Introduction to Data Analytics Course Overview

This Data Analytics course introduces beginners to the fundamental concepts of data analytics through real-world case studies and examples. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning; building an analytics framework, and using analytics tools to draw business insights.

Introduction to Data Analytics Key Features

  • 3 hours of online self-paced learning
  • Lifetime access to self-paced learning
  • Industry-recognized course completion certificate
  • Real-world case studies and examples

Skills Covered

  • Types of data analytics
  • Frequency distribution plots
  • Swarm plots
  • Data visualization
  • Data science methodologies
  • Analytics adoption frameworks
  • Trends in data analytics


The global data analytics market is expected to expand at a CAGR of 30 percent from 2017-2023 and reach the market valuation of $77.64 billion by the end of 2023. Skilled professionals will be eligible for more than 90,000 available jobs in data analytics globally.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Kuala Lumpur
    JPMorgan Chase hiring for Data Analyst professionals in Kuala Lumpur
    Genpact hiring for Data Analyst professionals in Kuala Lumpur
    VMware hiring for Data Analyst professionals in Kuala Lumpur
    LarsenAndTurbo hiring for Data Analyst professionals in Kuala Lumpur
    Citi hiring for Data Analyst professionals in Kuala Lumpur
    Accenture hiring for Data Analyst professionals in Kuala Lumpur
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Genpact hiring for Analytics Manager professionals in Kuala Lumpur
    CITI hiring for Analytics Manager professionals in Kuala Lumpur
    Wells Fargo hiring for Analytics Manager professionals in Kuala Lumpur
    Accenture hiring for Analytics Manager professionals in Kuala Lumpur
    Procter and Gamble hiring for Analytics Manager professionals in Kuala Lumpur
    Source: Indeed

Introduction to Data Analytics Course Curriculum


This Data Analytics for beginners course is ideal for anyone who wishes to learn the fundamentals of data analytics and pursue a career in this growing field. The course also caters to CxO-level and middle management professionals who want to improve their ability to derive business value and ROI from analytics.
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Learners need to possess an undergraduate degree or a high school diploma. This Introduction to Data Analytics for Beginners course has been designed for all levels, regardless of prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.
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Course Content

  • Introduction to Data Analytics

    • Lesson 1 - Course Introduction

      • 1.01 Course Introduction
    • Lesson 2 - Data Analytics Overview

      • 2.01 Introduction
      • 2.02 Data Analytics - Importance
      • 2.03 Digital Analytics: Impact on Accounting
      • 2.04 Data Analytics Overview
      • 2.05 Types of Data Analytics
      • 2.06 Descriptive Analytics
      • 2.07 Diagnostic Analytics
      • 2.08 Predictive Analytics
      • 2.09 Prescriptive Analytics
      • 2.10 Data Analytics - Amazon Example
      • 2.11 Data Analytics Benefits Decision-Making
      • 2.12 Data Analytics Benefits: Cost Reduction
      • 2.13 Data Analytics Benefits: Amazon Example
      • 2.14 Data Analytics: Other Benefits
      • 2.15 Key Takeaways
    • Lesson 3 - Dealing with Different Types of Data

      • 3.1 Introduction
      • 3.2 Terminologies in Data Analytics - Part One
      • 3.3 Terminologies in Data Analytics - Part Two
      • 3.4 Types of Data
      • 3.5 Qualitative and Quantitative Data
      • 3.6 Data Levels of Measurement
      • 3.7 Normal Distribution of Data
      • 3.8 Statistical Parameters
      • 3.09 Key Takeaways
    • Lesson 4 - Data Visualization for Decision making

      • 4.1 Introduction
      • 4.2 Data Visualization
      • 4.3 Understanding Data Visualization
      • 4.4 Commonly Used Visualizations
      • 4.5 Frequency Distribution Plot
      • 4.6 Swarm Plot
      • 4.7 Importance of Data Visualization
      • 4.8 Data Visualization Tools - Part One
      • 4.9 Data Visualization Tools - Part Two
      • 4.10 Languages and Libraries in Data Visualization
      • 4.11 Dashboard Based Visualization
      • 4.12 BI and Visualization Trends
      • 4.13 BI Software Challenges
      • 4.14 Key Takeaways
    • Lesson 5 - Data Science, Data Analytics, and Machine Learning

      • 5.01 Introduction
      • 5.02 The Data Science Domain
      • 5.03 Data Science, Data Analytics, and Machine Learning - Overlaps
      • 5.04 Data Science Demystified
      • 5.05 Data Science and Business Strategy
      • 5.06 Successful Companies Using Data Science
      • 5.7 Travel Industry
      • 5.8 Retail
      • 5.09 E-commerce and Crime agencies
      • 5.10 Analytical Platforms across Industries
      • 5.11 Key Takeaways
    • Lesson 6 - Data Science Methodology

      • 6.01 Introduction
      • 6.02 Data Science Methodology
      • 6.03 From Business Understanding to Analytic Approach
      • 6.04 From Requirements to Collection
      • 6.05 From Understanding to Preparation
      • 6.06 From Modeling to Evaluation
      • 6.07 From Deployment to Feedback
      • 6.08 Key Takeaways
    • Lesson 7 - Data Analytics in Different Sectors

      • 7.01 Introduction
      • 7.02 Analytics for Products or Services
      • 7.03 How Google Uses Analytics
      • 7.4 How LinkedIn Uses Analytics
      • 7.05 How Amazon Uses Analytics
      • 7.6 Netflix- Using Analytics to Drive Engagement
      • 7.7 Netflix- Using Analytics to Drive Success
      • 7.08 Media and Entertainment Industry
      • 7.09 Education Industry
      • 7.10 Healthcare Industry
      • 7.11 Government
      • 7.12 Weather Forecasting
      • 7.13 Key Takeaways
    • Lesson 8 - Analytics Framework and Latest trends

      • 8.1 Introduction
      • 8.2 Case Study: EY
      • 8.3 Customer Analytics Framework
      • 8.4 Data Understanding
      • 8.5 Data Preparation
      • 8.6 Modeling
      • 8.7 Model Monitoring
      • 8.8 Latest Trends in Data Analytics
      • 8.9 Graph Analytics
      • 8.10 Automated Machine Learning
      • 8.11 Open Source AI
      • 8.12 Key Takeaways

Introduction to Data Analytics Exam & Certification

  • Who provides the certification and how long it is valid for?

    Upon successful completion of the Intro to Data Analytics for beginners course, Simplilearn will provide you with an industry-recognized course completion certificate which has lifelong validity.

  • What do I need to do to unlock my Simplilearn certificate?

    To obtain the Introduction to Data Analytics course certification, you must:  

    • Complete the online self-learning course, and
    • Complete the course-end assessment with a minimum score of 80%

  • How to Become a Data Analyst?

    Data analysts play a unique role among the many data-centric jobs often found in today's businesses. A Data Analyst works closely with identifying patterns and trends in data sets, working alongside organizations within the business or the management team to establish business needs, define new data collection and analysis processes, and add real value to a company. Simplilearn’s Data Analyst Master’s program will give you sufficient insights into data analytics tools and methodologies to excel in your next role as a Data Analyst.

    Introduction to Data Analytics FAQs

    • What is Data Analytics?

      Data analytics is one of the major areas to explore in this digitalized world. To learn data analytics, you need to have an in-depth understanding of SQL, Python or R programming, data visualization techniques, statistics, regression, and more. While you can get started on your own, it is recommended to take this training program and learn data analytics from industry experts. You’ll get up-to-date knowledge of the field and know about the strategies and best practices that are actually followed by companies.

    • What is Data Analysis?

      Data analysis is a process of inspecting, cleaning, transforming and modeling data to discover useful information and support decision making to achieve business goals. There are various data analysis qualitative and quantitative methods and analytical or statistical tools used to extract the useful information and translate them into insights to make better business decisions,most of which are covered in this program.

    • Data Analysis vs Data Analytics

      Data analysis is the process of cleaning, transforming, and modeling data to find meaningful insights and make better decisions. Data analytics is a broad term that involves many diverse types of data analysis.

    • What are the different types of Data Analytics?

      There are four different types of data analytics:

      • Descriptive
      • Diagnostic
      • Predictive
      • Prescriptive

    • What qualifications do you need to become a Data Analyst?

      Most data analysts' job positions require a candidate to hold at least a bachelor’s degree in computer science, mathematics, statistics, or related field. It is, however, recommended to achieve a master’s degree in big data or data science or complete an online data analytics training course to enhance your credibility.

    • What are the different programming languages and tools used with data analytics?

      R, SAS, Python, and Apache Spark with Scala are the predominant languages used by Data Scientists and Data Analysts. Tableau, Excel, and QlikView are the most popular and effective analytics tools.

    • How can I learn more about improving my analytics skills?

      Simplilearn offers additional courses in Tableau and Business Analytics with Excel to enhance your learning and get you on the career path to becoming a Data Analyst. 

    • Who are the instructors and how are they selected?

      All of our highly qualified Data Analytics trainers are analytics industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

    • How do I enroll in Introduction to Data Analytics for beginners course?

      You can enroll for this Intro to Data Analytics for beginners course on our website and make an online payment using any of the following options:

      • Visa Credit or Debit Card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

      Once payment is received you will automatically receive a payment receipt and access information via email.

    • How can I learn more about this Introduction to Data Analytics for beginners course?

      Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    • What is Global Teaching Assistance?

      Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance.

    • Can I cancel my enrollment? Will I get a refund?

      Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our refund policy.

    • What is covered under the 24/7 Support promise?

      We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

    • Which are the advanced courses you should learn after completing Data Analytics for Beginners course?

      Here are some of the best advanced-level courses after completing the Data Analytics for Beginners course:

      1. Data Science with Python course
      2. Data Science with R Programming certification course
      3. Tableau certification course
      4. Business Analytics certification course
      5. Power BI certification course

      If you are looking for University partnered programs in Data Analytics, Simplilearn offers Post Graduate Program in Data Analytics that will give you broad exposure to key technologies and skills currently used in Data Analytics and Data Science, including Statistics, Python, R, Tableau, SQL, and Power BI.

    Our Kuala Lumpur Correspondence / Mailing address

    Simplilearn's Introduction to Data Analytics Course for Beginners in Kuala Lumpur, Malaysia

    Unit 32­01, Level 32, Tower B, The Vertical Corporate Towers Avenue 10, Bangsar, No.8, Jalan Kerinchi 59200 Kuala Lumpur Malaysia

    View Location
    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.