Data Science

1 Module: Python

Firstly, Python is the most important and necessary topic that every data scientist should have knowledge about.

• Environment set-up
• Jupyter overview
• Python Numpy
• Python Pandas
• Python Matplotlib

2 Module: R

Used for statistical and data analysis, Secondly, R programming language is one of the advanced statistical languages used in data science. Thirdly, This module teaches you how to explore data sets using R. Here you will learn –

• An introduction to R
• Data structures in R
• Data visualization with R
• Data analysis with R

3 Module: Statistics

When working with data, the knowledge of statistics is necessary and an important skill set that you must have. However, In this module, you will learn –

• Important statistical concepts used in data science
• Difference between population and sample
• Types of variables
• Measures of central tendency
• Measures of variability
• Coefficient of variance
• Skewness and Kurtosis

4 Module: Inferential statistics

Inferential statistics is used to make generalizations of populations, Firstly, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyze representative samples of large data sets. Therefore, In this module, you will learn –

• Normal distribution
• Test hypotheses
• Central limit theorem
• Confidence interval
• T-test
• Type I and II errors
• Student’s T distribution

5 Module: Regression and Anova

This lesson will help you understand how to establish a relationship between two or more objects. Firstly,  ANOVA or analysis of variance is used to analyze the differences among sample sets. However, Here you will learn –

• Regression
• ANOVA
• R square
• Correlation and causation

6 Module: Exploratory data analysis

In this lesson you will learn –

• Data visualization
• Missing value analysis
• The correction matrix
• Outlier detection analysis

7 Module: Supervised machine learning

Firstly, This is a comprehensive module to help you understand how to make machines or computers interpret human language. You will learn –

• Python tool
• Support vector machine
• Logistic and linear regression
• Decision tree classifier
• Neural networks

8 Module: Tableau

Tableau is a sophisticated business intelligence tool used for data visualization. In this lesson, you will learn –

• Working with Tableau
• Deep diving with data and connection
• Creating charts
• Mapping data in Tableau
• Dashboards and stories

9 Module: Machine learning on cloud

In this lesson, you will learn –

• ML on cloud platform
• ML on AWS
• ML on Microsoft Azure 

1 Module: What is Deep Learning?

• Applications
• Weights and Activation functions
• Perceptron
• Data Preprocessing
• Image augmentation in OpenCL

2 Module: Neural Networks

• Neural Networks
• Applications
• Loss function
• Backpropagation
• MNIST example for Neural Networks

3 Module: Convolutional Neural Networks (CNN)

• What are convolutional neural network and tensor flow
• Convolutional layer
• Pooling layer
• How to create the layers in Python

4 Module: Convolutional Neural Networks (CNN)

• Convolutional neural network with Python
• Transfer learning
• RCNN (Fast RCNN, Faster RCNN, Mask RCNN)

5 Module: Representation learning and Generative learning

• Auto encoder
• Generative adversarial Networks
• Simple example with MNIST dataset
• Limitation of GAN and Deep Convolutional GANs

6 Module: Deep Learning applications for Reinforcement Learning and NLP

• Key elements of Reinforcement Learning
• Open AI Gym Toolkit
• How is Deep Learning applied to RL?
• Robotic Manipulation using Deep RL
• Application of Deep learning to Natural Language Processing
• Automatic Language Translation
• Automatic Text Classification

• Introduction to ML

• Linear Regression
• Linear Discriminant Analysis
• Reinforcement Learning
• Multivariate Regression
• Linear Classification
• Unsupervised Learning
• Partial Least Squares
• Logistic Regression
• Supervised Learning
• Shrinkage Methods
• Project
• Support Vector Machines
• Artificial Neural Networks
• Regression Trees
• Hinge Loss Formulation
• Training and Validation
• Decision Trees
• Perceptron Learning
• Parameter Estimations
• Decision Trees Examples
• ROC Curve
• Random Forests
• Hidden Markov Models
• Evaluation Measures
• Bayesian Networks
• Tree width and belief
• Ensemble Methods
• Gradient Boosting
• Undirected Graphical Method
• Minimum Desc. Lgth Analysis
• Naive Bayes
• Variable Elimination
• Clustering
• Expectation Maximization
• Reinforcement Learning
• Birch and Cure Algorithms
• Gaussian Mixture Models
• Linear Theory

• Our AI syllabus contains several sub-sections which are mentioned below. These components from the AI syllabus can vary following different places and education institutes
• Perception and Intelligence
• Algorithms in AI
• Machine Learning
• Deep Learning & Neural Networks
• Humans and AI
• Ethical AI and Biases
• AI and Jobs
• PRE PREPARATORY CONTENT
• INTRODUCTION TO PYTHON FOR DATA ANALYSIS
• PYTHON FOR DATA SCIENCE
• MATH FOR MACHINE LEARNING
• DATA VISUALISATION IN PYTHON
• DATA ANALYSIS USING SQL
• ADVANCED SQL
• STATISTICS ESSENTIAL
• ANALYTICS PROBLEM SOLVING
• INVESTMENT ASSIGNMENT
• INFERENTIAL STATISTICS
• HYPOTHESIS TESTING
• EXPLORATORY DATA ANALYSIS
• GROUP PROJECT
• MACHINE LEARNING – 1
• LINEAR REGRESSION
• LINEAR REGRESSION ASSIGNMENT
• LOGISTIC REGRESSION
• NAIVE BAYES
• MODEL SELECTION
• MACHINE LEARNING – II
• ADVANCED REGRESSION
• SUPPORT VECTOR MACHINE (OPTIONAL)
• TREE MODELS
• MODEL SELECTION – PRACTICAL CONSIDERATIONS
• BOOSTING
• UNSUPERVISED LEARNING: CLUSTERING
• UNSUPERVISED LEARNING: PRINCIPAL COMPONENT ANALYSIS
• Natural Language Processing
• LEXICAL PROCESSING
• SYNTACTIC PROCESSING
• SYNTACTIC PROCESSING-ASSIGNMENT
• SEMANTIC PROCESSING
• BUILDING CHATBOTS WITH RASA
• FYI: Free NLP course!
• Deep Learning
• INTRODUCTION TO NEURAL NETWORKS
• SYNTACTIC PROCESSING
• NEURAL NETWORKS – ASSIGNMENT
• CONVOLUTIONAL NEURAL NETWORKS -INDUSTRY APPLICATIONS
• RECURRENT NEURAL NETWORKS
• NEURAL NETWORKS PROJECT-GESTURE RECOGNITION
• Get Machine Learning Certification online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
• REINFORCEMENT LEARNING
• CLASSICAL REINFORCEMENT LEARNING
• ASSIGNMENT -CLASSICAL
• REINFORCEMENT LEARNING
• DEEP REINFORCEMENT LEARNING
• REINFORCEMENT LEARNING PROJECT
• CAPSTONE
• DEPLOYMENT
• CAPSTONE

Language for statistical programming, various features of R, introduction to R Studio, statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools and learn about R-bind

• R Packages

R functions, code compilation and data in well-defined format called R Packages, R Package structure, package metadata and testing, CRAN (Comprehensive R Archive Network), vector creation and variables values assignment

• Sorting Data Frame

R functionality, Rep function, generating repeats, sorting and generating factor levels, transpose and stack function

• Matrices and Vectors

Introduction to matrix and vector in R, understanding various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions

• Reading Data from External Files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists and understanding how to read data from external files

• Generating Plots

Generate plots in R, graphs, bar plots, line plots, histograms and components of a pie chart

• Analysis of Variance (ANOVA)

Understanding analysis of variance (ANOVA) statistical technique, working with pie charts and histograms and deploying ANOVA with R, one-way ANOVA and two-way ANOVA

• K-Means Clustering

K-Means clustering for cluster and affinity analysis, cluster algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships

• Association Rule Mining

Introduction to Association Rule Mining, various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, algorithm and rules of Association Rule Mining and understanding single cardinality

• Regression in R

Understanding what is simple linear regression, various equations of line, slope, Y-intercept regression line, deploying analysis using regression, the least square criterion, interpreting the results and standard error to estimate and measure of variation

• Analyzing Relationship with Regression

Scatter plots, two-variable relationship, simple regression analysis and line of best fit

• Advanced Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-test, the test statistic with an F-distribution, advanced regression in R and prediction linear regression

• Logistic Regression

Logistic regression mean and logistic regression in R

• Advanced Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring if the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system and ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier

• Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC curve, converting the variable, data set partitioning, understanding how to check for multicollinearity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix and deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates

• Database Connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data and visualization of the performance of the algorithm using confusion matrix R Case Studies

• Logistic Regression Case Study

In this case study, you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns and uncover insights and more, all through the power of R programming. Due to this, the future advertisement spends can be decided and optimized for higher revenues.

• Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on various parameters. You will deploy multiple regression and note down the MPG for the car make, model, speed, load conditions, etc. It includes the model building, model diagnostic and checking the ROC curve, among other things.

• Receiver Operating Characteristic (ROC) Case Study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real-world data, check the ROC curve and more.