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Introduction to Data Analytics

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Introduction to Data Analytics

This course is part of multiple programs.

Instructor: IBM Skills Network Team

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5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

18 assignments¹

AI Graded see disclaimer

Taught in English

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Earn a shareable career certificate

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 videosTotal 40 minutes

Course Introduction3 minutes
Modern Data Ecosystem 5 minutes
Key Players in the Data Ecosystem6 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 Analyst5 minutes
A Day in the Life of a Data Analyst 5 minutes
Viewpoints: Applications of Data Analytics3 minutes

5 readingsTotal 34 minutes

A Quick Note for the Best Learning Experience2 minutes
Summary and Highlights10 minutes
Copy of Data Analytics vs. Data Analysis2 minutes
Generative AI: An essential Skill for today's Data Analysts10 minutes
Summary and Highlights10 minutes

4 assignmentsTotal 45 minutes

Practice Quiz: Modern Data Ecosystem and the Role of Data Analytics9 minutes
Practice Quiz: The Data Analyst Role6 minutes
Graded Quiz: Modern Data Ecosystem and the Role of Data Analytics15 minutes
Graded Quiz: The Data Analyst Role15 minutes

1 discussion promptTotal 5 minutes

Introduce yourself5 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 readings4 assignments1 plugin

11 videosTotal 67 minutes

Overview of the Data Analyst Ecosystem 4 minutes
Types of Data 4 minutes
Understanding Different Types of File Formats5 minutes
Sources of Data8 minutes
Languages for Data Professionals 8 minutes
Overview of Data Repositories5 minutes
RDBMS8 minutes
NoSQL8 minutes
Data Marts, Data Lakes, ETL, and Data Pipelines7 minutes
Foundations of Big Data5 minutes
Big Data Processing Tools6 minutes

2 readingsTotal 20 minutes

Summary and Highlights10 minutes
Summary and Highlights 10 minutes

4 assignmentsTotal 66 minutes

Practice Quiz: The Data Ecosystem and Languages for Data Professionals 15 minutes
Practice Quiz: Understanding Data Repositories and Big Data Platforms18 minutes
Graded Quiz: The Data Ecosystem and Languages for Data Professionals15 minutes
Graded Quiz: Understanding Data Repositories and Big Data Platforms18 minutes

1 pluginTotal 5 minutes

Real-World Applications of Data Warehouses, Data Marts, and Data Lakes5 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 readings4 assignments

7 videosTotal 40 minutes

Identifying Data for Analysis6 minutes
Data Sources5 minutes
How to Gather and Import Data 7 minutes
What is Data Wrangling?7 minutes
Tools for Data Wrangling6 minutes
Data Cleaning 6 minutes
Viewpoints: Data Preparation and Reliability4 minutes

2 readingsTotal 20 minutes

Summary and Highlights10 minutes
Summary and Highlights10 minutes

4 assignmentsTotal 48 minutes

Practice Quiz: Gathering Data9 minutes
Practice Quiz: Wrangling Data9 minutes
Graded Quiz: Gathering Data 15 minutes
Graded Quiz: Wrangling Data 15 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 readings4 assignments

8 videosTotal 44 minutes

Overview of Statistical Analysis8 minutes
What is Data Mining?5 minutes
Tools for Data Mining6 minutes
Overview of Communicating and Sharing Data Analysis Findings5 minutes
Viewpoints: Storytelling in Data Analysis3 minutes
Introduction to Data Visualization6 minutes
Introduction to Visualization and Dashboarding Software8 minutes
Viewpoints: Visualization Tools3 minutes

2 readingsTotal 20 minutes

Summary and Highlights10 minutes
Summary and Highlights10 minutes

4 assignmentsTotal 48 minutes

Practice Quiz: Analyzing and Mining Data 9 minutes
Practice Quiz: Communicating Data Analysis Findings9 minutes
Graded Quiz: Analyzing and Mining Data 15 minutes
Graded Quiz: Communicating Data Analysis Findings15 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 videosTotal 33 minutes

Career Opportunities in Data Analysis6 minutes
Viewpoints: Get into Data Profession3 minutes
Viewpoints: What do Employers look for in a Data Analyst?5 minutes
The Many Paths to Data Analysis4 minutes
Viewpoints: Career Options for Data Professionals3 minutes
Viewpoints: Advice for aspiring Data Analysts4 minutes
Viewpoints: Women in Data Professions3 minutes
Generative AI for Data Analytics 4 minutes

4 readingsTotal 32 minutes

Summary and Highlights10 minutes
Using Data Analysis for Detecting Credit Card Fraud10 minutes
Congratulations and Next Steps2 minutes
Course Credits and Acknowledgements10 minutes

2 assignmentsTotal 21 minutes

Practice Quiz: Opportunities and Learning Paths6 minutes
Graded Quiz: Opportunities and Learning Paths15 minutes

1 peer reviewTotal 60 minutes

Option 2: Peer Graded - Final Project Submission and Evaluation60 minutes

1 app itemTotal 20 minutes

Option 1: AI Graded - Final Project: Submission and Evaluation20 minutes

1 pluginTotal 5 minutes

Final Project Submission Guidelines and Deliverables5 minutes

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Instructor

IBM Skills Network Team

100 Courses3,030,333 learners

Offered by

IBM

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Frequently asked questions

Navigating data-driven organizations requires a clear understanding of who handles what. This course delineates the exact boundaries, daily responsibilities, and key toolsets for the core professions in the data domain:

Data Engineer: Architects data pipelines and provisions data repositories like warehouses and lakes.

Data Analyst: Examines datasets to spot trends, build dashboards, and answer specific business queries.

Data Scientist: Utilizes advanced statistical analysis, machine learning algorithms, and predictive modeling.

Business Analyst / BI Analyst: Translates data insights into corporate strategy, bridging the gap between technical metrics and executive decision-making.

Before you can analyze data, you must understand where it lives and how it is structured. You will explore a wide array of file formats (such as CSV, JSON, and XML) and learn how the Extract, Transform, Load (ETL) process moves information across various architecture models:

Databases: Optimized for capturing live, daily transaction records (OLTP).

Data Warehouses: Centralized, structured repositories designed for historical enterprise analytics.

Data Marts: Smaller, specialized subsets of a warehouse tailored to a specific department (e.g., Marketing or Finance).

Data Lakes: Massive storage repositories capable of holding raw, unstructured, or semi-structured data at scale.

This curriculum walks you through the entire end-to-end data lifecycle, establishing a repeatable workflow for data problem-solving. You will learn the mechanics of data gathering from disparate sources, followed by data wrangling and cleaning to eliminate duplicates, handle missing variables, and make datasets analysis-ready. From there, you will explore data mining techniques and statistical analysis to uncover correlations and trends, culminating in using visual analytics and dashboards to communicate a compelling, data-driven story to stakeholders.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.