Data Analysis Course & Certification 2023

Mode:	Online Based
Duration:	12 - 20 Weeks
Sessions:	50
Language:	English
Certificate:	Yes

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Information Technology

Data Analysis Course

Course Overview

The comprehensive data analysis course covers a range of topics, including advanced Excel, SQL, Python, machine learning, and Power BI. The course begins with an introduction to data analysis, followed by advanced Excel techniques, such as working with large datasets, creating PivotTables and PivotCharts, and using advanced functions. Next, students learn about SQL, including querying and joining databases.

After that, students are introduced to Python and machine learning, where they learn how to use Python for data analysis, build and evaluate predictive models, and work with algorithms such as linear regression and decision trees. Finally, students learn about Power BI, a data visualisation tool, which helps them create interactive dashboards, reports, and data visualisations, allowing them to communicate their findings effectively. Overall, the course provides students with a comprehensive understanding of the data analysis process and equips them with the skills necessary to succeed in a data-driven world.

Course Benefits

4 Reasons why you should study data analytics

Improved problem-solving: With data analytics, you develop the ability to identify patterns, detect anomalies, and solve complex problems using data-driven approaches.
High-Paying Career: Data analysts are significant, and with an approaching abilities deficiency not too far off as an ever increasing number of organisations and areas begin working with enormous information, this worth is simply going to increase.
Improved forecasting and planning: You can use data analytics to look at patterns and trends from the past to make accurate predictions and come up with effective business strategies.
Data analytics skills are applicable to everyday life: Many of the related skills you learn in data analytics can be used in everyday life. Learning how to effectively organise data can help you with personal budgeting, and explaining complex data can help you improve your communication skills.

Modules

Module 1 Excel (Basic, Intermediate, Advanced)

The basic IT functions
Creating and using a range of name
Locate formulas
IFERROR
VLOOKUP & HLOOKUP
INDEX & MATCH
Analyzing Your Data
- Set up Pivot tables
- Grouping your data
- Amend Pivot Tables with new data
- Use a slicer to filter your data
- Combining slicers to more than one Pivot Table
- Using a timeline
- Organize a Pivot chart
Reducing Your Audit Risk
- Data Recognition
- Use of Trace Precedents
- Use of Trace Dependents
- Eliminate Arrows
- Flaws checking
- Check out Formula
- Watch Window
Enhance Your Workflow
- Set up Macro security
- Recording Macros
- How to edit Macro
- Understanding the VBA edit window
- Allow & run a Macro from the ribbon
- Saving & using a Macro-enabled Workbook
- Deleting your Macro
Additional Topics
- Scenarios
- Goal Seek

Module 2 Mathematics & Statistics

Descriptive
Variability
Distribution
Probability
Linear Functions
Linear Algebra
Vectors
Matrices
Tensors

Module 3 SQL(Structured Query Language)

DDL and DML in MySQL and setup
ERD Diagrams and Relational Mapping
Data normalization
Basic Queries
Database Manipulation
Table Manipulation
Relational Algebra
Advanced SQL - Joining, Subquery, Views
Database Security
Multiple Activities to Perform

Module 4 Git and GitHub

Developers may build a strong portfolio displaying their coding skills and making their work available to a larger audience by using Git and GitHub. This platform provides an effective way to exchange code, cooperate with other members of the coding community, and showcase one's abilities, eventually promoting one's professional development and exposure.

Module 5 Python

Python Setup and What is Python?
Data Types and Syntax
Comparison Operators
Python Loop
Python Statements
Logical Operators
Methods and Functions
Error and Exception Handling
Modules Packages and libraries
Debugging
Advanced python Modules (DateTime)
File Management
Multiple Activities to Perform
Multiple Projects to Build

Module 6 Machine Learning

Data Preprocessing
Supervised Learning
Unsupervised Learning
Regression Models
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Random Forest Regression
- Topics such as
  - Assessing a Regression Model
  - Bias vs Variance
  - Regularisation
  - Gradient Descent
Classification Models
- Decision Tree Classification
- K-Nearest Neighbor
- Logistic Regression
- Naïve Bayes
- Random Forest Classification
- Support Vector Machines
- Additional Topics
  - Assessing a Classification Model
  - Adaboost
  - Gradient Boosting
  - XGBoost
  - Grid Search CV
Clustering Models
- Hierarchical
- K-Means Clustering
Association
- Apriori
- Eclat
Build Dashboards for Data Visualisation
Solved Sample Code Files for easy practice
Access to Multiple Datasets
7+ Real world data projects

Module 7 R Language

R Introduction
R Installing
R Syntax
Comments, Variables and Data types in R
Numbers, Math, Strings, Booleans and Operators in R
IF, IF Else, Else if and Nested IF in R
Loops in R
Functions in R
Data Structures in R
Graphics in R
R Statistics
Final Project of Data Analysis with R

Module 8 ETL/ELT

Introduction to ETL/ELT
- Extract
- Transform
- Load
Software Tools
- Talend Studio
- Apache Hadoop
- Apache Kafka
Real-world data projects

Module 9 Power BI

Introduction to Power BI
- Overview of Power BI
- Advantages of Power BI
- Power BI components
- Power BI service vs Power BI desktop
Data Sources and Connections
- Data sources overview
- Connecting to different data sources
- Working with data in Power BI
Data Transformation and Modeling
- Data transformation and cleaning
- Data modeling
- Creating relationships between data tables
Visualization
- Basic visualization types
- Customizing visualizations
- Working with visuals and filters
DAX Functions
- Introduction to DAX functions
- DAX formulas and expressions
- Aggregating and summarizing data with DAX
Sharing and Collaboration
- Sharing and publishing reports
- Managing access to reports
- Collaboration with Power BI
Advanced Topics
- Advanced data modeling
- Custom visuals and extensions
- Power BI embedded
Case Studies and Hands-On Projects
- Real-world case studies
- Hands-on projects with Power BI

Module 10 Tableau

Tableau Installation
Installing Tableau on your computer will allow you to undertake data analysis.
Tableau UI
You will learn how to utilise the Tableau user interface to do research and create data visualisations.
Tableau UI Components
Utilising elements for building visualisations, including as sheets and legends, using Tableau UI components.
Tableau Marks Card
Manages how data points look in a display.
Tableau Functions
Understanding built-in data manipulation algorithms using Tableau Functions.
Filters in Tableau
Using tools for data focus and visualisation refinement, such as filters in Tableau.
Forecasting with Tableau
Utilise automated predictions to forecast future data patterns when forecasting using Tableau.
Parameters
Customising an interactive visualisation by working with dynamic values.
Measures
The process for using quantitative data in calculations.
Dimensions
Data categorization and grouping using categorical variables.

Project In Tableau

House Data Dashobard - Project I
Jobs Data Dashboard - Project II

Projects

Weather Prediction
Predicting Cancer Malignant or Benign
Predicting Stock Prices
Wine Quality Prediction
Power BI Projects
Tableau Projects

Skills You’ll Learn from Data Analysis Course

Data visualisation: Skill to effectively present complex data through charts, graphs, and interactive visualisations, making it easier for stakeholders to understand and interpret.
Data mining: Competence in using techniques like clustering, classification, and association analysis to discover hidden patterns, trends, and insights from large datasets.
SQL and database querying: Proficiency in using SQL (Structured Query Language) to extract, manipulate, and analyse data from relational databases.
Programming skills: Knowledge of programming languages like Python or R to perform data analysis tasks efficiently and automate processes.
Business acumen: Understanding of business concepts and domain knowledge to align data analysis efforts with organisational goals and make data-driven recommendations.

Career After Data Analysis Certification

Data Analyst
Business Intelligence Analyst
Data Engineer
Power BI Analyst
Market Research Analyst
Financial Analyst
Data Quality Analyst
Healthcare Analyst

Certificate in Data Analysis Training

Participants who successfully complete the training will receive certifications from Up Skill Training to honour their accomplishment and the knowledge they have gained.