Data Science / Machine Learning                                                                        

Duration: 40-50 Hours

Prerequisites

• Basic knowledge of any programming Language.
• Basic knowledge of Database (SQL) and files (MS Excel, CSV etc.â?¦)
• Basic high school Algebra and Geometry

Course Content

1. Fundamentals of Data Science and Machine Learning

• Introduction to Data Science
• What is Data Analytics?
• Data, Data Types
• Data Distribution
• Need of Business Analytics
• BigData and Data Science
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
• Data Science Life Cycle,
• Different tools available for Data Science

2. Introduction to Statistics

• Types of data
• Measures of central tendency and dispersion
• Statistical Graphics

3. Probability and Probability Distributions

• Probability Theory
• Binomial Distribution
• Poisson Distribution
• Normal Distribution

4. R Programming Basics

• Introduction to R
• How to install and usages of R
• Operators in R
• Data Types
• Conditional Statements
• Control structure
• Loops in R
• Functions in R

5. Data Structure In R Programming

• Data Exploration
• Data Harmonization
• Creating Objects in R
• Data Structure in R
• Factors and Lists
• Import Functions in R
• Import an Excel File
• How to Import Minitab File?
• Importing Data from Database
• Table and CSV Files
• Reading data, Subsetting Data
• Visualizing the Data
• Input Output Sub setting

Data Science with Python

1. An Introduction to Python

• Why Python , its Unique Feature and where to use it?
• Python environment Setup
• Discuss about IDEâ??s like IDLE, Pycharm and Enthought Canopy
• Start programming on interactive shell.
• Python Identifiers, Keywords
• Discussion about installed module s and packages
• Access Command line arguments within programs
• Installing Anaconda
• Understanding the Spyder (IDE)

2. Conditional Statement, Loops and File Handling

• Python Data Types and Variable
• Condition and Loops in Python
• Decorators
• Python Modules & Packages
• Python Files and Directories manipulations
• Use various files and directory functions for OS operations

3. Python Core Objects and Functions

• Built in modules (Library Functions)
• Numeric and Mathâ??s Module
• String/List/Dictionaries/Tuple
• Complex Data structures in Python
• Arbitrary data types and their Data Structure
• Python built in function
• Python user defined functions
• Python packages and functions
• The anonymous Functions - Lambda Functions

4. Object Oriented Python

• OOPs Concepts
• Object , Classes and Destroying Objects
• Accessing attributes,Built-In Class Attributes
• Inheritance and Polymorphism
• Overriding Methods,Data Hiding
• Overloading Operators

6. Introduction to NumPy

• Understanding Data Types in Python
• The Basics of NumPy Arrays
• Computation on NumPyArrays
• Aggregations â?? Min Max and Everything In Between
• Introducing Broadcasting
• Rules of Broadcasting
• Broadcasting in practice
• Sorting Arrays
• Structure Data

7. Data Manupulation with Pandas

• Installing and Using Pandas
• Introducing Pandas Objects
• Data Indexing and Selection
• Operation on Data in Pandas
• Handling missing Data
• Hierarchical Indexing

8. Descriptive & Inferential Statistics

• Estimation Theory
  • Sampling Distribution
  • Point Estimation
  • Interval Estimation
• Test of Hypothesis
  • Inference about one population means
  • Inference about two populations means
  • Analysis of Variance Concept
• Inference about one & two population (Means & Proportion)
• Analysis of Variance ( 1 Way & 2 Way)

9. Machine Learning: Supervised Algorithms Classification

• Introduction to Machine Learning
• Naïve Bays Algorithm
• K-Nearest Neighbor Algorithm
• Decision Tress (SingleTree)
• Support Vector Machines
• Model Ensembling
  • Bagging
  • Random Forest
  • Boosting
  • Gradient Boosted Trees
  • Stacking
  • Stacking Classifications
• Optimization Algorithm
  • Gradient Descent/Ascent
  • Stochastic Gradient Descent
  • Grid Search & Random Search
• Cross Validation and Model performance
  • F1 & KS statistics
  • ROC, AUC etc...

10. Machine Learning: Regression

• Simple Linear Regression
• Multiple Linear Regression
• Count Regression
• Logistic Regression
• Decision Tree and Random Forest Regression

11. Machine Learning: Unsupervised Learning Algorithms

• Similarity Measures
• Principal Components Analysis
• Cluster Analysis and Similarity Measures
• Hierarchical Clustering
• K-means Clustering
• Soft Clustering
• Clustering using mix Data Type â?? Prototype clustering
• Association Rules Mining & Market Basket Analysis
• Neural Network

12. Text Mining

• Term Document Matrix
• TF-IDF
• Word Cloud

13. Time Series Analysis

• Moving Average, Simple Exponential Smoothening
• Holt-Winter's Method
• ARIMA Models

14. Data Visualization and Web Scraping

• Data Visualization and Matplotlib
• Python Libraries
• Features of Matplotlib
• Line Properties Plot with (x, y)
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple Plots and SubPlots
• Python Web Scraping and Data Science
• The Parser
• Searching & Modifying the Tree
• Printing, Formatting, Encoding

15. Visualization with Matplotlib

• General Matplotlib
• Importing matplotlib
• Setting Styles
• show() or No show()? How to Display Your Plots
• Simple Line Plots
• Simple Scatter Plots
• Visualizing Errors
• Density and Contour Plots
• Visualizing a Three-Dimensional Function
• Histograms, Binnings, and Density

Projects Covered

• Financial Analytics
• Logistics Analytics
• Text Analytics

Take Away

• Comprehensive Case Studies for each Topic.
• Various Approaches to Solve Data Science Problem.
• Pros and Cons of Various Algorithms and approaches.