Full Course Data Analysis For Data Science

Course Overview

In this course, you will learn how to use Business intelligence (BI) software and services to transform data into actionable insights that inform an organization’s business decisions. Data analysis is the process of extracting information from data. … Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.

Course Outline:

Microsoft Excel
Microsoft Power BI
Tableau & SQL
Python for Data Science
R Programming

Microsoft Excel

Excel Level 2

Chapter 01 – Working with Functions
Chapter 02 – Working with Lists
Chapter 03 – Analyzing Data
Chapter 04 – Visualizing Data with Charts
Chapter 05 – Using PivotTables and PivotCharts
Chapter 06 – Working with Graphical Objects
Chapter 07 – Using Array Formulas

Excel Level 3

Chapter 01 – Working with Multiple Worksheets and Workbooks
Chapter 02 – Sharing and Protecting Workbooks
Chapter 03 – Automating Workbook Functionality
Chapter 04 – Using Lookup Functions and Formula Auditing
Chapter 05 – Forecasting Data
Chapter 06 – Creating Sparklines and Mapping Data
Chapter 07 – Importing and Exporting Data
Chapter 08 – Internationalizing Workbooks
Chapter 09 – Working with Power Pivot
Chapter 10 – Advanced Customization Options
Chapter 11 – Working with Forms and Controls

Power BI

Overview

This course will discuss the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will also show how to access and process data from a range of data sources including both relational and non-relational data. This course will also explore how to implement proper security standards and policies across the Power BI spectrum including datasets and groups. The course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Finally, this course will show how to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.

After completing this course, students will be able to:
Ingest, clean, and transform data
Model data for performance and scalability
Design and create reports for data analysis
Apply and perform advanced report analytics
Manage and share report assets
Create paginated reports in Power BI

Power BI Course Outline

Module 1: Introduction
Module 2: Getting and Profiling Data
Module 3: Cleaning and Transforming Data
Module 4: Designing a Data Model
Module 5: Developing a Data Model
Module 6: Creating Model Calculations with DAX
Module 7: Optimizing Model Performance
Module 8: Creating Reports
Module 9: Enhancing Reports for Usability and Performance
Module 10: Creating Dashboards
Module 11: Enhancing Reports and Applying Advanced Analytics
Module 12: Managing and Sharing Power BI Assets
Module 13: Working with Paginated Reports in Power BI Tableau

The course is divided into 4 weeks. Over these 4 weeks, you will learn how to use Tableau to implement all types of visualizations and to help you find, and communicate, answers to business questions, as well as work with the Tableau functions that all data analysts should be familiar with. You will also learn how to use a different data set to work out analyses and how to use functions to include new calculations to your data.

WEEK 1. Get Started
WEEK 2. Data Visualization with Tableau
WEEK 3. Advanced Topics and Dashboards
WEEK 4. Dashboards and Dynamics in Tableau

SQL

Introduction to SQL
Basic SQL
SQL Joins
SQL Aggregations
Advanced SQL
Queries

Python for Data Science

Section 1: Introduction to Programming
Section 2: Control Flow
Section 3: Object Oriented Programming
Section 4: Advanced Python
Section 5: Data Science

Data Visualization using R Programming

Course Outline

Introduction to R Programming
History and overview of R
Install and configuration of R programming environment
Basic language elements and data structures
R+Knitr+Markdown+GitHub
Data input/output
Data storage formats
Subsetting objects
Vectorization
Control structures
Functions
Scoping Rules
Loop functions
Graphics and visualization
Grammar of data manipulation (dplyr and related tools)