:50
- Hypothesis-Testing-One-Way-Anova-Tests 00:19:00
- attachment-One-way-Anova.ipynb
- attachment-Hypothesis-Testing-T-tests.ipynb
- attachment-Hypothesis-Testing.ipynb
- Correlation 00:17:05
SECTION 3: PYTHON ESSENTIALS
6 Lessons
- Jupyter Notebook
- Python-Essentials-1 attachment-Python-Module1
- Python-Essentials-1 Attachment-Python-Quick-Tutorial
- Python-Essentials-1 DataMites-Python-Language-Basics
- Python-Essentials-2 Attachment-Python-Module1
- Python-Essentials-2attachment-Python-Quick-Tutorial
SECTION 4: DATA PREPARATION- NUMPY PANDAS
12 Lessons
00:36:24 Hours
- Resources Data Prepration Non-linear-methods
- Resources Data Prepration Univariate-Multivariate-Statistics
- Resources Data Prepration Linear methods and classification
- Attachment-Python-Numpy-Package
- DataMites-Python-Numpy
- Pandas-Essentials-1 Attachment-Python-Pandas-Module-3-4
- Pandas-Essentials-1-DataMites-Pandas-Material.pdf
- Data-Science-Training-Python-Pandas-Essentials2-Module3-Part2
- Python-Pandas-Essentials2-Module3-Part2. 00:26:16
- attachment-Pandas-Data-from-MySQL-server.ipynb
- pandas-mysql-data 00:00:00
- Google-Colab.mp4 00:10:08
SECTION 5: DATA VISUALIZATION - MATPLOTLIB
2 Lessons
00:30:28 Hours
- Visualization-M4P1 00:30:28
- DataMites-Visualization-Matplotlib
SECTION 6: MACHINE LEARNING ASSOCIATE
26 Lessons
02:22:06 Hours
- Machine-Learning-Introduction
- Machine-Learning-Introduction 00:30:58
- Linear-Regression-Deck
- Linear-Regression attachment-mtcars
- attachment-LinearRegression.ipynb
- Linear-Regression-Coding 00:35:00
- Logistic-Regression-Deck
- Learning-Logistic-Regression-Theory 00:24:08
- attachment-Logistic-Regression-Tut.ipynb
- attachment-Logistic-Regression-V1-0.pdf
- attachment-mtcars.csv
- attachment-pass.csv
- Logistic-Regression-Part1 00:00:00
- Logistic-Regression-Part2 00:00:00
- KMeans-Deck
- attachment-K-Means-V1-0.pdf
- attachment-KMeans-tut.ipynb
- K-Means-Part-1 00:00:00
- K-Nearest-Neighbour.pdf
- K-Nearest-Neighbour-attachment-mtcars
- attachment-K-Nearest-Neighbor-Tut.ipynb
- K-Nearest-Neighbour-part1 00:09:47
- Decision-Tree
- Decision-Tree-Theory attachment-cc-default.csv
- Decision-Tree-Part1. 00:16:17
- Machine-Learning-Decision-Tree-Coding 00:25:56
SECTION 7: MACHINE LEARNING EXPERT
23 Lessons
22:12:25 Hours
- Machine-Learning-Random-Forest-Deck
- Theory Attachment-Random-Forest-Tut.ipynb
- Machine-Learning-Random-Forest-Coding 00:18:24
- Machine-Learning-Random-Forest-Theory Part1 00:08:47
- Machine-Learning-Naive-Bayes-Deck
- Naive-Bayes-Coding attachment-spam.csv
- Naive-Bayes-Coding attachment
- Naive-Bayes-Theory Part1 00:17:03
- Naive-Bayes-Coding-Part 2 19:45:00
- Support-Vector-Machine-Deck
- Support-Vector-Machine-Theory attachment-SVM-basic
- Support-Vector-Machine-Theory attachment
- Support-Vector-Machine-Theory-Part1 00:12:19
- Support-Vector-Machine-Coding attachment-SVC-ML-gridsearch.ipynb
- Support-Vector-Machine-Coding 00:19:22
- Artificial-Neural-Network-ANN-Deck
- attachment-Artificial-Neural-Network.ipynb
- Artificial-Neural-Network-ANN-Theory 00:22:59
- Artificial-Neural-Network-ANN-Coding 00:25:22
- Principle-Component-Analysis-Deck
- Principle-Component-Analysis-Theory 00:13:07
- Principle-Component-Analysis-Coding 00:10:02
- ML-Deployment-END-to-END-ML-case-study
SECTION 8: TIME SERIES FORECASTING
5 Lessons
01:08:53 Hours
- Time-Series-Desk
- Time-Series-Forecasting-Theory-Part-1 00:30:33
- attachment-ARIMA-Time-Series.ipynb
- attachment-sales-cars.csv
- ARIM-in-Python-Time-Series-Forecasting-Part2 00:38:20
SECTION 9: TABLEAU FOUNDATION
5 Lessons
00:52:25 Hours
- Tableau Introduction,Connecting,Data-Source,Basic-Visualizations. 00:20:14
- 2_Tableau-Layout-Measures-and-Dimensions.mp4 00:09:58
- 3-Tableau-Visualizing-Super-Store-data.mp4 00:09:17
- 4-Tableau-Forecasting 00:06:24
- 5-Tableau-Publishing-and-Sharing 00:06:32
Requirements
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Description
Certified Data Scientist
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Other related courses
About the instructor
- 71 Reviews
- 7696 Students
- 40 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.
Student feedback
Reviews
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Tue, 01-Sep-2020 -
Tue, 06-Oct-2020Karan Shah -
Fri, 30-Oct-2020divya Vani
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Fri, 30-Oct-2020Usha RaniComprehensive and easy to understand.
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Tue, 22-Dec-2020Rahul Solanki
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Sat, 29-May-2021Jayant KumarGreat
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Sun, 06-Jun-2021Ramyadevi R
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