Business Intelligence and Data Analytics

Enrolled: 0 students
Duration: 108 hours
Lectures: 192
Level: Beginner

Working hours

Monday 10:00 am - 7:00 pm
Tuesday 10:00 am - 7:00 pm
Wednesday 10:00 am - 7:00 pm
Thursday 10:00 am - 7:00 pm
Friday 10:00 am - 7:00 pm
Saturday 10:00 am - 7:00 pm
Sunday 10:00 am - 7:00 pm

The primary goal of this quick start guide is to introduce you to Unreal Engine 4’s (UE4) development environment. By the end of this guide, you’ll know how to set up and develop C++ Projects in UE4. This guide shows you how to create a new Unreal Engine project, add a new C++ class to it, compile the project, and add an instance of a new class to your level. By the time you reach the end of this guide, you’ll be able to see your programmed Actor floating above a table in the level.

This quick start guide shows you how to add assets to your Unreal Engine (UE4) games. By the end of this guide, you’ll know how to use the Project Browser to create new projects and navigate the Content Browser to find and add content. You’ll also know where to find information on the FBX Content Pipeline while learning how to use the Material Editor to modify Materials before applying them to a Static Mesh Actor.

Main Features

  • Achieve Hollywood best quality visuals out of the box.
  • With complete C++ source code access, you can study.
  • Comes with designer-friendly Blueprint visual scripting.
  • Unreal Engine provides Robust Multiplayer Framework.
  • The built-in Cascade visual effects editor enables particles.
  • Unreal Engine 4’s Material Editor makes use of physically-based.

What is the target audience?

  • You might be thinking, all of the above – and that is fine. But as a complete beginner learning Unreal Engine 4.
  • The rendering system in Unreal Engine 4 is an all-new, DirectX 11 pipeline that includes deferred shading.

Learning a new game engine as a complete beginner is very intimidating. There are a lot of tutorials, documentation and advice already out but how do you start and proceed with learning Unreal Engine 4 is unclear. You get pulled into many different directions and end up confused and overwhelmed.

I have spent a lot of time deconstructing what it takes to learn a game engine from scratch. What it is that you should focus on first and what you should avoid until later.

Introduction to Data & Data science & Different Data Science fields

1
-Reasons behind Business & Data Science Buzzwords
2
-Analytics vs Analysis
3
-Business Analytics, Data Analytics & Data Science
4
-Adding BI, ML and AI to the picture
5
-Overview of Data Science Infographic
6
-Application of traditional data, big data, BI,ML and DS
7
-Why do we need each of these deciplines ?
8
-Traditional data techniques with real life example
9
-Traditional data techniques with real life example
10
-Big data techniques with real-life-example
11
-Business Intelligence techniques with real-life-example
12
-Traditional methods techniques with real-life-example
13
-ML techniques different types and real-life-example
14
-Programming langiages & software needed in data science
15
-Data Science job positions,what da they involve

Statistics - Descriptive statistics

1
-Population vs Sample
2
-Types of data & levels of measurement
3
-Categorical variable- visualization technique
4
-Numerical variable- Frequency distribution table
5
-Histogram
6
-Cross Table & Scatter plot
7
-Mean, Median and Mode
8
-Skewness
9
-Variance
10
-Standard Deviation
11
-Coefficient of Variation
12
-Covariance and Correlation Coefficient
13
-Statictics Practical Example

Statistics - Inferential Statistics

1
-What is distribution?
2
-The Normal Distribution
3
-The Standard Normal Distribution
4
-Central Limit Theorem
5
-Standard Error
6
-Estimator and Estimates
7
-What are Confidence Intervals?
8
-Population Variance known: Z-Score
9
-Confidence Intervals clarifications
10
-Students’t distribution
11
-Population Variance unknown: t-Score
12
-Margin of Error
13
-Confidence Intervals:Two Means- Dependent Samples
14
-Confidence Intervals:Two Means- Independent Samples
15
-Inferential Statistics Practical Example

Statistics-Hypothesis Testing

1
-The Null & Alternative Hypothesis
2
-Rejection Region & Significance level
3
-Type I & Type II error
4
-Test for Mean: Population variance known
5
-P-Value
6
-Test for Mean: Population variance unknown
7
-Test for Mean: Dependent Samples
8
-Test for Mean: Independent Samples
9
-Practical Application of Hypothesis testing

MySql

1
-Why use SQL and MySql
2
-Introduction to Databases & RDBMS fundamentals
3
-Comparing databases and spreadsheets-Important database terminology
4
-Concept of relational schemas: Primary Key & Foreign key
5
-Unique key & Null values
6
-Relationship between tables
7
-Install and get to know MySql
8
-MySql best practices
9
-Loading the employee database

Practical application of SQL SELECT statement

1
-Using SELECT-FROM
2
-Using WHERE
3
-Using AND, OR
4
-Using IN-NOT IN
5
-Using Operator Precedence and Logical Order
6
-Using LIKE- NOT LIKE
7
-Using Wildcard characters
8
-Using BETWEEN-AND
9
-Using BETWEEN-AND
10
-Using IS NOT NULL-IS NULL
11
-Using other Comparison Operators
12
-Using SELECT – DISTINCT
13
-Aggregate functions
14
-Using ORDER BY and GROUP BY
15
-Using Aliases (AS)
16
-Using HAVING
17
-WHERE vs HAVING
18
-Using LIMIT
19
-SUM, COUNT, MAX,MIN,ROUND, AVG functions

SQL Joins

1
-What are joins?
2
-INNER JOIN
3
-Duplicate rows
4
-LEFT JOIN
5
-RIGHT JOIN
6
-JOIN and WHERE together
7
-CROSS JOIN
8
-Combining Aggregate functions with JOIN
9
-JOIN more than two tables
10
-Difference between UNION and UNION ALL
11
-The SQL CASE statement

SQL Subqueries

1
-SQL subqueries with IN embedded inside WHERE
2
-SQL subqueries with EXISTS-NOT EXISTS embedded inside where
3
-SQL subqueries nested in SELECT and FROM

SQL Stored Routines

1
-Defining Stored routines
2
-Create stored procedure with MySQL syntax
3
-An example of Stored Procedures
4
-Creating a procedure in MySQL in another way
5
-Create stored procedure with an input parameter
6
-Create stored procedure with an output parameter
7
-SQL variables
8
-Benefit of user defined func in MySQL

Tableau Introduction

1
-Why use tableau? make your data make an impact
2
-Download and install tableau public
3
-Eonnecting data in tableau
4
-Exploring tableau’s interface
5
-Create our first chart in tableau

Tableau Functionalities

1
-Duplicating sheets
2
-Creating a table
3
-Creating custom fields
4
-Adding calculations to table
5
-Adding totals and subtotals
6
-Adding a custom calculation
7
-Inserting a filter
8
-Working with joins

Combining SQL and Tableau

1
-Introduction to software integration
2
-Combining SQL and tableau
3
-Loading the database
4
-Combing SQL and tableau case study

Introduction to programming with python

1
-What is programming?
2
-Why use python?
3
-Why use jupyter?
4
-Installing Anaconda Python and Jupyter
5
-Understanding jupyters interface-dashboard
6
-Prerequisites for coding
7
-Python variables
8
-Data types, number, string and boolean values
9
-The Arithmetic operators
10
-Double equaluity sign
11
-Reassign values
12
-Understanding line continuation
13
-Indexing elements
14
-Structure your code with indentation
15
-Comparison operators
16
-Logical & Identity operators
17
-IF , ELIF and ELSE statements
18
-Python functions introduction
19
-Creating function with and without parameters
20
-Creating function with and without parameters
21
-Conditional statements and function together
22
-Create function with few arguments
23
-Built-in functions in python
24
-Intro to lists
25
-Lists slicing
26
-Methods in python
27
-What is tupils?
28
-Python dictionaries
29
-For loops with and without end statement
30
-Loops with incrementing
31
-Range function to create lists
32
-Combing conditional statement and loops
33
-Conditional statements, functions and loops
34
-How to iterate over dictionaries?
35
-How to iterate over dictionaries?

OOP

1
-Introduction to object oriented programming
2
-Using modules and packages
3
-Standard library
4
-Import modules in python

Software Integration

1
-Data,servers,clients, requests , responses
2
-Data connectivity, APIs and end-points
3
-Become better acquinted with APIs
4
-Communicating through text files
5
-Software integration and its application

Integration

1
-Solving business exercise with python, SQL & Tableau
2
-Presenting the task-Absenteeism at work
3
-Presenting the data set
4
-A brief summary of Regression analysis
5
-Approch for solving the exercise
6
-Dropping variables we dont need
7
-Deeper look at reasons for absence column
8
-Splitting variables into multiple dummy variables
9
-Dropping dummy variable from data set
10
-Statistical perspective of dummy variable
11
-Categorizing various reasons for absence
12
-Concatenation in python
13
-Reordering columns in python data frame
14
-Use checkpoints to ease your work in jupyter
15
-Analyzing the date column
16
-Retrieving month values from date column
17
-Adding day of the week column
18
-Analysis of next 5 columns in DF
19
-Dealing with more categorical features behave like numeric

Integrating Python and SQL

1
-Using absenteeism module in python
2
-Creating the predicted output data base in MySQL
3
-Importing pymysql in python
4
-Creating a connection and cursor
5
-Creating predicted output table in MySQL
6
-Executing SQL SELECT statement from python
7
-Sending data from Jupyter to workbench

Using Tableau to analyze the predicted output

1
-Using tableau to analyze Age vs Probability
2
-Using tableau to analyze Reasons vs Probability
3
-Using Tableau Transportation expense vs probability
Faq Content 1
Faq Content 2

Productivity Hacks to Get More Done in 2018

— 28 February 2017

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