MS Excel | 20 HRS |
Python/R | 20 HRS |
Machine Learning | 20 HRS |
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 |
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 |
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) |
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) |
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.
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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.
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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. |