Data Science Course Curriculum

Course OutlineAlso on this course we offer the followingTraining HighlightsExpected Salary/ Pay Package

About us

LSA Training is an institution providing professional education to individuals pursuing career growth in an increasingly sophisticated and competitive world. We aim to promote excellence in education and training in both the academic and corporate world.
Duration: Extensive Accelerated Course

  • Week day Batches: 4 Days( Mon To Thur 10am to 3pm) 1 Week
  • Weekends Batches:4 weekends 10am to 4pm (Sat and Sun)
  • Evening Batches: 3 Weeks Mon to Fri (6pm to 9pm)
Course Info

Data science Introduction

  • Data Science motivating examples — Nate Silver, Netfilx, Money ball, okcupid, LinkedIn,
  • Introduction to Analytics, Types of Analytics,
  • Introduction to Analytics Methodulogy
  • Analytics Terminulogy, Analytics Touls
  • Introduction to Big Data
  • Introduction to Machine Learning

R software:

Introduction and Overview of R Language :

  • Origin of R, Interface of R,R coding Practices
  • R Downloading and Installing R
  • Getting Help on a function
  • Viewing Documentation

Data Inputting in R Data Types

  • Data Types, Data Objects, Data Structures
  • Creating a vector and vector operations
  • Sub-setting
  • Writing data
  • Reading tabular data files
  • Reading from csv files
  • Initializing a data frame
  • Selecting data frame culs by position and name
  • Changing directories
  • Re-directing R output

Data Manipulation in R

  • Appending data to a vector
  • Combining multiple vectors
  • Merging data frames
  • Data transformation
  • Contrul structures
  • Nested Loops

splitting

  • Strings and dates
  • Handling NAs and Missing Values
  • Matrices and Arrays
  • The str Function
  • Logical operations
  • Relational operators
  • generating Random Variables
  • Accessing Variables
  • Matrix Multiplication and Inversion
  • Managing Subset of data
  • Character manipulation
  • Data aggregation
  • Subscripting

Functions and Programming in R

  • Flow Contrul: For loop
  • If condition
  • While conditions and repeat loop
  • Debugging touls
  • Concatenation of Data
  • Combining Vars, cbind, rbind
  • sapply, lapply, tapply functions

Basic Statistics in R :

Part-I Session 1

  • Descriptive Statistics Introduction to Advanced Data Analytics
  • Statistical inferences for various Business problems
  • Types of Variables, measures of central tendency and dispersion
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Computing basic statistics
  • Comparing means of two samples
  • Testing a correlation for significance
  • Testing a proportion
  • Classical tests (t,z,F)
  • ANOVA
  • Summarizing Data
  • Data Munging Basics

Part-I Session 2

  • Test of Hypothesis Null/Alternative Hypothesis formulation 7
  • One Sample, two sample (Paired and Independent) T/Z Test
  • P Value Interpretation
  • Analysis of Variance (ANOVA)
  • Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.)

Part-I Session 3

  • Introduction to Correlation – Karl Pearson
  • Spearman Rank Correlation

Advanced Analytics :

Advanced Analytics with real world examples (Mini Projects)Part-II Session 1

  • Regression Theory
  • Linear regression
  • Logistic Regression Non Linear Regressions using Link functions
  • Logit Link Function
  • Binomial Propensity Modeling
  • Training-Validation approach

Part-II Session 2

  • Factor Analysis Introduction to Factor Analysis – PCA
  • Reliability Test 4
  • KMO MSA tests, Eigen Value Interpretation
  • Factor Rotation and Extraction

Part-II Session 3

  • Cluster Analysis Introduction to Cluster Techniques
  • Distance Methodulogies
  • Hierarchical and Non-Hierarchical Procedures
  • K-Means clustering
  • Wards Method

Time Series Analysis :

Part-III Session 1

  • Introduction and Exponential Smoothening Introduction to Time Series Data and Analysis
  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Exponential Smoothing (Single, double and triple)

Part-III Session 2

  • ARIMA Modeling Box – Jenkins Methodulogy
  • Introduction to Auto Regression and Moving Averages, ACF, PACF

Data Mining :

Machine learning with R:Part IV Session 1

  • Introduction to Machine learning and various machine learning techniques
  • Introduction to Data Mining
  • Introduction to Text Mining
  • Text analytic Process
  • Sentiment Analysis

Part IV

  • Statistical Analysis & Data Mining/Machine Learning
  • Cluster Analysis using R-Rattle
  • Association Rule Mining
  • Predictive Modeling using Decision Trees
  • Supervised learning
  • Un- Supervised learning
  • Reinforcement learning
  • Neural Network
  • Support Vector machine

Part IV Session 3

  • Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
  • ROC, Sensitivity, Specificity, Lift charts, Error Matrix
  • Deploying models using Score options
  • Opening and Saving models using Rattle

Analytics in Excel – 3 days

  • Data Preparation and Data Exploration in Excel
  • Network Analysis using NodeXL

Data Visualization in R

  • Creating a bar chart, dot plot
  • Creating a scatter plot, pie chart
  • Creating a histogram and box plot
  • Other plotting functions
  • Plotting with base graphics
  • Plotting with Lattice graphics
  • Plotting and culoring in R

Tableau with Case studiesSAS E Miner with use casesProject : Financial Project, Health care Project, Retail Project

Pre-Requisites:

  • Previous Educational Background in IT or experience in support of networking.
  • Hands on Experience
  • Real Time project work
  • Interview based Training
  • Instructor Led – Face2Face /True Live Online class
  • More interaction with student to faculty and student to student.
  • Detailed presentations. Soft copy of Material to refer any time.
  • Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
  • Mock interviews / group discussions / interview related questions.
  • Test Lab is in Cloud Technology – to practice on software tools if needed.
  • We discuss about the real time project domains.
  • The teaching methods / tools / topics we chosen are based on the current competitive job market.
  • Expected Salaries are as follows:
  • For Contractors £300 to £400 per day
  • Permanent Positions £40 to £60k per annum all depends on experience and skills set



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