Introduction to Data Analytics

Introduction to Data Analytics

This course is part of multiple programs.

Instructor: Rav Ahuja

Access provided by SR University

933,639 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

20,486 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

20,486 reviews

Beginner level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

What you'll learn

Explain what Data Analytics is and the key steps in the Data Analytics process
Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
Describe the different types of data structures, file formats, and sources of data
Describe the data analysis process involving collecting, wrangling, mining, and visualizing data

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 quizzes, 12 assignments¹

AI Graded see disclaimer

Taught in English

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There are 5 modules in this course

Ready to start a career in Data Analysis but don’t know where to begin? This course presents you with a gentle introduction to Data Analysis, the role of a Data Analyst, and the tools used in this job. You will learn about the skills and responsibilities of a data analyst and hear from several data experts sharing their tips & advice to start a career. This course will help you to differentiate between the roles of Data Analysts, Data Scientists, and Data Engineers.

You will familiarize yourself with the data ecosystem, alongside Databases, Data Warehouses, Data Marts, Data Lakes and Data Pipelines. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you’ll be able to understand the fundamentals of the data analysis process including gathering, cleaning, analyzing and sharing data and communicating your insights with the use of visualizations and dashboard tools. This all comes together in the final project where it will test your knowledge of the course material, and provide a real-world scenario of data analysis tasks. This course does not require any prior data analysis, spreadsheet, or computer science experience.

In this module, you will learn about the different types of data analysis and the key steps in a data analysis process. You will gain an understanding of the different components of a modern data ecosystem, and the role Data Engineers, Data Analysts, Data Scientists, Business Analysts, and Business Intelligence Analysts play in this ecosystem. You will also learn about the role, responsibilities, and skillsets required to be a Data Analyst, and what a typical day in the life of a Data Analyst looks like.

What's included

9 videos5 readings4 assignments1 discussion prompt

9 videos Total 40 minutes

Course Introduction 3 minutes
Modern Data Ecosystem 5 minutes
Key Players in the Data Ecosystem 6 minutes
Defining Data Analysis 6 minutes
Viewpoints: What is Data Analytics? 3 minutes
Responsibilities of a Data Analyst 5 minutes
Viewpoints: Qualities and Skills to be a Data Analyst 5 minutes
A Day in the Life of a Data Analyst 5 minutes
Viewpoints: Applications of Data Analytics 3 minutes

5 readings Total 34 minutes

A Quick Note for the Best Learning Experience 2 minutes
Summary and Highlights 10 minutes
Copy of Data Analytics vs. Data Analysis 2 minutes
Generative AI: An essential Skill for today's Data Analysts 10 minutes
Summary and Highlights 10 minutes

4 assignments Total 45 minutes

Practice Quiz: Modern Data Ecosystem and the Role of Data Analytics 9 minutes
Practice Quiz: The Data Analyst Role 6 minutes
Graded Quiz: Modern Data Ecosystem and the Role of Data Analytics 15 minutes
Graded Quiz: The Data Analyst Role 15 minutes

1 discussion prompt Total 5 minutes

Introduce yourself 5 minutes

In this module, you will learn about the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will gain an understanding of various types of data repositories such as Databases, Data Warehouses, Data Marts, Data Lakes, and Data Pipelines. In addition, you will learn about the Extract, Transform, and Load (ETL) Process, which is used to extract, transform, and load data into data repositories. You will gain a basic understanding of Big Data and Big Data processing tools such as Hadoop, Hadoop Distributed File System (HDFS), Hive, and Spark.

What's included

11 videos2 readings2 quizzes2 assignments1 plugin

11 videos Total 67 minutes

Overview of the Data Analyst Ecosystem 4 minutes
Types of Data 4 minutes
Understanding Different Types of File Formats 5 minutes
Sources of Data 8 minutes
Languages for Data Professionals 8 minutes
Overview of Data Repositories 5 minutes
RDBMS 8 minutes
NoSQL 8 minutes
Data Marts, Data Lakes, ETL, and Data Pipelines 7 minutes
Foundations of Big Data 5 minutes
Big Data Processing Tools 6 minutes

2 readings Total 20 minutes

Summary and Highlights 10 minutes
Summary and Highlights 10 minutes

2 quizzes Total 33 minutes

Graded Quiz: The Data Ecosystem and Languages for Data Professionals 15 minutes
Graded Quiz: Understanding Data Repositories and Big Data Platforms 18 minutes

2 assignments Total 33 minutes

Practice Quiz: The Data Ecosystem and Languages for Data Professionals 15 minutes
Practice Quiz: Understanding Data Repositories and Big Data Platforms 18 minutes

1 plugin Total 5 minutes

Real-World Applications of Data Warehouses, Data Marts, and Data Lakes 5 minutes

In this module, you will learn about the process and steps involved in identifying, gathering, and importing data from disparate sources. You will learn about the tasks involved in wrangling and cleaning data in order to make it ready for analysis. In addition, you will gain an understanding of the different tools that can be used for gathering, importing, wrangling, and cleaning data, along with some of their characteristics, strengths, limitations, and applications.

What's included

7 videos2 readings2 quizzes2 assignments

7 videos Total 40 minutes

Identifying Data for Analysis 6 minutes
Data Sources 5 minutes
How to Gather and Import Data 7 minutes
What is Data Wrangling? 7 minutes
Tools for Data Wrangling 6 minutes
Data Cleaning 6 minutes
Viewpoints: Data Preparation and Reliability 4 minutes

2 readings Total 20 minutes

Summary and Highlights 10 minutes
Summary and Highlights 10 minutes

2 quizzes Total 30 minutes

Graded Quiz: Gathering Data 15 minutes
Graded Quiz: Wrangling Data 15 minutes

2 assignments Total 18 minutes

Practice Quiz: Gathering Data 9 minutes
Practice Quiz: Wrangling Data 9 minutes

In this module, you will learn about the role of Statistical Analysis in mining and visualizing data. You will learn about the various statistical and analytical tools and techniques you can use in order to gain a deeper understanding of your data. These tools help you to understand the patterns, trends, and correlations that exist in data. In addition, you will learn about the various types of data visualizations that can help you communicate and tell a compelling story with your data. You will also gain an understanding of the different tools that can be used for mining and visualizing data, along with some of their characteristics, strengths, limitations, and applications.

What's included

8 videos2 readings2 quizzes2 assignments

8 videos Total 44 minutes

Overview of Statistical Analysis 8 minutes
What is Data Mining? 5 minutes
Tools for Data Mining 6 minutes
Overview of Communicating and Sharing Data Analysis Findings 5 minutes
Viewpoints: Storytelling in Data Analysis 3 minutes
Introduction to Data Visualization 6 minutes
Introduction to Visualization and Dashboarding Software 8 minutes
Viewpoints: Visualization Tools 3 minutes

2 readings Total 20 minutes

Summary and Highlights 10 minutes
Summary and Highlights 10 minutes

2 quizzes Total 30 minutes

Graded Quiz: Analyzing and Mining Data 15 minutes
Graded Quiz: Communicating Data Analysis Findings 15 minutes

2 assignments Total 18 minutes

Practice Quiz: Analyzing and Mining Data 9 minutes
Practice Quiz: Communicating Data Analysis Findings 9 minutes

In this module, you will learn about the different career opportunities in the field of Data Analysis and the different paths that you can take for getting skilled as a Data Analyst. At the end of the module, you will demonstrate your understanding of some of the basic tasks involved in gathering, wrangling, mining, analyzing, and visualizing data.

What's included

8 videos4 readings2 assignments1 peer review1 app item1 plugin

8 videos Total 33 minutes

Career Opportunities in Data Analysis 6 minutes
Viewpoints: Get into Data Profession 3 minutes
Viewpoints: What do Employers look for in a Data Analyst? 5 minutes
The Many Paths to Data Analysis 4 minutes
Viewpoints: Career Options for Data Professionals 3 minutes
Viewpoints: Advice for aspiring Data Analysts 4 minutes
Viewpoints: Women in Data Professions 3 minutes
Generative AI for Data Analytics 4 minutes

4 readings Total 32 minutes

Summary and Highlights 10 minutes
Using Data Analysis for Detecting Credit Card Fraud 10 minutes
Congratulations and Next Steps 2 minutes
Course Credits and Acknowledgements 10 minutes

2 assignments Total 21 minutes

Practice Quiz: Opportunities and Learning Paths 6 minutes
Graded Quiz: Opportunities and Learning Paths 15 minutes

1 peer review Total 60 minutes

Option 2: Peer Graded - Final Project Submission and Evaluation 60 minutes

1 app item Total 20 minutes

Option 1: AI Graded - Final Project: Submission and Evaluation 20 minutes

1 plugin Total 5 minutes

Final Project Submission Guidelines and Deliverables 5 minutes

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Instructor

Instructor ratings

(7,212 ratings)

Rav Ahuja

IBM

55 Courses 4,874,864 learners

Offered by

IBM

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Showing 3 of 20486

Reviewed on Jul 20, 2023

All content was related and helpful, the only issue I see is the final assessment, peer graded. It looks like some students who want to get feedback on others', do not read the answers at all !

Reviewed on Oct 16, 2021

Pretty good course. In my opinion the reading material was a bit brief and did not really cover the quiz questions. Other than that though it was pretty clear and relatively easy to understand.

Reviewed on Jun 12, 2022

This course offers a deep insight on the details of how to do the job of a data analyst. While it is an overview of the whole process, there is a lot of information to unpack. Highly recommend.

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