### Module 1. Python for Data Science

Fundamentals of Python
• Python Compiler and PVM
• Python installation and environment

Datatypes in Python
• strings
• char
• lists
• tuples
• range
• sets
• dictionaries

Operators in Python

Input / Output

Control Statements
• if statement
• if...else statement
• if...elif...else statement
• while loop
• for loop
• break statement
• continue statement
• pass statement

numpy arrays
• Array creation
• Array attributes
• 1D and 2D
• matrices

Functions in Python
• Built in and User defined functions
• Writing our own functions
• Importing functions

Modules and Packages
• Creating our own modules and packages
• Importing our modules and packages

Data Analysis in Python using pandas
• Series
• Dataframes
• Creation of Dataframes from different sources
• Viewing data in Dataframe
• Operations on Dataframe
• Handling missing data

Data visualization using matplotlib
• Line plot
• Bar graph
• Pie chart
• Sub plots
• Histogram

Data visualization using seaborn
• Distribution plot
• Kde plot
• Countplot
• Box plot
• Scatter plot
• Sub plots
• Lmplot
• Pair plots

### Module 2. Statistical Methods

Introduction to Statistics
• What is statistics?
• ypes of statistics
• Descriptive statistics
• Inferential statistics

Statistical terms
• Population
• Sample
• Variable (discrete and continuous)
• Data and types of data
• Qualitative (nominal and ordinal)
• Quantitative (interval scale and ratio scale)

Measures of Central tendency
• Mean
• Median
• Mode

Probability
• Probability with replacement
• Probability without replacement
• Probability Mass Function (PMF)
• Probability Density Function (PDF)

Measures of Shape
• Skewness
• Kurtosis

Measures of Dispersion or Variability
• variance
• std
• percentile
• quartile
• range
• IQR

Application of Variance or Std
• Empirical Rule
• Problems on Empirical Rule
• Chebyshev’s Theorem

Probability Distributions
• Normal distribution
• Standard normal distribution
• Sampling distribution of Sample means
• Central limit theorem
• T- Distribution
• Student T- Test
• Chi Square Test (Goodness of Fit)
• Binomial distribution
• Bernoulli distribution
• Geometric distribution
• Hypergeometric distribution
• Poisson distribution

Hypothesis Testing
• Upper tail test
• Lower tail test
• Two tail test

ANOVA
• One way ANOVA
• Two way ANOVA

### Module 3. Tableau

Introduction to Tableau
• Tableau tools
• Datatypes in Tableau
• Viewing data

Creating Pivot table

Data Blending

Cross Database Joins

Calculations on Data
• Aggregate functions
• Calculated fields

Data visualizations in Tableau
• Symbol maps
• Bar chart
• Stacked bar chart
• Line chart
• Pareto chart
• Heat map
• Pie chart
• Scatter plot
• Area chart
• Dual Axis chart
• Histogram
• Bubble chart

Dash board creation

### Module 4. Machine Learning in AI

Exploratory data analysis (EDA)

Outliers and their treatment

Supervised Learning vs Unsupervised Learning

Feature extraction and conversion
• One hot encoding using dummy variables
• One hot encoding using One hot encoder

Regression Models
• Simple Linear regression
• Multiple Linear regression
• Polynomial Linear regression
• Ridge regression
• Lasso regression
• ElasticNet regression

Classification Models
• Logistic regression
• Naïve Bayes (Gaussian NB and Multinomial NB)
• KNN Classifier
• SVM
• Regularization
• Kernel Trick
• Decision Tree
• Entropy
• Gini Index
• Random forest
• Confusion Matrix
• Bootstrapping, Bagging and Boosting

Unsupervised Learning
• K-Means clustering
• Elbow technique

Association Rule Learning
• Apriori Algorithm

Model selection
• Selecting appropriate model for our data

### Module 5. Deep Learning in AI

Introduction to Deep Learning
• Biological Neural Network
• Artificial Neural Network
• Perceptrons
• Layers of a Network

Activation functions
• Identity function
• Binary step function or Threshold function
• Logistic function or Sigmoid function
• ReLU function
• Hyperbolic Tangent function
• Softmax

Creating Neural Network in Python
• ANN
• ANN with Activation functions

TensorFlow and Keras
• Variables
• Constants
• Placeholders
• Graph / Tensor / Session
• Tensorflow programming

ANN in Tensorflow and Keras

Convolutional Neural Network

Recurrent Neural Network

### Module 6. Natural Language Processing in AI

NLP Concepts
• Tokenization
• Stemming
• Lemmatization
• Stop words
• POS

Feature Extraction
• CountVectorizer
• TfidfVectorizer

Text Classification using NLP

### Module 7. Computer Vision in AI

Object detection by Computer

### Course Highlights

• 100+ industry oriented tasks are solved that are actually needed by a Data Scientist
• Softcopy of the class notes
• Datasets with code
• Project with documentation
• Resume preparation
• All Interview Questions are discussed in the classes
• Softcopy of Interview Questions
• Placement Assistance