Drop us a line!





DS Program-3

MS Excel, Python/R, Machine Learning 3 Months (Weekend Batches)
35,000(Inclusive of Taxes)

Modules Included

MS Excel 20 HRS
Python/R 20 HRS
Machine Learning 20 HRS

Data Analytics with Excel

Relevance in industry & need of the hour
Types of analytics – Marketing, Risk, Operations, etc
Business & Technology drivers for analytics
Future of analytics & critical requirement
Types of problems and business objectives in various industries
Different phases of Analytics Project
Introduction to Excel
Working with Formulas and functions
Formating & Conditional Formating
Filtering, sorting, paste special etc
Functions (Logical & Text, Mathematical, Statistical etc)
Data Manipulation & Data Aggregation
Data Analysis using functions
Analyzing Data using Pivots
Descriptive Statistics
Creating Charts & Graphics
Data analytics tool (What -if analysis, Goal seek, Data Table, Solver)
Protecting Workbooks, worksheets and formulas
Working with VBE (Visual Basic Editor)
Introduction to Excel Object Model
Understanding of Sub and Function Procedures
Key Component of Programming Language
Understanding of If, Select Case, With End With Statements
Looping with VBA
User Defined Function
Some Commonly Used Macro Examples
Error Handling
Object and Memory Management in VBA
User Form Controls
ActiveX Controls
Communicating with Database MS Access through ADO - Exporting /Importing Data

Python

Why do we need Python?
Program structure in Python
Interactive Shell
Executable or script files.
User Interface or IDE
Numbers
Strings
List
Tuple
Dictionary
Other Core Types
Assignments, Expressions and prints
If tests and Syntax Rules
While and For Loops
Iterations and Comprehensions
Opening a file
Using Files
Other File tools
Function definition and call
Function Scope
Arguments
Function Objects
Anonymous Functions
Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python Packages for data manipulation (Pandas, Numpy etc)
Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - subsetting, methods
Exporting Data to various formats
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)
Basic Statistics - Measures of Central Tendencies and Variance
Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
Inferential Statistics -Sampling - Concept of Hypothesis Testing
Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square

R

Introduction R/R-Studio - GUI
Concept of Packages - Useful Packages (Base & other packages) in R
Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
Importing Data from various sources
Database Input (Connecting to database)
Exporting Data to various formats)
Viewing Data (Viewing partial data and full data)
Variable & Value Labels – Date Values
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formating etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
R Built-in Functions (Text, numeric, date, utility functions)
R User Defined Functions
R Packages for data manipulation(base, dplyr, plyr, reshape,car, sqldf etc)
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
R Packages for Graphical Analysis (base, ggplot, lattice etc)

Machine Learning

What is machine learning?
What are the use case of Machine learning?
Statistical learning vs. Machine learning
Iteration and evaluation
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
Introduction to CARET package
Introduction to H2O package
Linear Regression
Logistic regression
Generalization & Non Linearity
Recursive Partitioning(Decision Trees)
Ensemble Models(Random Forest, Bagging & Boosting(ada, gbm etc))
Artificial Neural Networks(ANN)
Support Vector Machines(SVM)
K-Nearest neighbours
Naive Bayes
K-means clustering
Challenges of unsupervised learning and beyond K-means
Market Basket Analysis
Collaborative Filtering
Social Media – Characteristics of Social Media
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Text Analytics – Sentiment Analysis using R
Text Analytics – Word cloud analysis using R
Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Vector space models; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Handling big graphs
The purpose of it all: Finding patterns in data
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)

What You Get?

Learn from our comprehensive collection of project case-studies, hand-picked by industry experts, to give you an in-depth understanding of how data science moves industries like telecom, transportation, e-commerce & more.
  1. Global Sales Store Data Analytics - WallMart
  2. Service Calls & Engineers Utilization Data Analytics – HCL Services
  3. Clinical Data Analytics of Cancer Patients Diagnosis & Medication – Global Health Care
  4. Data Analytics of Training & Development Program of Defense Forces – Indian Navy ...many more...
You will be having the opportunity of 10-15 Hrs e-learning exercises along with instructor-led-training which enable candidates to get the maximum out of the subjects and empowering them to build logics to hand any new requirement.
This program has been designed in collaboration with some of the most influential analytics leader and top academician in data science.

Final Outcome

Thanks to the digital revolution that is sweeping the world and India in particular, data scientists are now the most sought-after professionals by big corporations as well as startups. And companies across industries are rewarding good data analysts and scientists with desirable career growth and salaries.