Sedulous Software Training Hub

• Learn the basics of data science by introducing key concepts, such as the data science life cycle.
• Students are introduced to Python or R programming, their versions, and an in-depth study of the programming languages.
• Read through fundamental statistics, the application of statistical analysis, and distinguish between inferential and descriptive statistics.
• Data Science Models and Algorithms include predictive models, predictive analyses, linear regression, and polynomial regression.
• Being trained with supervised and unsupervised learning algorithms, hypothesis testing, and reinforcement learning algorithms.

• Understanding Data Science
• The Data Science Life Cycle
• Understanding Artificial Intelligence (AI)
• Overview of Implementation of Artificial Intelligence
• Machine Learning
• Deep Learning
• Artificial Neural Networks (ANN)
• Natural Language Processing (NLP)
• How R connected to Machine Learning
R - as a tool for Machine Learning Implementation

• What is Python and history of Python
• Python-2 and Python-3 differences
• Install Python and Environment Setup
• Python Identifiers, Keywords and Indentation
• Comments and document interlude in Python
• Command line arguments and Getting User Input
• Python Basic Data Types and Variables

• Understanding Lists in Python
• Understanding Iterators
• Generators, Comprehensions and Lambda Expressions
• Understanding and using Ranges

• Introduction to the section
• Python Dictionaries and More on Dictionarie
• Sets and Python Sets Examples

• Reading and writing text files
• Appending to Files
• Writing Binary Files Manually and using Pickle Module

• Python user defined functions
• Python packages functions
• The anonymous Functions
• Loops and statement in Python
• Python Modules & Packages

• What is Exception?
• Handling an exception
• try….except…else
• try-finally clause
• Argument of an Exception
• Python Standard Exceptions
• Raising an exceptions
• User-Defined Exceptions

• What are regular expressions?
• The match Function and the Search Function
• Matching vs Searching
• Search and Replace
• Extended Regular Expressions and Wildcard

• Collections – named tuples, default dicts
• Debugging and breakpoints, Using IDEs

• Understanding different types of Data
• Understanding Data Extraction
• Managing Raw and Processed Data
• Wrangling Data using Python
• Using Mean, Median and Mode
• Variation and Standard Deviation
• Probability Density and Mass Functions
• Understanding Conditional Probability
• Exploratory Data Analysis (EDA)
• Working with Numpy, Scipy and Pandas

• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Mode
• Improving the Performance of the Model

• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting

• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

• Understanding the Supervised Learning Algorithm
• Boosted Trees
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding the Supervised Learning Algorithms
• Understanding Classifications
• Working with different types of Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding Unsupervised Learning
• Exploring K-means
• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering
• Exploring Hierarchical Clustering
• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering
• Understanding Dimensionality Reduction
• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing

• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing
• Normalization
• Standard Normalization
• Parameters of Hypothesis Testing
• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test

• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff

• What is R?
• History and Features of R
• Introduction to R Studio
• Installing R and Environment Setup
• Command Prompt
• Understanding R programming Syntax
• Understanding R Script Files

• Data types in R
• Creating and Managing Variables
• Understanding Operators
• Assignment Operators
• Arithmetic Operators
• Relational and Logical Operators
• Other Operators
• Understanding and using Decision Making Statements
• The IF Statement
• The IF…ELSE statement
• Switch Statement
• Understanding Loops and Loop Control
• Repeat Loop
• While Loop
• For Loop
• Controlling Loops with Break and Next Statements

• Understanding the Vector Data type
• Introduction to Vector Data type
• Types of Vectors
• Creating Vectors and Vectors with Multiple Elements
• Accessing Vector Elements
• Understanding Arrays in R
• Introduction to Arrays in R
• Creating Arrays
• Naming the Array Rows and Columns
• Accessing and manipulating Array Elements
• Understanding the Matrices in R
• Introduction to Matrices in R
• Creating Matrices
• Accessing Elements of Matrices
• Performing various computations using Matrices
• Understanding the List in R
• Understanding and Creating List
• Naming the Elements of a List
• Accessing the List Elements
• Merging different Lists
• Manipulating the List Elements
• Converting Lists to Vectors
• Understanding and Working with Factors
• Creating Factors
• Data frame and Factors
• Generating Factor Levels
• Changing the Order of Levels
• Understanding Data Frames
• Creating Data Frames
• Matrix Vs Data Frames
• Sub setting data from a Data Frame
• Manipulating Data from a Data Frame
• Joining Columns and Rows in a Data Frame
• Merging Data Frames
• Converting Data Types using Various Functions
• Checking the Data Type using Various Functions

• Understanding Functions in R
• Definition of a Function and its Components
• Understanding Built in Functions
• Character/String Functions
• Numerical and Statistical Functions
• Date and Time Functions
• Understanding User Defined Functions (UDF)
• Creating a User Defined Function
• Calling a Function
• Understanding Lazy Evaluation of Functions

• Understanding External Data
• Understanding R Data Interfaces
• Working with Text Files
• Working with CSV Files
• Understanding Verify and Load for Excel File
• Using WriteBin() and ReadBin() to manipulate Binary Files
• Understanding the RMySQL Package to Connect and Manage MySQL Databases

• What is Data Visualization
• Understanding R Libraries for Charts and Graphs
• Using Charts and Graphs for Data Visualizations
• Exploring Various Chart and Graph Types
• Pie Charts and Bar Charts
• Box Plots and Scatter Plots
• Histograms and Line Graphs

• Understanding the Basics of Statistical Analysis
• Uses and Advantages of Statistical Analysis
• Understanding and using Mean, Median and Mode
• Understanding and using Linear, Multiple and Logical Regressions
• Generating Normal and Binomial Distributions
• Understanding Inferential Statistics
• Understanding Descriptive Statistics and Measure of Central Tendency

• Understanding Packages
• Installing and Loading Packages
• Managing Packages

• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model

• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting

• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbor
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means
• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering
• Exploring Hierarchical Clustering
• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering
• Understanding Dimensionality Reduction
• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)

• What is Hypothesis Testing in Machine Learnings
• Advantages of using Hypothesis Testing
• Basics of Hypotheis
• Parameters of Hypothesis Testing
• Null Hypothesis
• Alternative Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test

• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff