Course Outline for Data Analytics

Course Outline for Data Analytics

Duration: 5 days.

Course Description

In today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. One needs proper Data Analytics proficiency in order to stay ahead of the competition and gain market share. This course equips the trainee to acquire a broad spectrum of data analytics tools and techniques which would enable him to stay ahead of the industry trends. This course is taught by top professionals in data technology, analytics and mathematics.

What You’ll Learn

By the end of the course you shall be able to:

• Curate relevant data for analysis.
• Use various analytics and visual tools to discover trends.
• Form and test market hypothesis in a scientific manner.
• Understand and apply the full life-cycle of a data analytics project.
• Add value to your marketing, research, product and sales team by providing insights gained from scientific data analytics research.
• Understand the technology ecosystem surrounding data analytics and hence make informed decisions.
• Understand various issues surrounding data governance and form policies.

Course Outline

1. Data Curation
   1. Importance of Data Curation.
   2. What is data curation? An overview
   3. Various methods of data curation.
   4. Technologies used in data curation, storage and retrieval.
2. Data Science Project Presentation
   1. Overview.
   2. Key features.
   3. Categories of presentation material.
   4. Different categories of Audience.
   5. Powerpoint Slides Presentation.
   6. Jupyter/Colab notebook presentation.
3. Data Analytics Project Cycle.
   1. The seven fundamental steps.
   2. Understanding the Business.
   3. Data procurement.
   4. Data Exploration and Cleaning.
   5. Data enrichment.
   6. Data Visualization
   7. Predictive Analytics
   8. Iterations.
4. Business Impact by Data Analytics
   1. Areas of Impact.
   2. Augmented Analytics.
   3. Augmented Data Management.
   4. Continuous Intelligence.
   5. Explainable AI.
   6. Graph Analytics.
   7. Data Fabric.
   8. NLP and Conversational Analytics.
   9. Commercial AI and Machine Learning.
   10. Blockchain.
5. Data Visualization Technologies.
   1. Matplotlib.
   2. Pandas.
   3. Seaborn.
   4. ggplot.
   5. Plotly.
6. Database Concepts.
   1. Database Management System.
   2. Database Components and Characteristics.
   3. Types of Databases.
   4. Database Technologies.
7. Trend Analysis with Descriptive Statistics.
   1. Measures of Central Tendency.
   2. Measures of Variability.
   3. Univariate Analysis.
   4. Bivariate Analysis.
   5. Multivariate Analysis.
   6. Sample Size.
   7. Trend Detection.
8. Hypothesis Testing
   1. A/B Testing.
   2. Null Hypothesis.
   3. Normal Distribution.
   4. Test Statistics.
   5. P-value.
   6. Case Studies.
   7. Outlier Detection.
9. Data Governance.
10. Conclusions and Summary.