PYTHON FOR DATA SCIENCE

1.1. FUNDAMENTALS OF PYTHON

• Advantages of Python

• Python compiler and PVM

• Python instillation and environment

1.2. DATATYPES IN PYTHON

• strings

• char

• lists

• tuples

• range

• sets

• dictionaries

1.3. OPERATORS IN PYTHON

1.4. INPUT/OUTPUT

1.5. CONTROL STATEMENTS

• if statement

• if…else statement

• if…elif…else statement

• while loop

• for loop

• break statement

• continue statement

• pass statement

1.6. NUMPY ARRAYS

• Array creation

• Array attributes

• 1D and 2D Arrays

• Matrix

1.7. FUNCTIONS IN PYTHON

• Built in and User defined functions

• Writing your own functions

• Importing functions

1.8. MODULES AND PACKAGES

• Modules

• Packages

• Imports

1.9. DATA ANALYSIS IN PYTHON USING PANDAS

• Series

• Dataframes

• Creation of dataframes from different sources

• Viewing data in dataframe

• Operations on dataframe

• Handling missing data

1.10..DATA VISUALIZATION USING MATPLOTLIB

• Line plot

• Bar graph

• Pie chart

• Subplots

• Histogram

1.11. DATA VISUALIZATION USING SEABORN

STATISTICAL METHODS

2.1. INTRODUCTION TO STATISTICS

• What is statistics?

• Types of statistics

• Descriptive statistics

• Inferential statistics

2.2. STATISTICAL TERMS

• Population

• Sample

• Variable (discrete and continuous)

• Data and types of data

• Qualitative (nominal and ordinal)

• Quantitative (interval scale and ratio scale)

2.3. MEASURES OF CENTRAL TENDENCY

• Mean

• Median

• Mode

2.4. PROBABILITY

• Probability with replacement

• Probability without replacement

• Probability Mass Function (PMF)

• Probability Density Function (PDF)

2.5. MEASURES OF SHAPE

• Skewness

• Kurtosis

2.6. MEASURES OF DISPERSION OR VARIABILITY

• variance

• std

• percentile

• quartile

• range

• IQR

2.7. PROBABILITY DISTRIBUTIONS

• Normal distritution

• Standard normal distribution

• Sampling distribution of sample means

• Central limit theorem

• T- Distribution

• Student T- Test

• Chi Square Test (Goodness of Fit)

• Binomial distribution

2.8. HYPOTHESIS TESTING

• Upper tail test

• Lower tag test

• Two tag test

2.9. ANOVA

• 1-way ANOVA

• 2-way ANOVA

TABLEAU

3.1. INTRODUCTION TO TABLEAU

• Tableau tools

• Datatmes in Tableau

• Viewing data

3.2. CREATING PIVOT TABLE

3.3. DATA BLENDING

3.4. CROSS DATABASE JOIN

• Aggregate functions

3.6. DATA VISUALIZATION

• Symbol maps

• Bar chart

• Stacked bar chin

• Line chart

• Heat map

• Pie chart

• Scatter plot

• Area chart

• Dual Axis chart

• Histogram

• Bubble chart

MACHINE LEARNING 

4.1. EXPLORATORY DATA ANALYSIS (EDA)

4.2. OUTLIERS AND THEIR TREATMENT

4.3. SUPERVISED LEARNING VS UNSUPERVISED LEARNING

4.4. FEATURE EXTRACTION AND CONVERSION

• One hot encoding using dummy variables
• One hot encoding using One hot encoder

4.5. REGRESSION MODELS

• Simple Linear regression
• Multiple Linear regression
• Polynomial Linear regression
• Ridge regression
• Bias and Variance tradeoff
• Lasso regression
• Elasticnet regression

4.6. CLASSIFICATION MODELS

• Logistic regression
• Naive Bayes (Gaussian NB and Multinomial NB)
• KNN Classifier
• SVM
• Regularization
• Decision Tree
• Entropy
• Gini Index
• Random Forest
• Confusion Matrix

4.7. UNSUPERVISED LEARNING

• K-Means Clustering
• Elbow technique

4.8. ASSOCIATION RULE LEARNING

• Apriori Algorithm

4.9. MODEL SELECTION

• Selecting appropriate model for our data

Data Analytics

• Introduction
• Essential Python Libraries
• Data Wrangling
• Data Aggregation and Group operations
• Advanced pandas
• Time series data analytics

COMPUTER VISION 

7.1. OBJECT DETECTION BY COMPUTER

• DATA PREPROCESSING

  • Data profiling
  • Data cleansing
  • Data reduction
  • Data transformation
  • Feature engineering
  • EDA

: LIBRARIES

• NUMPY
• PANDAS
• MATPLOTLIB
• SEABORN
• SCIKIT-LEARN
• SCIPY