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Data Analytics

Course Overview

Data Analysis is an ever-evolving discipline with lots of focus on new predictive modelling techniques coupled with rich analytical tools that keep increasing our capacity to handle big data. However, in order to chart a coherent path forward, it is necessary to understand where the discipline has come from since its inception.

This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish.

What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You'll develop skills and a perspective that will make you more productive faster and allow you to become an asset to your organization. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.

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Learning Outcomes

  • Learn the terms, jargon, and impact of business intelligence and data analytics.
  • Gain knowledge of the scope and application of data analysis.
  • Explore ways to measure the performance of and improvement opportunities for business processes.
  • Be able to describe the need for tracking and identifying the root causes of deviation or failure.
  • Review the basic principles, properties, and application of Probability Theory.
  • Discuss data distribution including Central Tendency, Variance, Normal Distribution, and non-normal distributions.
  • Learn about Statistical Inference and drawing conclusions about a Data Population.
  • Learn about Forecasting, including introduction to simple Linear Regression analysis.
  • Learn about Sample Sizes and Confidence Intervals and Limits, and how they influence the accuracy of your analysis.
  • Explore different methods and easy algorithms for forecasting future results and to reduce current and future risk.

Course Contents

Sr. No Item Description
1 Data Fundamentals
  • Course Overview and Level Set :- Objectives of the Class, Expectations for the Class
  • Understanding "Real-World" Data :- Unstructured vs. Structured, Relationships, Outliers, Data growth
  • Types of Data: - Flavours of Data, Sources of Data, Internal vs. External Data, Time Scope of Data (Lagging, Current, Leading)
  • Data-Related Risk: - Common Identified Risks, Effect of Process on Results, Effect of Usage on Results, Opportunity Costs, Tool Investment, Mitigation of Risk
  • Data Quality:- Cleansing, Duplicates, SSOT, Field standardization, Identify sparsely populated fields, How to fix common issues
2 Analysis Foundations
  • Statistical Practices: Overview, Comparing Programs and Tools, Words in English vs. Data, Concepts Specific to Data Analysis, Domains of Data Analysis, Descriptive Statistics, Inferential Statistics, Analytical Mindset, Describing and Solving Problems
3 Analyzing Data
  • Averages in Data :- Mean, Median, Mode, Range
  • Central Tendency :- Variance, Standard Deviation, Sigma Values, Percentiles, Use Concepts for Estimating
  • Hands-On - Central Tendency
  • Analytical Graphics for Data
  • Categorical :- Bar Charts
  • Continuous :- Histograms
  • Time Series :- Line Charts
  • Bivariate Data :- Scatter Plots
  • Distribution :- Box Plot
4 Analytics & Modeling
  • Overview of Commonly Useful Distributions :- Probability Distribution, Cumulative Distribution, Bimodal Distributions, Skewness of Data, Pareto Distribution, Correlation, Distributions, Predictive Analytics, A Discussion about Patterns, Regression and Time Series for Prediction
  • Hands-On - Linear Regression, Simulation, Pseudorandom Sequences, Monte Carlo Analysis, Demo / Lab: Monte Carlo in Excel
  • Understanding Clustering
  • Segmentation
  • Common
  • Algorithms :- K-MEANS
5 Hands-On Introduction to R and R Studio
  • R Basics
  • Descriptive Statistics
  • Importing and Manipulating Data
  • R Scripting
  • Data Visualization with R
  • Regression in R
  • K-MEANS in
  • Monte Carlo in R
  • Demo/Lab: Hands-on R work
6 Visualizing & Presenting Data
  • Goals of Visualization :- Communication and Narrative, Decision Enablement, Critical Characteristics
  • Visualization Essentials :- Users and Stakeholders, Stakeholder Cheat Sheet, Common Missteps
  • Communicating Data-Driven Knowledge :- Alerting and Trending, To Self-Serve or Not, Formats & Presentation Tools, Design Considerations

Job Opportunities

  • Python programmer
  • Software engineer
  • Data analyst
  • Data scientist
  • Entry level developer
  • Business analyst
  • Software developer
  • Product analyst
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