← Data Science & Analytics

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The complete curriculum

01 Introduction to DS 3 lessons ▶

1. 1.1What is Data Science Trial
2. 1.2What is Data Science Full
3. 1.3Data Science Case Studies

02 Introduction to Python Opearator datatype 3 lessons ▶

1. 2.1Getting Start with python
2. 2.2Identifiers_datatypes
3. 2.3Python Operators

03 Python Conditional statements 3 lessons ▶

1. 3.1Conditional if elif
2. 3.2Loop while for
3. 3.3Nested loop continue break

04 Python Data Structure 4 lessons ▶

1. 4.1String part1
2. 4.2String part2
3. 4.3List Comprehension
4. 4.4Tuple Sets

05 Functions Lamda in Python 2 lessons ▶

1. 5.1Function map lamda
2. 5.2Function Casestudy banking

06 Numpy 2 lessons ▶

1. 6.1Numpy Insurance
2. 6.2Numpy casestudy

07 Pandas 3 lessons ▶

1. 7.1Pandas 1
2. 7.2Pandas 2 NA sort group
3. 7.3Pandas 3 Casestudy healthcare

08 Visualisation matplot seaborn plotly 4 lessons ▶

1. 8.1Matplotlib Visualisation
2. 8.2Seaborn Visualisation
3. 8.3Casestudy Matplot Seaborn
4. 8.4Plotly

09 EDA 2 lessons ▶

1. 9.1EDA 1
2. 9.2EDA 2 Tranform FeatureEngg

10 Data Preprocessing 2 lessons ▶

1. 10.1Data wragling File Handling
2. 10.2Data Preprocessing

11 Statistics 6 lessons ▶

1. 11.1Descriptive stats
2. 11.2Inferential stats
3. 11.3Probabilty
4. 11.4Hypothesis Tesing 1
5. 11.5Hypothesis test case study python prog
6. 11.6Type 1 Type 2 Error

12 ML Concept Types 1 lesson ▶

1. 12.1ML Concepts Types

13 Simple Linear Regression 2 lessons ▶

1. 13.1Linear Regr Concept Python OLS
2. 13.2Simple Lin Reg 1

14 Multiple Linear Regression 3 lessons ▶

1. 14.1MLR Evaluation Techniques
2. 14.2Multi Linear Reg Python
3. 14.3Non Linear Polynomial

15 Logistic Regression ML model 3 lessons ▶

1. 15.1Logistic Case study claimant
2. 15.2Logistic Regr Python Churn
3. 15.3Logistic Regression Concept

16 Decision Tree 3 lessons ▶

1. 16.1Decision Tree Concept
2. 16.2Dec Tree Gini Python imple
3. 16.3Decison Tree Case study Mushroom

17 Random Forest 3 lessons ▶

1. 17.1Random Forest Concept
2. 17.2Random Forest Maths
3. 17.3Python red wine Random Forest

18 Boosting Stacking 3 lessons ▶

1. 18.1AdaBoost
2. 18.2Gardient Boosting
3. 18.3Stacking Ensemble

19 KNN 2 lessons ▶

1. 19.1KNN Case study Diabetic
2. 19.2KNN Concept python

20 Naive Bayes 2 lessons ▶

1. 20.1Naive Bayes
2. 20.2Naive Bayes Titanic Python Case study

21 Unsupervised Learning K Means 6 lessons ▶

1. 21.1Unsupervised ML Clustering
2. 21.2K-means Algo
3. 21.3Kmeans Python Univ
4. 21.4Hirarchical Clustering
5. 21.5Hirarchical Customer Segment
6. 21.6PCA Concept Casestudy

22 Model Evaluation & Tuning 1 lesson ▶

1. 22.1Cross Validation Techniques

23 NLP 5 lessons ▶

1. 23.1NLP Concept Token
2. 23.2NLP lemit stemming
3. 23.3NLP Case study stemVslemat
4. 23.4TF IDF NLP case study
5. 23.5NLP NB Email Spam

24 Time Series Forecasting 6 lessons ▶

1. 24.1Time Series Concept
2. 24.2Time Series visuali data prep
3. 24.3TimeSeries Stationarity
4. 24.4ACF PACF
5. 24.5ARIMA Model
6. 24.6SARIMA Model Forecasting

25 Capstone Project 2 lessons ▶

1. 25.1Capstone project - Cardiac Diagnosis Part-1
2. 25.2Capstone project - Cardiac Diagnosis Part-2

26 Model Deployment 1 lesson ▶

1. 26.1Model Deployment

Tools you'll use

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