| 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 |
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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.
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