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World’s #1 Online Bootcamp

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

Benefits

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
  • Data Analyst
    Annual Salary
    $43KMin
    $62KAverage
    $95KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Montreal
    JPMorgan Chase hiring for Data Analyst professionals in Montreal
    Genpact hiring for Data Analyst professionals in Montreal
    VMware hiring for Data Analyst professionals in Montreal
    LarsenAndTurbo hiring for Data Analyst professionals in Montreal
    Citi hiring for Data Analyst professionals in Montreal
    Accenture hiring for Data Analyst professionals in Montreal
    Source: Indeed
  • Analytics Manager
    Annual Salary
    $70KMin
    $97KAverage
    $139KMax
    Source: Glassdoor
    Hiring Companies
    Genpact hiring for Analytics Manager professionals in Montreal
    CITI hiring for Analytics Manager professionals in Montreal
    Wells Fargo hiring for Analytics Manager professionals in Montreal
    Accenture hiring for Analytics Manager professionals in Montreal
    Procter and Gamble hiring for Analytics Manager professionals in Montreal
    Source: Indeed

Introduction to Data Analytics Course Curriculum

Eligibility

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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Pre-requisites

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

    Preview

    • Lesson 1 - Course Introduction

      02:09Preview
      • 1.01 Course Introduction
        02:09

    • Lesson 2 - Data Analytics Overview

      23:10Preview
      • 2.01 Introduction
        00:35
      • 2.02 Data Analytics - Importance
        00:46
      • 2.03 Digital Analytics: Impact on Accounting
        03:08
      • 2.04 Data Analytics Overview
        02:33
      • 2.05 Types of Data Analytics
        00:42
      • 2.06 Descriptive Analytics
        00:57
      • 2.07 Diagnostic Analytics
        01:14
      • 2.08 Predictive Analytics
        01:16
      • 2.09 Prescriptive Analytics
        01:17
      • 2.10 Data Analytics - Amazon Example
        01:18
      • 2.11 Data Analytics Benefits Decision-Making
        01:27
      • 2.12 Data Analytics Benefits: Cost Reduction
        03:30
      • 2.13 Data Analytics Benefits: Amazon Example
        02:21
      • 2.14 Data Analytics: Other Benefits
        01:28
      • 2.15 Key Takeaways
        00:38

    • Lesson 3 - Dealing with Different Types of Data

      16:03Preview
      • 3.1 Introduction
        00:29
      • 3.2 Terminologies in Data Analytics - Part One
        02:39
      • 3.3 Terminologies in Data Analytics - Part Two
        01:19
      • 3.4 Types of Data
        02:22
      • 3.5 Qualitative and Quantitative Data
        02:41
      • 3.6 Data Levels of Measurement
        02:56
      • 3.7 Normal Distribution of Data
        00:45
      • 3.8 Statistical Parameters
        02:35
      • 3.09 Key Takeaways
        00:17

    • Lesson 4 - Data Visualization for Decision making

      26:18Preview
      • 4.1 Introduction
        00:25
      • 4.2 Data Visualization
        01:03
      • 4.3 Understanding Data Visualization
        02:57
      • 4.4 Commonly Used Visualizations
        02:27
      • 4.5 Frequency Distribution Plot
        01:35
      • 4.6 Swarm Plot
        01:23
      • 4.7 Importance of Data Visualization
        01:59
      • 4.8 Data Visualization Tools - Part One
        02:21
      • 4.9 Data Visualization Tools - Part Two
        01:49
      • 4.10 Languages and Libraries in Data Visualization
        02:09
      • 4.11 Dashboard Based Visualization
        03:01
      • 4.12 BI and Visualization Trends
        03:38
      • 4.13 BI Software Challenges
        01:01
      • 4.14 Key Takeaways
        00:30

    • Lesson 5 - Data Science, Data Analytics, and Machine Learning

      17:25Preview
      • 5.01 Introduction
        00:27
      • 5.02 The Data Science Domain
        01:25
      • 5.03 Data Science, Data Analytics, and Machine Learning - Overlaps
        01:25
      • 5.04 Data Science Demystified
        02:50
      • 5.05 Data Science and Business Strategy
        02:18
      • 5.06 Successful Companies Using Data Science
        02:58
      • 5.7 Travel Industry
        01:16
      • 5.8 Retail
        00:47
      • 5.09 E-commerce and Crime agencies
        02:04
      • 5.10 Analytical Platforms across Industries
        01:23
      • 5.11 Key Takeaways
        00:32

    • Lesson 6 - Data Science Methodology

      09:15Preview
      • 6.01 Introduction
        00:26
      • 6.02 Data Science Methodology
        01:20
      • 6.03 From Business Understanding to Analytic Approach
        01:02
      • 6.04 From Requirements to Collection
        01:06
      • 6.05 From Understanding to Preparation
        01:10
      • 6.06 From Modeling to Evaluation
        01:53
      • 6.07 From Deployment to Feedback
        01:52
      • 6.08 Key Takeaways
        00:26

    • Lesson 7 - Data Analytics in Different Sectors

      22:18Preview
      • 7.01 Introduction
        00:33
      • 7.02 Analytics for Products or Services
        01:53
      • 7.03 How Google Uses Analytics
        02:30
      • 7.4 How LinkedIn Uses Analytics
        00:37
      • 7.05 How Amazon Uses Analytics
        02:03
      • 7.6 Netflix- Using Analytics to Drive Engagement
        00:56
      • 7.7 Netflix- Using Analytics to Drive Success
        02:49
      • 7.08 Media and Entertainment Industry
        01:10
      • 7.09 Education Industry
        02:57
      • 7.10 Healthcare Industry
        01:39
      • 7.11 Government
        02:31
      • 7.12 Weather Forecasting
        02:21
      • 7.13 Key Takeaways
        00:19

    • Lesson 8 - Analytics Framework and Latest trends

      13:00Preview
      • 8.1 Introduction
        00:29
      • 8.2 Case Study: EY
        01:05
      • 8.3 Customer Analytics Framework
        00:59
      • 8.4 Data Understanding
        01:42
      • 8.5 Data Preparation
        00:50
      • 8.6 Modeling
        02:05
      • 8.7 Model Monitoring
        01:11
      • 8.8 Latest Trends in Data Analytics
        01:11
      • 8.9 Graph Analytics
        00:45
      • 8.10 Automated Machine Learning
        01:24
      • 8.11 Open Source AI
        00:52
      • 8.12 Key Takeaways
        00:27

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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.

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