Faysal AzizEnquire
FA-0465Data & Analytics

Course Outline for Data Analytics

Duration: 5 days.

Introduction

Why this course

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.
Training outline

10 modules

·
01Data Curation4 topics
  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.
02Data Science Project Presentation6 topics
  1. Overview.
  2. Key features.
  3. Categories of presentation material.
  4. Different categories of Audience.
  5. Powerpoint Slides Presentation.
  6. Jupyter/Colab notebook presentation.
03Data Analytics Project Cycle.8 topics
  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.
04Business Impact by Data Analytics10 topics
  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.
05Data Visualization Technologies.5 topics
  1. Matplotlib.
  2. Pandas.
  3. Seaborn.
  4. ggplot.
  5. Plotly.
06Database Concepts.4 topics
  1. Database Management System.
  2. Database Components and Characteristics.
  3. Types of Databases.
  4. Database Technologies.
07Trend Analysis with Descriptive Statistics.7 topics
  1. Measures of Central Tendency.
  2. Measures of Variability.
  3. Univariate Analysis.
  4. Bivariate Analysis.
  5. Multivariate Analysis.
  6. Sample Size.
  7. Trend Detection.
08Hypothesis Testing7 topics
  1. A/B Testing.
  2. Null Hypothesis.
  3. Normal Distribution.
  4. Test Statistics.
  5. P-value.
  6. Case Studies.
  7. Outlier Detection.
09Data Governance.0 topics
10Conclusions and Summary.0 topics

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Course Outline for Data Analytics
FA-0465

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Course Outline for Data Analytics