Last Update On:23-12-2024

Data Science with Python

Complete Practical & Real-time Training

1. Complete Practical and Real-time Scenarios
2. Study Material and Practice Labs as required
3. Session wise notes & Doubts Clarifications
4. Mock Interview

Intro to R Programming

• Introduction to R
• Loading, Querying and manipulating data in R
• Cleaning raw data for Modeling

R Programming Concepts

• The datatypes in R and its uses
• Built-in functions in R
• Subsetting methods
• Summarize data using functions
• Use of functions like head(), tail(), for inspecting data
• Use-cases for problem solving using R

Data Manipulation in R

• Various phases of Data Cleaning
• Functions used in Inspection
• Data Cleaning Techniques
• Uses of functions involved
• Use-cases for Data Cleaning using R

Data Import Techniques in R

• Import data from spreadsheets and text files into R
• Importing data from statistical formats
• Packages installation for database import
• Connecting to RDBMS from R using ODBC and basic SQL queries in R
• Web Scraping
• Other concepts on Data Import Techniques

Exploratory Data Analysis (EDA) using R

• What is EDA& Why we need it.
• Goals & Types of EDA
• Implementing of EDA
• Boxplots, cor() in R
• EDA functions
• Multiple packages in R for data analysis
• Extending R with user-defined packages.

Data Visualization in R

• Story telling with Data
• Principle tenets
• Elements of Data Visualization
• Infographics vs Data Visualization
• Investigating characteristics of data set through Visualization.
• Identifying outlier in data.

Machine Learning Understanding

• ML Fundamentals& Use Cases.
• Understanding Supervised and Unsupervised Learning Techniques
• Clustering& Common Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models&Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study
• Implementing Association rule mining
• Understanding Process flow of Supervised Learning Techniques
• Decision Tree Classifier
• How to build Decision trees
• Random Forest Classifier
• What is Random Forests
• Features of Random Forest&Out of Box Error Estimate and Variable Importance
• Naive Bayes Classifier.
• Problem Statement and Analysis
• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.
• Linear Regression& Implementation
• Logistic Regression& Implementation
• Text Mining&Sentimental Analysis.

PYTHON PROGRAMMING

• Python Overview& Languages
• Advantages/Disadvantages of Python pydoc.
• Starting Python& Path Configuration
• Running a Python Script& Debugging.
• Python Scripts on UNIX/Windows
• Anaconda Python Editors and IDEs.
• Using Variables& Keywords
• Built-in Functions& Different String Literals with Usage.
• Math Operators and Expressions
• Writing to the Screen& String Formatting.
• Command Line Parameters and Flow Control.

Sequences and File Operations

• Lists ,Tuples
• Indexing and Slicing
• Funtions,Iterating through a Sequence
• Using Enumerate
• Operators and Keywords for Sequences
• The xrangefunction& List Comprehensions
• List Comprehensions&Generator Expressions
• Dictionaries and Sets.

Quick Revise – Functions Sorting Errors and Exception Handling

• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys& Lambda Functions.
• Sorting Collections of Collections
• Sorting Dictionaries, Lists in Place
• Errors and Exception Handling
• Handling Multiple Exceptions
• The Standard Exception Hierarchy
• Using Modules
• The Import Statement
• Module Search Path
• Package Installation Ways.

RE Packages and Object Oriented Programming in Python

• The Sys Module&Interpreter Information
• STDIO&Launching External Programs
• PathsDirectories and Filenames
• Math Function,Random Numbers
• Zipped Archives
• Defining Classes,Initializers
• Instance Methods, Properties
• Class Methods and DataStatic Methods
• Private Methods and Inheritance
• Module Aliases and Regular Expressions.

Real time Overview & Layout skeleton

• Dealing with Errors&Using Unit Tests
• Project Layout and Overview
• Required Packages& Creating Skeleton
• Final Directory Structure
• Testing your Setup
• Creating a Database with SQLite 3
• CRUD Operations

ML Using Python Language

• Areas of Implementation of Machine Learning
• Why Python&Major Classes of Learning Algorithms
• Supervised vs Unsupervised Learning
• Learning NumPy&Scipy
• Basic plotting using Matplotlib
• Machine Learning application

Algorithm

• General Approach to kNN
• Building the Classifier from Scratch
• Testing the Classifier
• Measuring the Performance of the Classifier.
• Clustering Problem
• What is K-Means Clustering
• Clustering with k-Means in Python.

Application Implementation

• Introduction to Pandas& Creating Data Frames
• Grouping Sorting &Plotting Data
• Creating Functions
• Converting Different Formats
• Combining Data from Various Formats