Data Analyst Online Course

During the Data Analyst course, you’ll engage with knowledge and real-life case studies as you develop practical skills and techniques for immediate application to data analysis projects, or within your organization. You will be benefited from the unique pedagogy and multidisciplinary approach of Blackboard Learning—an institution at the forefront of research and online learning—as you develop data analysis skills to better understand the creating the sample database, data science & machine learning concepts, and data science career and the factors that contribute to career success and failure.

Throughout this Data Analyst course, developed by industry experts, you’ll get the opportunity to learn from experts with diverse experience. Guided by experts, the Data Analyst course prepares you to become a change-maker with the skills to drive your career or organization forward.

The Data Analyst course will demystify data analysis and give you the toolkit to make better contributions and become an even greater asset to your organization. It will also allow you to communicate more effectively and confidently about data analysis issues, whether it is with the relevant people in your own business or with those outside your workplace.

After completing the Data Analyst course from Blackboard Learning, you will be more skillful with more knowledge, along with practical tips and advice that will help you to learn the essential aspects of data analysis. Skills development in data analysis leads you to career development in the data analysis sector.

Courses included in this Data Analyst bundle course:

Enroll in the Data Analyst course and get started with the Data Analyst journey!

This Data Analyst course is a course consisting of 6 courses with many data analysis-related topics.

You will get in this bundle course-

Course 1: Excel Data Analysis

Course 2: SQL Queries 101

Course 3: MySQL Course

Course 4: Learn Python for Data Science & Machine Learning from A-Z

Course 5: Building Robust Excel Models with Power Query, formulas, and VBA

Course 6: Compare two workbooks to find matches and variances with Excel VBA Tool

The Data Analyst course contains important modules that teach learners about their professional needs and succession. In the United Kingdom, Blackboard Learning is one of the most popular online Data Analyst course providers. You will get a solid foundation of knowledge about data analysis in this Data Analyst course. You will be able to think critically about Data Analyst and comprehend basic data analysis theories and methods. This Data Analyst course was created to provide you with the tools and methods you'll need to make a measurable effect in your career, whether your objective is to land a job, improve your abilities, or make a good influence in some other way.

Curriculum for Data Analyst bundle courses:

Course 1: Excel Data Analysis

• Excel Data Analysis - Part 1

• Excel Data Analysis - Part 2

• Excel Data Analysis - Part 3

• Excel Data Analysis - Part 4

• Excel Data Analysis - Part 5

• Excel Data Analysis - Part 6

• Excel Data Analysis - Part 7

• Excel Data Analysis - Part 8

• Excel Data Analysis – Part 9

Course 2: SQL Queries 101

• Unzipping the sample files.

• Creating the sample database.

• Basic Select Statements.

• Sorting the query with the order by statement.

• Using the where statement to filter the query.

• Creating subtotals using the group by statement.

• Introduction to using the join statement to create queries from more than 1 table.

Course 3: MySQL Course

• Section 1 Introduction

• Intro

• Section 2 Installation

• Installing Amp

• Section 3 First Steps and Basics

• Creating and dropping DBs

• Creating Tables

• Dropping Tables

• Inserting into Tables

• Updating and Deleting

• Data Types

• Operators

• Section 4 Constraints and Functions

• Primary Keys

• Foreign Keys

• Functions

• Section 5 Clauses

• Select with Where

• Order By

• Group By

• Section 6 Alters

• Alters

• Section 7 Joins

• Aliases

• Inner Join

• Left and Right Joins

Course 4: Learn Python for Data Science & Machine Learning from A-Z

• Section 1: Introduction to Python for Data Science & Machine Learning from A-Z

• Who is this course for?

• Data Science + Machine Learning Marketplace

• Data Science Job Opportunities

• Data Science Job Roles

• What is a Data Scientist?

• How to Get a Data Science Job

• Data Science Projects Overview

• Section 2: Data Science & Machine Learning Concepts

• Why We Use Python

• What is Data Science?

• What is Machine Learning?

• Machine Learning Concepts & Algorithms

• What is Deep Learning?

• Machine Learning Vs Deep Learning

• Section 3: Python for Data Science

• What is Programming?

• Why Python for Data Science?

• What is Jupiter?

• What is Google Collab?

• Python Variables, Booleans

• Getting Started with Google Collab

• Python Operators

• Python Numbers & Booleans

• Python Strings

• Python Conditional Statements

• Python For Loops and While Loops

• Python Lists

• More about Lists

• Python Tuples

• Python Dictionaries

• Python Sets

• Compound Data Types & When to use each one?

• Python Functions

• Object-Oriented Programming in Python

• Section 4: Statistics for Data Science

• Intro To Statistics

• Descriptive Statistics

• Measure of Variability

• The measure of Variability Continued

• Measures of Variable Relationship

• Inferential Statistics

• Measure of Asymmetry

• Sampling Distribution

• Section 5: Probability and Hypothesis Testing

• What Exactly is Probability?

• Expected Values

• Relative Frequency

• Hypothesis Testing Overview

• Section 6: Numbly Data Analysis

• Intro Numbly Array Data Types

• Numbly Arrays

• Numbly Arrays Basics

• Numbly Array Indexing

• Numbly Array Computations

• Broadcasting

• Section 7: Pandas Data Analysis

• Intro To Pandas

• Intro To Pandas Continued

• Section 8: Python Data Visualization

• Data Visualization Overview

• Different Data Visualization Libraries in Python

• Python Data Visualization Implementation

• Section 9: Introduction to Machine Learning

• Intro to Machine Learning

• Section 10: Data Loading & Exploration

• Exploratory Data Analysis

• Section 11: Data Cleaning

• Feature Scaling

• Data Cleaning

• Section 12: Feature Selecting and Engineering

• Feature Engineering

• Section 13: Linear and Logistic Regression

• Linear Regression Intro

• Gradient Descent

• Linear Regression + Correlation Methods

• Linear Regression Implementation

• Section 14: K Nearest Neighbors

• Parametric vs non-parametric models

• EDA on Iris Dataset

• The KNN Intuition

• Implement the KNN algorithm from scratch

• Compare the result with the Sklearn Library

• Hyperparameter tuning using the cross-validation

• The decision boundary visualization

• Manhattan vs Euclidean Distance

• Feature scaling in KNN

• Curse of dimensionality

• KNN use cases

• KNN pros and cons

• Section 15: Decision Trees

• Decision Trees Section Overview

• EDA on Adult Dataset

• What is Entropy and Information Gain?

• The Decision Tree ID3 algorithm from Scratch Part 1

• The Decision Tree ID3 algorithm from scratch Part 2

• The Decision Tree ID3 algorithm from scratch Part 3

• ID3 - Putting Everything Together

• Evaluating our ID3 implementation

• Compare with Sclera implementation

• 10. Visualizing the tree

• Plot the Important Features

• Decision Trees Hyper-parameters

• Pruning

• [Optional] Gain Ration

• Decision Trees Pros and Cons

• [Project] Predict whether income exceeds $50K/yr - Overview

• Section 16: Ensemble Learning and Random Forests

• Ensemble Learning Section Overview

• What is Ensemble Learning?

• What is Bootstrap Sampling?

• What is Bagging?

• Out-of-Bag Error (OOB Error)

• Implementing Random Forests from Scratch Part 1

• Implementing Random Forests from Scratch Part 2

• Compare with sklearn implementation

• Random Forests Hyper-Parameters

• Random Forests Pros and Cons

• What is Boosting?

• Adobos Part 1

• Adobos Part 2

• Section 17: Support Vector Machines

• SVM Outline

• SVM intuition

• Hard vs Soft Margins

• C hyper-parameter

• Kernel Trick

• SVM - Kernel Types

• SVM with Linear Dataset (Iris)

• SVM with Non-linear Dataset

• SVM with Regression

• [Project] Voice Gender Recognition using SVM

• Section 18: K-Means

• Unsupervised Machine Learning Intro

• Unsupervised Machine Learning Continued

• Data Standardization

• Section 19: PCA

• PCA Section Overview

• What is PCA?

• PCA Drawbacks

• PCA Algorithm Steps (Mathematics)

• Covariance Matrix vs SVD

• PCA - Main Applications

• PCA - Image Compression

• PCA - Supervised vs Unsupervised

• PCA - Visualization

• Section 20: Data Science Career

• 1. Creating A Data Science Resume

• Data Science Cover Letter

• How to Contact Recruiters

• Getting Started with Freelancing

• Top Freelance Websites

• Personal Branding

• Networking

• Importance of a Website

Course 5: Building Robust Excel Models with Power Query, formulas, and VBA

Course 6: Compare two workbooks to find matches and variances with Excel VBA Tool

Why Blackboard Learning:

Blackboard Learning is an online learning platform through which students from any corner of the world can learn their desired course. Using online learning, we assist students in realizing their full potential and advancing their careers. Today, our goal is to be the world's leading provider of online learning experiences with a global impact. By leveraging online learning, we assist students in preparing for bright futures in world-changing jobs. We provide a wide range of categories, including Accounting & IT, Programming, Creative, and more. Our courses are designed to stretch students intellectually through state-of-the-art online learning.

This Data Analyst course is for anyone looking to develop their skills and knowledge in data analysis-related fields, as well as for those-

• Want to enhance Data Analyst related skills and knowledge?
• Use data analysis-related knowledge in his career or profession.
• Needs data analysis-related skills for new job applications and opportunities.
• Who wants to learn Data Analyst and apply it in real life?
• Anyone who wants to demonstrate Data Analyst to prospective employers or jobs.
• Anyone who wants to apply Data Analyst course-related skills and dive into relevant career paths.

The Data Analyst course does not require prior knowledge or experience. Anyone with a PC, tablet, or mobile phone can do the Data Analyst course. It would be ideal for the learner to have:

• An open mind, a spirit of self-inspection, and a willingness to improve himself/herself.
• A desire to improve business (and personal) knowledge and skills.
• The desire to enhance skills in data analysis.

This Data Analyst course is exciting as it opens the doors to many professions related to data analysis. Prospective Data Analyst course-related career paths that include but are not limited to-

• Business Intelligence Analyst
• Data Analyst
• Data Analytics Consultant
• Operations Analyst
• Marketing Analyst

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Certificate of completion

Digital certificate - Included