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Data Science

Data Science

ethans data sci


About the Course


Level 1 - 42 Hours class room program  - 7 Weekends. 
Level 2 - 30 Hours Class room program - 4 More Weekends
Level 3 - 12 Hours Class room program - 2 More Weekends 

Prerequisites: Basic computer knowledge, any data related experience will be advantageous.

After the classes:

Ethans Data sciences modules are precisely designed ensuring all industry requirements are met & making students eligible for plethora of job openings in the field of data analytics.

Students will easily crack interviews on Business analytics, data visualization (Tableau) related positions. Any interview for entry level data analyst position would be a cake walk for the candidates

Who get this training?

  • Any graduate/post graduate or students in final stages of graduation + People willing to align careers in analytics
  • Team leaders working with data and often need basic data analysis
  • Engineers looking for career opportunities in IT/ITES industry
  • Management students looking for strategic positions
  • People already working with huge datasets
  • Hadoop Professionals
  • CA, CS, CFA



Course Syllabus: 

Level 1: 

Introduction to Data Science (3 Hours)

  • Introduction to Data analytics
  • Understanding Business Applications
  • Data types and data Models
  • Type of Business Analytics

Data Analytics using Excel (6 Hours)

  • Introduction to Microsoft Excel
  • Multiple Data formatting options
  • Data security options
  • Excel shortcuts – A cheat sheet to remember
  • Multiple Data handling techniques (Filters, validations, grouping, etc.)
  • Using formulae and functions in excel
  • Advanced functions (vlookup, pivot tables)
  • Data Analysis and simulation (what is analysis, scenario builder, solver, etc.)
  • Introduction to Macros
  • Data locking, sharing and tracking
  • Excel Charting & Dashboarding techniques

Introduction to Statistics (3 Hours)

  • Basic Statistics
    • Measure of central tendency
    • Types of Distributions
    • Anova
    • F-Test
    • Central Limit Theorem & applications
  • Hypothesis Testing
  • Multiple Linear Regression
  • Logistic Regression
  • Market Basket Analysis
  • Clustering (Hierarchical Clustering & K-means Clustering)
  • Classification (Decision Trees)
  • Time Series Analysis (Simple Moving Average, Exponential smoothening, ARIMA+)

Data Analytics with R/R Studio (30 Hours)

  • Introduction to R & R Studio
  • Why prefer R?
  • Demonstration of R installation of multiple OS
  • Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
  • Data Structures in R
  • Sub-setting in R Language, Data Structures in R
  • Variables, Operators, Functions, Conditionals Statement, loops etc
  • Import CSV, XLS, SQL files to R
  • Data Import and Data Export in R
  • Control Structure and User Defined Functions
  • Data Manipulation techniques in R
  • Visualization using R
  • Hypothesis testing
  • Machine Learning with R
  • Statistical model building, validation & prediction using R
    • Multiple Linear Regression
    • Logistic Regression
    • Market Basket Analysis
    • Clustering (Hierarchical Clustering & K-means Clustering)
    • Classification (Decision Trees)
    • Time Series Analysis (Simple Moving Average, Exponential smoothening, ARIMA+)
  • Prediction using statistical models
  • Case Study - Real time project with Twitter Data Analytics
  • Case Study - Real time project with Google Finance
  • Case Study - Real time project with IMDB Website

Level 2:

Tableau Analytics (12 Hours)

  • Tableau Introduction
  • Data connection to Tableau
  • Calculated fields, parameters, sets, groups in Tableau
  • Various visualizations Techniques in Tableau
  • Map based visualization using Tableau
  • Creating Tableau Dashboard
  • Creating Story using Tableau
  • Analytics using Tableau
  • Clustering using Tableau
  • Time series analysis using Tableau
  • Simple Linear Regression using Tableau
  • R integration in Tableau
  • Integrating R code with Tableau
  • Creating statistical model with dynamic inputs
  • Visualizing R output in Tableau

Level 3:

Introduction to Python (30 Hours)

  • What is Python and history of Python?
  • Why Python and where to use it?
  • Discussion about Python 2 and Python 3
  • Set up Python environment for development
  • Demonstration on Python Installation
  • Discuss about IDE’s like IDLE, Pycharm and Enthought Canopy
  • Discussion about unique feature of Python
  • Write first Python Program
  • Start programming on interactive shell.
  • Using Variables, Keywords
  • Interactive and Programming techniques
  • Comments and document interlude in Python
  • Practical use cases using data analysis
  • Introduction to Hadoop

       Core Objects and Built-in Functions

  • Python Core Objects and builtin functions
  • Number Object and operations
  • String Object and Operations
  • List Object and Operations
  • Tuple Object and operations
  • Dictionary Object and operations
  • Set object and operations
  • Boolean Object and None Object
  • Different data Structures, data processing

      Conditional Statements and Loops

  • What are conditional statements?
  • How to use the indentations for defining if, else, elif block
  • What are loops?
  • How to control the loops
  • How to iterate through the various object
  • Sequence and iterable objects

     UDF Functions and Object Functions

  • What are various type of functions
  • Create UDF functions
  • Parameterize UDF function, through named and unnamed parameters
  • Defining and calling Function
  • The anonymous Functions - Lambda Functions
  • String Object functions
  • List and Tuple Object functions
  • Dictionary Object functions

Data Analytics Using Python

  Machine Learning with Python

  • Understanding Machine Leaning?
  • Areas of Implementation of Machine Learning,
  • Why companies prefer ML
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning, Learning
  • Why Numpy?
  • Learn Numpy and Scipy,
  • Basic plotting using Matplotlib
  • Algorithms using Skikit learn

 Data Analysis with Pandas

  • What is Pandas
  • Creating Series and Data Frames
  • Data Mining, Data Extraction, Data Analysis
  • Data Filter, Data Merging and Data Appending
  • Grouping, Sorting
  • Reading CSV, JSON, XML, XLS using pandas
  • Plotting Data/Create Graphs and Analysis using Matplotlib
  • Data analysis with data set