Introduction to Data Science

What is data science?

What is difference between AI, Data Science, Machine Learning, Deep Learning

Job Land scape and Preparation Time

Who are data scientists?

What is day to day job of Data Scientist

End to End Data Science Project Life Cycle

Data Science roles – functions, pay across domains, experience

Business Statistics

Data types
1. Continuous variables
2. Ordinal Variables
3. Categorical variables
4. Time Series
5. Miscellaneous
6. Common Data Science Terminology

Descriptive statistics
1. Basics concepts of probability
2. Frequentist versus Bayesian Probability
3. Axioms of probability theory,
4. Permutations and combination
5. Conditional and marginal probability
6. Joint Probability
7. Bayes Theorem
8. Probability Mass Function and Probability Density Function
9. Cumulative Mass Function and Cumulative Density Function

Central Tendencies
1. Mean
2. Median
3. Mode
4. Spread
5. Variance
6. Standard Deviation
7. Effects on central tendencies after transformations
8. Quartile Analysis
9. Implementation of central tendencies using python
10. Box Plots for outlier identification
11. Drawing Box plots using python

Sampling
1. Need for Sampling?
2. Different types of Sampling
3. Simple random sampling
4. Systematic sampling
5. Stratified Sampling
6. Implementation of sampling techniques using python

Data distributions
1. Normal Distribution
2. Binomial Distribution
3. Binomial Approximated to Normal
4. Implementation of distributions using python

Inferential statistics
1. Why inferential statistics?
2. Z score calculation
3. Defining p value and implementations using python
4. Inferring from sample to population
5. Sampling distribution of sample means

Hypothesis testing
1. Confidence Interval
2. Testing the hypothesis
3. Type I error
4. Type II error
5. Null and alternate hypothesis
6. Reject or acceptance criterion

Introduction to R

A Primer to R programming

What is R? Similarities to OOP and SQL

Types of objects in R – lists, matrices, arrays, data.frames etc.

Creating new variables or updating existing variables

If statements and conditional loops – For, while etc.

String manipulations

Sub setting data from matrices and data.frames

Casting and melting data to long and wide format

Merging datasets

Python for Data Science

Understanding the reason of Python’s popularity

Basics of Python: Operations, loops, functions, dictionaries

Numpy – creating arrays, reading, writing, manipulation techniques

Ground-up for Deep-Learning

Exploratory Data Analysis with Python

Getting to understand structure of Matplotlib

Configuring grid, ticks.

text, color map, markers, widths with Matplotlib

configuring axes, grid

hist, scatterplots

bar charts

multiple plots

3D plots

Correlation matrix plotting

Data Munging with Python

Introduction to pandas

Data loading with Pandas

Data types with python

Descriptive Statistics with Pandas

Quartile analysis with Pandas

Sort, Merge, join with Pandas

Indexing and Slicing with pandas

Pivot table, Aggregate and cross tab with pandas

Apply function for parallel processing with Python

Cleaning Data with python

Determining correlation

Handling missing values

Plotting with Pandas

Time series with Pandas

Introduction to Artificial Intelligence

Dealing Prediction problem

Forecasting for industry

Optimization in logistics

Segmentation in customer analytics

Supervised learning

Unsupervised Learning

Optimization

Types of AI : Statistical Modelling, Machine Learning, Deep Learning, Optimization, Natural Language Processing, Computer vision, Speech Processing, Robotics

Artificial Intelligence I – Statistical Modelling

Linear Regression
1. Assumptions
2. Model development and interpretation
3. Sum of least squares

Logistic Regression
1. Need for logistic regression
2. Logit link function
3. Maximum likelihood estimation
4. Model development and interpretation
5. Confusion Matrix – error measurement
6. ROC curve
7. Measuring sensitivity and specificity
8. Advantages and disadvantages of logistic regression models

Time series analysis – Forecasting
1. Simple moving averages
2. Exponential smoothing
3. Time series decomposition
4. ARIMA

Model validation and deployment
1. RMSE – Root Mean squared error
2. MAPE – Mean Average Percentage Error
3. Confusion matrix and Misclassification rate
4. Area under the curve (AUC) , ROC curve

Artificial Intelligence II – Machine Learning

Supervised Learning

Decision trees and Random Forest
1. C5.0
2. Classification and Regression trees(CART)
3. Process of tree building
4. Entropy and Gini Index
5. Problem of over fitting
6. Pruning a tree back
7. Trees for Prediction (Linear) – example
8. Tress for classification models – example
9. Advantages of tree-based models?

Association Rule Mining
1. Rules generation from decision trees,
2. Apriori algorithm
3. Support, confidence and lift measures

Support Vector Machines
1. Linear learning machines
2. SVM case for linearly separable data
3. Kernel space

Neural Networks
1. Motivation for Neural Networks
2. Perceptron and Single Layer Neural Network
3. Back Propagation algorithm
4. Feed Forward Neural Net
5. Sigmoid parameters
6. Weights initialization,
7. Decay of weights
8. Learning rate
9. Momentum

Ensemble Techniques
1. Bagging
2. Boosting
3. Stacking
4. Gradient Boosting Machines
5. Unsupervised Learning

Clustering Techniques
1. Hierarchical clustering
2. K-Means clustering
3. Distance measures
4. Applications of cluster analysis – Customer Segmentation

Collaborative Filtering, PCA

Artificial Intelligence III – Natural Language Processing

NLP I – Text Preprocessing

1. Tokenization
2. Stemming
3. Lemmatization

NLP II – Text Modelling

1. POS tagging
2. TFIDF and classification

Artificial Intelligence IV – Deep Learning

ReLU

Sigmoid, Depth vs Width tradeoffs

Convolutional networks

Concepts of filters

Sliding

Pooling and Padding

Comparison between DL and ML performances over the MNIST dataset

Practical use cases of AI and best practices in AI

Business problem to an analytical problem

Guidelines in model development

Big Data, Azure for AI, Data Science applications

Big data and analytics?

Leverage Big data platforms for Data Science

Introduction to evolving tools

Machine learning with Spark

Creation of R-Server clusters

Computation of Big-Data ML algorithms over the Azure cloud

Analytical Visualisation with Tableau

Why is it important for Data-Analyst

Tableau workbook walkthrough

Instruction of creation of your own workbooks

Demo of few more workbooks

What we are offering as a part of this course?

2 REAL TIME projects End to End explanations with Pseudo code

All classes explained with REAL TIME projects experience

Data sets with code

End to End Data Science Project work flow explanation

Free online mock test for Data Science Interview preparation

Free Mock Interviews for best performers in exam

Hand written notes copy and slides copy from institute

Detailed assistance in Resume preparation. Special attention for experienced people on previous experience

Real time interview questions and answers e-book

Trainer available for doubts answering on Slack Channel

Latest resources, blogs and articles sharing on slack channel

Special focus on building profile for experienced people