Korea Software HRD Center

Data Analytics

This course covers the fundamentals of data analytics, including statistics, data processing, and machine learning. You will learn how to work with Python, SQL, Excel, and Power BI for data analysis and visualization.

Objective

Understand basic and advanced statistics for data analysis.
Learn data collection, web scraping, and data processing techniques.
Use Python and SQL for data manipulation and analysis.
Visualize data using Excel and Power BI.
Build predictive models and apply machine learning concepts.

Curriculum

Basic Statistics

What is Statistics?
Type of statistics.
Difference between Population and Sample
What is Descriptive Statistics?
What is the Measure of Central Tendency? (Mean, Median, Mode)
What is Measure of Variability? (Range, IQR, Variance, Standard Deviation)
What is Normal Distribution?

Advanced Statistics

What is Inferential Statistics?
What is Hypothesis Testing? (Level of Significant, Confidence Interval, Test Statistic)
What is ANOVA?
What is Regression?
What is Correlation?
Type of Correlation.

Introduction to Data Analytics

What is Data Analytics?
Data Analysis Steps?
Type of Data Analytics
Data Analytics Use Cases
Lifecycle of Data Analytics
Benefits of Data Analytics
Data Analytics Techniques
Data Analytics Tools
How to Become a Data Analyst?
Skills needed to become a Data Analyst
Data Analyst and Data Scientist

Python I: Fundamental

Installation Python
Tool to use (Jupyter)
Array
Matrix Operation
Dictionaries

Data Collection & Web Scraping

Introduction to data collection
Introduction to Web Scraping & Web Crawling
HTML parsing with BeautifulSoup
Scrapy Fundamental
XPath with Scrapy
Scrapy with Selenium
Scrapy with Splash

SQL for Analytics

Introduction to SQL
Database Normalization and Entity Relationship Model
SQL Operators
Working with SQL: Join, Tables, and Variables
Working with Subqueries (Nested subqueries, Correlated subqueries, Common Table Expressions)
SQL Views, Stored Procedures, Triggers
Deep Dive into SQL Functions
Deep Dive into User-defined Functions

Excel for Data Analytics

Why using Excel?
Formula and Function

Excel for Data Analytics ('Data Preparation')

Load Data into Excel (File (txt,csv), Database)
What is power query?
Why need power query?
How to use power query to clean and transform data?
Data Validation
Sort and Filtering
Conditional Formatting
All Kind of Count in Excel

Excel for Data Analytics ('Data Extraction')

Split text
Auto fill
Transpose
Concatenate
Trim
Change Data Type (Convert Data)

Data Analysis with Excel

What if Analysis in Excel?
Goal Seek
Scenario Manager
Data Table
Solver
Histogram
Correlation
Rank and Percentile
Descriptive Statistics (Regression in Excel)
Forecast Sheet

Data Visualization with Excel

Pivot Table, Chart, Slicer
Create and Modify Pivot Tables
Use Pivot Tables to solve a variety of Business Problems
Create an Interactive Dashboard with Pivot Charts and Slicers
Bring data to life using conditional formatting & custom number formatting
Mastering charts in Excel
Complete the dashboard with creative visualizations and dynamic charts (Interaction Dashboard)

Data Warehouse (Ready Data to Analyze)

Data Warehouse Overview
Type of Data Warehouse
Data Warehouse architecture
Data Warehouse Characteristics
Data Warehouse vs Database
ETL and ELT

Power BI

Introduction to Power BI
Understanding Power BI
Power BI Desktop and the Power BI service
Creating a Power BI account

Data Preparation and Modeling With Power BI

Creating reports and dashboards
Advanced features (Drillthrough, Slicers, Filters, etc.)
Sharing and publishing reports
RLS (Role Level Security) in Power BI

Data Visualization With Power BI

Creating reports and dashboards
Using visualizations such as charts, tables, and maps
Creating custom visuals and using marketplace visuals
Designing and formatting reports

Advanced Features of Power BI

Creating and managing data hierarchies
Using drillthrough and drilldown features
Creating dynamic reports with slicers, filters, and bookmarks
Using Power BI mobile app
Sharing and publishing reports to Power BI service and dashboards
Workspace in Power BI
Role User in Power BI workspace
RLS (Role Level Security) in Power BI and why do we need it?
Sharing and collaboration (Workspace)
Using the Power BI service to collaborate with others
Setting up security and permissions for reports and data

Python II: Working With Libraries

Dataframe of Pandas with Python
Series of Pandas with Python
Access Data from Dataframe of Pandas
Data Processing with Pandas (Load Data into Pandas and Clean)
Dynamic Array of Numpy with Python
Create Chart, Plots Using matplotlib with Python

Exploratory Data Analysis

What is Exploratory Data Analysis (EDA)?
Why is it important?
Type of EDA
What is cross validation?

Feature Engineering (Data Processing)

Feature Selection
Handling missing values
Handling imbalanced data
Handling outliers
Binning
Encoding
Feature Scaling

Predictive Modeling

Machine learning Algorithms
Type of machine learning
Regression models
Decision Tree

Time Series Forecasting

Introduction to time-series
Correlation Forecasting
Autoregressive models

Data Processing with PySpark

Introduction to time-series
Correlation Forecasting
Installing PySpark
Spark Structure API DataFrame
RDD
Spark Architecture and Components
Tuple
Pair RDD
FlatMaps and Filters
Sorts and Coalesce
Joins
Spark SQL
Datasets
DataFrames API
Pivot Tables
User Defined Function
HashAggregation
SparkML for Machine Learning
Linear Regression Model
Training Data
Model Fitting Parameters
Feature Selection
Non-Numeric Data
Pipelines
K Means Clustering
Spark Streaming and Structured Streaming with Kafka

Business Problem Solving, Insights and Storytelling

Business domains
Understanding the business problem and formulating hypotheses
Exploratory data analysis
Data storytelling
Project on deriving business insights and storytelling

Business Case Studies

Real world Business Analytics
Focus on Real world problem
Business Required

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