The Analytics 360 Course
A comprehensive course for beginners: 5 modules comprising of Excel, Python, Power BI, SQL, R and Base Analytics. Read more about the course structure below.
Course Outline
ADVANCED EXCEL
DURATION: 24 HOURS
Excel is by far the world's most popular spreadsheet, used pretty much everywhere you look in the business world, especially in areas where people are adding up numbers a lot, like marketing, business development, sales, finance, etc. Thus, Excel in its universality is now a must have skill in every business environment.
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SQL
DURATION: 24 HOURS
Structured Query language (SQL) is a computer language used to access a database. It is used for updating data on a database.
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
R
DURATION: 16 HOURS
The R tool is an open source framework, which grew as a result of a strong push by Google. Today, R is poised to overtake SAS as the most widely used tool in statistical analyses. With over 20,000 packages currently available, it has near limitless potential for business application.
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
POWER BI
Power BI is a business analytics service by Microsoft. It aims to provide interactive visualisations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.
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
DURATION: 16 HOURS
BASE ANALYTICS
DURATION: 40 HOURS
Analytics Base (AB) includes fundamental concepts of statistics, and guides in building predictive models using multiple linear and logistic regressions. All of this is taught using live case studies with data from 18 different industries, at ATI.
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
PYTHON
DURATION: 24 HOURS
Python is an open source scripting language, known for its simplicity.
It is extremely powerful and can be used for almost any statistical or analytical operation. As it is widely used in web development, it acts as an ideal bridge to support analytics in web based applications.
TOPIC 1: INTRODUCTION TO PYTHON

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

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

Exceptions 
Sorting 
List Compressions 
Generators and Iterators 
Randomness 
Regular Expressions 
Object Oriented Programming
TOPIC 3: PLOTTING DATA

Plotting Functions using Matplotlib module 
Line Plots 
Bar Plots 
Histograms and Density Plots 
Scatter Plots 
Saving plots to file
TOPIC 4: NUMPY

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

Expressing Conditional Logic as Array Operations 
Mathematical and Statistical Methods 
Methods for Boolean Arrays 
Sorting 
Unique and Other Set Logic
TOPIC 5: PANDAS

Series 
DataFrame 
Index Objects

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

Correlation and Covariance 
Unique Values, Value Counts, and Membership

Filtering Out Missing Data 
Filling in Missing Data

Reordering and Sorting Levels 
Summary Statistics by Level 
Using a DataFrame’s Columns
TOPIC 6: DATA WRANGLING: CLEAN, TRANSFORM, MERGE, RESHAPE

Databasestyle DataFrame Merges 
Merging on Index 
Concatenating Along an Axis 
Combining Data with Overlap

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

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

Iterating Over Groups 
Selecting a Column or Subset of Columns 
Grouping with Dicts and Series 
Grouping with Functions 
Grouping by Index Levels
ADVANCED ANALYTICS
DURATION: 40 HOURS
With the aid of live projects, Advanced Analytics teaches you how to:
Make your data look good using data visualization
Forecast using time series
Find patterns in large amount of text using text analytics
 Machine Learning.
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