Course Description

  • What are the career benefits of this course?

    Knowledge of Data Analytics will help enhance your problem solving and data interpretation skills to drive better business decisions in your organization.


    According to Glassdoor, Data Analytics certified professionals can earn a range of  $67K-$72 average annual salary in the U.S. There are more than 90,000 available jobs in Data Analytics globally  (Source: Indeed.com). The global data analytics market is expected to expand at a CAGR of 30 percent from 2017-2023 and reach the market valuation of USD $77.64 billion by the end of 2023.

  • What are the course objectives?

    The focus of this Data Analytics online course is to introduce beginners to the fundamental concepts of Data Analytics through real-world case studies and examples. The courseware covers the importance of data analytics and visualization for decision-making in a business setting, the difference between data analytics, data science and machine learning, and how to build an analytics framework and use analytics tools to uncover meaningful business insights.

  • What skills will you learn from this Data Analytics course?

    Upon completion of this course, you will be able to:

    • Understand how to solve analytical problems in real-world scenarios
    • Define effective objectives for analytics projects
    • Work with different types of data
    • Understand the importance of data visualization to help make more effective business decisions
    • Understand charts, graphs and tools used for analytics and visualization and use them to derive meaningful insights
    • Create an analytics adoption framework
    • Identify upcoming trends in the data analytics field

  • Who should take this Introduction to Data Analytics course?

    The 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.

  • What are the prerequisites for this Data Analytics course?

    This Introduction to Data Analytics course has been designed for all levels, regardless of prior knowledge of analytics, statistics or coding. Familiarity with mathematics is helpful for this course.

Course Preview

    • Lesson 1 - Course Introduction

      02:09
      • 1.01 Course Introduction
        02:09
    • Lesson 2 - Data Analytics Overview

      23:10
      • 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

      16:03
      • 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

      26:18
      • 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

      17:25
      • 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

      09:15
      • 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

      22:18
      • 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

      13:00
      • 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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Exam & Certification

  • 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%

      FAQs

      • 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. 

      • How do I enroll for online training?

        You can enroll for this training 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.

      • 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.
         

          Contact Us

          +1-844-532-7688

          (Toll Free)

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