In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst/Analytics Manager/Actuarial Scientist/Business Analytic Practitioners. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data mugging, etc. There are three components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is manual calculations will be shown on how formulae’s are used behind the logics. The third is a practical introduction to the tools that will be used in the program like R/Python Programming and EXCEL.

- Any Graduate, No programming and statistics knowledge or skills required.

- Mr. Srinivas Reddy is Research Scholar (Ph.D in DATA SCIENCE & AI)
- Having 10+ Years of Experience in Software Development & Trainings.
- Masters of Technology in Computer Science & Engineering, MICROSOFT Certified Professional, IBM Certified Professional & Certified from IIT Kanpur & IIT Ropar.
- Data Science Management Consultant with over 7+ years of Experience in Finance, Retail, Transport domains.
- Expertise in Data Science, Data Analytics, Machine Learning, Deep Learning, Artificial Intelligence, Python, R, Weka, Data Management & BI Technologies.

**Introduction to Data Science**

- What is a Data Science?
- Who is a Data Scientist?
- Who can become a Data Scientist?
- What is an Artificial Intelligence?
- What is a Machine Learning?
- What is a Deep Learning?
- Artificial Intelligence Vs Machine Learning Vs Deep Learning
- Real Time Process of Data Science
- Data Science Real Time Applications
- Technologies used in Data Science
- Prerequisites Knowledge to Learn Data Science

**Introduction to Machine Learining**

- What is a Machine Learning?
- Machine Learning Vs Statistics
- Traditional Programming Vs Machine Learning
- How Machine Will Learn like Human Learning
- Machine Learning Engineer Responsibilities
- Types of Machine Learning
- Supervised learning
- Un-Supervised learning
- Reinforcement Learning

**Core Python Programming**

- PYTHON Programming Introduction
- History of Python
- Python is Derived from?
- Python Features
- Python Applications
- Why Python is Becoming Popular Now a Day?
- Existing Programming Vs Python Programming
- Writing Programs in Python
- Top Companies Using Python
- Python Programming Modes
- Interactive Mode Programming
- Scripting Mode Programming

- Flavors in Python, Python Versions
- Download & Install the Python in Windows & Linux
- How to set Python Environment in the System?
- Anaconda – Data Science Distributor
- Downloading and Installing Anaconda, Jupyter Notebook &Spyder
- Python IDE – Jupyter Notebook Environment
- Python IDE – Spyder Environment
- Python Identifiers(Literals), Reserved Keywords
- Variables, Comments
- Lines and Indentations, Quotations
- Assigning Values to Variables
- Data Types in Python
- Mutable Vs Immutable
- Fundamental Data Types: int, float, complex, bool, str
- Number Data Types: Decimal, Binary, Octal, Hexa Decimal & Number Conversions
- Inbuilt Functions in Python
- Data Type Conversions
- Priorities of Data Types in Python
- Python Operators
- Slicing & Indexing
- Forward Direction Slicing with +ve Step
- Backward Direction Slicing with -ve Step

- Decision Making Statements
- if Statement
- if-else Statement
- elif Statement

- Looping Statements
- Conditional Statements
- break Statement
- continue Statement
- Pass Statement

**Advanced Python Programming**

- Advanced Data Types: List, Tuple, Set, Frozenset, Dictionary, Range, Bytes & Bytearray, None
- List Data Structure
- Tuple Data Structure
- Set Data Structure
- Frozenset Data Structure
- Dictionary Data Structure
- List Vs Tuple Vs Set Vs Frozenset Vs Dict
- Range, Bytes, Bytearray & None
- Python Functions
- Python Built-in Functions
- Python Lambda Functions
- String with Functions
- Python File Handling
- Python Exceptions
- Python Packages
- Python Libraries
- Python Modules
- Collection Module
- Math Module
- OS Module
- Random Module
- Statistics Module
- Sys Module
- Date & Time Module

- Loading the Module in our Python Code
- import Statement
- from-import Statement

- Renaming a Module

**Data Analysis with Python Numpy**

- NumPy Introduction
- What is NumPy
- The Need of NumPy

- NumPy Environment Setup
- N-Dimensional Array (Ndarray)
- Creating a Ndarray Object
- Finding the Dimensions of the Array
- Finding the Size of Each Array Element
- Finding the Data Type of Each Array Item
- Finding the Shape and Size of the Array
- Reshaping the Array Objects
- Slicing in the Array
- Finding the Maximum, Minimum, and Sum of the Array Elements
- NumPy Array Axis
- Finding Square Root and Standard Deviation
- Arithmetic Operations on the Array
- Array Concatenation

- NumPy Datatypes
- NumPydtype
- Creating a Structured Data Type

- Numpy Array Creation
- empty
- Zeros
- ones

- Numpy Array from Existing Data
- asarray

- Numpy Arrays within the Numerical Range
- arrange
- linspace
- logspace

- NumPy Broadcasting
- Broadcasting Rules

- NumPy Array Iteration
- Order of Iteration
- F-Style Order
- C-Style Order

- Array Values Modification

- Order of Iteration
- NumPy String Functions
- NumPy Mathematical Functions
- Trigonometric Functions
- Rounding Functions

- NumPy Statistical functions
- Finding the Min and Max Elements from the Array
- Calculating Median, Mean, and Average of Array Items

- NumPy Sorting and Searching
- NumPy Copies and Views
- NumPy Matrix Library
- NumPy Linear Algebra
- NumPy Matrix Multiplication in Python

**Data Analysis with Python Pandas**

- Pandas Introduction& Pandas Environment Setup
- Key Features of Pandas
- Benefits of Pandas
- Python Pandas Data Structure
- Series
- DataFrame
- Panel

- Pandas Series
- Creating a Series
- Create an Empty Series
- Create a Series using Inputs

- Accessing Data from Series with Position
- Series Object Attributes
- Retrieving Index Array and Data Array of a Series Object
- Retrieving Types (dtype) and Size of Type (itemsize)
- Retrieving Shape
- Retrieving Dimension, Size and Number of Bytes
- Checking Emptiness and Presence of NaNs
- Series Functions

- Creating a Series
- Pandas DataFrame
- Create a DataFrame
- Create an Empty DataFrame
- Create a DataFrame using Inputs

- Create a DataFrame
- Column Selection, Addition & Deletion
- Row Selection, Addition & Deletion
- DataFrame Functions
- Merging, Joining & Combining DataFrames
- Pandas Concatenation
- Viewing/Inspecting Data (loc&iloc)
- Data Cleaning
- Filter, Sort, and Groupby
- Statistics on DataFrame
- Pandas Vs NumPy
- DataFrame Plotting
- Line: Line Plot (Default)
- Bar: Vertical Bar Plot
- Barh: Horizontal Bar Plot
- Hist: Histogram Plot
- Box: Box Plot
- Pie: Pie Chart
- Scatter: Scatter Plot

**DBMS – Structured Query Language**

- Introduction & Models of DBMS
- SQL & Sub Language of SQL
- Data Definition Language (DDL)
- Data Manipulation Language (DML)
- Data Query/Retrieval Language (DQL/DRL)
- Transaction Control Language (TCL)
- Data Control Language (DCL)
- Installation of MySQL & Database Normalization
- Sub Queries & Key Constraints
- Aggregative Functions, Clauses & Views

**Importing & Exporting Data**

- Data Extraction from CSV (pd.read_csv)
- Data Extraction from TEXT File (pd.read_table)
- Data Extraction from CLIPBOARD (pd.read_clipboard)
- Data Extraction from EXCEL (pd.read_excel)
- Data Extraction from URL (pd.read_html)
- Writing into CSV (df.to_csv)
- Writing into EXCEL (df.to_excel)
- Data Extraction from DATABASES
- Python MySQL Database Connection
- Import mysql.connector Module
- Create the Connection Object
- Create the Cursor Object
- Execute the Query

- Python MySQL Database Connection

**Data Visualization with Python Matplotlib**

- Data Visualization Introduction
- Tasks of Data Visualization
- Benefit of Data Visualization
- Plots for Data Visualization
- Matplotlib Architecture
- General Concept of Matplotlib
- MatPlotLib Environment Setup
- Verify the MatPlotLib Installation
- Working with PyPlot
- Formatting the Style of the Plot
- Plotting with Categorical Variables
- Multi-Plots with Subplot Function
- Line Graph
- Bar Graph
- Histogram
- Scatter Plot
- Pie Plot
- 3Dimensional – 3D Graph Plot
- mpl_toolkits
- Functions of MatPlotLib
- Contour Plot, Quiver Plot, Violin Plot
- 3D Contour Plot
- 3D Wireframe Plot
- 3D Surface Plot
- Box Plot
- What is a Boxplot?
- Mean, Median, Quartiles, Outliers
- Inter Quartile Range (IQR), Whiskers
- Data Distribution Analysis
- Boxplot on a Normal Distribution
- Probability Density Function
- 68–95–99.7 Rule (Empirical rule)

**Machine Learning**

- What is Machine Learning
- Importance of Machine Learning
- Need for Machine Learning
- Statistics Vs Machine Learning
- Traditional Programming Vs Machine Learning
- How Machine Learning like Human Learning
- How does Machine Learning Work?
- Machine Learning Engineer Responsibilities
- Life Cycle of Machine Learning
- Gathering Data
- Data preparation
- Data Wrangling
- Analyze Data
- Train the model
- Test the model
- Deployment

- Features of Machine Learning
- History of Machine Learning
- Applications of Machine Learning
- Types of Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning

**Supervised Machine Learning**

- How Supervised Learning Works?
- Steps Involved in Supervised Learning
- Types of supervised Machine Learning Algorithms
- Classification
- Regression

- Advantages of Supervised Learning
- Disadvantages of Supervised Learning

**Unsupervised Machine Learning**

- How Unsupervised Learning Works?
- Why use Unsupervised Learning?
- Types of Unsupervised Learning Algorithm
- Clustering
- Association

- Advantages of Unsupervised Learning
- Disadvantages of Unsupervised Learning
- Supervised Vs Unsupervised Learning
- Reinforcement Machine Learning
- How to get Datasets for Machine Learning?
- What is a Dataset?
- Types of Data in Datasets
- Popular Sources for Machine Learning Datasets

**Data Preprocessing in Machine Learning**

- Why do we need Data Preprocessing?
- Getting the Dataset
- Importing Libraries
- Importing Datasets
- Finding Missing Data
- By Deleting the Particular Row
- By Calculating the Mean

- Encoding Categorical Data
- LableEncoder
- OneHotEncoder

- Splitting Dataset into Training and Test Set
- Feature Scaling
- Standardization
- Normalization

**Classification Algorithms in Machine Learning**

- What is the Classification Algorithm?
- Types of Classifications
- Binary Classifier
- Multi-class Classifier

- Learners in Classification Problems
- Lazy Learners
- Eager Learners

- Types of ML Classification Algorithms
- Linear Models
- Logistic Regression
- Support Vector Machines

- Non-linear Models
- K-Nearest Neighbors
- Naïve Bayes
- Decision Tree Classification
- Random Forest Classification
- Kernel SVM

- Evaluating a Classification Model
- Confusion Matrix
- What is a Confusion Matrix?
- True Positive
- True Negative
- False Positive – Type 1 Error
- False Negative – Type 2 Error

- Why need a Confusion matrix?
- Precision
- Recall
- Precision vs Recall
- F1-score
- Confusion Matrix in Scikit-Learn
- Confusion Matrix for Multi-Class Classification

- What is a Confusion Matrix?
- Log Loss or Cross-Entropy Loss
- AUC-ROC curve

- Confusion Matrix

- Linear Models
- Use cases of Classification Algorithms

**K-Nearest Neighbor (KNN) Algorithm in Machine Learning**

- Why do we need a K-NN Algorithm?
- How does K-NN work?
- What is Euclidean Distance
- How it Calculates the Distance

- How to Select the Value of K in the K-NN Algorithm?
- Advantages of KNN Algorithm
- Disadvantages of KNN Algorithm
- Python Implementation of the KNN Algorithm
- Analysis on Social Network Ads Dataset
- Steps to Implement the K-NN Algorithm
- Data Pre-processing Step
- Fitting the K-NN algorithm to the Training Set
- Predicting the Test Result
- Test Accuracy of the Result (Creation of Confusion Matrix)
- Visualizing the Test Set Result.
- Improve the Performance of the K-NN Model

**Naïve Bayes Classifier Algorithm in Machine Learning**

- Why is it Called Naïve Bayes?
- Naïve Means?
- Bayes Means?

- Bayes’ Theorem
- Posterior Probability
- Likelihood Probability
- Prior Probability
- Marginal Probability

- Working of Naïve Bayes’ Classifier
- Advantages of Naïve Bayes Classifier
- Disadvantages of Naïve Bayes Classifier
- Applications of Naïve Bayes Classifier
- Types of Naïve Bayes Model
- Gaussian Naïve Bayes Classifier
- Multinomial Naïve Bayes Classifier
- Bernoulli Naïve Bayes Classifier

- Python Implementation of the Naïve Bayes Algorithm
- Steps to Implement the Naïve Bayes Algorithm
- Data Pre-processing Step
- Fitting Naive Bayes to the Training set
- Predicting the Test Result
- Test Accuracy of the Result (Creation of Confusion matrix)
- Visualizing the Test Set Result
- Improve the Performance of the Naïve Bayes Model

**Decision Tree Classification Algorithm in Machine Learning**

- Why use Decision Trees?
- Types of Decision Trees
- Categorical Variable Decision Tree
- Continuous Variable Decision Tree

- Decision Tree Terminologies
- How does the Decision Tree Algorithm Work?
- Attribute Selection Measures
- Entropy
- Information Gain
- Gini index
- Gain Ratio

- Algorithms used in Decision Trees
- ID3 Algorithm → (Extension of D3)
- 5 Algorithm→ (Successor of ID3)
- CART Algorithm → (Classification & Regression Tree)

- How to Avoid/Counter Overfitting in Decision Trees?
- Pruning Decision Trees
- Random Forest

- Pruning: Getting an Optimal Decision tree
- Advantages of the Decision Tree
- Disadvantages of the Decision Tree
- Python Implementation of Decision Tree
- Steps to Implement the Decision Tree Algorithm
- Data Pre-processing Step
- Fitting a Decision-Tree Algorithm to the Training Set
- Predicting the Test Result
- Test Accuracy of the Result (Creation of Confusion matrix)
- Visualizing the Test Set Result
- Improve the Performance of the Decision Tree Model

**Random Forest Classifier Algorithm in Machine Learning**

- Working of the Random Forest Algorithm
- Assumptions for Random Forest
- Why use Random Forest?
- How does Random Forest Algorithm Work?
- Ensemble Techniques
- Bagging (Bootstrap Aggregation)

- Applications of Random Forest
- Disadvantages of Random Forest
- Python Implementation of Random Forest Algorithm
- Steps to Implement the Random Forest Algorithm:
- Data Pre-processing Step
- Fitting the Random Forest Algorithm to the Training Set
- Predicting the Test Result
- Test Accuracy of the Result (Creation of Confusion Matrix)
- Visualizing the Test Set Result
- Improving the Performance of the Random Forest Model

**Logistic Regression Algorithm in Machine Learning**

- Logistic Function (Sigmoid Function)
- Assumptions for Logistic Regression
- Logistic Regression Equation
- Type of Logistic Regression
- Binomial Logistic Regression
- Multinomial Logistic Regression
- Ordinal Logistic Regression

- Python Implementation of Logistic Regression (Binomial)
- Steps to Implement the Logistic Regression:
- Data Pre-processing Step
- Fitting Logistic Regression to the Training Set
- Predicting the Test Result
- Test Accuracy of the Result(Creation of Confusion Matrix)
- Visualizing the Test Set Result
- Improve the Performance of the Logistic Regression Model

**Support Vector Machine Algorithm**

- Types of Support Vector Machines
- Linear Support Vector Machine
- Non-Linear Support Vector Machine

- Hyperplane in the SVM Algorithm
- Support Vectors in the SVM Algorithm
- How does SVM Works?
- How does Linear SVM Works?
- How does Non-Linear SVM Works?

- Python Implementation of Support Vector Machine
- Steps to Implement the Support Vector Machine:
- Data Pre-processing Step
- Fitting Support Vector Machine to the Training Set
- Predicting the Test Result
- Test Accuracy of the Result(Creation of Confusion Matrix)
- Visualizing the Test Set Result
- Improve the Performance of the Support Vector Machine Model

**Regression Algorithms in Machine Learning**

- Terminologies Related to the Regression Analysis
- Dependent Variable
- Independent Variable
- Outliers
- Multi-collinearity
- Under fitting and Overfitting
- Why do we use Regression Analysis?
- Types of Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Ridge Regression
- Lasso Regression

**Linear Regression in Machine Learning**

- Types of Linear Regression
- Simple Linear Regression
- Multiple Linear Regression

- Linear Regression Line
- Positive Linear Relationship
- Negative Linear Relationship

- Finding the Best Fit Line
- Assumptions of Linear Regression

**Simple Linear Regression in Machine Learning**

- SLR Model
- Implementation of Simple Linear Regression Algorithm using Python
- Data Pre-processing Step
- Fitting Simple Linear Regression to the Training Set
- Predicting the Test Result
- Test Accuracy of the
- Visualizing the Test Set Result.
- Try to Improve the Performance of the Model

**Multiple Linear Regression in Machine Learning**

- MLR Equation
- Assumptions for Multiple Linear Regression
- Implementation of Multiple Linear Regression model using Python
- Data Pre-processing Step
- Fitting Multiple Linear Regression to the Training Set
- Predicting the Test Result
- Test Accuracy of the
- Visualizing the Test Set Result.
- Try to Improve the Performance of the Model

**Polynomial Regression in Machine Learning**

- Need for Polynomial Regression
- Equation of the Polynomial Regression Model
- Implementation of Polynomial Regression using Python
- Steps for Polynomial Regression:
- Data Pre-processing
- Build a Linear Regression Model
- Build a Polynomial Regression Model
- Visualize the Result for Linear Regression Model
- Visualize the Result for Polynomial Regression Model
- Predicting the Final Result with the Linear Regression Model
- Predicting the Final Result with the Polynomial Regression Model

- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Linear Regression Vs Logistic Regression
- Classification vs Regression

**Clustering Algorithms in Machine Learning**

- Types of Clustering Methods
- Hierarchical Clustering

- Clustering Algorithms
- K-Means Algorithm
- Agglomerative Hierarchical Algorithm

- Applications of Clustering

**Hierarchical Clustering Algorithm in Machine Learning**

- Hierarchical Clustering Technique Approaches
- Why Hierarchical Clustering?
- Agglomerative Hierarchical Clustering
- Working of Dendrogram in Hierarchical Clustering
- Hierarchical Clustering Example with Scratch Data
- Python Implementation of Agglomerative Hierarchical Clustering
- Steps for Implementation of Agglomerative Hierarchical Clustering using Python
- Data Pre-processing
- Finding the Optimal Number of Clusters using the Dendrogram
- Training the Hierarchical Clustering Model
- Visualizing the Clusters

**K-Means Clustering Algorithm in Machine Learning**

- What is K-Means Algorithm?
- How does the K-Means Algorithm Work?
- How to Choose the Value of “K Number of Clusters” in K-Means Clustering?
- Elbow Method
- Within Cluster Sum of Squares (WCSS)

- K-Means Clustering Example with Scratch Data
- Python Implementation of K-means Clustering Algorithm
- Steps to Implement of K-means Clustering Algorithm
- Data Pre-processing
- Finding the Optimal Number of Clusters using the Elbow Method
- Training the K-means Algorithm on the Training Dataset
- Visualizing the Clusters

**Association Rules in Machine Learning**

- Association Rules
- Pattern Detection
- Market Basket Analysis
- Support, Confidence, Expected Confidence, Lift
- Finding Item Sets with High Support
- Finding Item Rules with High Confidence or Lift

**Apriori Algorithm in Machine Learning**

- Apriori Algorithm
- How does Apriori Algorithm Works?
- Apriori Algorithm Example
- Implementation of Apriori Algorithm using Python
- Limitations of Apriori Algorithm

**Statistics**

- Mean, Median and Mode
- Data Variability, Range, Quartiles
- IQR, Calculating Percentiles
- Variance, Standard Deviation, Statistical Summaries
- Types of Distributions – Normal, Binomial, Poisson
- Probability Distributions & Skewness
- Data Distribution, 68–95–99.7 rule (Empirical rule)
- Descriptive Statistics and Inferential Statistics
- Statistics Terms and Definitions, Types of Data
- Data Measurement Scales, Normalization, Standardization
- Measure of Distance, Euclidean Distance
- Probability Calculation – Independent & Dependent
- Entropy, Information Gain
- Regression

**Natural Language Processing**

- Natural Language Processing Introduction
- Components of NLP
- Applications of NLP
- How to build an NLP Pipeline?

**Exploring Features of NLTK**

- Open the Text File for Processing
- Import Required Libraries
- Sentence Tokenizing
- Word Tokenizing
- Find the Frequency Distribution
- Plot the Frequency Graph
- Remove Punctuation Marks
- Plotting Graph without Punctuation Marks
- List of Stopwords
- Removing Stopwords
- Final Frequency Distribution

- Word Cloud
- Bag of Words
- TF-IDF

**Deploying a Machine Learning Model on a Web using Flask**

- What is Model Deployment?
- What is Flask?
- Installing Flask on your Machine
- Understanding the Problem Statement
- Build our Machine Learning Model
- Create the Webpage
- Connect the Webpage with the Model
- Working of the Deployed Model

**Deep Learning Introduction**

- What is Deep Learning?
- Deep learning Process
- Types of Deep Learning Networks
- TensorFlow
- Installation of TensorFlow through pip & conda
- Advantage and Disadvantage of TensorFlow
- TensorFlow Playground
- Introduction to Keras, OpenCV&Theano
- Implementation of Deep Learning

**Artificial Intelligence Introduction**

- What is Artificial Intelligence?
- Why Artificial Intelligence?
- Goals of Artificial Intelligence
- What Comprises to Artificial Intelligence?
- Advantages of Artificial Intelligence
- Disadvantages of Artificial Intelligence

- Applications of Artificial Intelligence
- History of Artificial Intelligence
- Types of Artificial Intelligence
- Types of AI Agents
- Search Algorithms in Artificial Intelligence
- Subsets of Artificial Intelligence
- Implementation of Artificial Intelligence

**R Programming**

- Why R Programming is Important?
- Why Learn R?
- History of Python
- Features of R
- Applications of R
- Comparison between R and Python
- Which is Better to Choose
- Pros and Cons of R
- Companies using R
- R Packages
- Downloading and Installing R
- What is CRAN?
- Setting R Environment:
- Search Packages in R Environment
- Search Packages in Machine with inbuilt function and manual searching
- Attach Packages to R Environment
- Install Add-on Packages from CRAN
- Detach Packages from R Environment
- Functions and Packages Help

- R Programming IDE
- RStudio
- Downloading and Installing RStudio

- Variable Assignment
- Displaying Variables
- Deleting Variables

- Comments
- Single Line
- Multi Line Comments

- Data Types
- Logical
- Integer
- Double
- Complex
- Character

- Operators
- Arithmetic Operators
- Relational Operators
- Logical Operators
- Assignment Operators
- R as Calculator
- Performing different Calculations

- Functions
- Inbuilt Functions
- User Defined Functions

- STRUCTURES
- Vector
- List
- Matrix
- Data frame
- Array
- Factors

- Inbuilt Constants & Functions
- Vectors
- Vector Creation
- Single Element Vector
- Multiple Element Vector
- Vector Manipulation
- Sub setting& Accessing the Data in Vector

- Lists
- Creating a List
- Naming List Elements
- Accessing List Elements
- Manipulating List Elements
- Merging Lists
- Converting List to Vector

- Matrix
- Creating a Matrix
- Accessing Elements of a Matrix
- Matrix Manipulations
- Dimensions of Matrix
- Transpose of Matrix

- Data Frames
- Create Data Frame
- Vector to Data Frame
- Character Data Converting into Factors: StringsAsFactors
- Convert the columns of a data frame to characters
- Extract Data from Data Frame
- Expand Data Frame, Column Bind and Row Bind

- Merging / Joining Data Frames
- Inner Join
- Outer Join
- Cross Join

- Arrays
- Create Array with Multiple Dimensions
- Naming Columns and Rows
- Accessing Array Elements
- Manipulating Array Elements
- Calculations across Array Elements

- Factors
- Factors in Data Frame
- Changing the Order of Levels
- Generating Factor Levels
- Deleting Factor Levels

**Loading and Reading Data in R**

- Data Extraction from CSV
- Getting and Setting the Working Directory
- Input as CSV File, Reading a CSV File
- Analyzing the CSV File, Writing into a CSV File

- Data Extraction from URL
- Data Extraction from CLIPBOARD
- Data Extraction from EXCEL
- Install “xlsx” Package
- Verify and Load the “xlsx” Package, Input as “xlsx” File
- Reading the Excel File, Writing the Excel File

- Data Extraction from DATABASES
- RMySQL Package, Connecting to MySql
- Querying the Tables, Query with Filter Clause
- Updating Rows in the Tables, Inserting Data into the Tables
- Creating Tables in MySql, Dropping Tables in MySql
- Using dplyr and tidyr package

**Machine Learning using R**

- Data Pre-processing
- Classification Algorithms
- K Nearest Neighbors Classification
- Naive Bayes Classification
- Decision Tree Classification
- Random Forest Classification
- Support Vector Machine Classification
- Logistic Regression
- Kernel SVM

- Regression Algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression

- Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering

- Association Rule Algorithms
- Apriori

Course | Date | Timings | Duration | Trainer | Location |

A report by AIM had found out that the average salary for a data scientist in India is ₹12.7 lakh per annum in 2018. However, this trend has levelled off with the average analytics salary capped at ₹12.6 lakh per annum across all experience levels in 2019. In fact, Data Analytics professionals are currently benefitting from the big data wave with analytics professionals earning 26% higher than an average software engineer in India.

Data analysts dissect data to tell a story. Data scientists have the same skills as data analysts, but they also have a strong foundation in modeling, statistics, analytics and computer science. Unlike data analysts, they typically have machine learning skills.

The two most commonly used languages required for gaining expertise in Data Science are Python and R. These languages are used for statistical analysis or ML projects. R is an open-source language used for statistical computing and graphics, while Python is a more preferred language for Data Science as it is faster as compared to R.

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