Data Science

Data Science

Course Feature

Data Science (

Data Science

  • Duration 3 Months
  • Class Timings 1:30 hr
  • Eligibility Knowledge of C Language

MODULE - I

Introduction to Python Programming

  • Introduction to Data Science
  • Introduction to Python
  • Basic Operations in Python
  • Fundamental of Computer Graphics.
  • Variable Assignment
  • Creating your first Digital Painting
  • Functions: in-built functions, user defined functions

MODULE - II

Data Structure - Introduction

  • List: Different Data Types in a List
  • Operations on a list: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a List
  • Applying functions on a List
  • Dictionary: Index, Value
  • Operation on a Dictionary: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a Dictionary
  • Applying functions on a Dictionary
  • Operations on Array: Slicing, Splicing, Sub-setting
  • Conditional(T/F) on an Array
  • Loops: For, While
  • Shorthand for For
  • Conditions in shorthand for For

MODULE - III

Basics of Statistics

  • Statistics & Plotting
  • Seabourn & Matplotlib - Introduction
  • Univariate Analysis on a Data
  • Plot the Data - Histogram plot
  • Find the distribution
  • Find mean, median and mode of the Data
  • Take multiple data with same mean but different sd, same mean and sd
  • different kurtosis: find mean, sd, plot
  • Multiple data with different distributions
  • Bootstrapping and sub-setting
  • Making samples from the Data
  • Making stratified samples - covered in bi-variate analysis
  • Find the mean of sample
  • Central limit theorem
  • Plotting
  • Hypothesis testing + DOE
  • Bi-variate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples
  • Categorical variables
  • Class variable

MODULE - IV

Use of Pandas

  • File I/O
  • Series: Data Types in series, Index
  • Data Frame
  • Series to Data Frame
  • Re-indexing
  • Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
  • Pandas
  • Stat operations on Data Frame
  • Reading from different sources
  • Missing data treatment
  • Merge, join
  • Options for look and feel of data frame
  • Writing to file

MODULE - V

Data Manipulation & Visualization

  • Data Aggregation, Filtering and Transforming
  • Lamda Functions
  • Apply, Group-by
  • Map, Filter and Reduce
  • Visualization
  • as
  • Matplotlib, pyplot
  • Seaborn
  • Scatter plot, histogram, density, heat-map, bar charts

MODULE - VI

Linear Regression

  • Regression - Introduction
  • Linear Regression: Lasso, Ridge
  • Variable Selection
  • Forward & Backward Regression

MODULE - VII

Logistic Regression

  • Logistic Regression: Lasso, Ridge
  • Naive Bayes

MODULE - VIII

Unsupervised Learning

  • Unsupervised Learning - Introduction
  • Distance Concepts
  • Classification
  • Clustering
  • Multidimensional Scaling
  • PCA

MODULE - IX

Random Forest

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting

We Will Contact You, At a Time Which Suits You Best

app_image

Discover Our App

Access your courses anywhere, anytime & prepare with practice tests