+ TOPIC 1: INTRODUCTION TO EXCEL

• OVERVIEW
• HOME TAB
• CONDITIONAL FORMAT
• PASTE SPECIAL
• GO TO SPECIAL

+ TOPIC 2: DATA TAB

• OVERVIEW
• SIMPLE SORT AND FILTER
• ADVANCED SORT AND FILTER
• WHAT IF ANALYSIS
• DATA VALIDATION

+ TOPIC 3: FUNCTIONS

• TEXT FUNCTIONS LIKE CONCATENATE, TRIM, SEARCH AND SUBSTITUTE
• LOGICAL FUNCTIONS LIKE IF, AND, OR
• LOOKUP FUNCTIONS LIKE VLOOKUP, HLOOKUP, REFERENCE, INDEX AND MATCH
• ADVANCED FUNCTIONS LIKE DCOUNT, DSUM

+ TOPIC 4: DYNAMIC CHARTS AND PIVOT TABLES

• OVERVIEW
• CREATING CHARTS AND USING DYNAMIC CHARTS
• CONNECTING TO FORM CONTROLS
• SIMPLE PIVOT TABLES AND FUNCTIONS IN PIVOT TABLES
• INTEGRATING CHARTS WITH TABLES

+ TOPIC 5: VBA/MACROS

• WRITING SUB ROUTINES
• LOOPS
• CONDITIONAL STATEMENTS
• PRIVATE SUB ROUTINES
• UDFS

+ TOPIC 6: USERFORMS

• CREATING USERFORMS
• USING SIMPLE VALIDATIONS

+ TOPIC 1: INTRODUCTION TO SQL

• WHAT IS SQL
• WHY AND WHERE IS SQL USED
• WHY SHOULD ONE LEARN SQL
• DATABASE FUNDAMENTALS
• WHAT IS A DATABASE
• WHAT ARE THE DIFFERENT FEATURES IN A DATABASE (EG PRIMARY KEY, FOREIGN KEY AND CANDIDATE KEY)

+ TOPIC 2: BASIC RELATIONAL DATABASE MANAGEMENT CONCEPTS

• HOW TO ACCESS ONE TABLE FROM ANOTHER
• THE DIFFERENT RELATIONSHIPS POSSIBLE BETWEEN TWO TABLES
• HOW TO USE SQL COMMANDS IN ACCESS
• NORMALIZATION
• 1NF, 2NF..BCN
• MODEL A NORMALIZED DATABASE

+ TOPIC 3: INTRODUCTION TO THE CONCEPT OF TABLES

• OVERVIEW
• HOW TO CREATE A TABLE
• HOW TO IMPORT DATA FROM EXCEL, TEXT FILES
• HOW TO CHECK TABLES FOR CONSISTENCY
• FUNCTIONS
• THE SELECT FUNCTION - HOW, WHERE, WHY
• THE INSERT, UPDATE, DELETE FUNCTION - HOW, WHERE, WHY

+ TOPIC 4: DATABASE FUNCTIONS

• "GROUP BY"OPTION - HOW, WHERE, WHY
• "COUNT" OPTION - HOW, WHERE, WHY
• "WHERE" OPTION - HOW, WHERE, WHY
• MATHEMATICAL FUNCTIONS - AVG, SUM, MIN, MAX, FIRST, LAST
• SCALAR FUNCTIONS - UCASE, LCASE, MID, LEN, NOW, ROUND, FORMAT
• PRIMARY KEY COMSTRAINT
• IN CLASS PROJECT AROUND FUNCTIONS

+ TOPIC 1: BASIC R PROGRAMMING

• R ENVIRONMENT AND WINDOWS SYSTEM
• R OBKECTS, DATA PERMANENCY AND REMOVING OBJECTS
• R HELP AND SEARCH WITH FUNCTIONS
• R COMMANDS AND CASE SENSITIVITY
• DATA IMPORT AND EXPORT
• PACKAGES
• R INSTALLATION
• SIMPLE MANIPULATIONS: NUMBERS AND VECTORS
• WRITING YOUR OWN FUNCTIONS

+ TOPIC 2: ADVANCED APPLICATIONS OF R

• DATA MANIPULATION: MERGING, SORTING, FILTERING, DE-DUPING
• USER DEFINED FUNCTIONS
• VISUALIZATIONS: HISTOGRAM, BAR PLOT, BOX PLOT, MOSAICS PLOT, GEOGRAPHICS PLOT

+ TOPIC 1: INTRODUCTION TO POWER BI

• INSTALLING POWER BI DESKTOP
• EXPLORING VARIOUS DATA SOURCES
• CONNECT TO DATA IN POWER BI DESKTOP

+ TOPIC 2: QUERY EDITOR IN POWER BI

• RENAMING QUERIES
• COMBINING QUERIES: APPEND
• FIXING METADATA
• FILTERING ROWS
• ELIMINATING COLUMNS
• COMBINING QUERIES: MERGE
• ADDING A COLUMN

+ TOPIC 3: TRANSFORMING DATA

• DIRECT QUERY VS IMPORT DATA
• SHAPE & COMBINE
• DATA TYPES
• GROUPS JOINS AND BINS
• SPLIT COLUMN

+ TOPIC 4: FILTERS & SLICERS

• BASIC FILTERS & ADVANCED FILTERS
• PAGE AND REPORT FILTERS
• DRILL THROUGH FILTERS

+ TOPIC 5: CHARTS & CONCEPTS

• VARIOUS CHARTS
• MATRIX, TABLE MAP, R VISUALS

+ TOPIC 6: CREATING REPORTS

• CREATING REPORTS
• RENAMING A DATASET AND REPORT
• GET INSIGHTS

+ TOPIC 7: DAX FUNCTIONS

• STRING MATH AND LOGICAL
• AGGREGATE
• DATE AND TIME INTELLIGENCE

+ TOPIC 8: CREATING DATASETS

• CREATE AND MANAGE RELATIONSHIPS
• CALCULATED FIELDS
• COLUMNS (CALCULATED AND CONDITIONAL)
• MEASURES HIERARCHIES AND TABLES
• PERFORMING A LOOKUP TO A RELATED TABLES
• TRANSLATING A VALUE
• ENHANCING THE DATA MODEL

+ TOPIC 9: SHARING & EXPLORING POWER BI DASHBOARDS

• ADDING TITLE
• ACTIONS AND REPORTS
• POWER BI Q&A

+ TOPIC 1: INTRODUCTION TO ANALYTICS

• OVERVIEW
• NEED FOR ANALYTICS
• USE OF ANALYTICS ACROSS DIFFERENT INDUSTRIES
• CHALLENGES IN ADOPTION OF ANALYTICS

+ TOPIC 2: DESCRIPTIVE ANALYTICS

• OVERVIEW
• UNDERSTANDING DIFFERENT OUTPUTS
• TABULAR AND GRAPHICAL METHOD
• SUMMARY STATISTICS

+ TOPIC 3: STATISTICAL TESTING

• HYPOTHESIS TESTING
• Z TEST, T TEST, CHI SQUARE TEST, ANOVA
• PARAMETRIC AND NON PARAMETRIC TEST

+ TOPIC 4: REGRESSION AND CORRELATION

• OVERVIEW
• HOW TO CARRY OUT REGRESSION
• TYPES OF REGRESSION - LOGISTIC AND LINEAR
• CASE STUDIES

+ TOPIC 5: MODELING TECHNIQUES

• OVERVIEW
• CONCEPTS OF SEGMENTATION
• USE OF SEGMENTATION
• CLUSTER ANALYSIS FACTOR ANALYSIS

+ TOPIC 1: TEXT ANALYTICS

• PROBLEMS WITH UNSTRUCTURED DATA
• TERMINOLOGY IN TEXT ANALYTICS: CORPUS, TDM, PARSING, STEMMING,STOPWORDS, CHUNKING ETC
• CLASSIFICATION AND TAGGING
• IN CLASS PROJECT: DOCUMENT CLASSIFIER AND SENTIMENT ANALYSIS

+ TOPIC 2: AUTOMATION IN TIME SERIES

• TIME SERIES DECOMPOSITION
• COMMON TECHNIQUES LIKE MOVING AVERAGES, SMOOTHING ETC
• ARIMA
• IN CLASS PROJECT TO AUTOMATE FORECASTING

+ TOPIC 3: MACHINE LEARNING

• WHAT IS MACHINE LEARNING
• TREE BASED LEARNING
• COMMON LEARNERS: KNN, RANDOM FORESTS, GBM ETC
• IN CLASS PROJECT

+ TOPIC 1: INTRODUCTION TO PYTHON

Python is used for different applications, and there is no single solution for setting up python and required packages. This module focuses on setting up python for data analysis. We discuss pros and cons of different IDE’s that are currently used in industry along with overview of python libraries currently used in data science domain.

• Installing Python language and library modules
• Python 2 and Python 3 (A very brief overview of differences will be taught)
• Brief overview of Integrated Development Environments (IDE’s)
• Jupyter Notebook basics
• Import conventions
• Essential Python Libraries i. Numpy, Scipy ii. Pandas iii. Matplotlib

+ TOPIC 2: USING PYTHON- THE BASICS

This module focus on basics features of the Python language. It covers basic programming fundamentals in python which can be thought of as a crash course in python.

• Whitespace Formatting
• Modules
• Arithmetic
• Functions
• Strings
• Lists
• Tuples
• Dictionaries
• Sets
• Control Flow
• Reading from and writing to data files on disk

In this module we will introduce Python code to illustrate the above features. In addition, we will briefly describe other Python features listed below, and explain them in more detail when used for the first time later in the course. This will provide better context for understanding these features, better retention of concepts and learning outcomes.

• Exceptions
• Sorting
• List Compressions
• Generators and Iterators
• Randomness
• Regular Expressions
• Object Oriented Programming

+ TOPIC 3: PLOTTING DATA

Making plots and static or interactive visualizations is one of the most important tasks in data analysis. It may be a part of the exploratory process; for example, helping identify outliers, needed data transformations, or coming up with ideas for models.

Plotting Functions using Matplotlib module

• Line Plots
• Bar Plots
• Histograms and Density Plots
• Scatter Plots
• Saving plots to file

+ TOPIC 4: NUMPY

NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis. While NumPy by itself does not provide very much high-level data analytical functionality, having an understanding of NumPy arrays and array-oriented computing will help you use tools like Pandas much more effectively

The NumPy ndarray: A Multidimensional Array Object

• Creating ndarrays
• Data Types for ndarrays
• Operations between Arrays and Scalars
• Basic Indexing and Slicing
• Boolean Indexing
• Fancy Indexing
• Transposing Arrays and Swapping Axes

• Universal Functions: Fast Element-wise Array Functions

• Data Processing Using Arrays

• Expressing Conditional Logic as Array Operations
• Mathematical and Statistical Methods
• Methods for Boolean Arrays
• Sorting
• Unique and Other Set Logic

+ TOPIC 5: PANDAS

Pandas will be the primary library of interest which contains high-level data structures and manipulation tools designed to make data analysis fast and easy in Python. Pandas is built on top of NumPy and makes it easy to use in NumPy-centric applications.

• Introduction to pandas Data Structures

• Series
• DataFrame
• Index Objects

• Essential Functionality

• Reindexing
• Dropping entries from an axis
• Indexing, selection, and filtering
• Arithmetic and data alignment
• Function application and mapping
• Sorting and ranking
• Axis indexes with duplicate values

• Summarizing and Computing Descriptive Statistics

• Correlation and Covariance
• Unique Values, Value Counts, and Membership

• Handling Missing Data

• Filtering Out Missing Data
• Filling in Missing Data

• Hierarchical Indexing

• Reordering and Sorting Levels
• Summary Statistics by Level
• Using a DataFrame’s Columns

+ TOPIC 6: DATA WRANGLING: CLEAN, TRANSFORM, MERGE, RESHAPE

Much of the programming work in data analysis and modelling is spent on data preparation: loading, cleaning, transforming, and rearranging. Sometimes the way that data is stored in files or databases is not the way you need it for a data processing application and hence before any data analysis and modelling, data wrangling is a must exercise.

• Combining and Merging Data Sets

• Database-style DataFrame Merges
• Merging on Index
• Concatenating Along an Axis
• Combining Data with Overlap

• Reshaping and Pivoting

• Reshaping with Hierarchical Indexing
• Pivoting “long” to “wide” Format

• Data Transformation

• Removing Duplicates
• Transforming Data Using a Function or Mapping
• Replacing Values
• Renaming Axis Indexes
• Discretization and Binning
• Detecting and Filtering Outliers
• Permutation and Random Sampling
• Computing Indicator/Dummy Variables

+ TOPIC 7: DATA AGGREGATION AND GROUP OPERATIONS

Categorizing a data set and applying a function to each group, whether an aggregation or transformation, is often a critical component of a data analysis workflow. Pandas provides a flexible and high-performance group by facility, enabling you to slice and dice, and summarize data sets in a natural way.

• GroupBy Mechanics

• Iterating Over Groups
• Selecting a Column or Subset of Columns
• Grouping with Dicts and Series
• Grouping with Functions
• Grouping by Index Levels

• Group-wise Operations and Transformations

• Pivot tables and Cross-tabulation