Data Science using Python & R

Choose from these three incredible options to start your journey with Handson!

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Data Science using Python & R Overview

Data Science course teaches you to master the concepts of Python & R programming. Through this  Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. 
 
Upon course completion, you will master the essential tools of Data Science with Python & R.
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Course Fees:Rs.25000

  • Duration : 5 months
  • No Cost EMI : INR 5000 x 5

NEFT Payment :Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

Support :9830247087

Course Description!

Data Science(Python,R) Course Curriculum

The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

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Course content

  • What is analytics & Data Science?
  • Different between Data Science Big data and data analysis
  • Data understanding: real life example
  • Why data science and machine learning is future
  • Analytics vs. Data warehousing
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Overview of analytics tools & their popularity
  • List of steps in Data science projects
  • Process of Data science projects
  • .Identify the most appropriate solution design for the given problem
    statement
  • Necessary Programming Languages and Software Used in Data Science
  • Why R and Python for data science?
  • Introduction to python
  • History of Python
  • Internal & External IDLE
  • Installation of Python & Anaconda
  • Compiler & Interpreter
  • Write your first program
  • Data types, Input and output function
  • Types of Operators
  • Conditional Statement: if-else, if-elif-else, Nested if else
  • Loop: While loop, For loop
  • Nested while loop, Nested for loop
  • Break, Continue and Pass
  • Mutable and Immutable data types
  • Numeric types and it’s operation
  • String and it’s operation
  • Tuple and it’s operation
  • List and it’s operation
  • Dictionary and it’s operation
  • Sets and It’s operation
  • Conditional statement
  • Loop
  • Dictionary & List
  • Comprehension techniqus
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • local and global variable
  • Recursion, Anonymous (lambda) function
  • User define functions
  • OOPS concepts Defining
  • Class Creating object, Constructor
  • Method vs function Calling methods
  • Method Overriding, List of objects Inheritance
  • Defining module, Importing module
  • Dir(), Module search path, Sys module, Os module
  • Namespace
  • Defining and create package
  • Installing third party packages
  • Module and package
  • Funciton
  • Inheritance
  • Defining a file, Types of file and it’s operations
  • Opening a File, Closing file, File modes, File attributes
  • Writing to file, Reading from file, Appending to file
  • File positions, Binary file
  • . Pickle module
  • Introduction to Numpy, Pandas, Matplotlib
  • Process of Data science projects
  • Identify the most appropriate solution design for the given problem
    statement
  • Array, Array indexing, Array operation
  • Data frame, series, Groupby
  • Missing values
  • Data input and output
  • Analysis exercise
  • Chart prepration
  • Box plot, Scatter plot, Chart styling
  • Histogram, Bar chart etc.
  • Groupby ploting
  • Data visualization exercise
  • Types of Machine learning
  • Related concepts: Splitting the dataset into train set and test set
  • Processing CSV data
  • Correlation, Mean square error, R- Squared
  • Confusion Matrix, ROC & AUC Curve
  • Data cleaning techniques
  • Linear Regression Technique
  • Non- Linear Regression Techniques
  • What is Logistic Regression
  • Concept and theory
  • Sigmoid function & Mathematical approach
  • K-Nearest Neighbors
  • Concept and theory
  • Mathematical approach, Distance functions: Euclidean, Minkowski
  • Support Vector machine
  • Introduction to Support Vector machine
  • Mathematical Approach
  • Theory on hyperplane
  • Dataset with problem description
  • Practical application on Python
  • Significance of using Decision Tree
  • Different kinds of Decision Tree
  • Procedure and technique of Decision Tree
  • Random Forest
  • Theory and mathematical concepts
  • Entropy and Decision Tree
  • Dataset with problem description
  • Classification & Regression using random forest on Python
  • Introduction of Naïve Bayes
  • Theory of classification
  • Concept of probability: prior and posterior
  • Bayes Theorem
  • Mathematical concepts
  • Limitation of Naïve Bayes
  • Dataset with problem description
  • Practical application on Python
  • Introduction of clustering
  • K-mean clustering
  • Hierachical clustering
  • Dataset with problem description
  • Practical application on Python
  • Introduction to Artificial Intelligence
  • Applications of Artificial Intelligence
  • Types of Artificial Intelligence
  • Keras, Tensorflow
  • Appliction of Neural Network
  • Plan of attack
  • Activation function
  • Gradient descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Connectionism
  • Practical approach with python
  • Introduction of Convolution Neural Network
  • How a computer read an image
  • Plan of attack
  • Convolution Operation
  • ReLU layers
  • Pooling layers
  • Flattening
  • Different layers
  • What is reinforcement learning
  • Bellman equation
  • Markov Decision process
  • Agent environment problem
  • Reinforcement process
  • Reward Maximization
  • Q-learning algorithm
  • Practical approach with python
  • History of R-language
  • Why to learn R-language
  • Importance of R-language
  • Installation and setup Environment
  • Packages interfaces and library
  • Arithmetic in R
  • Variables
  • Vector Basics
  • Vector Operations
  • Vector Indexing and Slicing
  • Creating a Matrix
  • Matrix Arithmetic
  • Matrix Selection and Indexing
  • Factor and Categorical Matrices
  • Data Frame Indexing and Selection
  • Operations on Data Frame
  • List Basics operations
  • CSV Files with R
  • Excel Files with R
  • SQL with R
  • Operators
  • Conditional Statements
  • Loops & Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Package Building
  • Understanding & Working with Graph Libraries
  • Overview of ggplot2
  • Histograms
  • Scatterplots
  • Bar Plot
  • Boxplots
  • 2 Variable Plotting
  • Coordinates and Faceting
  • Data Manipulation Using Dplyr
  • Pipe Operator
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Introduction to Machine Learning
  • Data Munging in R
  • Cyclical vs Seasonal Analysis
  • Introduction to Regression and Classification algorithm
  • Linear Regression with R
  • Logistic Regression with R
  • K Nearest Neighbors
  • Explanation and introduction Support Vector Machine in R-language
  • Example of Decision Tree with R- programming
  • Explanation and introduction Random Forests in R-language
  • What is clusters
  • Type of clusters
  • K Means Clustering with R
  • Overview of Shiny
  • Creating a populated dashboard: UI, Server
  • Shiny input: Text input, Numeric input, Checkbox input, Slider input, Radio
    button
  • Shiny output: text output, table output, plot output, data table output
  • Single page layout
  • Data download, Data upload
  • Progress indicator
  • Build your own app: Shiny dashboard
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
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Why to choose Handson?

Hands-On Training

Using a hands-on approach to training, participants will adopt the form of learning that typically benefit from the most. It allows people to learn by doing.

 

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