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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.

Which version of Sylenth1 do i need for this soundbank ?


In order to use the soundbank you will need Sylenth1 version 2.2.1 or higher




Which DAW will i need for this pack ?


In order to use the soundbanks you will need any DAW that can load VST-Plugins like Sylenth1 and Serum.
To use the Sample Pack you will need any DAW that can load WAV samples.
For the Project Files you will need FL Studio 12 (we recommend version 12.5)




How do i recieve my product ?


As soon as you purchased this product we will process your order almost instantly and then the download will start automaticaly. Also you will recieve an email along with the download link




What payment methods are accepted ?


In order to make the checkout process as simple and fast as possible we process all payments through PayPal. However, NO PayPal account is required, if you use Credit Card or Direct debit.Contact our support if you need any assistance with your payments. ultrasonicofficial@yahoo.com





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


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 NumPyndarray: 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

  1. Series
  2. DataFrame
  3. Index Objects
• Essential Functionality
  1. Reindexing
  2. Dropping entries from an axis
  3. Indexing, selection, and filtering
  4. Arithmetic and data alignment
  5. Function application and mapping
  6. Sorting and ranking
  7. Axis indexes with duplicate values
• Summarizing and Computing Descriptive Statistics
  1. Correlation and Covariance
  2. Unique Values, Value Counts, and Membership
• Handling Missing Data
  1. Filtering Out Missing Data
  2. Filling in Missing Data
• Hierarchical Indexing
  1. Reordering and Sorting Levels
  2. Summary Statistics by Level
  3. 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

  1. Database-style DataFrame Merges
  2. Merging on Index
  3. Concatenating Along an Axis
  4. Combining Data with Overlap
• Reshaping and Pivoting
  1. Reshaping with Hierarchical Indexing
  2. Pivoting “long” to “wide” Format
• Data Transformation
  1. Removing Duplicates
  2. Transforming Data Using a Function or Mapping
  3. Replacing Values
  4. Renaming Axis Indexes
  5. Discretization and Binning
  6. Detecting and Filtering Outliers
  7. Permutation and Random Sampling
  8. 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

  1. Iterating Over Groups
  2. Selecting a Column or Subset of Columns
  3. Grouping with Dicts and Series
  4. Grouping with Functions
  5. Grouping by Index Levels
• Group-wise Operations and Transformations • Pivot tables and Cross-tabulation





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