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Data Analyst

Course Description

This Data Analyst Master’s program in collaboration with IBM will make you an expert in data analysis. In this Data Analytics course, you’ll learn analytics tools and techniques, how to work with SQL databases, the languages of R and Python, how to create data visualizations, and how to apply statistics and predictive analytics in a business environment.

About the Data Analyst Master’s program developed in collaboration with IBM

IBM is the second-largest Predictive Analytics and Machine Learning solutions provider globally (source: The Forrester Wave report, September 2018). A joint partnership with Simplilearn and IBM introduces students to integrated blended learning, making them experts in Data Analytics and Data Science. The Data Analyst Master’s program in collaboration with IBM will make students industry-ready for Data Analytics and Data Science job roles.

IBM is a leading cognitive solution and cloud platform company, headquartered in Armonk, New York, offering a plethora of technology and consulting services. Each year, IBM invests $6 billion in research and development and has achieved five Nobel prizes, nine US National Medals of Technology, five US National Medals of Science, six Turing Awards, and 10 Inductions in US Inventors Hall of Fame.

What can I expect from this Simplilearn’s Data Analyst program developed in collaboration with IBM?

Upon completion of this Data Analyst Master’s program, you will receive the certificates from IBM and Simplilearn in the Data Analytics courses in the learning path*. These certificates will testify to your skills as an expert in Data Analyst. You will also receive the following:

  • USD 1200 worth of IBM cloud credits that you can leverage for hands-on exposure
  • Access to IBM cloud platforms featuring IBM Watson and other software for 24/7 practice
  • Industry-recognized Data Analyst Master’s certificate from Simplilearn

What are the learning objectives?

Simplilearn’s Data Analyst Master’s program developed in collaboration with IBM will provide you with extensive expertise in the booming data analytics field. This Data Analytics certification training course will teach you how to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extracts, and forecasting. Through this course, you will also gain expertise in data visualization techniques using Tableau and Power BI, learning how to organize data and design dashboards.

In this Data Analyst certification online course, a special emphasis is placed on those currently employed in the non-technical workforce. Through this course, those with a basic understanding of mathematical concepts will be able to complete the course and become an expert in data analytics.

This learning experience melds the knowledge of Data Analytics with hands-on demos and projects via CloudLab. Upon completing this course, you will have all the skills required to become a successful Data Analyst.

Why become a Data Analyst?

By 2020, the World Economic Forum forecasts that data analysts will be in demand due to increasing data collection and usage. Organizations view data analysis as one of the most crucial future specialties due to the value that can be derived from data. Data is more abundant and accessible than ever in today’s business environment. In fact, 2.5 quintillion bytes of data are created each day. With an ever-increasing skill gap in data analytics, the value of Data Analysers is continuing to grow, creating a new job, and career advancement opportunities.

The facts are that professionals who enter the Data Science field will have their pick of jobs and enjoy lucrative salaries. According to an IBM report, data and analytics jobs are predicted to increase by 15 percent to 2.72 million jobs by 2020, with the most significant demand for data analysts in finance, insurance, and information technology. Data analysts earn an average pay of $67,377 in 2019 according to Glassdoor.

What Data Analysis skills will you learn in this course?

The learning path in this Data Analyst Master’s program is specifically designed to give you the tools you need to be successful in Data Science and help you obtain a competitive edge. By the end of this Data Analytics courses, you will:

Understand essential statistical concepts including measures of central tendency, dispersion, correlation, and regression

Master SQL concepts such as Universal Query Tool and SQL command

Write your first Python program by implementing concepts of variables, strings, functions, loops, and conditions

  • Understand the nuances of lists, sets, dictionaries, conditions and branching, objects and classes in Python
  • Work with data in Python, including reading and writing files, loading, working, and saving data with Pandas
  • Learn how to interpret data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and execute machine learning using Scikit-Learn
  • Perform data analytics using popular Python libraries
  • Gain insights on several data visualization libraries in Python; including Matplotlib, Seaborn, and Folium
  • Gain an in-depth understanding of the basics of R, learning how to write your own R scripts
  • Master R programming and understand how various statements are executed in R
  • Understand and use linear and non-linear regression models and classification techniques for data analysis
  • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
  • Gain a foundational understanding of business analytics using Excel
  • Get introduced to the latest Microsoft analytics and visualization tools (Power BI)
  • Grasp the concepts of Tableau Desktop 10, become proficient with Tableau statistics and build interactive dashboards
  • Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto, Gantt chart, and market basket analysis

What Data Analysis projects are included in this course?

This Data Analyst Master’s program includes 15+ real-life, industry-based projects on different domains to help you master concepts of business analytics and intelligence, such as decision trees, data visualization, data blending, and more. Projects are as follows:

Project 1: Examine how large companies like Amazon and Flipkart make use of business intelligence tools to perform category analysis.
Project Title: Category Performance Analysis
Description: According to the Performance Evaluation Program, the subcategories yielding a consistent profit for the last four years are awarded the Best Performing Subcategories. Help the manager identify the top subcategories based on profits and use advanced dashboard features to portray a complete picture for subcategory sales.

Project 2: Learn how stock markets like NASDAQ, NSE, and BSE leverage data science and analytics to arrive at consumable data from complex datasets.
Domain: Stock Market
Description: As a part of the project, you will import data using Yahoo data reader for the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. Perform fundamental analytics, including plotting closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all stocks.

Project 3: See how banks like Citigroup, Bank of America, ICICI, and HDFC make use of data science to stay ahead of the competition.
Domain: Banking
Description: A Portuguese banking institution ran a marketing campaign to convince potential customers to invest in a bank term deposit. Their marketing campaigns were conducted through phone calls, and sometimes the same customer was contacted more than once. Your job is to analyze the data collected from the marketing campaign.

Project 4: Learn how leading healthcare industry leaders make use of data science to leverage their business.
Domain: Healthcare
Description: Predictive analytics can be used in healthcare to mediate hospital readmissions. In healthcare and other industries, predictors are most useful when they can be transferred into action, but historical and real-time data alone are worthless without intervention. More importantly, to judge the efficiency and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.

Project 5: Understand how leading retail companies like Walmart, Amazon, and Target make use of data science to analyze and optimize their product placements and inventory.
Domain: Retail
Description: Analytics is used in optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them insights into regular occurrences in the retail sector.

Project 6: Understand how insurance leaders like Berkshire Hathaway, AIG, and AXA make use of data science by working on a real-life insurance-based project.
Domain: Insurance
Description: The use of predictive analytics has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.

Who should take this Data Analytics course?

Aspiring professionals of any educational background with an analytical frame of mind are best suited to pursue the Data Analytics course, including:

  • IT professionals
  • Banking and finance professionals
  • Marketing managers
  • Sales professionals
  • Supply chain network managers
  • Beginners in the data analytics domain
  • Students in UG/ PG programs

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Course Syllabus

Course 1

Introduction to Data Analytics
Simplilearn’s Data Analytics for beginners course will give you insights into how to apply data and analytics principles in your business. Learning analytics, data visualization, and data science methodologies through this course will make you capable of driving better business decisions and ROI.

Introduction to Data Analytics

Lesson 1 – Course Introduction02:09
1.01 Course Introduction02:09
Lesson 2 – Data Analytics Overview23:10
2.01 Introduction00:35
2.02 Data Analytics – Importance00:46
2.03 Digital Analytics: Impact on Accounting03:08
2.04 Data Analytics Overview02:33
2.05 Types of Data Analytics00:42
2.06 Descriptive Analytics00:57
2.07 Diagnostic Analytics01:14
2.08 Predictive Analytics01:16
2.09 Prescriptive Analytics01:17
2.10 Data Analytics – Amazon Example01:18
2.11 Data Analytics Benefits Decision-Making01:27
2.12 Data Analytics Benefits: Cost Reduction03:30
2.13 Data Analytics Benefits: Amazon Example02:21
2.14 Data Analytics: Other Benefits01:28
2.15 Key Takeaways00:38
Lesson 3 – Dealing with Different Types of Data16:03
3.1 Introduction00:29
3.2 Terminologies in Data Analytics – Part One02:39
3.3 Terminologies in Data Analytics – Part Two01:19
3.4 Types of Data02:22
3.5 Qualitative and Quantitative Data02:41
3.6 Data Levels of Measurement02:56
3.7 Normal Distribution of Data00:45
3.8 Statistical Parameters02:35
3.09 Key Takeaways00:17
Lesson 4 – Data Visualization for Decision making26:18
4.1 Introduction00:25
4.2 Data Visualization01:03
4.3 Understanding Data Visualization02:57
4.4 Commonly Used Visualizations02:27
4.5 Frequency Distribution Plot01:35
4.6 Swarm Plot01:23
4.7 Importance of Data Visualization01:59
4.8 Data Visualization Tools – Part One02:21
4.9 Data Visualization Tools – Part Two01:49
4.10 Languages and Libraries in Data Visualization02:09
4.11 Dashboard Based Visualization03:01
4.12 BI and Visualization Trends03:38
4.13 BI Software Challenges01:01
4.14 Key Takeaways00:30
Lesson 5 – Data Science, Data Analytics, and Machine Learning17:25
5.01 Introduction00:27
5.02 The Data Science Domain01:25
5.03 Data Science, Data Analytics, and Machine Learning – Overlaps01:25
5.04 Data Science Demystified02:50
5.05 Data Science and Business Strategy02:18
5.06 Successful Companies Using Data Science02:58
5.7 Travel Industry01:16
5.8 Retail00:47
5.09 E-commerce and Crime agencies02:04
5.10 Analytical Platforms across Industries01:23
5.11 Key Takeaways00:32
Lesson 6 – Data Science Methodology09:15
6.01 Introduction00:26
6.02 Data Science Methodology01:20
6.03 From Business Understanding to Analytic Approach01:02
6.04 From Requirements to Collection01:06
6.05 From Understanding to Preparation01:10
6.06 From Modeling to Evaluation01:53
6.07 From Deployment to Feedback01:52
6.08 Key Takeaways00:26
Lesson 7 – Data Analytics in Different Sectors22:18
7.01 Introduction00:33
7.02 Analytics for Products or Services01:53
7.03 How Google Uses Analytics02:30
7.4 How LinkedIn Uses Analytics00:37
7.05 How Amazon Uses Analytics02:03
7.6 Netflix- Using Analytics to Drive Engagement00:56
7.7 Netflix- Using Analytics to Drive Success02:49
7.08 Media and Entertainment Industry01:10
7.09 Education Industry02:57
7.10 Healthcare Industry01:39
7.11 Government02:31
7.12 Weather Forecasting02:21
7.13 Key Takeaways00:19
Lesson 8 – Analytics Framework and Latest trends13:00
8.1 Introduction00:29
8.2 Case Study: EY01:05
8.3 Customer Analytics Framework00:59
8.4 Data Understanding01:42
8.5 Data Preparation00:50
8.6 Modeling02:05
8.7 Model Monitoring01:11
8.8 Latest Trends in Data Analytics01:11
8.9 Graph Analytics00:45
8.10 Automated Machine Learning01:24
8.11 Open Source AI00:52
8.12 Key Takeaways00:27

Course 2

Business Analytics with Excel
Boost your analytics career with powerful new Microsoft® Excel skills by taking this Business Analytics with Excel course, which includes Power BI training. These two commonly used tools, combined with official business analytics certification, will put you on the path of a successful career.

Business Analytics with Excel

Lesson 00 – Introduction05:27
0.001 Course Introduction05:27
Lesson 01 – Introduction to Business Analytics09:52
1.001 Introduction02:15
1.002 What Is in It for Me00:10
1.003 Types of Analytics02:18
1.004 Areas of Analytics04:06
Knowledge Check
1.006 Key Takeaways00:52
1.007 Conclusion00:11
Lesson 02 – Formatting Conditional Formatting and Important Fuctions38:29
2.001 Introduction02:12
2.002 What Is in It for Me00:21
2.003 Custom Formatting Introduction00:55
2.004 Custom Formatting Example03:24
2.005 Conditional Formatting Introduction00:44
2.006 Conditional Formatting Example101:47
2.007 Conditional Formatting Example202:43
2.008 Conditional Formatting Example301:37
2.009 Logical Functions04:00
2.010 Lookup and Reference Functions00:28
2.011 VLOOKUP Function02:14
2.012 HLOOKUP Function01:19
2.013 MATCH Function03:13
2.014 INDEX and OFFSET Function03:50
2.015 Statistical Function00:24
2.016 SUMIFS Function01:27
2.017 COUNTIFS Function01:13
2.019 STDEV, MEDIAN and RANK Function03:02
2.020 Exercise Intro00:35
2.21 Exercise
Knowledge Check
2.023 Key Takeaways00:53
2.024 Conclusion00:09
Lesson 03 – Analyzing Data with Pivot Tables19:32
3.001 Introduction01:47
3.002 What Is in It for Me00:22
3.003 Pivot Table Introduction01:03
3.004 Concept Video of Creating a Pivot Table02:47
3.005 Grouping in Pivot Table Introduction00:24
3.006 Grouping in Pivot Table Example 101:42
3.007 Grouping in Pivot Table Example 201:57
3.008 Custom Calculation01:14
3.009 Calculated Field and Calculated Item00:25
3.010 Calculated Field Example01:22
3.011 Calculated Item Example02:52
3.012 Slicer Intro00:35
3.013 Creating a Slicer01:22
3.014 Exercise Intro00:58
3.15 Exercise
Knowledge Check
3.017 Key Takeaways00:35
3.018 Conclusion00:07
Lesson 04 – Dashboarding32:07
4.001 Introduction01:18
4.002 What Is in It for Me00:18
4.003 What is a Dashboard00:45
4.004 Principles of Great Dashboard Design02:16
4.005 How to Create Chart in Excel02:26
4.006 Chart Formatting01:45
4.007 Thermometer Chart03:32
4.008 Pareto Chart02:26
4.009 Form Controls in Excel01:08
4.010 Interactive Dashboard with Form Controls04:13
4.011 Chart with Checkbox05:48
4.012 Interactive Chart04:37
4.013 Exercise Intro00:55
4.14 Exercise1
4.15 Exercise2
Knowledge Check
4.017 Key Takeaways00:34
4.018 Conclusion00:06
Lesson 05 – Business Analytics With Excel25:48
5.001 Introduction02:12
5.002 What Is in It for Me00:24
5.003 Concept Video Histogram05:18
5.004 Concept Video Solver Addin05:00
5.005 Concept Video Goal Seek02:57
5.006 Concept Video Scenario Manager04:16
5.007 Concept Video Data Table02:03
5.008 Concept Video Descriptive Statistics01:58
5.009 Exercise Intro00:52
5.10 Exercise
Knowledge Check
5.012 Key Takeaways00:39
5.013 Conclusion00:09
Lesson 06 – Data Analysis Using Statistics31:57
6.001 Introduction01:51
6.002 What Is in It for Me00:21
6.003 Moving Average02:50
6.004 Hypothesis Testing04:20
6.005 ANOVA02:47
6.006 Covariance01:56
6.007 Correlation03:38
6.008 Regression05:15
6.009 Normal Distribution06:49
6.010 Exercise1 Intro00:34
6.11 Exercise 1
6.012 Exercise2 Intro00:17
6.13 Exercise 2
6.014 Exercise3 Intro00:19
6.15 Exercise 3
Knowledge Check
6.017 Key Takeaways00:52
6.018 Conclusion00:08
Lesson 07 – Power BI14:01
7.001 Introduction01:17
7.002 What Is in It for Me00:18
7.003 Power Pivot04:16
7.004 Power View02:36
7.005 Power Query02:45
7.006 Power Map02:06
Knowledge Check
7.008 Key Takeaways00:32
7.009 Conclusion00:11

Tableau Training

This Tableau certification course helps you master Tableau Desktop, a world-wide utilized data visualization, reporting, and business intelligence tool. Advance your career in analytics by learning Tableau and how to best use this training in your work.

Tableau 10

Lesson 01 – Course Introduction05:04
1.01 Course Introduction05:04
Lesson 02 – Getting Started with Tableau10:22
2.01 Getting Started with Tableau00:29
2.02 Download and Install Tableau Public02:01
2.03 Load Data from Excel03:42
2.04 User Interface of Tableau Public03:52
2.05 Key Takeaways00:18
Knowledge Check
Lesson 03 – Core Topics in Tableau11:38
3.01 Core Topics in Tableau00:31
3.02 Dimension vs Measures02:42
3.03 Discrete vs. Continuous01:27
3.04 Application of Discrete and Continuous Fields04:05
3.05 Aggregation in Tableau02:33
3.06 Key Takeaways00:20
Knowledge Check
Lesson 04 – Creating Charts in Tableau33:11
4.01 Creating Charts in Tableau00:43
4.02 Bar Chart02:51
4.03 Stacked Bar Chart02:01
4.04 Line Chart03:38
4.05 Scatter Plot02:55
4.06 Dual-Axis Charts05:42
4.07 Combined-Axis Chart02:01
4.08 Funnel Chart02:54
4.09 Cross Tabs01:50
4.10 Highlight Tables02:22
4.11 Maps03:17
4.12 Measure Name and Measure Values02:38
4.13 Key takeaways00:19
Knowledge Check
Customer Analysis
Lesson 05 – Working with Metadata13:57
5.01 Working with Metadata00:35
5.02 Data Types05:08
5.03 Rename, Hide, Unhide and Sort Columns03:42
5.04 Default Properties of Fields04:09
5.05 Key takeaways00:23
Knowledge Check
Lesson 06 – Filters in Tableau40:22
6.01 Filters in Tableau00:43
6.02 Dimension Filter07:38
6.03 Date Filter06:25
6.04 Measure Filter03:39
6.05 Visual Filter06:00
6.06 Interactive Filter08:13
6.07 Data source Filter02:27
6.08 Context Filter05:02
6.09 Key takeaways00:15
Knowledge Check
Product Analysis
Lesson 07 – Applying Analytics to the Worksheet57:02
7.01 Applying Analytics to the Worksheet00:42
7.02 Sets06:54
7.03 Parameters05:22
7.04 Group05:50
7.05 Calculated Fields06:16
7.06 Date Functions05:37
7.07 Text Functions05:28
7.08 Bins and Histogram04:05
7.09 Sort03:15
7.10 Reference and Trend Lines05:06
7.11 Table Calculations03:49
7.12 Pareto Chart02:52
7.13 Waterfall Chart01:26
7.14 Key Takeaways00:20
Knowledge Check
Lesson 08 – Dashboards01:13:25
8.01 Dashboards in Tableau00:41
8.02 Dashboard05:17
8.03 Working with Layout07:42
8.04 Objects in Dashboard09:37
8.05 Making Interactive Dashboard04:10
8.06 Actions in Dashboard08:23
8.07 Best Practices for Dashboard Creation00:59
8.08 Dashboards for Mobile03:28
8.09 Story03:22
Case Study29:20
8.11 Key Takeaways00:26
Knowledge Check
Sales Dashboard
Lesson 09 – Modifications to Data Connections17:59
9.01 Modifications to Data Connections00:37
9.02 Edit Data Source02:33
9.03 Union03:32
9.04 Joins07:04
9.05 Data Blending03:55
9.06 Key Takeaways00:18
Knowledge Check
Lesson 10 – Level of Detail17:11
10.01 Level of Detail00:32
10.02 Introduction to Level of Detail (LOD)02:54
10.03 Fixed LOD05:09
10.04 Include LOD03:33
10.05 Exclude LOD02:58
10.06 Publish to Tableau Public01:43
10.07 Key Takeaways00:22
Knowledge Check

Course 4

Power BI
Simplilearn’s Power BI certification training will help you learn Power BI concepts like Microsoft Power BI Desktop layouts, BI reports, dashboards, Power BI DAX commands, and functions. In this Power BI certification course, you will explore to experiment, fix, prepare, and present data quickly and easily.

Microsoft Power BI Recipes

Lesson 1 – Get and Prep Data like a Super Nerd41:21
1.1 The Course Overview03:16
1.2 Desktop Layout, Features, and Views05:41
1.3 Connecting to Common Data sources11:31
1.4 Query Editor Layout and Functionality14:43
1.5 Creating Relationships in Your Data Model06:10
Lesson 2 – Develop Your Data Nerd Prowess42:16
2.1 Built-in Aggregations05:54
2.2 Calculated Columns and Measures10:51
2.3 A Visual Demonstration12:50
2.4 Some Slightly Advanced Measures12:41
Lesson 3 – Developing Reports and Dashboards45:21
3.1 Data Visualization Best Practices14:32
3.2 Report and Dashboard Layout07:59
3.3 Creating a Sales Analysis Report12:35
3.4 Creating a Project Management Report10:15
Lesson 4 – Tips, Tricks, and Capstone Project48:36
4.1 Telling the Story of Your Data14:54
4.2 Thinking Outside the Visual Box22:36
4.3 Theme-ing It Up06:28
4.4 Capstone Project04:38

Course 5 Online Classroom Flexi Pass

Programming Basics and Data Analytics with Python
Learn how to analyze data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas, use the SciPy library of mathematical routines, and perform machine learning using scikit-learn. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.

Section 01 – Programming Basics and Data Analytics with Python (Self Learning Curriculum)

Lesson 1 Learning Objective
Course End Objectives
Lesson 2 Introduction16:02
Learning Objectives
Getting Started Analyzing Data in Python04:14
Importing and Exporting Data in Python04:13
Introduction to Data Analysis with Python00:50
Python Packages for Data Science02:28
The Problem01:51
Understanding the Data02:26
Lesson 3 Data Wrangling18:50
Learning Objectives
Binning in Python01:47
Data Formatting in Python03:23
Data Normalization in Python03:34
Dealing with Missing Values in Python05:57
Indicator variables in Python02:00
Pre-processing Data in Python02:09
Lesson 4 Exploratory Data Analysis18:23
Learning Objectives
Analysis of Variance (ANOVA)03:58
Correlation – Statistics02:37
Descriptive Statistics04:39
Exploratory Data Analysis01:20
GroupBy in Python03:20
Lesson 5 Model Development26:07
Learning Objectives
Linear Regression and Multiple Linear Regression06:34
Model Evaluation using Visualization04:44
Polynomial Regression and Pipelines04:25
Measures for In-Sample Evaluation03:37
Prediction and Decision Making05:03
Lesson 6 Model Evaluation22:54
Learning Objectives
Model Evaluation07:30
Overfitting Underfitting and Model Selection04:20
Grid Search04:33
Model Evaluation and Refinement00:21
Ridge Regression04:26
Unlocking IBM Certificate

Course 6 Online Classroom Flexi Pass

Data Science Certification Training – R Programming
Simplilearn’s Data Science certification with R Programming training makes you an expert in data analytics using the R programming language. This course enables you to take your Data Science skills into a variety of companies, helping them analyze data and make more informed business decisions.

Data Science with R

Lesson 00 – Course Introduction01:31
Course Introduction01:31
Lesson 01 – Introduction to Business Analytics21:06
1.001 Overview00:44
1.002 Business Decisions and Analytics04:33
1.003 Types of Business Analytics03:53
1.004 Applications of Business Analytics08:57
1.005 Data Science Overview01:29
1.006 Conclusion01:30
Knowledge Check
Lesson 02 – Introduction to R Programming26:35
2.001 Overview00:31
2.002 Importance of R05:20
2.003 Data Types and Variables in R02:14
2.004 Operators in R04:39
2.005 Conditional Statements in R02:45
2.006 Loops in R05:07
2.007 R script01:44
2.008 Functions in R02:58
2.009 Conclusion01:17
Knowledge Check
Lesson 03 – Data Structures50:57
3.001 Overview01:04
3.002 Identifying Data Structures13:14
3.003 Demo Identifying Data Structures14:05
3.004 Assigning Values to Data Structures04:51
3.005 Data Manipulation09:23
3.006 Demo Assigning values and applying functions07:46
3.007 Conclusion00:34
Knowledge Check
Lesson 04 – Data Visualization29:40
4.001 Overview00:29
4.002 Introduction to Data Visualization03:03
4.003 Data Visualization using Graphics in R18:50
4.004 ggplot205:14
4.005 File Formats of Graphic Outputs01:08
4.006 Conclusion00:56
Knowledge Check
Lesson 05 – Statistics for Data Science-I14:10
5.001 Overview00:21
5.002 Introduction to Hypothesis02:06
5.003 Types of Hypothesis03:13
5.004 Data Sampling02:48
5.005 Confidence and Significance Levels04:33
5.006 Conclusion01:09
Knowledge Check
Lesson 06 – Statistics for Data Science-II29:55
6.001 Overview00:28
6.002 Hypothesis Test00:47
6.003 Parametric Test14:36
6.004 Non-Parametric Test08:31
6.005 Hypothesis Tests about Population Means02:09
6.006 Hypothesis Tests about Population Variance00:45
6.007 Hypothesis Tests about Population Proportions01:11
6.008 Conclusion01:28
Knowledge Check
Lesson 07 – Regression Analysis45:04
7.001 Overview00:26
7.002 Introduction to Regression Analysis01:11
7.003 Types of Regression Analysis Models01:38
7.004 Linear Regression08:59
7.005 Demo Simple Linear Regression07:29
7.006 Non-Linear Regression03:49
7.007 Demo Regression Analysis with Multiple Variables13:29
7.008 Cross Validation01:48
7.009 Non-Linear to Linear Models02:06
7.010 Principal Component Analysis02:45
7.011 Factor Analysis00:26
7.012 Conclusion00:58
Knowledge Check
Lesson 08 – Classification01:05:14
8.001 Overview00:31
8.002 Classification and Its Types04:24
8.003 Logistic Regression03:35
8.004 Support Vector Machines04:26
8.005 Demo Support Vector Machines11:13
8.006 K-Nearest Neighbours02:34
8.007 Naive Bayes Classifier02:53
8.008 Demo Naive Bayes Classifier06:15
8.009 Decision Tree Classification09:47
8.010 Demo Decision Tree Classification06:25
8.011 Random Forest Classification02:01
8.012 Evaluating Classifier Models06:04
8.013 Demo K-Fold Cross Validation04:09
8.014 Conclusion00:57
Knowledge Check
Lesson 09 – Clustering28:10
9.001 Overview00:17
9.002 Introduction to Clustering02:57
9.003 Clustering Methods07:47
9.004 Demo K-means Clustering11:15
9.005 Demo Hierarchical Clustering05:02
9.006 Conclusion00:52
Knowledge Check
Lesson 10 – Association23:13
10.001 Overview00:15
10.002 Association Rule06:20
10.003 Apriori Algorithm05:19
10.004 Demo Apriori Algorithm10:37
10.005 Conclusion00:42
Knowledge Check

Course 7

Data Analyst Capstone
Simplilearn’s Data Analyst Capstone project will give you an opportunity to implement the skills you learned in the Data Analyst master’s program. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem.The project is the final step in the learning path and will help you to showcase your expertise to employers.

Data Analyst Capstone

Lesson 01: Data Analyst Capstone
Data Analyst Capstone


  • Vast selection of courses and labs Access
  • Unlimited access from all devices
  • Learn from industry expert instructors
  • Assessment quizzes and monitor progress
  • Vast selection of courses and labs Access
  • Blended Learning with Virtual Classes
  • Access to new courses every quarter
  • 100% satisfaction guarantee

You Will Get Certification After Completetion This Course.

Instructor Led Lectures
All IT Tutor Pro Formerly It Nuggets Courses replicate a live class experience with an instructor on screen delivering the course’s theories and concepts.These lectures are pre-recorded and available to the user 24/7. They can be repeated, rewound, fast forwarded.
Visual Demonstrations, Educational Games & Flashcards
IT Tutor Pro Formerly It Nuggets recognizes that all students do not learn alike and different delivery mediums are needed in order to achieve success for a large student base. With that in mind, we delivery our content in a variety of different ways to ensure that students stay engaged and productive throughout their courses.
Mobile Optimization & Progress Tracking
Our courses are optimized for all mobile devices allowing students to learn on the go whenever they have free time. Students can access their courses from anywhere and their progress is completely tracked and recorded.
Practice Quizzes And Exams
IT Tutor Pro Formerly It Nuggets Online’s custom practice exams prepare you for your exams differently and more effectively than the traditional exam preps on the market. Students will have practice quizzes after each module to ensure you are confident on the topic you are learning.
World Class Learning Management System
IT Tutor Pro Formerly It Nuggets provides the next generation learning management system (LMS). An experience that combines the feature set of traditional Learning Management Systems with advanced functionality designed to make learning management easy and online learning engaging from the user’s perspective.

Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.